From 6fe2bfb9e76c6af8e585a3c0f63c2583c574daab Mon Sep 17 00:00:00 2001 From: James Kent Date: Tue, 22 Oct 2024 09:46:39 -0500 Subject: [PATCH 01/42] adding testing --- .github/workflows/test.yml | 32 + .gitignore | 2 + README | 26 - README.md | 130 + pipelines/__init__.py | 0 pipelines/dataset.py | 152 + .../participant_demographics/__init__.py | 0 pipelines/pipeline.py | 9 + pipelines/umls_disease/__init__.py | 0 pipelines/word_count/__init__.py | 0 pipelines/word_count/run.py | 82 + pyproject.toml | 44 + scripts/run_all.py | 4 +- tests/conftest.py | 8 + .../39ua7DtHYLoW/identifiers.json | 1 + .../processed/ace/coordinates.csv | 15 + .../39ua7DtHYLoW/processed/ace/metadata.json | 11 + .../39ua7DtHYLoW/processed/ace/text.txt | 724 +++ .../processed/pubget/coordinates.csv | 15 + .../processed/pubget/metadata.json | 11 + .../39ua7DtHYLoW/processed/pubget/text.txt | 120 + .../39ua7DtHYLoW/source/ace/28527986.html | 239 + .../39ua7DtHYLoW/source/pubget/28527986.xml | 1613 ++++++ .../source/pubget/tables/table_000.csv | 17 + .../source/pubget/tables/table_000_info.json | 1 + .../source/pubget/tables/tables.xml | 2 + .../6ZqhJVpfYDzu/identifiers.json | 1 + .../processed/ace/coordinates.csv | 77 + .../6ZqhJVpfYDzu/processed/ace/metadata.json | 11 + .../6ZqhJVpfYDzu/processed/ace/text.txt | 68 + .../6ZqhJVpfYDzu/source/ace/16513147.html | 3901 +++++++++++++ .../Lb3HDCyzoerL/identifiers.json | 1 + .../processed/pubget/coordinates.csv | 21 + .../processed/pubget/metadata.json | 11 + .../Lb3HDCyzoerL/processed/pubget/text.txt | 107 + .../Lb3HDCyzoerL/source/pubget/29113357.xml | 4829 +++++++++++++++++ .../source/pubget/tables/table_000.csv | 19 + .../source/pubget/tables/table_000_info.json | 1 + .../source/pubget/tables/table_001.csv | 6 + .../source/pubget/tables/table_001_info.json | 1 + .../source/pubget/tables/table_002.csv | 20 + .../source/pubget/tables/table_002_info.json | 1 + .../source/pubget/tables/table_003.csv | 8 + .../source/pubget/tables/table_003_info.json | 1 + .../source/pubget/tables/table_004.csv | 15 + .../source/pubget/tables/table_004_info.json | 1 + .../source/pubget/tables/tables.xml | 2 + tests/test_dataset.py | 7 + tests/test_word_count.py | 21 + 49 files changed, 12360 insertions(+), 28 deletions(-) create mode 100644 .github/workflows/test.yml delete mode 100644 README create mode 100644 README.md create mode 100644 pipelines/__init__.py create mode 100644 pipelines/dataset.py create mode 100644 pipelines/participant_demographics/__init__.py create mode 100644 pipelines/pipeline.py create mode 100644 pipelines/umls_disease/__init__.py create mode 100644 pipelines/word_count/__init__.py create mode 100644 pipelines/word_count/run.py create mode 100644 pyproject.toml create mode 100644 tests/conftest.py create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/identifiers.json create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/coordinates.csv create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/metadata.json create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/28527986.xml create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json create mode 100644 tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml create mode 100644 tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json create mode 100644 tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv create mode 100644 tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json create mode 100644 tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt create mode 100644 tests/data/sample_inputs/6ZqhJVpfYDzu/source/ace/16513147.html create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/identifiers.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/coordinates.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/metadata.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json create mode 100644 tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml create mode 100644 tests/test_dataset.py create mode 100644 tests/test_word_count.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..f8a01bc --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,32 @@ +name: Install and Test + +on: + push: + branches: + - main + pull_request: + branches: + - main + +concurrency: + group: testing-${{ github.ref }} + cancel-in-progress: true + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.8' + + - name: Install dependencies + run: pip install .[tests] + + - name: Test with pytest + run: pytest diff --git a/.gitignore b/.gitignore index 8a7884b..a745ea2 100644 --- a/.gitignore +++ b/.gitignore @@ -106,3 +106,5 @@ venv.bak/ *.swp .swo .swn + +_version.py diff --git a/README b/README deleted file mode 100644 index e5be4a5..0000000 --- a/README +++ /dev/null @@ -1,26 +0,0 @@ -# ns-text-extraction-workflows - -This repository contains pipelines and scripts for extracting features from text using Natural Language Processing (NLP), Large Language Models (LLMs), -and other algorithms across thousands of articles in the NeuroStore database. - -## Installation - -To install the necessary dependencies, run: - - pip install -r requirements.txt - - -## Usage -### Running pipelines -Executable workflows in `pipelines/{pipeline_name}/run.py` will take as input standardized pubget-style text inputs (row row per article). - - -Run all available pipelines and harmonize outputs using CLI (todo) - - -### Pipeline outputs -Pipeline results are output to `data/outputs/{input_hash}/{pipeline_name}. -Outputs include extracted features `features.csv`, feature descriptions `descriptions.json`, and extraction information `info.json`. - -Pipeline outputs are not stored as part of this repository. -See `ns-text-extraction-outputs` sub repository. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..279b329 --- /dev/null +++ b/README.md @@ -0,0 +1,130 @@ +# ns-text-extraction-workflows + +This repository contains pipelines and scripts for extracting features from text using Natural Language Processing (NLP), Large Language Models (LLMs), +and other algorithms across thousands of articles in the NeuroStore database. + +## Installation + +To install the necessary dependencies, run: + + pip install -r requirements.txt + + +## Usage +### Running pipelines +Executable workflows in `pipelines/{pipeline_name}/run.py` will take as input standardized pubget-style text inputs (row row per article). + + +Run all available pipelines and harmonize outputs using CLI (todo) + + +### Pipeline outputs +Pipeline results are output to `data/outputs/{input_hash}/{pipeline_name}. +Outputs include extracted features `features.csv`, feature descriptions `descriptions.json`, and extraction information `info.json`. + +Pipeline outputs are not stored as part of this repository. +See `ns-text-extraction-outputs` sub repository. + +### Types of pipelines + + +#### Each study is indepentently processed + +1) scenerio 1: nothing changed +2) scenerio 2: a study was added +3) scenerio 3: a study was changed + +`info.json` in the output directory +increment (value): 0 +date: 2021-09-01 + +ns-pond: no hashing +we will hash based on the inputs to the pipeline and then store the hash in the info.json in the output directory. + +have a place for the raw output of the API/external service. +raw.json +and clean.json +clean function for a pipeline output, that can be used to clean the output of a pipeline +#### Each study is processed in the context of all other studies + + + +Have a dev version +only include openaccess papers +pipeline name plus version then hash runs +pipeline/v1.0.0/hash_run-01 + +the hash is just the hash of the pipeline config + + +independent studies: copy over the studies that have been processed and havent been changed +independent studies: re-run the pipeline on studies that have been changed + + +## Notes + +# study independent results: +/pipline_name/v1.0.0/conf-#000A/run-01/study-01/input.json + /study-02/input.json + /results.json + +/pipline_name/v1.0.0/conf-#000A/run-02/study-03/ + +# study dependent results: +/pipline_name/v1.0.0/#sbqA_run-01/study-01 + /study-02 +/pipline_name/v1.0.0/#sbqA_run-02/study-01 + /study-02 + /study-03 + +Re-Run study independent pipeline: +1. Update with new - create new directory with only updated studies +2. Force re-run for a given set of inputs (from a particular directory, we are not using inheritance here) + +Re-Run study dependent pipeline: +1. Re-run all + + +after update: +database.study_results_table +id, study, conf, run: +0 01 #000A, 01 +1 02 #000A, 01 +2 03 #000A, 02 + + +after re-run: +database.study_results_table +id, study, conf, run: +0 01 #000A, 01 +1 02 #000A, 01 +2 03 #000A, 02 +3 01 #000A, 02 +4 02 #000A, 02 + +## Tf-idf gets it's own unique table +## participant demographics get their own unique table + + +## have a table for feature names? +database.study_results_values_table +id, study_results_table_fk, feature(name), value, certainty + + +database.pipeline_table +id, pipline_name, pipline_description, version, study_dependent?, ace_compatiable?, pubget_compat?, Derivative +0, gpt3_embed, wat, 1.0.0, False, True, True, False +1, HDBSCABN, wat, 1.0.0, True, False, False, True +2, TF-IDF, wat, 1.0.0, True, False, True, False +3, embed_and_HDBSCAN, wat, 1.0.0, True, True, True, False + +database.pipeline_configs_table +id, pipline_fk, configuration, configuration_hash, +0, 0, {use_cheap_option: true}, #000A +1, 1, {dimensions: 10}, #XXXX + +database.pipeline_run_table +id, pipline_fk, config_hash_fk, run_index, description, date + + +## TODO: how do I represent results in the database? diff --git a/pipelines/__init__.py b/pipelines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipelines/dataset.py b/pipelines/dataset.py new file mode 100644 index 0000000..cf95d10 --- /dev/null +++ b/pipelines/dataset.py @@ -0,0 +1,152 @@ +"""Dataset creation for processing inputs.""" +from dataclasses import dataclass +from pathlib import Path +import re +import json + + + +@dataclass +class AceRaw: + html: Path + +@dataclass +class PubgetRaw: + xml: Path + tables: dict = None + tables_xml: Path = None + +@dataclass +class ProcessedData: + coordinates: Path + text: Path + metadata: Path + +@dataclass +class Study: + dbid: str + doi: str = None + pmid: str = None + pmcid: str = None + ace: ProcessedData = None + pubget: ProcessedData = None + ace_raw: AceRaw = None + pubget_raw: PubgetRaw = None + + +class Dataset: + """Dataset class for processing inputs.""" + + def __init__(self, input_directory): + """Initialize the dataset.""" + self.data = self.load_directory(input_directory) + + def load_directory(self, input_directory): + """Load the input directory. + input_directory (str): The input directory containing the text. + processed (bool): Whether the input text is already processed. + source (str): The source of the input text. + (ace or pubget, if None, tries to find both) + """ + pattern = re.compile(r'^[a-zA-Z0-9]{12}$') + + sub_directories = input_directory.glob("[0-9A-Za-z]*") + + study_directories = [ + dir_ for dir_ in sub_directories + if dir_.is_dir() and pattern.match(dir_.name) + ] + + dset_data = {} + + for study_dir in study_directories: + + study_id = study_dir.name + study_obj = Study(dbid=study_id) + # associate IDs with study object + with open((study_dir / "identifiers.json"), "r") as ident_fp: + ids = json.load(ident_fp) + + study_obj.doi = ids["doi"] or None + study_obj.pmid = ids["pmid"] or None + study_obj.pmcid = ids["pmcid"] or None + + source_dir = study_dir / "source" + + # check if the source ace directory exists and load appropriate files + if (source_dir / "ace").exists(): + study_obj.ace_raw = AceRaw(html=source_dir / "ace" / f"{study_obj.pmid}.html") + + # check if the source pubget directory exists and load appropriate files + if (source_dir / "pubget").exists(): + study_obj.pubget_raw = PubgetRaw( + xml=source_dir / "pubget" / f"{study_obj.pmcid}.xml", + ) + study_obj.pubget_raw.tables_xml = source_dir / "pubget" / "tables" / "tables.xml" + + tables_files = (source_dir / "pubget" / "tables").glob("*.xml") + tables_files = [t for t in tables_files if t.name != "tables.xml"] + + num_tables = len(tables_files) // 2 + study_obj.pubget_raw.tables = { + '{0:03}'.format(t): {"metadata": None, "contents": None} + for t in range(num_tables) + } + + for tf in tables_files: + table_number = tf.stem.split("_")[1] + if tf.suffix == ".json": + key = "metadata" + else: + key = "contents" + + study_obj.pubget_raw.tables[table_number][key] = tf + + # processed directory + processed_dir = study_dir / "processed" + if (processed_dir / "ace").exists(): + study_obj.ace = ProcessedData( + coordinates=processed_dir / "ace" / "coordinates.csv", + text=processed_dir / "ace" / "text.txt", + metadata=processed_dir / "ace" / "metadata.json" + ) + + if (processed_dir / "pubget").exists(): + study_obj.pubget = ProcessedData( + coordinates=processed_dir / "pubget" / "coordinates.csv", + text=processed_dir / "pubget" / "text.txt", + metadata=processed_dir / "pubget" / "metadata.json" + ) + + dset_data[study_id] = study_obj + + return dset_data + + def __len__(self): + """Return the length of the dataset.""" + return len(self.data) + + def __getitem__(self, idx): + """Return an item from the dataset.""" + return self.data[idx] + + + +class PipelineInputFilter: + """Filter for pipeline inputs.""" + + def __init__(self, pipeline, output_directory, overwrite=False): + """Initialize the filter. + + pipeline (Pipeline): The pipeline to filter. + output_directory (str): The output directory where the pipeline has been previously run. + overwrite (bool): Whether to overwrite the existing output + """ + + def filter(self, dataset): + """Filter the dataset.""" + pass + + def load_outputs(self): + """Load the outputs.""" + pass diff --git a/pipelines/participant_demographics/__init__.py b/pipelines/participant_demographics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipelines/pipeline.py b/pipelines/pipeline.py new file mode 100644 index 0000000..6f980ca --- /dev/null +++ b/pipelines/pipeline.py @@ -0,0 +1,9 @@ +class Node: + """Processing step for the data. + + input is a dataset object + output is a dataset object + """ + def hash(self, dataset): + """Return a hash of the input dataset.""" + pass diff --git a/pipelines/umls_disease/__init__.py b/pipelines/umls_disease/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipelines/word_count/__init__.py b/pipelines/word_count/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipelines/word_count/run.py b/pipelines/word_count/run.py new file mode 100644 index 0000000..65d5dc6 --- /dev/null +++ b/pipelines/word_count/run.py @@ -0,0 +1,82 @@ +from datetime import datetime +import hashlib +import json +# from neurostore_text_extraction.pipelines.dataset import Dataset +# from neurostore_text_extraction.pipelines.pipeline import Node + +class WordCountExtraction: + """Extract word count from the documents. + + Extract word counts from the text data. + """ + _version = "1.0.0" + + _pipeline_type = "independent" + + _hash_args = ['prefer'] + + def __init__(self, prefer="pubget"): + """Initialize the word count extraction pipeline.""" + self.prefer = prefer + + def run(self, dataset, output_dir): + """Run the word count extraction pipeline. + Output metadata: + - version + - date + - source + """ + + # dataset hash + dataset_str = "_".join(list(dataset.data.keys())) + # argument hash + arg_str = '_'.join([str(getattr(self, arg)) for arg in self._hash_args]) + + # full hash + hash_str = hashlib.shake_256(f"{dataset_str}_{arg_str}".encode()).hexdigest(6) + + # find the most recent restults across all directories + existing_results = {} # main key is study id, contains dates, inputs, and hash where the file came from + # extract the hash from the directory + for d in output_dir.glob(f"{self._version}/**/*"): + if d.is_dir() and d.name in set(dataset.data.keys()): + if (d / "info.json").exists(): + with open(d / "info.json", "r") as f: + info = json.load(f) + found_info = {"date": info["date"], "inputs": info["inputs"], "hash": hashlib.md5(str(info).encode()).hexdigest()} + if existing_results.get(d.name) is None or info["date"] > existing_results[d.name]["date"]: + existing_results[d.name] = found_info + + hash_outdir = (output_dir / self._version / hash_str) + hash_outdir.mkdir(parents=True, exist_ok=True) + + for db_id, study in dataset.data.items(): + if self.prefer == "pubget" and getattr(study.pubget, "text", None) is not None: + text_file = study.pubget.text + elif self.prefer == "ace" and getattr(study.ace, "text", None) is not None: + text_file = study.ace.text + else: + text_file = getattr(study.pubget, "text", None) or getattr(study.ace, "text", None) + + if text_file is None: + raise ValueError(f"No text found for {db_id}") + + with open(text_file, "r") as f: + text = f.read() + + text_len = len(text.split()) + + # make the directory if it doesn't exist + study_outdir = (hash_outdir / db_id) + study_outdir.mkdir(parents=True, exist_ok=True) + with open(study_outdir / "results.json", "w") as f: + json.dump({"word_count": text_len}, f) + + with open(study_outdir / "info.json", "w") as f: + json.dump({ + "date": datetime.now().isoformat(), + "inputs": { + str(text_file): hashlib.md5(text.encode()).hexdigest() + } + }, f + ) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c59e2ec --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,44 @@ +[build-system] +requires = ["hatchling", "hatch-vcs"] +build-backend = "hatchling.build" + +[project] +name = "neurostore-text-extractor" +authors = [{name = "James Kent", email = "jamesdkent21@gmail.com"}] +description = "A package for extracting text features from the NeuroStore database." +readme = "README.md" +keywords = ["neurostore", "neurosynth", "neuroimaging", "meta-analysis"] +license = {text = "BSD 3-Clause License"} +classifiers = [ + "License :: OSI Approved :: BSD License", + "Programming Language :: Python :: 3", +] +dynamic = ["version"] +# dependencies = ["pandas"] + + +[project.optional-dependencies] +participant_demographics = [ + "pandas", + "numpy", + "pydantic", + "publang", + "openai", +] +umls_disease = [ + "pandas", + "numpy", + "labelrepo", + "spacy", + "scispacy", + "tqdm", +] +word_count = [ + "pandas", +] + +[tool.hatch.version] +source = "vcs" + +[tool.hatch.build.hooks.vcs] +version-file = "pipelines/_version.py" diff --git a/scripts/run_all.py b/scripts/run_all.py index 847b3a5..5753b59 100644 --- a/scripts/run_all.py +++ b/scripts/run_all.py @@ -16,7 +16,7 @@ def run_pipeline(pipeline_name, input_data_path, output_dir): # Create a subdirectory for the hash of the input data hash_dir = os.path.join(output_dir, hash(input_data_path)) os.makedirs(hash_dir, exist_ok=True) - + # Execute the {pipeline_name}/run.py script with the input data pipeline_script = os.path.join(pipeline_name, "run.py") subprocess.run(["python", pipeline_script, input_data_path, hash_dir]) @@ -36,4 +36,4 @@ def main(): run_pipeline(pipeline_name, args.input_data_path, args.output_dir) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..31cc7c4 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,8 @@ +from pathlib import Path +import pytest + + +@pytest.fixture +def sample_data(): + """Return a sample dataset.""" + return Path(__file__).parents[1] / "tests/data/sample_inputs" diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/identifiers.json b/tests/data/sample_inputs/39ua7DtHYLoW/identifiers.json new file mode 100644 index 0000000..c2b9993 --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/identifiers.json @@ -0,0 +1 @@ +{"pmid": "28527986", "doi": "10.1016/j.dcn.2017.05.004", "pmcid": "PMC6987902"} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv new file mode 100644 index 0000000..5be8f6d --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv @@ -0,0 +1,15 @@ +table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,-94.0,6.0,0.004,Middle OG,,2.82,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,54.0,-56.0,20.0,0.003,Angular G,,2.76,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,32.0,22.0,-14.0,0.003,Posterior OFG,,2.73,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,28.0,52.0,-4.0,0.005,Anterior OFG,,2.54,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,20.0,-16.0,0.001,Posterior OFG,,3.17,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,32.0,50.0,-8.0,0.002,Anterior OFG,,2.93,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,20.0,-18.0,0.001,Posterior OFG,,3.04,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,34.0,50.0,-10.0,0.004,Anterior OFG,,2.63,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-46.0,-18.0,-32.0,0.003,Anterior ITG,,2.73,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-8.0,-94.0,-18.0,0.004,Occipital G,,2.67,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-44.0,-18.0,-32.0,0.003,Anterior ITG,,2.72,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-8.0,-94.0,-18.0,0.003,Occipital G,,2.63,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-44.0,-20.0,-34.0,0.004,Anterior ITG,,2.64,INHIBITION Angry > Happy +8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-34.0,-4.0,-32.0,0.005,Hippocampus,,2.64,INHIBITION Angry > Happy diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json new file mode 100644 index 0000000..459df10 --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json @@ -0,0 +1,11 @@ +{ + "title": "The temporal and spatial brain dynamics of automatic emotion regulation in children.", + "authors": "Urbain, Charline;Sato, Julie;Pang, Elizabeth W;Taylor, Margot J", + "journal": "Developmental cognitive neuroscience", + "keywords": null, + "abstract": "Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (\u223c10 years old) who performed a go/no-go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425ms. These activations involved the right angular and occipital gyri from 100 to175ms, the right orbito-frontal gyrus (OFG) from 250 to 325ms (p<0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.", + "publication_year": 2017, + "coordinate_space": "MNI", + "license": null, + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt new file mode 100644 index 0000000..636c866 --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt @@ -0,0 +1,724 @@ + + + + + + + + + + + + + + + + + + +The temporal and spatial brain dynamics of automatic emotion regulation in children - PMC + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Back to Top + +Skip to main content + + + + + + +An official website of the United States government + +Here's how you know + + + + + + + + +The .gov means it’s official. + + Federal government websites often end in .gov or .mil. 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Inclusion in an NLM database does not imply endorsement of, or agreement with, + the contents by NLM or the National Institutes of Health. + Learn more: + PMC Disclaimer + | + + PMC Copyright Notice + + + + + + +Dev Cogn Neurosci. 2017 Aug; 26: 62-68. Published online 2017 May 17. doi: 10.1016/j.dcn.2017.05.004PMCID: PMC6987902PMID: 28527986The temporal and spatial brain dynamics of automatic emotion regulation in childrenCharline Urbain,a,⁎ Julie Sato,b,c Elizabeth W. Pang,c,e and Margot J. Taylorb,c,d,eCharline UrbainaUR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, BelgiumFind articles by Charline UrbainJulie SatobDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadaFind articles by Julie SatoElizabeth W. PangcNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaFind articles by Elizabeth W. PangMargot J. TaylorbDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadadDepartment of Psychology, University of Toronto, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaFind articles by Margot J. TaylorAuthor information Article notes Copyright and License information PMC DisclaimeraUR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, BelgiumbDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadadDepartment of Psychology, University of Toronto, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaCharline Urbain: eb.ca.blu@niabruc ⁎Corresponding author. eb.ca.blu@niabrucReceived 2016 Nov 29; Revised 2017 May 8; Accepted 2017 May 8.Copyright © 2017 The AuthorsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).This article has been corrected. See Dev Cogn Neurosci. 2020 October; 45: 100843.Associated DataSupplementary Materialsmmc1.doc (34K)GUID: E42B178D-0AA0-4256-9F73-D312A046CE43AbstractMechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.Keywords: MEG, Social cognition, Cognitive control, Orbito-frontal cortex, Temporal pole1. IntroductionDuring development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002, Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007, Todd et al., 2008, Hum et al., 2013a, Hum et al., 2013b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007, Hum et al., 2013a, Hum et al., 2013b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011, Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007, Todd et al., 2008, Todd et al., 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004, Gross and Muñoz, 1995, Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004, Hopp et al., 2011).Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.2. Material and methods2.1. ParticipantsParticipants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.2.2. Experimental MEG task and procedureThe children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.Open in a separate windowFig. 1(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (p = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (p < 0.002) but no differences between emotions were found in the vigilance condition (p > 0.88).Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.2.3. MEG data acquisitionMEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.2.4. MRI data acquisitionEach child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.2.5. Behavioural analysesAt the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.2.6. MEG analysesWith MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).2.6.1. Preprocessing steps MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.2.6.2. Source reconstruction Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005, Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).Table 1Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).INHIBITION Angry > HappyBrain regionsTime window (ms)z-valuep-valueMNI coordinatesxyzRMiddle OG100–1752.820.00426−946RAngular G125–1752.760.00354−5620RPosterior OFG225–2752.730.0033222−14RAnterior OFG225–2752.540.0052852−4RPosterior OFG250–3003.170.0012620−16RAnterior OFG250–3002.930.0023250−8RPosterior OFG275–3253.040.0012620−18RAnterior OFG275–3252.630.0043450−10LAnterior ITG325–3752.730.003−46−18−32LOccipital G325–3752.670.004−8−94−18LAnterior ITG350–4002.720.003−44−18−32LOccipital G350–4002.630.003−8−94−18LAnterior ITG375–4252.640.004−44−20−34LHippocampus375–4252.640.005−34−4−32Open in a separate windowNote: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.3. Results3.1. Behavioural resultsBehavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p > 0.88).3.2. MEG resultsAnalyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).Open in a separate windowFig. 23D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).Open in a separate windowFig. 3Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (p = 0.023) with the Inhibition Angry condition driving the interaction.4. DiscussionIn this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010, Cohen Kadosh et al., 2014).At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a, Hum et al., 2013b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998, Hillyard et al., 1998, Corbetta and Shulman, 2002, Singh-Curry and Husain, 2009, Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007, Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006, Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009, Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999, Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, Todd et al., 2012).Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010, Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002, Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.Conflict of Interest:None.AcknowledgementsThe authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.FootnotesAppendix ASupplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2017.05.004.Appendix A. Supplementary dataThe following is Supplementary data to this article:Click here to view.(34K, doc)ReferencesAlbert J., Lopez-Martin S., Carretie L. 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Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (\u223c10 years old) who performed a go/no\u2013go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425\u202fms. These activations involved the right angular and occipital gyri from 100 to175\u202fms, the right orbito-frontal gyrus (OFG) from 250 to 325\u202fms (p \u202f< \u202f0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425\u202fms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context. \n ", + "publication_year": 2017, + "coordinate_space": "MNI", + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt b/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt new file mode 100644 index 0000000..c38857b --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt @@ -0,0 +1,120 @@ + +## Introduction + +During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions ( ). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly ( , ). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus ( ). + +Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces ( ), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces ( ). + +Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues ( , , , ). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in ). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; ( )) or gender ( , , ) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process ( ), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels ( , ; see , for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental ( , , ). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not ( and see for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth ( , , ). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms ( , ). + +Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children ( ). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; ). Although fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction. + +The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition. + + +## Material and methods + +### Participants + +Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs). + +Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task. + + +### Experimental MEG task and procedure + +The children completed an emotional go/no–go task (see a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing. +(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task ( p  = . 000001; Inhibition < Vigilance) and Emotion ( p  = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition ( p  = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition ( p  < 0.002) but no differences between emotions were found in the vigilance condition ( p  > 0.88). + Fig. 1 + +Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants. + +The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions ( ). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used. + +All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials. + + +### MEG data acquisition + +MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain. + + +### MRI data acquisition + +Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil. + + +### Behavioural analyses + +At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press). + +To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors. + + +### MEG analyses + +With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014). + +#### Preprocessing steps + +MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip ( ). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature ( ). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected. + + +#### Source reconstruction + +Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms). + +Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise ( ). Head modeling was computed using a single shell head model ( ) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design ( ). + +Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t- statistics [SPM(T)], reported significant at p  < 0.005. A family-wise error correction (p  < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction ( ) which adjusted the p -value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM ( , ), and adapted to MEG ( ), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix ( ). The correction corresponded effectively to a significance level of P < 0.002 (see in bold). +Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset). + Table 1 + + + + +## Results + +### Behavioural results + +Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p  = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p  = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p  = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p  < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p  > 0.88). + + +### MEG results + +Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see and ) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms). +3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms). + Fig. 2 + +These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see for an example of these results in the right OFG). +Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion ( p  = 0.023) with the Inhibition Angry condition driving the interaction. + Fig. 3 + + + +## Discussion + +In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces). + +Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues ( ), consistent with adolescent and adult studies ( , ). + +At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition. + +Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces. + +The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing ( ), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies ( , ). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures ( ) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks ( , , , , ). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context. + +The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., , ). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see in bold). + +The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children ( ) and adults ( , ). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions ( ) and, thus plays an important role in flexible social behaviour [for reviews see, , ]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children ( , ). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see ) or avoidance behaviour ( ) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity ( , ). + +Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone ( , ). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes ( , ). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes ( and see for a review of temporal pole functions). + +In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children. + + +## Conflict of Interest: + +None. + + \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html b/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html new file mode 100644 index 0000000..ba86267 --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html @@ -0,0 +1,239 @@ + + + + + + + + + + + + + + + + + + + + + The temporal and spatial brain dynamics of automatic emotion regulation in children - ScienceDirect + + + + + + + + + + + + + + + + + Skip to main content + +
 
Elsevier

Developmental Cognitive Neuroscience

Volume 26, August 2017, Pages 62-68
open access
Developmental Cognitive Neuroscience

The temporal and spatial brain dynamics of automatic emotion regulation in children

Under a Creative Commons license

Abstract

Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.

Keywords

MEG
Social cognition
Cognitive control
Orbito-frontal cortex
Temporal pole

1. Introduction

During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002; Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).

Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).

Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007; Todd et al., 2008; Hum et al., 2013a,b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007; Hum et al., 2013a,b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011; Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007; Todd et al., 2008, 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004; Gross and Muñoz, 1995; Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004; Hopp et al., 2011).

Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.

The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

2. Material and methods

2.1. Participants

Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).

Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.

2.2. Experimental MEG task and procedure

The children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.

Fig. 1

Fig. 1. (A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (< 0.002) but no differences between emotions were found in the vigilance condition (> 0.88).

Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.

The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.

All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.

2.3. MEG data acquisition

MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.

2.4. MRI data acquisition

Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.

2.5. Behavioural analyses

At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).

To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.

2.6. MEG analyses

With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).

2.6.1. Preprocessing steps

MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.

2.6.2. Source reconstruction

Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).

Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).

Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005; Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).

Table 1. Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

3. Results

3.1. Behavioural results

Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, > 0.88).

3.2. MEG results

Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).

Fig. 2

Fig. 2. 3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).

These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).

Fig. 3

Fig. 3. Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (= 0.023) with the Inhibition Angry condition driving the interaction.

4. Discussion

In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).

Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010; Cohen Kadosh et al., 2014).

At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.

The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a,b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998; Hillyard et al., 1998; Corbetta and Shulman, 2002; Singh-Curry and Husain, 2009; Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.

The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007; Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).

The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006; Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009; Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999; Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, 2012).

Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010; Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002; Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).

In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.

Acknowledgements

The authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.

Appendix A. Supplementary data

The following is Supplementary data to this article:

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V. Singh-Curry, M. HusainThe functional role of the inferior parietal lobe in the dorsal and ventral stream dichotomy
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R.M. Todd, M.D. Lewis, L.A. Meusel, P.D. ZelazoThe time course of social-emotional processing in early childhood: ERP responses to facial affect and personal familiarity in a Go-Nogo task
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+ + + + Dev Cogn Neurosci + Dev Cogn Neurosci + + Developmental Cognitive Neuroscience + + 1878-9293 + 1878-9307 + + Elsevier + + + + 28527986 + 6987902 + S1878-9293(16)30233-X + 10.1016/j.dcn.2017.05.004 + + + Original Research + + + + The temporal and spatial brain dynamics of automatic emotion regulation in children + + + + + Urbain + Charline + + curbain@ulb.ac.be + a + + + + + Sato + Julie + + b + c + + + + Pang + Elizabeth W. + + c + e + + + + Taylor + Margot J. + + b + c + d + e + + + UR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium + Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada + Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada + Department of Psychology, University of Toronto, Toronto, Canada + Division of Neurology, The Hospital for Sick Children, Toronto, Canada + + Corresponding author. curbain@ulb.ac.be + + + 17 + 5 + 2017 + + + + 8 + 2017 + + + 17 + 5 + 2017 + + 26 + 62 + 68 + + + 29 + 11 + 2016 + + + 8 + 5 + 2017 + + + 8 + 5 + 2017 + + + + © 2017 The Authors + 2017 + + This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). + + + +

Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.

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+ + Keywords + MEG + Social cognition + Cognitive control + Orbito-frontal cortex + Temporal pole + +
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+ + + + Introduction +

During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002, Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).

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Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).

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Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007, Todd et al., 2008, Hum et al., 2013a, Hum et al., 2013b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007, Hum et al., 2013a, Hum et al., 2013b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011, Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007, Todd et al., 2008, Todd et al., 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004, Gross and Muñoz, 1995, Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004, Hopp et al., 2011).

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Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.

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The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

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+ + + Material and methods + + + Participants +

Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).

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Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.

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+ + + Experimental MEG task and procedure +

The children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.

(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (p = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (p < 0.002) but no differences between emotions were found in the vigilance condition (p > 0.88).

Fig. 1

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Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.

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The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.

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All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.

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+ + + MEG data acquisition +

MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.

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+ + + MRI data acquisition +

Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.

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+ + + Behavioural analyses +

At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).

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To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.

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+ + + MEG analyses +

With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).

+ + + Preprocessing steps +

MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.

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+ + + Source reconstruction +

Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).

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Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).

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Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005, Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).

Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

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+
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+ + + Results + + + Behavioural results +

Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p > 0.88).

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+ + + MEG results +

Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).

3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).

Fig. 2

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These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).

Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (p = 0.023) with the Inhibition Angry condition driving the interaction.

Fig. 3

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+
+ + + Discussion +

In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).

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Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010, Cohen Kadosh et al., 2014).

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At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

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Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.

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The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a, Hum et al., 2013b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998, Hillyard et al., 1998, Corbetta and Shulman, 2002, Singh-Curry and Husain, 2009, Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.

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The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007, Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).

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The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006, Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009, Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999, Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, Todd et al., 2012).

+

Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010, Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002, Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).

+

In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.

+
+ + Conflict of Interest: +

None.

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The following is Supplementary data to this article:

+
+ + Acknowledgements +

The authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.

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+ + + +

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2017.05.004.

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diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv new file mode 100644 index 0000000..6c600c8 --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv @@ -0,0 +1,17 @@ +INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy +Unnamed: 0_level_1,Brain regions,Time window (ms),z-value,p-value,MNI coordinates,MNI coordinates,MNI coordinates +Unnamed: 0_level_2,Unnamed: 1_level_2,Unnamed: 2_level_2,Unnamed: 3_level_2,Unnamed: 4_level_2,x,y,z +R,Middle OG,100–175,2.82,0.004,26,−94,6 +R,Angular G,125–175,2.76,0.003,54,−56,20 +R,Posterior OFG,225–275,2.73,0.003,32,22,−14 +R,Anterior OFG,225–275,2.54,0.005,28,52,−4 +R,Posterior OFG,250–300,3.17,0.001,26,20,−16 +R,Anterior OFG,250–300,2.93,0.002,32,50,−8 +R,Posterior OFG,275–325,3.04,0.001,26,20,−18 +R,Anterior OFG,275–325,2.63,0.004,34,50,−10 +L,Anterior ITG,325–375,2.73,0.003,−46,−18,−32 +L,Occipital G,325–375,2.67,0.004,−8,−94,−18 +L,Anterior ITG,350–400,2.72,0.003,−44,−18,−32 +L,Occipital G,350–400,2.63,0.003,−8,−94,−18 +L,Anterior ITG,375–425,2.64,0.004,−44,−20,−34 +L,Hippocampus,375–425,2.64,0.005,−34,−4,−32 diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json new file mode 100644 index 0000000..6bfe18f --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json @@ -0,0 +1 @@ +{"table_id": "tbl0005", "table_label": "Table 1", "table_caption": "Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0\u2013500\u202fms post-stimulus onset).", "table_foot": "Note: Statistical significance was set for the significant voxels of activations at puncorr\u202f<\u202f0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr\u202f<\u202f0.05.", "n_header_rows": 3, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml new file mode 100644 index 0000000..7311d1f --- /dev/null +++ b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml @@ -0,0 +1,2 @@ + +6987902285279866987902S1878-9293(16)30233-X10.1016/j.dcn.2017.05.004tbl0005Table 1Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

Table 1
Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).
Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32
Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.
\ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json b/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json new file mode 100644 index 0000000..f72d946 --- /dev/null +++ b/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json @@ -0,0 +1 @@ +{"pmid": "16513147", "doi": "10.1016/j.neuropsychologia.2006.01.005", "pmcid": null} \ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv new file mode 100644 index 0000000..0e3dc1e --- /dev/null +++ b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv @@ -0,0 +1,77 @@ +table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-39.0,57.0,3.0,,Left middle frontal gyrus (BA 10),6,3.9,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,42.0,57.0,9.0,,Right middle frontal gyrus (BA 10),23,4.4,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,54.0,21.0,-6.0,,Right inferior frontal gyrus (BA 47),21,3.9,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-42.0,18.0,-6.0,,Left inferior frontal gyrus (BA 47),9,3.9,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,48.0,15.0,12.0,,Right insula (BA 13),8,3.7,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-39.0,0.0,27.0,,Left insula (BA 13),15,4.4,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,45.0,-42.0,51.0,,Right inferior parietal lobule (BA 40),37,4.1,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,33.0,-57.0,51.0,,Right superior parietal lobule (BA 7),25,4.4,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,42.0,-57.0,48.0,,Right angular gyrus (BA 39),8,3.7,Cue identification PM > uncontaminated ongoing +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,0.0,48.0,-6.0,,Medial anterior prefrontal cortex (BA 10),23,3.9,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,30.0,39.0,51.0,,Right superior frontal gyrus (BA 8),5,3.4,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,6.0,12.0,36.0,,Right cingulate gyrus (BA 24),7,3.9,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-42.0,-15.0,42.0,,Left precentral gyrus (BA 4/3),11,4.2,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-3.0,-21.0,57.0,,Left medial frontal gyrus (BA 6/31),17,3.7,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,6.0,-24.0,69.0,,Right medial frontal gyrus (BA 6),19,3.9,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,24.0,-24.0,72.0,,Right precentral gyrus (BA 4),11,3.6,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,3.0,-33.0,54.0,,Right precuneus (BA 7),13,3.7,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-3.0,-48.0,60.0,,Left precuneus (BA 7),14,3.5,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,12.0,-51.0,6.0,,Right lingual gyrus (BA 18/19),17,3.6,Uncontaminated ongoing > cue identification PM +3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-9.0,-63.0,12.0,,Left lingual gyrus/cuneus (BA 18/30),24,4.0,Uncontaminated ongoing > cue identification PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-39.0,57.0,3.0,,Left middle frontal gyrus (BA 10),8,3.6,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,39.0,54.0,15.0,,Right middle frontal gyrus (BA 10),203,5.1,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,27.0,54.0,-12.0,,Right middle/superior frontal gyrus (BA 11),16,3.7,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,21.0,-15.0,,Right inferior frontal gyrus (BA 47),22,4.1,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,9.0,21.0,48.0,,Right medial frontal gyrus (BA 8),15,3.6,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-39.0,18.0,-6.0,,Left insula (BA 13),20,3.9,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,12.0,39.0,,Right precentral gyrus (BA 9),24,3.7,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,51.0,12.0,9.0,,Right precentral gyrus/insula (BA 44),36,4.0,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-51.0,9.0,39.0,,Left inferior/middle central gyrus (BA 9),31,4.5,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-42.0,3.0,27.0,,Left precentral gyrus (BA 6),54,5.1,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,-42.0,51.0,,Right inferior parietal lobule (BA 40),199,5.2,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-48.0,-45.0,48.0,,Left inferior parietal lobule (BA 40),155,5.0,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,12.0,-66.0,39.0,,Right precuneus (BA 19),12,4.3,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,36.0,-90.0,-12.0,,Right inferior occipital gyrus (BA 18),46,4.0,Intention retrieval PM > uncontaminated ongoing +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,0.0,48.0,-6.0,,Medial anterior prefrontal cortex (BA 10),243,5.3,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,12.0,36.0,,Right cingulate gyrus (BA 24),5,3.7,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,54.0,-12.0,57.0,,Right lateral parietal cortex (BA 3),30,4.4,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-42.0,-15.0,45.0,,Left precentral gyrus (BA 4/3),13,3.6,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-3.0,-18.0,57.0,,Left medial frontal gyrus (BA 6/31),19,4.0,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,-30.0,72.0,,Right medial frontal gyrus (BA 6/4),13,3.7,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,3.0,-33.0,57.0,,Right precuneus (BA 7),8,3.6,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,12.0,-51.0,6.0,,Right lingual gyrus (BA 18),8,3.6,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-6.0,-57.0,15.0,,Left lingual gyrus/cuneus (BA 30),48,4.7,Uncontaminated ongoing > intention retrieval PM +3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,-87.0,24.0,,Right cuneus (BA 18),6,3.7,Uncontaminated ongoing > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,3.0,36.0,-12.0,,Right anterior cingulate (BA 32/11),53,3.7,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-3.0,15.0,-6.0,,Left anterior cingulate (BA 25),31,4.5,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-24.0,0.0,-9.0,,Left putamen,51,5.0,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,57.0,-18.0,21.0,,Right insula (BA 40),24,3.6,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-54.0,-21.0,21.0,,Left postcentral gyrus/insula (BA 40),113,4.5,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-54.0,-72.0,24.0,,Left middle temporal gyrus (BA 39/19),7,3.4,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-21.0,-78.0,-6.0,,Left lingual gyrus (BA 18),17,3.8,Cue identification PM > intention retrieval PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-33.0,51.0,18.0,,Left middle frontal gyrus (BA 10),6,3.4,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,27.0,51.0,-3.0,,Right superior frontal gyrus (BA 10),12,3.8,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-18.0,42.0,21.0,,Left medial frontal gyrus (BA 9),5,3.4,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-12.0,27.0,27.0,,Left anterior cingulate (BA 32/24),11,3.9,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,36.0,24.0,-3.0,,Right insula (BA 13/47),13,4.2,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-3.0,21.0,45.0,,Left cingulate gyrus (BA 32),364,6.3,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,24.0,12.0,63.0,,Right lateral frontal cortex (BA 6),118,4.5,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,3.0,9.0,30.0,,Right cingulate gyrus (BA 24),5,4.0,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-27.0,6.0,66.0,,Left lateral frontal cortex (BA 6),46,5.1,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-42.0,0.0,57.0,,Left middle frontal gyrus (BA 6),5,3.4,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-45.0,-54.0,57.0,,Left inferior parietal lobule (BA 40),5,3.4,Intention retrieval PM > cue identification PM +3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-12.0,-75.0,33.0,,Left cuneus (BA 18),246,5.8,Intention retrieval PM > cue identification PM +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,36.0,30.0,-6.0,,Right inferior frontal gyrus (BA 47),31,4.1,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-42.0,21.0,15.0,,Left insula (BA 13),9,4.3,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-51.0,9.0,33.0,,Left precentral gyrus (BA 6),11,3.5,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-27.0,6.0,-6.0,,Left putamen,30,3.9,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-42.0,-48.0,-9.0,,Left fusiform gyrus (BA 37),9,3.8,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-30.0,-93.0,21.0,,Left middle occipital gyrus (BA 19),9,4.3,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-15.0,-99.0,-3.0,,Left occipital lingual gyrus (BA 18),79,4.9,Cue identification ongoing > intention retrieval ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-21.0,54.0,6.0,,Left medial frontal gyrus (BA 10),15,4.2,Intention retrieval ongoing > cue identification ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,18.0,18.0,66.0,,Right middle frontal gyrus (BA 6),8,3.7,Intention retrieval ongoing > cue identification ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-12.0,-18.0,60.0,,Left paracentral lobule (BA 6),14,3.8,Intention retrieval ongoing > cue identification ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,12.0,-30.0,66.0,,Right paracentral lobule (BA 6),20,3.9,Intention retrieval ongoing > cue identification ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-15.0,-30.0,66.0,,Left postcentral gyrus (BA 4),10,4.0,Intention retrieval ongoing > cue identification ongoing +3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,3.0,-75.0,36.0,,Left cuneus (BA 18),6,3.9,Intention retrieval ongoing > cue identification ongoing diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json new file mode 100644 index 0000000..fc3e0ea --- /dev/null +++ b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json @@ -0,0 +1,11 @@ +{ + "title": "Differential components of prospective memory? Evidence from fMRI.", + "authors": "Simons, Jon S;Sch\u00f6lvinck, Marieke L;Gilbert, Sam J;Frith, Chris D;Burgess, Paul W", + "journal": "Neuropsychologia", + "keywords": null, + "abstract": "Two of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (\"cue identification\") and remembering the action to be performed (\"intention retrieval\"). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.", + "publication_year": 2006, + "coordinate_space": "MNI", + "license": null, + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt new file mode 100644 index 0000000..388975f --- /dev/null +++ b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt @@ -0,0 +1,68 @@ + + + + + + + + + + + + + + + + + + + + +Differential components of prospective memory?: Evidence from fMRI - ScienceDirect + + + + + + + + + + + + + + + + JavaScript is disabled on your browser. + Please enable JavaScript to use all the features on this page. + + +Skip to main contentSkip to article +ScienceDirectJournals & BooksHelpSearchMy accountThe University of Texas at AustinView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Methods3. Results4. DiscussionAcknowledgementsReferencesShow full outlineCited by (253)Figures (2)Tables (5)Table 1Table 2Table 3Table 4Table 5NeuropsychologiaVolume 44, Issue 8, 2006, Pages 1388-1397Differential components of prospective memory?: Evidence from fMRIAuthor links open overlay panelJon S. Simons a, Marieke L. Schölvinck a, Sam J. Gilbert a, Chris D. Frith b, Paul W. Burgess aShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.neuropsychologia.2006.01.005Get rights and contentAbstractTwo of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (“cue identification”) and remembering the action to be performed (“intention retrieval”). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.Previous article in issueNext article in issueKeywordsAnterior prefrontal cortexRostral prefrontal cortexFrontal lobesFunctional magnetic resonance imaging (fMRI)Executive function1. IntroductionProspective memory (PM1), remembering to perform an intended action after a delay (Meacham & Singer, 1977), may involve a number of processing stages: forming an intention, maintaining the intention in memory over an interval while being engaged in another (or ongoing) task, executing the intended action at the appropriate moment, and evaluating the outcome (Freud, 1901, Ellis, 1996). Much research into PM has focused on the third of these stages, involving recognition of the appropriate moment to act and remembering what action was to be performed (see Table 1 in Burgess, Scott, & Frith, 2003, for a list of cardinal properties of PM). The most often studied example is where an action needs to be performed when an external event occurs, such as remembering to stop and buy a loaf of bread when you drive past the grocery store (“event-based PM”; Einstein, Holland, McDaniel, & Guynn, 1992). McDaniel and Einstein (1992) proposed a division of event-based PM into two components: cue identification and intention retrieval. Cue identification involves the detection of the cue event (e.g., the grocery store) signaling that the intended action should be performed; intention retrieval involves the subsequent recovery of that intention (e.g., buying the bread) from memory. There are a considerable number of behavioral studies that have investigated these components (e.g., Brandimonte & Passolunghi, 1994; Cohen, West, & Craik, 2001; Marsh, Hicks, Cook, Hansen, & Pallos, 2003; Einstein et al., 1992; Einstein, McDaniel, Manzi, Cochran, & Baker, 2000; Ellis & Milne, 1996; West, Herndon, & Crewdson, 2001; West & Ross-Munroe, 2002; West, Wymbs, Jakubek, & Herndon, 2003).Much of this research has focused on one or other of the two components, such as on the effect of cue characteristics in triggering a response. It has been shown that cues that are particularly salient tend to be noticed more frequently (Einstein et al., 2000), that unfamiliar cues benefit prospective remembering (Brandimonte & Passolunghi, 1994), and that when an intention has been formed to respond to a particular category of cues, highly typical category members evoke the intention more often than less typical exemplars (Ellis & Milne, 1996). Studies of intention retrieval have concentrated primarily on the association between the cue and the stored intention. When this association is strong, retrieval may be relatively automatic, as opposed to more effortful processing when the association is weak (McDaniel & Einstein, 2000; McDaniel, Einstein, Guynn, & Breneiser, 2004).Despite the extensive research on PM processes, however, relatively few studies have investigated whether cue identification and intention retrieval might rely on separable cognitive processes. Cohen et al. (2001) evaluated in separate experiments the hypothesis that cue identification and intention retrieval are primarily supported by stimulus-driven and conceptually driven processes, respectively. Cue identification was manipulated by a change in format of the cue from study session to test session and, in a second experiment, intention retrieval was manipulated by a change in semantic relatedness between the cue and the intention from study session to test session. The authors found that a change in cue format reduced the number of PM cues detected, while semantically unrelated intentions were less often correctly recalled upon detection of the cue, consistent with their hypothesis. However, only accuracy data were reported, although many PM studies have reported an effect of prospective memory retrieval on reaction times (e.g., Burgess, Scott, & Frith, 2003; Marsh, Hicks, & Watson, 2002; Marsh et al., 2003; West et al., 2001). Marsh et al. (2003) examined reaction times while investigating the extent to which maintenance of a PM intention might affect cognitive processing of the ongoing task at the time a PM cue is encountered (see also Smith, 2003). Marsh et al. manipulated cue identification and intention retrieval demands separately and found differential effects on reaction times in the ongoing task. However, despite showing a differential effect of maintaining a PM intention on performance of the ongoing task, these authors did not study the effect of manipulating cue identification and intention retrieval on the PM task itself.The few available results thus suggest that cue identification and intention retrieval might be separable behaviorally, in that manipulating the demand on the two components may differentially affect error rates and/or reaction times. However, even if this is the case, it may not necessarily follow that the two components are supported by exclusively different brain regions. Previous neuroimaging studies of PM have used a variety of paradigms which, although not designed to manipulate cue identification and intention retrieval as experimental variables, have nevertheless involved cue identification and intention retrieval processes to differing extents. In all of these studies, a consistent pattern of activation has been observed, involving particularly anterior prefrontal cortex (approximating Brodmann area 10) (Burgess, Quayle, & Frith, 2001; Burgess et al., 2003; den Ouden, Frith, Frith, & Blakemore, 2005; Okuda et al., 1998). It is not clear, therefore, whether the anterior prefrontal cortex network is involved in PM function to a similar degree irrespective of the demands on cue identification and intention retrieval, or whether varying cue identification and intention retrieval as experimental variables will reveal that the key neural correlate of PM reflects processing relating to one of the hypothesized components more than the other.The experiment presented here examined these issues by scanning participants using fMRI while they were undertaking a task in which PM trials were embedded in an ongoing task in such a way as to prevent participants from actively rehearsing the intentions. Two PM conditions were used, one with high cue identification demand and low intention retrieval demand (the ‘cue identification PM condition’), and one with low cue identification demand and high intention retrieval demand (the ‘intention retrieval PM condition’). Cue identification was manipulated by altering the perceptual salience of the PM cues (Brandimonte & Passolunghi, 1994; Einstein et al., 2000). In the low cue identification demand condition, the cues were perceptually distinct from the ongoing trials, while in the high demand condition, the cues were perceptually similar but conceptually distinct. Intention retrieval demand was manipulated by varying the number of actions participants needed to perform in order to determine the appropriate response. If, as predicted by previous neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), an anterior prefrontal cortex network supports PM function regardless of the demands on cue identification and intention retrieval, then substantial overlap can be expected between the patterns of activation associated with each PM condition.The use of word and shape versions of the task enabled analysis involving conjunction contrasts across tasks to identify brain regions that are commonly activated across stimulus types, and might be considered to reflect “central”, task-independent PM processes, as opposed to those that might be specific to a particular stimulus type or task. In addition, to examine the effect of maintaining a PM intention on performance of the ongoing task (Marsh et al., 2003, Smith, 2003), a session consisting solely of ongoing trials was included at the beginning of the experiment, before participants had received any instructions concerning PM trials. Previous studies have shown that once instructed about a PM condition, the expectation that a PM trial will occur continues even if participants are subsequently instructed that there will be no PM trials in the upcoming block (Burgess et al., 2003; Einstein et al., 2005; Holbrook, Bost, & Cave, 2003). Ongoing trials presented before exposure to a PM condition should not be contaminated by the expectation of a PM trial, so were termed ‘uncontaminated’ ongoing trials, with ongoing trials occurring after presentation of PM instructions termed ‘contaminated’ ongoing trials. Burgess et al. (2001) have shown that not only the execution, but also the expectation, of a PM trial can be associated with lateral anterior prefrontal cortex activation. If this region is involved in maintenance of the PM intention, it should show greater activation in the present experiment during contaminated versus uncontaminated ongoing trials and, indeed, between contaminated ongoing trials in the intention retrieval versus cue identification PM conditions.2. Methods2.1. ParticipantsSixteen right-handed native speakers of English (eight males, eight females, mean age 23.4 years, range 18-30 years) volunteered to take part in the experiment. They were screened using a comprehensive medical questionnaire and written informed consent was obtained before participating.2.2. Design and materialsTwo PM tasks, a word and a shape task, were administered to each participant. Each task consisted of ongoing trials, prospective memory trials with high cue identification and low intention retrieval demands (termed cue identification PM trials), and prospective memory trials with low cue identification and high intention retrieval demands (termed intention retrieval PM trials). The two PM conditions occurred in separate sessions.Each trial in the word task consisted of two nouns presented in the middle of the screen, one of which was written in upper case and the other in lower case letters (see Fig. 1). Words were drawn from the MRC Psycholinguistic Database (Wilson, 1988), and were matched for written frequency, familiarity, and concreteness. For ongoing trials, participants were instructed to indicate using a keypad whether the left or the right of the two words contained more letters. In the cue identification PM condition, in which trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the words belonged to the same semantic category, for example cow and horse. Conversely, in the intention retrieval PM condition, in which trials were perceptually distinct from the ongoing condition, words were written in the same case and participants were required to retrieve a greater number of actions than in the cue identification PM condition: count up the syllables of both words and press one key if the total was four or less, or another key if the total was higher than four. To avoid interference between the instructions, words of the same semantic category were never written in the same case.Download : Download full-size imageFig. 1. The words (top) and shapes (bottom) experimental tasks. An ongoing trial, a cue identification PM trial, and an intention retrieval PM trial are shown for each task.The shape task consisted of a 4 × 4 grid, in which a colored triangle and a random other shape, such as a pentagon, were presented (see Fig. 1). Each shape was drawn in a different color, selected from six possible hues. Irregular shapes were used to avoid recognition at first glance. For ongoing trials, participants were instructed to indicate whether the shape other than the triangle was presented to the left or the right of the triangle (see Fig. 1). In the cue identification PM condition in which, as before, trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the two shapes were a chess knight's move away from each other. In the intention retrieval condition, in which trials were perceptually distinct from the ongoing condition, the two shapes were of the same color and participants were required to retrieve a greater number of actions than in the cue identification PM condition: determine the number of sides of the shape other than the triangle, and press one key if this number was five or less, and another key if this number was higher than five. Again, to avoid conflicting instructions, shapes were never both a knight's move away from each other and drawn in the same color.Word and shape tasks were administered in short blocks of approximately 35 s, interrupted by approximately 10 s of an unrelated task which was used as a baseline condition, common to all scanning sessions, against which hemodynamic activity relating to the different experimental conditions could be contrasted across sessions. In the unrelated task, participants were asked to press two keys alternately to make a row of Xs flip as quickly as possible between a horizontal and a vertical configuration. The inter-trial interval in the unrelated task was varied randomly between 300 and 700 ms to induce subjects to pay attention to the stimuli.2.3. ProcedureEach trial consisted of 500 ms of a fixation cross, followed by presentation of the stimulus (the two words or shapes) for a maximum of 3000 ms. The tasks were subject-paced to prevent rehearsal of the given instructions (cf. Burgess et al., 2003) and ensure variable onset times of the trials, which improves fMRI design estimation efficiency (Henson, 2003). There was an inter-trial interval of 250 ms.The tasks were administered in six sessions. The first two sessions (one words, one shapes, with task order counterbalanced between subjects) consisted of “uncontaminated” ongoing trials only without any expectation of a PM trial (no mention of PM conditions was made in the instructions for these first sessions). Two PM sessions then followed for each task, one session containing “contaminated” ongoing and cue identification PM trials, and one session containing “contaminated” ongoing and intention retrieval PM trials. The order of the PM conditions was also counterbalanced between subjects. Each session consisted of blocks of approximately 35 s of task (range 34-36 s, randomized) alternating with around 10 s of the unrelated task (range 9-11 s, randomized), with a 1 s pause between blocks. All sessions were preceded by instructions and a practice round. Each of the four PM sessions (two PM conditions for each of the two tasks) consisted of a number of ongoing trials interspersed with one group of four PM trials per 35 s block. This group of PM trials was always presented after approximately 20 s of ongoing task, to ensure that the participant would be fully engaged in the ongoing task and to control for the time between the last PM trial of a group and the first PM trial of the next group. To minimize the possibility that the appearance of the PM trials could be anticipated, two of the 35 s blocks in each condition did not contain any PM trials at all, and in two other 35 s blocks the PM trials were presented close to the beginning of the block. Anticipation of the PM task on a trial to trial basis was reduced further by placing an extra ongoing trial randomly somewhere in between the four PM trials and by varying the position of the group of PM trials within the block. In total, 32 PM trials were presented per session. The four PM sessions each lasted 517 s.2.4. Image acquisition and data analysisT2-weighted echo-planar functional images were acquired using a 3T Siemens Allegra system. For each subject, two time series of 21 followed by four time series of 227 whole-brain images were obtained (TR = 2.34 s, TE = 30 ms, 36 sequential axial slices aligned at approximately 10° to the AC-PC transverse plane, 2 mm thickness, 1 mm inter-slice skip). The first six images of each session were discarded to allow for T1 equilibration. Prior to the actual experiment, a magnetic fieldmap was acquired for each subject, which was used in the pre-processing of the functional images to reduce the distorting effects of the sinus area on the prefrontal cortex.FMRI data were pre-processed and analyzed using the statistical parametric mapping procedure as implemented in SPM2 (Wellcome Department of Imaging Neuroscience, London). All images were realigned to the first image to correct for motion (using 4th-degree B-spline interpolation), after which the magnetic fieldmap was used to create a mean undistorted image. After realignment, all images were resampled in time to match the middle slice, to correct for differences in slice acquisition timing. The images were then normalized to an EPI template in MNI stereotactic space (Cocosco, Kollokian, Kwan, & Evans, 1997). Normalized images were resampled into 3 mm cubic voxels and smoothed using a Gaussian filter (8 mm FWHM kernel). A high-pass filter of 1/128 Hz was used to remove low-frequency noise, and an AR(1) + white noise model corrected for temporal autocorrelation. Finally, the time series was scaled to a grand mean of 100 across voxels and scans within each session.Random effects statistical analysis was undertaken twice, once using a block design to estimate the main effects of interest, and once using an event-related design to allow investigation of effects that might be correlated with reaction time. Each analysis was conducted in two stages of a mixed effects model. In the first analysis, 16 conditions were defined: baseline and ongoing conditions for all six sessions, and a PM condition in the last four sessions. Blocks of PM condition lasted from the moment that the first PM trial which the subject responded correctly to appeared on the screen until the subject responded to the last PM trial in a group. Blocks of all conditions were modeled by convolving a boxcar that had a specific onset time and duration with a canonical haemodynamic response function. In the second analysis, the aforementioned conditions were re-defined as event-types for separate trials; a separate regressor coded for missed responses in each session. In both analyses, parameter values for each covariate were then estimated using a subject-specific fixed-effects model. Movement parameters in the three directions of motion and 3° of rotation were included as confounds, as well as a single covariate representing the mean session effect. In the event-related analysis, RTs were included as a parametric modulator of each event-type regressor.Subject-specific estimates for the contrasts of interest were obtained using linear contrasts across sessions. To control for between-session signal differences, the effects of interest within each session were contrasted against the baseline condition from that same session. These estimates were entered into the second stage of analysis treating subjects as a random effect, using a one-sample t-test across subjects. Since activations that were independent of the type of stimuli involved (words or shapes) were of principal theoretical interest, a cognitive conjunction analysis was applied to the data which identifies as significant only those brain regions that are commonly activated in both tasks. Statistical parametric maps of the independent word and shape contrasts of interest were constructed and a one-way ANOVA on the 16 subjects was used to reveal brain regions significantly activated across both tasks, at an uncorrected threshold of p < 0.001. The anatomical locations and approximate Brodmann areas of significant cluster maxima of at least five contiguous voxels were localized using the Talairach and Tournoux atlas (Talairach & Tournoux, 1988) after adjusting coordinates to allow for differences between the MNI and Talairach templates (Brett, Christoff, Cusack, & Lancaster, 2001). The activation in prefrontal cortex associated with both PM conditions was examined in more detail by extracting mean percentage signal change magnitude relative to the baseline conditions from the subject-specific parameter estimates of cluster maxima, and subjecting them to a repeated-measures analysis that included region (left lateral BA 10, right lateral BA 10, and medial BA 10) and PM condition as repeated factors.3. Results3.1. Behavioral resultsThe accuracy and reaction time data as a function of task and condition are displayed in Table 1. There was no main effect of task in terms of accuracy, F(1,15) = 0.67, n.s., but a significant main effect of condition, F(3,45) = 102.74, p < 0.0001, and a trend towards an interaction between the two factors, F(3,45) = 2.73, p = 0.055. Accuracy scores were lower on PM trials compared to both types of ongoing trials, F(1,15) = 53.57, p < 0.0001. The accuracy difference between the cue identification and intention retrieval PM conditions was also significant, participants being less accurate in the intention retrieval PM condition compared to the cue identification PM condition, F(1,15) = 15.44, p < 0.001.Table 1. Accuracy (%) and reaction time (ms) data per task (standard deviations in parentheses)Empty CellWords taskShapes taskUncontaminated ongoing Accuracy98.9 (1.7)97.4 (3.0) RT566 (151)720 (129)Contaminated ongoing Accuracy97.0 (1.7)96.8 (2.2) RT991 (260)839 (102)Cue identification PM Accuracy80.6 (12.0)83.3 (13.6) RT1133 (169)836 (113)Intention retrieval PM Accuracy75.9 (10.2)68.3 (7.9) RT1596 (190)1394 (207)Uncontaminated ongoing: ongoing trials where no PM trial was expected. Contaminated ongoing: ongoing trials where a PM trial was expected. Cue identification PM: prospective memory trials with high demand on cue identification and low demand on intention retrieval. Intention retrieval PM: prospective memory trials with high demand on intention retrieval and low demand on cue identification.Reaction times were significantly increased in the word compared to the shape task, F(1,15) = 23.34, p < 0.0001, and showed a main effect of condition, F(3,45) = 145.20, p < 0.0001, and an interaction, F(3,45) = 23.07, p < 0.0001. Both tasks showed an increase in reaction times between uncontaminated and contaminated ongoing trials, F(1,15) = 38.08, p < 0.0001, and between cue identification PM and intention retrieval PM trials, F(1,15) = 191.08, p < 0.0001. A comparison between the contaminated ongoing trials in the two PM conditions revealed that ongoing trials in the cue identification PM condition were associated with significantly longer RTs than in the intention retrieval PM condition, t(15) = 4.55, p < 0.001, though when separated per task this difference was only significant in the words task, t(15) = 4.93, p < 0.001.3.2. Neuroimaging resultsThe results from the block analysis are reported first.2 To identify the brain areas involved in prospective memory, each of the PM conditions in the word and shape tasks was contrasted with the uncontaminated ongoing condition, with between-session differences controlled for using the common baseline condition as described in the Methods section. Because we are interested in the brain regions associated with prospective memory that are not dependent on the kind of task used, a conjunction contrast was used to identify activations that were common to both word and shape tasks. The conjunction contrast revealed a remarkably consistent pattern of significant activation in both the cue identification and intention retrieval PM conditions in anterior prefrontal cortex (BA 10), with activation bilaterally in lateral BA 10 and deactivation in medial BA 10 (see Fig. 2A and B). Examination of the signal confirmed that there was no significant effect of PM condition on activation in the anterior prefrontal cortex clusters identified, F(1,15) = 3.22, n.s. Significant activation was also found in a number of other areas including ventrolateral prefrontal and lateral parietal cortex in both PM conditions, and dorsolateral prefrontal cortex and orbitofrontal cortex in the intention retrieval PM condition (see Table 2, Table 3).Download : Download full-size imageFig. 2. Group functional activation maps of percentage signal change, in a conjunction across the word and the shape task. Activations are shown in yellow/red, deactivations are shown in blue, with activations of particular interest circled. Z coordinates are shown in top left corner. (A) In the cue identification PM > uncontaminated ongoing contrast, bilateral BA 10 activation and medial BA 10 deactivation was observed. A highly similar pattern was observed in (B) the intention retrieval PM > uncontaminated ongoing contrast. Differences between conditions emerge in (C), the direct intention retrieval PM > cue identification PM contrast, with significantly greater activation in anterior prefrontal cortex bilaterally in the intention retrieval PM condition, and evidence of deactivation in medial anterior BA 10. Left lateral BA 10 activation was found in (D) the contaminated ongoing > uncontaminated ongoing condition (left: cue identification PM ongoing condition; right: intention retrieval PM ongoing condition) (for interpretation of the references to color in this figure legend, the reader is referred to the web version of the article).Table 2. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification PM > uncontaminated ongoing Left middle frontal gyrus (BA 10)-395733.96 Right middle frontal gyrus (BA 10)425794.423 Right inferior frontal gyrus (BA 47)5421-63.921 Left inferior frontal gyrus (BA 47)-4218-63.99 Right insula (BA 13)4815123.78 Left insula (BA 13)-390274.415 Right inferior parietal lobule (BA 40)45-42514.137 Right superior parietal lobule (BA 7)33-57514.425 Right angular gyrus (BA 39)42-57483.78Uncontaminated ongoing > cue identification PM Medial anterior prefrontal cortex (BA 10)048-63.923 Right superior frontal gyrus (BA 8)3039513.45 Right cingulate gyrus (BA 24)612363.97 Left precentral gyrus (BA 4/3)-42-15424.211 Left medial frontal gyrus (BA 6/31)-3-21573.717 Right medial frontal gyrus (BA 6)6-24693.919 Right precentral gyrus (BA 4)24-24723.611 Right precuneus (BA 7)3-33543.713 Left precuneus (BA 7)-3-48603.514 Right lingual gyrus (BA 18/19)12-5163.617 Left lingual gyrus/cuneus (BA 18/30)-9-63124.024Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Table 3. Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellIntention retrieval PM > uncontaminated ongoing Left middle frontal gyrus (BA 10)-395733.68 Right middle frontal gyrus (BA 10)3954155.1203 Right middle/superior frontal gyrus (BA 11)2754-123.716 Right inferior frontal gyrus (BA 47)4521-154.122 Right medial frontal gyrus (BA 8)921483.615 Left insula (BA 13)-3918-63.920 Right precentral gyrus (BA 9)4512393.724 Right precentral gyrus/insula (BA 44)511294.036 Left inferior/middle central gyrus (BA 9)-519394.531 Left precentral gyrus (BA 6)-423275.154 Right inferior parietal lobule (BA 40)45-42515.2199 Left inferior parietal lobule (BA 40)-48-45485.0155 Right precuneus (BA 19)12-66394.312 Right inferior occipital gyrus (BA 18)36-90-124.046Uncontaminated ongoing > intention retrieval PM Medial anterior prefrontal cortex (BA 10)048-65.3243 Right cingulate gyrus (BA 24)612363.75 Right lateral parietal cortex (BA 3)54-12574.430 Left precentral gyrus (BA 4/3)-42-15453.613 Left medial frontal gyrus (BA 6/31)-3-18574.019 Right medial frontal gyrus (BA 6/4)6-30723.713 Right precuneus (BA 7)3-33573.68 Right lingual gyrus (BA 18)12-5163.68 Left lingual gyrus/cuneus (BA 30)-6-57154.748 Right cuneus (BA 18)6-87243.76Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Separate examination of the word and shape task contrasts revealed additional, potentially task-dependent, activation associated with cue identification in left premotor cortex, precuneus, and occipital cortex for words, and parahippocampal cortex for shapes. Similarly, the intention retrieval PM condition was associated with additional activation in left cingulate gyrus, premotor cortex, and precuneus for words, and a more anterior part of cingulate gyrus and postcentral gyrus for shapes.Returning to the task-independent conjunction contrasts, despite the similarities in activation patterns associated with the two PM conditions versus the uncontaminated ongoing condition, a number of brain regions were identified in the direct task-independent contrast between cue identification PM and intention retrieval PM (see Fig. 2C). These results provide evidence of additional differential BA 10 involvement in the PM conditions. Significantly greater activation was found in the intention retrieval PM condition bilaterally in regions of BA 10 that peaked slightly less laterally than those observed in the contrasts above. Medial BA 10 appeared to be more active in the cue identification PM condition (see blue activation in Fig. 2C), although this effect did not exceed the whole-brain threshold of p < 0.001. At a threshold of p < 0.05 corrected for the voxels in an 8-mm sphere around the peak that was identified in both the cue identification PM versus ongoing and intention retrieval PM versus ongoing contrasts (0, 48, -6; see Table 2, Table 3), however, significant medial BA 10 activation did emerge in the cue identification versus intention retrieval PM contrast (0, 45, -9; Z = 3.5; voxels = 19). Other brain regions which were more active at the whole-brain threshold in the cue identification PM condition included the anterior cingulate, whereas in the intention retrieval PM condition more extensive areas of the cingulate gyri were among regions showing greater activation (see Table 4).Table 4. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification PM > intention retrieval PM Right anterior cingulate (BA 32/11)336-123.753 Left anterior cingulate (BA 25)-315-64.531 Left putamen-240-95.051 Right insula (BA 40)57-18213.624 Left postcentral gyrus/insula (BA 40)-54-21214.5113 Left middle temporal gyrus (BA 39/19)-54-72243.47 Left lingual gyrus (BA 18)-21-78-63.817Intention retrieval PM > cue identification PM Left middle frontal gyrus (BA 10)-3351183.46 Right superior frontal gyrus (BA 10)2751-33.812 Left medial frontal gyrus (BA 9)-1842213.45 Left anterior cingulate (BA 32/24)-1227273.911 Right insula (BA 13/47)3624-34.213 Left cingulate gyrus (BA 32)-321456.3364 Right lateral frontal cortex (BA 6)2412634.5118 Right cingulate gyrus (BA 24)39304.05 Left lateral frontal cortex (BA 6)-276665.146 Left middle frontal gyrus (BA 6)-420573.45 Left inferior parietal lobule (BA 40)-45-54573.45 Left cuneus (BA 18)-12-75335.8246Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Since the behavioral data showed longer RTs in the intention retrieval PM than in the cue identification PM condition, it is possible that the greater bilateral BA 10 activation in the intention retrieval PM condition might be attributable to differences in time on task. To test this possibility, a second analysis was conducted including RT as a parametric modulator to identify brain regions where activation was positively or negatively correlated with RTs. No significant correlation was found between RT and activation in the regions of BA 10 identified in the previous contrasts, even when the threshold was dropped to p < 0.1 uncorrected. It thus seems safe to attribute the greater bilateral BA 10 activation during intention retrieval PM trials primarily to the demands on recovering the delayed intention.To examine the effect of maintaining a PM intention on performance of the ongoing task, the contaminated ongoing trials in both PM conditions were contrasted with the uncontaminated ongoing trials. In both PM conditions, contaminated ongoing trials were associated with greater activation in left lateral BA 10/47 (-48, 39, 0 for cue identification; -48, 42, -3 for intention retrieval; see Fig. 2D). Again, no correlation was found between RTs and activation in these regions in the analysis that included RT as a parametric modulator. Contrasting directly the contaminated ongoing trials in both PM conditions, significantly greater activation in left lateral BA 10 was observed during the intention retrieval PM condition than the cue identification PM condition (see Table 5). This is consistent with the idea that activation in this region reflects maintenance of the intended PM actions (Burgess et al., 2001).Table 5. Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification ongoing > intention retrieval ongoing Right inferior frontal gyrus (BA 47)3630-64.131 Left insula (BA 13)-4221154.39 Left precentral gyrus (BA 6)-519333.511 Left putamen-276-63.930 Left fusiform gyrus (BA 37)-42-48-93.89 Left middle occipital gyrus (BA 19)-30-93214.39 Left occipital lingual gyrus (BA 18)-15-99-34.979 Right cuneus (BA 18)15-102124.458Intention retrieval ongoing > cue identification ongoing Left medial frontal gyrus (BA 10)-215464.215 Right middle frontal gyrus (BA 6)1818663.78 Left paracentral lobule (BA 6)-12-18603.814 Right paracentral lobule (BA 6)12-30663.920 Left postcentral gyrus (BA 4)-15-30664.010 Left cuneus (BA 18)3-75363.96Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.4. DiscussionThe main finding of the present experiment was that, despite behavioral differences between the cue identification and intention retrieval PM conditions, a strikingly similar pattern of hemodynamic changes, involving anterior prefrontal cortex (BA 10), was observed across both conditions. Finding such consistent results even when the demands on cue identification and intention retrieval were manipulated sufficiently to produce significant behavioral effects, confirms the view from previous neuroimaging studies that this region is likely to be of central importance to prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005).For example, Okuda et al. (1998) reported activation in the left frontal pole (BA 10), as well as right dorsolateral and ventrolateral prefrontal cortex (BA 8/9/47) and anterior cingulate (BA 24), when participants remembered and acted upon a list of target words relative to performing an ongoing routine activity (word repetition). Activation in the frontal pole (BA 10, bilaterally) was also found by Burgess et al. (2001) across several cognitive tasks. In that study, activation during an ongoing task was compared to activation in two PM conditions: one in which PM trials were expected but never actually occurred, and one in which PM trials were expected and acted upon. In both PM conditions, activation in bilateral frontal pole, right lateral prefrontal and parietal cortex, plus precuneus, was found. Burgess et al. (2003) extended these results by showing that the bilateral activation of lateral BA 10 associated with retrieving a delayed intention was accompanied by deactivation in medial BA 10. Recently, den Ouden et al. (2005) reported activation in lateral BA 10, lateral parietal cortex, and precuneus, associated with keeping an intention in mind while performing an ongoing task responding to questions about intentions and actions.In the present experiment, the reliability of the consistently observed lateral BA 10 activation was assessed by manipulating the demands placed on cue identification and intention retrieval processes as experimental variables. Of course, both PM conditions involved cue identification and intention retrieval to some degree; the experimental manipulation was not all-or-none. However, the demands on the two PM components were manipulated sufficiently for significant behavioral differences to emerge between them: accuracy was lower, and reaction times longer, in the intention retrieval condition than in the cue identification condition, although correlational analysis indicated that these behavioral differences could not explain the consistent hemodynamic changes observed across conditions. Significant lateral BA 10 activation was seen bilaterally in both PM conditions, confirming the characteristic nature of this activation, as reported by a number of neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005). Moreover, both PM conditions were additionally associated with medial BA 10 deactivation when contrasted with ongoing trials, consistent with the findings of Burgess et al. (2003), who also observed reduced medial BA 10 activation during PM trials versus ongoing trials. Of course, the limitations of fMRI do not allow us to speculate as to whether precisely the same neurons were recruited by both PM conditions, but it does seem clear that, at the macro level of Brodmann areas at least, similar patterns of activation in BA 10 were observed during both cue identification and intention retrieval PM conditions.Despite finding that similar regions of BA 10 were activated (and deactivated) in both PM conditions, there was evidence that the level of activation in lateral BA 10 was greater in the intention retrieval PM condition than in the cue identification PM condition. The correlational RT analysis showed this effect was not attributable to differences in reaction time as the activations observed did not correlate with RT durations, consistent with results observed by Burgess et al. (2003). This suggests that the results cannot be explained by a simple task difficulty account, since greater difficulty (in terms at least of the amount of effort required to perform the task) could be expected to be reflected in increased RTs. Left lateral BA 10 was also significantly activated in the contrast between contaminated and uncontaminated PM trials, suggesting that activation in the region is affected by prior exposure to a PM intention. This is consistent with the finding from Burgess et al. (2001) that BA 10 activation is seen when PM trials are expected, regardless of whether they actually occur, and perhaps indicates that the region contributes to the maintenance of the delayed intention, a conclusion supported by the finding in the present experiment that BA 10 activation during contaminated ongoing trials was greater in the intention retrieval than the cue identification PM conditions. Moreover, there are echoes in these results of data from a number of electrophysiology studies which found that intention retrieval was associated with a sustained frontal slow-wave modulation that appeared to be particularly evident when retrieval of the PM intention was made more demanding (West et al., 2001; West & Ross-Munroe, 2002). Increasing the number of intentions to be remembered resulted in a larger amplitude of this frontal slow-wave (West et al., 2003). Although it is problematic to compare electrophysiological and neuroimaging results directly, there are similarities between the ERP increase over frontal sites and the present finding of greater lateral anterior prefrontal cortex activation when intention retrieval is more demanding.The greater lateral BA 10 activation associated with intention retrieval can be conceived of in terms of differential attention towards external events and internally generated thought processes (Burgess, Simons, Dumontheil, & Gilbert, 2005; Gilbert, Frith, & Burgess, 2005; Gilbert, Simons, Frith, & Burgess, in press; Simons, Owen, Fletcher, & Burgess, 2005; Simons, Gilbert, Owen, Fletcher, & Burgess, 2005; see also Christoff & Gabrieli, 2000; Gusnard, Akbudak, Shulman, & Raichle, 2001). By this account, when one's primary aim is to detect a cue, attention must be turned towards the external world; once the cue has been detected, however, one must disengage from the external stimuli and attend to internal representations so that the relevant intention can be retrieved from memory. Thus, placing higher demands on cue identification implies biasing attention more towards external stimuli, whereas a higher load on intention retrieval implies an increase of attentional focus upon internally generated thought.A number of previous studies have shown that lateral BA 10 plays an important role in the recollection of details about the context in which previous events occurred (Ranganath, Johnson, & D’Esposito, 2000; Rugg, Fletcher, Chua, & Dolan, 1999; Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005), an ability that requires retrieval of internally represented mnemonic information (Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005). In contrast, medial BA 10 has been associated with performance of tasks that emphasize the processing of externally presented stimuli (Gilbert et al., 2005, Janata et al., 2002, Small et al., 2003). Thus, Burgess et al. (2005) have suggested that lateral areas of BA 10 may play a role in maintaining attention towards internal cognition and more medial areas in maintaining attention towards external stimuli. The results from the PM versus ongoing contrasts in the present experiment can be interpreted along these lines, with medial BA 10 deactivation reflecting disengagement from the external ongoing task stimuli and the lateral BA 10 activation being associated with the directing of attention towards the internally represented PM intention (see Burgess et al., 2003, for a similar suggestion). Consistent with this view, lateral BA 10 activation was greater when the demands on intention retrieval were higher, and there was evidence of greater medial BA 10 activation associated with the cue identification PM condition, in which external cue processing demands were greater. Taken as a whole, these results are in agreement with the hypothesis that BA 10 acts as a “gateway”, biasing attention between externally derived perceptual information used to detect the occurrence of a PM cue and internal thought processes relating to the stored PM intention (Burgess et al., 2005; Gilbert et al., 2005; Simons, Gilbert, et al., 2005).PM-related activation was also seen in a number of areas outside the anterior PFC region of interest. When contrasted against uncontaminated ongoing trials, the cue identification and intention retrieval PM conditions provoked activation in regions of lateral PFC and parietal cortex, among other areas. A number of regions showed differential activation in the two PM conditions. These included anterior cingulate cortex, which was activated more in the cue identification PM condition, and posterior cingulate and precuneus, which showed greater activation in the intention retrieval PM condition. Activation in all these regions has been observed in previous neuroimaging studies of prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), and has been linked with a number of processes relevant to PM retrieval. For example, lateral PFC, anterior cingulate and lateral parietal cortex have been proposed to constitute a cognitive control network involved in sustained attention and vigilance to particularly visual stimuli (Burgess et al., 2001; Coull, Frith, Frackowiak, & Grasby, 1996; Cabeza et al., 2003). Posterior cingulate, precuneus, and lateral parietal cortex have been linked in many studies with functions related to retrieval of stored mnemonic information such as recollection, retrieval confidence, and imagery (see Wagner, Shannon, Kahn, & Buckner, 2005, for a recent review).In conclusion, the present experiment demonstrated that it is possible to tease apart behaviorally cue identification and intention retrieval components of PM, but that they may reflect the operation of similar underlying processes supported by anterior prefrontal cortex (BA 10). Evidence suggests that lateral BA 10 may reflect the maintenance and/or retrieval of the stored PM intention, showing greater activation not only during intention retrieval PM trials, but also when maintaining an intention in memory during the ongoing task.AcknowledgementsWe are grateful to Daniel Glaser and Hanneke den Ouden for discussion and advice, and the staff of the Functional Imaging Laboratory for scanning assistance. 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HerndonEffects of intention load and background context on prospective remembering: An event-related brain potential study.Psychophysiology, 40 (2003), pp. 260-276View in ScopusGoogle ScholarWilson, 1988M.D. WilsonThe MRC psycholinguistic databaseBehavior Research Methods, Instruments, and Computers, 20 (1988), pp. 6-11View in ScopusGoogle ScholarCited by (253)Sensory modality affects the spatiotemporal dynamics of alpha and theta oscillations associated with prospective memory2024, International Journal of PsychophysiologyShow abstractThe maintenance of an intention in memory (Prospective Memory, PM) while performing a task is associated with a cost in terms of both performance (longer response times and lower accuracy) and neurophysiological modulations, which extent depends on several features of the stimuli.This study explores the neural patterns associated with PM in different sensory modalities, to identify differences depending on this variable and discuss their functional meaning.Data were collected using a High-Density EEG during a baseline and a PM condition, proposed in a visual and an auditory version. Theta and alpha oscillations were compared between the two conditions within each modality using a cluster-based permutation approach.PM conditions were associated with clusters of decreased alpha and theta activity in both modalities. However, different spatiotemporal dynamics were elicited as a function of sensory modality: alpha decreases displayed an overlapping onset between modalities, but different durations, lasting longer in the auditory modality. Conversely, the clusters of decreased theta activity presented similar durations between modalities, but different temporal and spatial onsets, appearing at different moments over the respective sensory areas.The similar spatiotemporal properties of alpha suppression between modalities indicate that such oscillations may represent a supramodal, top-down process, presumably reflecting the external direction of attention to successfully detect the prospective cue (strategic monitoring). In theta, the clusters showed more modality-specific differences, which temporal and spatial properties correspond to the ones necessary to perform the ongoing task, suggesting a shift in resource allocation in favor of the PM task.Dynamic functional connectivity associated with prospective memory success in children2022, Neuroimage: ReportsShow abstractTo remember the prospective intention successfully, going back and forth between the background task and the intention, i.e., the dynamics of these multiple processes can be critical. An executive function like task switching has been associated with the success of prospective memory (PM) in children, but the neural mechanism of PM in children has not been investigated. The purpose of this study was to reveal the dynamic functional connectivity underlying the success of PM in children. Healthy 108 children, aged 7 to 15, were engaged in a single trial PM task, with a 30-min delay. Temporal variabilities in their resting-state functional connectivity were analyzed, using sliding windows with seed regions of interest ROIs of the PM network. About 70% of children successfully remembered the intention; they showed greater dynamics in neural connectivity between the right dorsolateral prefrontal cortex (DLPFC) and intraparietal sulcus, and between the right DLPFC and insula as compared to children with PM failure. Everyday activities and the usual attention to ongoing processes can be associated with alertness in the right frontoparietal network and internal-state monitoring in the insula network, and those dynamics might be associated with one-time event PM success in children.Prefrontal cortical activation associated with prospective memory while walking around a real-world street environment2022, NeuroImageShow abstractRostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in “real world” situations outside the laboratory in freely ambulant individuals. The addition of a “brain-first” approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. This can be extremely valuable when trying to link brain and cognition.Serial multitasking2021, Encyclopedia of Behavioral Neuroscience: Second EditionShow abstractSerial multitasking is the ability to coordinate the completion of several tasks to achieve an overall goal (e.g., preparing a meal for friends). In everyday situations requiring serial multitasking, only one task can be attempted at a time; individuals are seeking to perform several tasks within a specific period but cannot perform the tasks from start to finish in strict succession. This chapter reviews the contribution of neuropsychology and cognitive aging research to the understanding of serial multitasking and discusses the cognitive processes that act cooperatively with one another to allow us to achieve our complex cognitive goals.Inferior parietal lobule involved in representation of “what” in a delayed-action Libet task2021, Consciousness and CognitionShow abstractIntentional motor action is typically characterized by the decision about the timing, and the selection of the action variant, known as the “what” component. We compared free action selection with instructed action, where the movement type was externally cued, in order to investigate the action selection and action representation in a Libet’s task. Temporal and spatial locus of these processes was examined using the combination of high-density electroencephalography, topographic analysis of variance, and source reconstruction. Instructed action, engaging representation of the response movement, was associated with distinct negativity at the parietal and centro-parietal channels starting around 750 ms before the movement, which has a source particularly in the bilateral inferior parietal lobule. This suggests that in delayed-action tasks, the process of action representation in the inferior parietal lobule may play an important part in the larger parieto-frontal activity responsible for movement selection.The neurological and neuropsychological effects of child maltreatment2020, Aggression and Violent BehaviorShow abstractDue to the increase in annual cases, child maltreatment has become a significant focus in research. There are four types of childhood abuse that have been identified and discussed throughout this review: sexual abuse, physical abuse, emotional abuse, and neglect. In order for psychologists and other related professionals to recognize the contributions maltreatment has on psychiatric disorders, it is necessary to identify and understand the complex sequela of childhood abuse and neglect that may appear throughout the victim's lifespan. This paper reviewed current research on child maltreatment and its relationship to neurological impairments and neuropsychological effects. Additionally, a comparison between typical and impaired neurological morphology emphasized alterations in volumetric structures and stress response functions occurring in the HPA axis, amygdala, hippocampus, and corpus callosum. Recent studies have discovered that changes to the preceding neurological structures can significantly impact different neuropsychological domains including working memory, processing speed, language, visual-spatial abilities, and motor skills.View all citing articles on Scopus1Delegates at the 2nd International Conference on Prospective Memory, Zurich, July 2005, voted that the use of the abbreviation “PM” for prospective memory was preferable to other extant forms.2An event-related analysis was also conducted as verification, which produced activation in similar brain regions as the block analysis with reduced effect sizes, consistent with the lower statistical power associated with event-related designs (Henson, 2003).View AbstractCopyright © 2006 Elsevier Ltd. All rights reserved.Recommended articlesProspective memory in multiple sclerosis: The impact of cue distinctiveness and executive functioningBrain and Cognition, Volume 109, 2016, pp. 66-74Emmanuelle Dagenais, …, Pierre DuquetteView PDFEye movements provide insights into the conscious use of context in prospective memoryConsciousness and Cognition, Volume 52, 2017, pp. 68-74Vanessa K. Bowden, …, Shayne LoftView PDFRisk for Alzheimer’s disease: A review of long-term episodic memory encoding and retrieval fMRI studiesAgeing Research Reviews, Volume 62, 2020, Article 101133Ian M. McDonough, …, Meagan M. 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Elsevier

Neuropsychologia

Volume 44, Issue 8, 2006, Pages 1388-1397
Neuropsychologia

Differential components of prospective memory?: Evidence from fMRI

https://doi.org/10.1016/j.neuropsychologia.2006.01.005Get rights and content

Abstract

Two of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (“cue identification”) and remembering the action to be performed (“intention retrieval”). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.

Keywords

Anterior prefrontal cortex
Rostral prefrontal cortex
Frontal lobes
Functional magnetic resonance imaging (fMRI)
Executive function

1. Introduction

Prospective memory (PM1), remembering to perform an intended action after a delay (Meacham & Singer, 1977), may involve a number of processing stages: forming an intention, maintaining the intention in memory over an interval while being engaged in another (or ongoing) task, executing the intended action at the appropriate moment, and evaluating the outcome (Freud, 1901, Ellis, 1996). Much research into PM has focused on the third of these stages, involving recognition of the appropriate moment to act and remembering what action was to be performed (see Table 1 in Burgess, Scott, & Frith, 2003, for a list of cardinal properties of PM). The most often studied example is where an action needs to be performed when an external event occurs, such as remembering to stop and buy a loaf of bread when you drive past the grocery store (“event-based PM”; Einstein, Holland, McDaniel, & Guynn, 1992). McDaniel and Einstein (1992) proposed a division of event-based PM into two components: cue identification and intention retrieval. Cue identification involves the detection of the cue event (e.g., the grocery store) signaling that the intended action should be performed; intention retrieval involves the subsequent recovery of that intention (e.g., buying the bread) from memory. There are a considerable number of behavioral studies that have investigated these components (e.g., Brandimonte & Passolunghi, 1994; Cohen, West, & Craik, 2001; Marsh, Hicks, Cook, Hansen, & Pallos, 2003; Einstein et al., 1992; Einstein, McDaniel, Manzi, Cochran, & Baker, 2000; Ellis & Milne, 1996; West, Herndon, & Crewdson, 2001; West & Ross-Munroe, 2002; West, Wymbs, Jakubek, & Herndon, 2003).

Much of this research has focused on one or other of the two components, such as on the effect of cue characteristics in triggering a response. It has been shown that cues that are particularly salient tend to be noticed more frequently (Einstein et al., 2000), that unfamiliar cues benefit prospective remembering (Brandimonte & Passolunghi, 1994), and that when an intention has been formed to respond to a particular category of cues, highly typical category members evoke the intention more often than less typical exemplars (Ellis & Milne, 1996). Studies of intention retrieval have concentrated primarily on the association between the cue and the stored intention. When this association is strong, retrieval may be relatively automatic, as opposed to more effortful processing when the association is weak (McDaniel & Einstein, 2000; McDaniel, Einstein, Guynn, & Breneiser, 2004).

Despite the extensive research on PM processes, however, relatively few studies have investigated whether cue identification and intention retrieval might rely on separable cognitive processes. Cohen et al. (2001) evaluated in separate experiments the hypothesis that cue identification and intention retrieval are primarily supported by stimulus-driven and conceptually driven processes, respectively. Cue identification was manipulated by a change in format of the cue from study session to test session and, in a second experiment, intention retrieval was manipulated by a change in semantic relatedness between the cue and the intention from study session to test session. The authors found that a change in cue format reduced the number of PM cues detected, while semantically unrelated intentions were less often correctly recalled upon detection of the cue, consistent with their hypothesis. However, only accuracy data were reported, although many PM studies have reported an effect of prospective memory retrieval on reaction times (e.g., Burgess, Scott, & Frith, 2003; Marsh, Hicks, & Watson, 2002; Marsh et al., 2003; West et al., 2001). Marsh et al. (2003) examined reaction times while investigating the extent to which maintenance of a PM intention might affect cognitive processing of the ongoing task at the time a PM cue is encountered (see also Smith, 2003). Marsh et al. manipulated cue identification and intention retrieval demands separately and found differential effects on reaction times in the ongoing task. However, despite showing a differential effect of maintaining a PM intention on performance of the ongoing task, these authors did not study the effect of manipulating cue identification and intention retrieval on the PM task itself.

The few available results thus suggest that cue identification and intention retrieval might be separable behaviorally, in that manipulating the demand on the two components may differentially affect error rates and/or reaction times. However, even if this is the case, it may not necessarily follow that the two components are supported by exclusively different brain regions. Previous neuroimaging studies of PM have used a variety of paradigms which, although not designed to manipulate cue identification and intention retrieval as experimental variables, have nevertheless involved cue identification and intention retrieval processes to differing extents. In all of these studies, a consistent pattern of activation has been observed, involving particularly anterior prefrontal cortex (approximating Brodmann area 10) (Burgess, Quayle, & Frith, 2001; Burgess et al., 2003; den Ouden, Frith, Frith, & Blakemore, 2005; Okuda et al., 1998). It is not clear, therefore, whether the anterior prefrontal cortex network is involved in PM function to a similar degree irrespective of the demands on cue identification and intention retrieval, or whether varying cue identification and intention retrieval as experimental variables will reveal that the key neural correlate of PM reflects processing relating to one of the hypothesized components more than the other.

The experiment presented here examined these issues by scanning participants using fMRI while they were undertaking a task in which PM trials were embedded in an ongoing task in such a way as to prevent participants from actively rehearsing the intentions. Two PM conditions were used, one with high cue identification demand and low intention retrieval demand (the ‘cue identification PM condition’), and one with low cue identification demand and high intention retrieval demand (the ‘intention retrieval PM condition’). Cue identification was manipulated by altering the perceptual salience of the PM cues (Brandimonte & Passolunghi, 1994; Einstein et al., 2000). In the low cue identification demand condition, the cues were perceptually distinct from the ongoing trials, while in the high demand condition, the cues were perceptually similar but conceptually distinct. Intention retrieval demand was manipulated by varying the number of actions participants needed to perform in order to determine the appropriate response. If, as predicted by previous neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), an anterior prefrontal cortex network supports PM function regardless of the demands on cue identification and intention retrieval, then substantial overlap can be expected between the patterns of activation associated with each PM condition.

The use of word and shape versions of the task enabled analysis involving conjunction contrasts across tasks to identify brain regions that are commonly activated across stimulus types, and might be considered to reflect “central”, task-independent PM processes, as opposed to those that might be specific to a particular stimulus type or task. In addition, to examine the effect of maintaining a PM intention on performance of the ongoing task (Marsh et al., 2003, Smith, 2003), a session consisting solely of ongoing trials was included at the beginning of the experiment, before participants had received any instructions concerning PM trials. Previous studies have shown that once instructed about a PM condition, the expectation that a PM trial will occur continues even if participants are subsequently instructed that there will be no PM trials in the upcoming block (Burgess et al., 2003; Einstein et al., 2005; Holbrook, Bost, & Cave, 2003). Ongoing trials presented before exposure to a PM condition should not be contaminated by the expectation of a PM trial, so were termed ‘uncontaminated’ ongoing trials, with ongoing trials occurring after presentation of PM instructions termed ‘contaminated’ ongoing trials. Burgess et al. (2001) have shown that not only the execution, but also the expectation, of a PM trial can be associated with lateral anterior prefrontal cortex activation. If this region is involved in maintenance of the PM intention, it should show greater activation in the present experiment during contaminated versus uncontaminated ongoing trials and, indeed, between contaminated ongoing trials in the intention retrieval versus cue identification PM conditions.

2. Methods

2.1. Participants

Sixteen right-handed native speakers of English (eight males, eight females, mean age 23.4 years, range 18–30 years) volunteered to take part in the experiment. They were screened using a comprehensive medical questionnaire and written informed consent was obtained before participating.

2.2. Design and materials

Two PM tasks, a word and a shape task, were administered to each participant. Each task consisted of ongoing trials, prospective memory trials with high cue identification and low intention retrieval demands (termed cue identification PM trials), and prospective memory trials with low cue identification and high intention retrieval demands (termed intention retrieval PM trials). The two PM conditions occurred in separate sessions.

Each trial in the word task consisted of two nouns presented in the middle of the screen, one of which was written in upper case and the other in lower case letters (see Fig. 1). Words were drawn from the MRC Psycholinguistic Database (Wilson, 1988), and were matched for written frequency, familiarity, and concreteness. For ongoing trials, participants were instructed to indicate using a keypad whether the left or the right of the two words contained more letters. In the cue identification PM condition, in which trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the words belonged to the same semantic category, for example cow and horse. Conversely, in the intention retrieval PM condition, in which trials were perceptually distinct from the ongoing condition, words were written in the same case and participants were required to retrieve a greater number of actions than in the cue identification PM condition: count up the syllables of both words and press one key if the total was four or less, or another key if the total was higher than four. To avoid interference between the instructions, words of the same semantic category were never written in the same case.

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Fig. 1. The words (top) and shapes (bottom) experimental tasks. An ongoing trial, a cue identification PM trial, and an intention retrieval PM trial are shown for each task.

The shape task consisted of a 4 × 4 grid, in which a colored triangle and a random other shape, such as a pentagon, were presented (see Fig. 1). Each shape was drawn in a different color, selected from six possible hues. Irregular shapes were used to avoid recognition at first glance. For ongoing trials, participants were instructed to indicate whether the shape other than the triangle was presented to the left or the right of the triangle (see Fig. 1). In the cue identification PM condition in which, as before, trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the two shapes were a chess knight's move away from each other. In the intention retrieval condition, in which trials were perceptually distinct from the ongoing condition, the two shapes were of the same color and participants were required to retrieve a greater number of actions than in the cue identification PM condition: determine the number of sides of the shape other than the triangle, and press one key if this number was five or less, and another key if this number was higher than five. Again, to avoid conflicting instructions, shapes were never both a knight's move away from each other and drawn in the same color.

Word and shape tasks were administered in short blocks of approximately 35 s, interrupted by approximately 10 s of an unrelated task which was used as a baseline condition, common to all scanning sessions, against which hemodynamic activity relating to the different experimental conditions could be contrasted across sessions. In the unrelated task, participants were asked to press two keys alternately to make a row of Xs flip as quickly as possible between a horizontal and a vertical configuration. The inter-trial interval in the unrelated task was varied randomly between 300 and 700 ms to induce subjects to pay attention to the stimuli.

2.3. Procedure

Each trial consisted of 500 ms of a fixation cross, followed by presentation of the stimulus (the two words or shapes) for a maximum of 3000 ms. The tasks were subject-paced to prevent rehearsal of the given instructions (cf. Burgess et al., 2003) and ensure variable onset times of the trials, which improves fMRI design estimation efficiency (Henson, 2003). There was an inter-trial interval of 250 ms.

The tasks were administered in six sessions. The first two sessions (one words, one shapes, with task order counterbalanced between subjects) consisted of “uncontaminated” ongoing trials only without any expectation of a PM trial (no mention of PM conditions was made in the instructions for these first sessions). Two PM sessions then followed for each task, one session containing “contaminated” ongoing and cue identification PM trials, and one session containing “contaminated” ongoing and intention retrieval PM trials. The order of the PM conditions was also counterbalanced between subjects. Each session consisted of blocks of approximately 35 s of task (range 34–36 s, randomized) alternating with around 10 s of the unrelated task (range 9–11 s, randomized), with a 1 s pause between blocks. All sessions were preceded by instructions and a practice round. Each of the four PM sessions (two PM conditions for each of the two tasks) consisted of a number of ongoing trials interspersed with one group of four PM trials per 35 s block. This group of PM trials was always presented after approximately 20 s of ongoing task, to ensure that the participant would be fully engaged in the ongoing task and to control for the time between the last PM trial of a group and the first PM trial of the next group. To minimize the possibility that the appearance of the PM trials could be anticipated, two of the 35 s blocks in each condition did not contain any PM trials at all, and in two other 35 s blocks the PM trials were presented close to the beginning of the block. Anticipation of the PM task on a trial to trial basis was reduced further by placing an extra ongoing trial randomly somewhere in between the four PM trials and by varying the position of the group of PM trials within the block. In total, 32 PM trials were presented per session. The four PM sessions each lasted 517 s.

2.4. Image acquisition and data analysis

T2-weighted echo-planar functional images were acquired using a 3T Siemens Allegra system. For each subject, two time series of 21 followed by four time series of 227 whole-brain images were obtained (TR = 2.34 s, TE = 30 ms, 36 sequential axial slices aligned at approximately 10° to the AC–PC transverse plane, 2 mm thickness, 1 mm inter-slice skip). The first six images of each session were discarded to allow for T1 equilibration. Prior to the actual experiment, a magnetic fieldmap was acquired for each subject, which was used in the pre-processing of the functional images to reduce the distorting effects of the sinus area on the prefrontal cortex.

FMRI data were pre-processed and analyzed using the statistical parametric mapping procedure as implemented in SPM2 (Wellcome Department of Imaging Neuroscience, London). All images were realigned to the first image to correct for motion (using 4th-degree B-spline interpolation), after which the magnetic fieldmap was used to create a mean undistorted image. After realignment, all images were resampled in time to match the middle slice, to correct for differences in slice acquisition timing. The images were then normalized to an EPI template in MNI stereotactic space (Cocosco, Kollokian, Kwan, & Evans, 1997). Normalized images were resampled into 3 mm cubic voxels and smoothed using a Gaussian filter (8 mm FWHM kernel). A high-pass filter of 1/128 Hz was used to remove low-frequency noise, and an AR(1) + white noise model corrected for temporal autocorrelation. Finally, the time series was scaled to a grand mean of 100 across voxels and scans within each session.

Random effects statistical analysis was undertaken twice, once using a block design to estimate the main effects of interest, and once using an event-related design to allow investigation of effects that might be correlated with reaction time. Each analysis was conducted in two stages of a mixed effects model. In the first analysis, 16 conditions were defined: baseline and ongoing conditions for all six sessions, and a PM condition in the last four sessions. Blocks of PM condition lasted from the moment that the first PM trial which the subject responded correctly to appeared on the screen until the subject responded to the last PM trial in a group. Blocks of all conditions were modeled by convolving a boxcar that had a specific onset time and duration with a canonical haemodynamic response function. In the second analysis, the aforementioned conditions were re-defined as event-types for separate trials; a separate regressor coded for missed responses in each session. In both analyses, parameter values for each covariate were then estimated using a subject-specific fixed-effects model. Movement parameters in the three directions of motion and 3° of rotation were included as confounds, as well as a single covariate representing the mean session effect. In the event-related analysis, RTs were included as a parametric modulator of each event-type regressor.

Subject-specific estimates for the contrasts of interest were obtained using linear contrasts across sessions. To control for between-session signal differences, the effects of interest within each session were contrasted against the baseline condition from that same session. These estimates were entered into the second stage of analysis treating subjects as a random effect, using a one-sample t-test across subjects. Since activations that were independent of the type of stimuli involved (words or shapes) were of principal theoretical interest, a cognitive conjunction analysis was applied to the data which identifies as significant only those brain regions that are commonly activated in both tasks. Statistical parametric maps of the independent word and shape contrasts of interest were constructed and a one-way ANOVA on the 16 subjects was used to reveal brain regions significantly activated across both tasks, at an uncorrected threshold of p < 0.001. The anatomical locations and approximate Brodmann areas of significant cluster maxima of at least five contiguous voxels were localized using the Talairach and Tournoux atlas (Talairach & Tournoux, 1988) after adjusting coordinates to allow for differences between the MNI and Talairach templates (Brett, Christoff, Cusack, & Lancaster, 2001). The activation in prefrontal cortex associated with both PM conditions was examined in more detail by extracting mean percentage signal change magnitude relative to the baseline conditions from the subject-specific parameter estimates of cluster maxima, and subjecting them to a repeated-measures analysis that included region (left lateral BA 10, right lateral BA 10, and medial BA 10) and PM condition as repeated factors.

3. Results

3.1. Behavioral results

The accuracy and reaction time data as a function of task and condition are displayed in Table 1. There was no main effect of task in terms of accuracy, F(1,15) = 0.67, n.s., but a significant main effect of condition, F(3,45) = 102.74, p < 0.0001, and a trend towards an interaction between the two factors, F(3,45) = 2.73, p = 0.055. Accuracy scores were lower on PM trials compared to both types of ongoing trials, F(1,15) = 53.57, p < 0.0001. The accuracy difference between the cue identification and intention retrieval PM conditions was also significant, participants being less accurate in the intention retrieval PM condition compared to the cue identification PM condition, F(1,15) = 15.44, p < 0.001.

Table 1. Accuracy (%) and reaction time (ms) data per task (standard deviations in parentheses)

Empty CellWords taskShapes task
Uncontaminated ongoing
 Accuracy98.9 (1.7)97.4 (3.0)
 RT566 (151)720 (129)

Contaminated ongoing
 Accuracy97.0 (1.7)96.8 (2.2)
 RT991 (260)839 (102)

Cue identification PM
 Accuracy80.6 (12.0)83.3 (13.6)
 RT1133 (169)836 (113)

Intention retrieval PM
 Accuracy75.9 (10.2)68.3 (7.9)
 RT1596 (190)1394 (207)

Uncontaminated ongoing: ongoing trials where no PM trial was expected. Contaminated ongoing: ongoing trials where a PM trial was expected. Cue identification PM: prospective memory trials with high demand on cue identification and low demand on intention retrieval. Intention retrieval PM: prospective memory trials with high demand on intention retrieval and low demand on cue identification.

Reaction times were significantly increased in the word compared to the shape task, F(1,15) = 23.34, p < 0.0001, and showed a main effect of condition, F(3,45) = 145.20, p < 0.0001, and an interaction, F(3,45) = 23.07, p < 0.0001. Both tasks showed an increase in reaction times between uncontaminated and contaminated ongoing trials, F(1,15) = 38.08, p < 0.0001, and between cue identification PM and intention retrieval PM trials, F(1,15) = 191.08, p < 0.0001. A comparison between the contaminated ongoing trials in the two PM conditions revealed that ongoing trials in the cue identification PM condition were associated with significantly longer RTs than in the intention retrieval PM condition, t(15) = 4.55, p < 0.001, though when separated per task this difference was only significant in the words task, t(15) = 4.93, p < 0.001.

3.2. Neuroimaging results

The results from the block analysis are reported first.2 To identify the brain areas involved in prospective memory, each of the PM conditions in the word and shape tasks was contrasted with the uncontaminated ongoing condition, with between-session differences controlled for using the common baseline condition as described in the Methods section. Because we are interested in the brain regions associated with prospective memory that are not dependent on the kind of task used, a conjunction contrast was used to identify activations that were common to both word and shape tasks. The conjunction contrast revealed a remarkably consistent pattern of significant activation in both the cue identification and intention retrieval PM conditions in anterior prefrontal cortex (BA 10), with activation bilaterally in lateral BA 10 and deactivation in medial BA 10 (see Fig. 2A and B). Examination of the signal confirmed that there was no significant effect of PM condition on activation in the anterior prefrontal cortex clusters identified, F(1,15) = 3.22, n.s. Significant activation was also found in a number of other areas including ventrolateral prefrontal and lateral parietal cortex in both PM conditions, and dorsolateral prefrontal cortex and orbitofrontal cortex in the intention retrieval PM condition (see Table 2, Table 3).

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Fig. 2. Group functional activation maps of percentage signal change, in a conjunction across the word and the shape task. Activations are shown in yellow/red, deactivations are shown in blue, with activations of particular interest circled. Z coordinates are shown in top left corner. (A) In the cue identification PM > uncontaminated ongoing contrast, bilateral BA 10 activation and medial BA 10 deactivation was observed. A highly similar pattern was observed in (B) the intention retrieval PM > uncontaminated ongoing contrast. Differences between conditions emerge in (C), the direct intention retrieval PM > cue identification PM contrast, with significantly greater activation in anterior prefrontal cortex bilaterally in the intention retrieval PM condition, and evidence of deactivation in medial anterior BA 10. Left lateral BA 10 activation was found in (D) the contaminated ongoing > uncontaminated ongoing condition (left: cue identification PM ongoing condition; right: intention retrieval PM ongoing condition) (for interpretation of the references to color in this figure legend, the reader is referred to the web version of the article).

Table 2. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification PM > uncontaminated ongoing
 Left middle frontal gyrus (BA 10)−395733.96
 Right middle frontal gyrus (BA 10)425794.423
 Right inferior frontal gyrus (BA 47)5421−63.921
 Left inferior frontal gyrus (BA 47)−4218−63.99
 Right insula (BA 13)4815123.78
 Left insula (BA 13)−390274.415
 Right inferior parietal lobule (BA 40)45−42514.137
 Right superior parietal lobule (BA 7)33−57514.425
 Right angular gyrus (BA 39)42−57483.78

Uncontaminated ongoing > cue identification PM
 Medial anterior prefrontal cortex (BA 10)048−63.923
 Right superior frontal gyrus (BA 8)3039513.45
 Right cingulate gyrus (BA 24)612363.97
 Left precentral gyrus (BA 4/3)−42−15424.211
 Left medial frontal gyrus (BA 6/31)−3−21573.717
 Right medial frontal gyrus (BA 6)6−24693.919
 Right precentral gyrus (BA 4)24−24723.611
 Right precuneus (BA 7)3−33543.713
 Left precuneus (BA 7)−3−48603.514
 Right lingual gyrus (BA 18/19)12−5163.617
 Left lingual gyrus/cuneus (BA 18/30)−9−63124.024

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Table 3. Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Intention retrieval PM > uncontaminated ongoing
 Left middle frontal gyrus (BA 10)−395733.68
 Right middle frontal gyrus (BA 10)3954155.1203
 Right middle/superior frontal gyrus (BA 11)2754−123.716
 Right inferior frontal gyrus (BA 47)4521−154.122
 Right medial frontal gyrus (BA 8)921483.615
 Left insula (BA 13)−3918−63.920
 Right precentral gyrus (BA 9)4512393.724
 Right precentral gyrus/insula (BA 44)511294.036
 Left inferior/middle central gyrus (BA 9)−519394.531
 Left precentral gyrus (BA 6)−423275.154
 Right inferior parietal lobule (BA 40)45−42515.2199
 Left inferior parietal lobule (BA 40)−48−45485.0155
 Right precuneus (BA 19)12−66394.312
 Right inferior occipital gyrus (BA 18)36−90−124.046

Uncontaminated ongoing > intention retrieval PM
 Medial anterior prefrontal cortex (BA 10)048−65.3243
 Right cingulate gyrus (BA 24)612363.75
 Right lateral parietal cortex (BA 3)54−12574.430
 Left precentral gyrus (BA 4/3)−42−15453.613
 Left medial frontal gyrus (BA 6/31)−3−18574.019
 Right medial frontal gyrus (BA 6/4)6−30723.713
 Right precuneus (BA 7)3−33573.68
 Right lingual gyrus (BA 18)12−5163.68
 Left lingual gyrus/cuneus (BA 30)−6−57154.748
 Right cuneus (BA 18)6−87243.76

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Separate examination of the word and shape task contrasts revealed additional, potentially task-dependent, activation associated with cue identification in left premotor cortex, precuneus, and occipital cortex for words, and parahippocampal cortex for shapes. Similarly, the intention retrieval PM condition was associated with additional activation in left cingulate gyrus, premotor cortex, and precuneus for words, and a more anterior part of cingulate gyrus and postcentral gyrus for shapes.

Returning to the task-independent conjunction contrasts, despite the similarities in activation patterns associated with the two PM conditions versus the uncontaminated ongoing condition, a number of brain regions were identified in the direct task-independent contrast between cue identification PM and intention retrieval PM (see Fig. 2C). These results provide evidence of additional differential BA 10 involvement in the PM conditions. Significantly greater activation was found in the intention retrieval PM condition bilaterally in regions of BA 10 that peaked slightly less laterally than those observed in the contrasts above. Medial BA 10 appeared to be more active in the cue identification PM condition (see blue activation in Fig. 2C), although this effect did not exceed the whole-brain threshold of p < 0.001. At a threshold of p < 0.05 corrected for the voxels in an 8-mm sphere around the peak that was identified in both the cue identification PM versus ongoing and intention retrieval PM versus ongoing contrasts (0, 48, −6; see Table 2, Table 3), however, significant medial BA 10 activation did emerge in the cue identification versus intention retrieval PM contrast (0, 45, −9; Z = 3.5; voxels = 19). Other brain regions which were more active at the whole-brain threshold in the cue identification PM condition included the anterior cingulate, whereas in the intention retrieval PM condition more extensive areas of the cingulate gyri were among regions showing greater activation (see Table 4).

Table 4. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification PM > intention retrieval PM
 Right anterior cingulate (BA 32/11)336−123.753
 Left anterior cingulate (BA 25)−315−64.531
 Left putamen−240−95.051
 Right insula (BA 40)57−18213.624
 Left postcentral gyrus/insula (BA 40)−54−21214.5113
 Left middle temporal gyrus (BA 39/19)−54−72243.47
 Left lingual gyrus (BA 18)−21−78−63.817

Intention retrieval PM > cue identification PM
 Left middle frontal gyrus (BA 10)−3351183.46
 Right superior frontal gyrus (BA 10)2751−33.812
 Left medial frontal gyrus (BA 9)−1842213.45
 Left anterior cingulate (BA 32/24)−1227273.911
 Right insula (BA 13/47)3624−34.213
 Left cingulate gyrus (BA 32)−321456.3364
 Right lateral frontal cortex (BA 6)2412634.5118
 Right cingulate gyrus (BA 24)39304.05
 Left lateral frontal cortex (BA 6)−276665.146
 Left middle frontal gyrus (BA 6)−420573.45
 Left inferior parietal lobule (BA 40)−45−54573.45
 Left cuneus (BA 18)−12−75335.8246

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Since the behavioral data showed longer RTs in the intention retrieval PM than in the cue identification PM condition, it is possible that the greater bilateral BA 10 activation in the intention retrieval PM condition might be attributable to differences in time on task. To test this possibility, a second analysis was conducted including RT as a parametric modulator to identify brain regions where activation was positively or negatively correlated with RTs. No significant correlation was found between RT and activation in the regions of BA 10 identified in the previous contrasts, even when the threshold was dropped to p < 0.1 uncorrected. It thus seems safe to attribute the greater bilateral BA 10 activation during intention retrieval PM trials primarily to the demands on recovering the delayed intention.

To examine the effect of maintaining a PM intention on performance of the ongoing task, the contaminated ongoing trials in both PM conditions were contrasted with the uncontaminated ongoing trials. In both PM conditions, contaminated ongoing trials were associated with greater activation in left lateral BA 10/47 (−48, 39, 0 for cue identification; −48, 42, −3 for intention retrieval; see Fig. 2D). Again, no correlation was found between RTs and activation in these regions in the analysis that included RT as a parametric modulator. Contrasting directly the contaminated ongoing trials in both PM conditions, significantly greater activation in left lateral BA 10 was observed during the intention retrieval PM condition than the cue identification PM condition (see Table 5). This is consistent with the idea that activation in this region reflects maintenance of the intended PM actions (Burgess et al., 2001).

Table 5. Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification ongoing > intention retrieval ongoing
 Right inferior frontal gyrus (BA 47)3630−64.131
 Left insula (BA 13)−4221154.39
 Left precentral gyrus (BA 6)−519333.511
 Left putamen−276−63.930
 Left fusiform gyrus (BA 37)−42−48−93.89
 Left middle occipital gyrus (BA 19)−30−93214.39
 Left occipital lingual gyrus (BA 18)−15−99−34.979
 Right cuneus (BA 18)15−102124.458

Intention retrieval ongoing > cue identification ongoing
 Left medial frontal gyrus (BA 10)−215464.215
 Right middle frontal gyrus (BA 6)1818663.78
 Left paracentral lobule (BA 6)−12−18603.814
 Right paracentral lobule (BA 6)12−30663.920
 Left postcentral gyrus (BA 4)−15−30664.010
 Left cuneus (BA 18)3−75363.96

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

4. Discussion

The main finding of the present experiment was that, despite behavioral differences between the cue identification and intention retrieval PM conditions, a strikingly similar pattern of hemodynamic changes, involving anterior prefrontal cortex (BA 10), was observed across both conditions. Finding such consistent results even when the demands on cue identification and intention retrieval were manipulated sufficiently to produce significant behavioral effects, confirms the view from previous neuroimaging studies that this region is likely to be of central importance to prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005).

For example, Okuda et al. (1998) reported activation in the left frontal pole (BA 10), as well as right dorsolateral and ventrolateral prefrontal cortex (BA 8/9/47) and anterior cingulate (BA 24), when participants remembered and acted upon a list of target words relative to performing an ongoing routine activity (word repetition). Activation in the frontal pole (BA 10, bilaterally) was also found by Burgess et al. (2001) across several cognitive tasks. In that study, activation during an ongoing task was compared to activation in two PM conditions: one in which PM trials were expected but never actually occurred, and one in which PM trials were expected and acted upon. In both PM conditions, activation in bilateral frontal pole, right lateral prefrontal and parietal cortex, plus precuneus, was found. Burgess et al. (2003) extended these results by showing that the bilateral activation of lateral BA 10 associated with retrieving a delayed intention was accompanied by deactivation in medial BA 10. Recently, den Ouden et al. (2005) reported activation in lateral BA 10, lateral parietal cortex, and precuneus, associated with keeping an intention in mind while performing an ongoing task responding to questions about intentions and actions.

In the present experiment, the reliability of the consistently observed lateral BA 10 activation was assessed by manipulating the demands placed on cue identification and intention retrieval processes as experimental variables. Of course, both PM conditions involved cue identification and intention retrieval to some degree; the experimental manipulation was not all-or-none. However, the demands on the two PM components were manipulated sufficiently for significant behavioral differences to emerge between them: accuracy was lower, and reaction times longer, in the intention retrieval condition than in the cue identification condition, although correlational analysis indicated that these behavioral differences could not explain the consistent hemodynamic changes observed across conditions. Significant lateral BA 10 activation was seen bilaterally in both PM conditions, confirming the characteristic nature of this activation, as reported by a number of neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005). Moreover, both PM conditions were additionally associated with medial BA 10 deactivation when contrasted with ongoing trials, consistent with the findings of Burgess et al. (2003), who also observed reduced medial BA 10 activation during PM trials versus ongoing trials. Of course, the limitations of fMRI do not allow us to speculate as to whether precisely the same neurons were recruited by both PM conditions, but it does seem clear that, at the macro level of Brodmann areas at least, similar patterns of activation in BA 10 were observed during both cue identification and intention retrieval PM conditions.

Despite finding that similar regions of BA 10 were activated (and deactivated) in both PM conditions, there was evidence that the level of activation in lateral BA 10 was greater in the intention retrieval PM condition than in the cue identification PM condition. The correlational RT analysis showed this effect was not attributable to differences in reaction time as the activations observed did not correlate with RT durations, consistent with results observed by Burgess et al. (2003). This suggests that the results cannot be explained by a simple task difficulty account, since greater difficulty (in terms at least of the amount of effort required to perform the task) could be expected to be reflected in increased RTs. Left lateral BA 10 was also significantly activated in the contrast between contaminated and uncontaminated PM trials, suggesting that activation in the region is affected by prior exposure to a PM intention. This is consistent with the finding from Burgess et al. (2001) that BA 10 activation is seen when PM trials are expected, regardless of whether they actually occur, and perhaps indicates that the region contributes to the maintenance of the delayed intention, a conclusion supported by the finding in the present experiment that BA 10 activation during contaminated ongoing trials was greater in the intention retrieval than the cue identification PM conditions. Moreover, there are echoes in these results of data from a number of electrophysiology studies which found that intention retrieval was associated with a sustained frontal slow-wave modulation that appeared to be particularly evident when retrieval of the PM intention was made more demanding (West et al., 2001; West & Ross-Munroe, 2002). Increasing the number of intentions to be remembered resulted in a larger amplitude of this frontal slow-wave (West et al., 2003). Although it is problematic to compare electrophysiological and neuroimaging results directly, there are similarities between the ERP increase over frontal sites and the present finding of greater lateral anterior prefrontal cortex activation when intention retrieval is more demanding.

The greater lateral BA 10 activation associated with intention retrieval can be conceived of in terms of differential attention towards external events and internally generated thought processes (Burgess, Simons, Dumontheil, & Gilbert, 2005; Gilbert, Frith, & Burgess, 2005; Gilbert, Simons, Frith, & Burgess, in press; Simons, Owen, Fletcher, & Burgess, 2005; Simons, Gilbert, Owen, Fletcher, & Burgess, 2005; see also Christoff & Gabrieli, 2000; Gusnard, Akbudak, Shulman, & Raichle, 2001). By this account, when one's primary aim is to detect a cue, attention must be turned towards the external world; once the cue has been detected, however, one must disengage from the external stimuli and attend to internal representations so that the relevant intention can be retrieved from memory. Thus, placing higher demands on cue identification implies biasing attention more towards external stimuli, whereas a higher load on intention retrieval implies an increase of attentional focus upon internally generated thought.

A number of previous studies have shown that lateral BA 10 plays an important role in the recollection of details about the context in which previous events occurred (Ranganath, Johnson, & D’Esposito, 2000; Rugg, Fletcher, Chua, & Dolan, 1999; Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005), an ability that requires retrieval of internally represented mnemonic information (Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005). In contrast, medial BA 10 has been associated with performance of tasks that emphasize the processing of externally presented stimuli (Gilbert et al., 2005, Janata et al., 2002, Small et al., 2003). Thus, Burgess et al. (2005) have suggested that lateral areas of BA 10 may play a role in maintaining attention towards internal cognition and more medial areas in maintaining attention towards external stimuli. The results from the PM versus ongoing contrasts in the present experiment can be interpreted along these lines, with medial BA 10 deactivation reflecting disengagement from the external ongoing task stimuli and the lateral BA 10 activation being associated with the directing of attention towards the internally represented PM intention (see Burgess et al., 2003, for a similar suggestion). Consistent with this view, lateral BA 10 activation was greater when the demands on intention retrieval were higher, and there was evidence of greater medial BA 10 activation associated with the cue identification PM condition, in which external cue processing demands were greater. Taken as a whole, these results are in agreement with the hypothesis that BA 10 acts as a “gateway”, biasing attention between externally derived perceptual information used to detect the occurrence of a PM cue and internal thought processes relating to the stored PM intention (Burgess et al., 2005; Gilbert et al., 2005; Simons, Gilbert, et al., 2005).

PM-related activation was also seen in a number of areas outside the anterior PFC region of interest. When contrasted against uncontaminated ongoing trials, the cue identification and intention retrieval PM conditions provoked activation in regions of lateral PFC and parietal cortex, among other areas. A number of regions showed differential activation in the two PM conditions. These included anterior cingulate cortex, which was activated more in the cue identification PM condition, and posterior cingulate and precuneus, which showed greater activation in the intention retrieval PM condition. Activation in all these regions has been observed in previous neuroimaging studies of prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), and has been linked with a number of processes relevant to PM retrieval. For example, lateral PFC, anterior cingulate and lateral parietal cortex have been proposed to constitute a cognitive control network involved in sustained attention and vigilance to particularly visual stimuli (Burgess et al., 2001; Coull, Frith, Frackowiak, & Grasby, 1996; Cabeza et al., 2003). Posterior cingulate, precuneus, and lateral parietal cortex have been linked in many studies with functions related to retrieval of stored mnemonic information such as recollection, retrieval confidence, and imagery (see Wagner, Shannon, Kahn, & Buckner, 2005, for a recent review).

In conclusion, the present experiment demonstrated that it is possible to tease apart behaviorally cue identification and intention retrieval components of PM, but that they may reflect the operation of similar underlying processes supported by anterior prefrontal cortex (BA 10). Evidence suggests that lateral BA 10 may reflect the maintenance and/or retrieval of the stored PM intention, showing greater activation not only during intention retrieval PM trials, but also when maintaining an intention in memory during the ongoing task.

Acknowledgements

We are grateful to Daniel Glaser and Hanneke den Ouden for discussion and advice, and the staff of the Functional Imaging Laboratory for scanning assistance. This work was supported by Wellcome Trust Grant 061171.

References

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1

Delegates at the 2nd International Conference on Prospective Memory, Zurich, July 2005, voted that the use of the abbreviation “PM” for prospective memory was preferable to other extant forms.

2

An event-related analysis was also conducted as verification, which produced activation in similar brain regions as the block analysis with reduced effect sizes, consistent with the lower statistical power associated with event-related designs (Henson, 2003).

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Here, we used Seed-based d Mapping, a coordinate-based meta-analytic approach to identify consistent regions of GM anomalies across studies of PSP. Totally, 18 original VBM studies, comprising 284 patients with PSP and 367 healthy controls were included. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the frontal motor cortices, medial (including anterior cingulate cortex) and lateral frontal cortices, insula, superior temporal gyrus, striatum (putamen and caudate nucleus), thalamus, midbrain, and anterior cerebellum. Our study further suggests that many confounding factors, such as age, male ratio, motor severity, cognitive impairment severity, and illness duration of PSP patients, and scanner field-strength, could contribute to the heterogeneity of GM alterations in PSP across studies. Our comprehensive meta-analysis demonstrates a specific neuroanatomical pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries that mediate vertical supranuclear gaze palsy, motor disabilities (postural instability with falls and parkinsonism), and cognitive-behavioral disturbances. Confounding factors merit attention in future studies. \n ", + "publication_year": 2017, + "coordinate_space": "MNI", + "license": "http://creativecommons.org/licenses/by/3.0/", + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt b/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt new file mode 100644 index 0000000..437be6a --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt @@ -0,0 +1,107 @@ + +## INTRODUCTION + +Progressive supranuclear palsy (PSP) is a clinical syndrome characterized mainly by early postural instability with falls, vertical supranuclear gaze palsy, parkinsonism, and cognitive-behavioral disturbances that lead to significant disabilities with a mean survival of 6.38 years [ – ]. PSP is a rapidly progressive neurodegenerative disorder, pathologically confirmed by the accumulation of tau protein and neuropil threads in cortical and subcortical structures [ , ]. Several clinical subtypes of PSP have been identified, of which the classic Richardson's syndrome (PSP-RS) and the PSP-parkinsonism variant (PSP-P) are the most common [ ]. No effective treatments are available for PSP [ ]. Its differential diagnosis from other parkinsonian disorders is critical but presents challenges in clinical practice, especially in the early disease stages [ , ]. Despite impressive advances in understanding its pathophysiology, the reliably validated biomarkers for the ante-mortem diagnosis and the prognosis of PSP have not yet been established [ , , , ]. + +Major improvements in modern magnetic resonance imaging (MRI) techniques increase our ability to identify brain structural alterations in vivo that shed light on the neuroanatomical basis of PSP and hold promise for its diagnosis [ , – ]. Midbrain atrophy is a hallmark of PSP [ , ]. Recent evidence from whole-brain voxel-based morphometry (VBM) studies in PSP has additionally demonstrated gray matter (GM) atrophy in a number of brain regions, such as the thalamus, basal ganglia, insula, frontal cortices, temporal cortices, parietal cortices, and cerebellum [ – ]. Compared with conventional MRI investigations that draw regions of interest (ROIs) for morphometric comparisons, VBM is a hypothesis-free analytic tool to quantify regional structural differences between groups at a whole-brain level [ ]. VBM has been widely used in neurodegenerative disorders [ – ]. Despite the strengths, inconsistent results across different VBM studies were reported [ ]. Shi, et al. in 2012, Shao, et al. in 2013, and Yu, et al. in 2014 thus conducted three coordinate-based meta-analyses to test the consistency of GM changes in PSP, which included nine, nine, and 12 VBM studies, respectively [ – ]. However, these meta-analyses had several limitations. First, these meta-analyses did not examine the confounding variables, such as age, gender, disease duration, and symptom severity that potentially lead to heterogeneity of the structural alterations associated with PSP [ ]. Second, the quantitative voxel-based meta-analytic tools have been modified [ – ]. Several complementary analyses, such as jackknife sensitivity, heterogeneity, and publication bias analyses could be further performed to explore the robustness of the findings [ , ]. Third, the numbers of VBM studies included in these meta-analyses were limited. To achieve sufficient power for moderate effects, Eickhoff and co-workers recently recommended that 17 or more experiments were needed for a coordinate-based meta-analysis to detect moderately sized effects [ ]. In recent two years, we identified six more VBM studies of PSP eligibly for the meta-analysis. As such, results from previous meta-analyses cannot be considered conclusive and will need replication in a more exhaustive meta-analysis. + +Against this background, we aimed to conduct an updated meta-analysis based on 18 whole-brain VBM studies in PSP using a modified Seed-based d Mapping (SDM) approach to obtain more accurate results. In addition, analyses of jackknife sensitivity, heterogeneity, publication bias and meta-regression were comprehensively performed to examine the robustness and replicability of GM abnormalities associated with PSP. + + +## RESULTS + +### Characteristics of included studies + +Figure presents the flow diagram for inclusion/exclusion of studies in the meta-analysis. Totally, 18 original studies reporting GM differences between 284 patients with PSP and 367 healthy controls were included in this meta-analysis [ – ]. Across the 18 studies, one study included all pathologically confirmed patients with nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) and PSP (nfvPPA-PSP) [ ] and another two studies included some pathologically confirmed patients with PSP in the samples [ , ]. Patients with PSP in the remaining 15 studies were clinically diagnosed. Three of the 17 studies explicitly indicated that the samples included two subtypes of PSP, PSP-RS and PSP-P [ , , ]. Patients in the remaining 14 of 17 studies were clinically diagnosed based on the NINDS-SPSP clinical criteria [ , ], which has over 95% sensitivity and specificity in diagnosing PSP-RS [ ]. No significant differences between patients with PSP and healthy controls regarding mean age (standardized mean difference = 0.17; 95% confidence interval [CI] = -0.003 to 0.344, z = 0.61, p = 0.055) or gender distribution (relative risk = 1.12, 95% CI = 0.974 to 1.289, z = 0.159, p = 0.112) were observed. Mean Unified Parkinson's Disease Rating Scale-motor examination (UPDRS-III) score in 11/18 studies (rang from 20.4 to 52.9), Hoehn and Yahr disability scale (H&Y) in 7/18 studies (rang from 2.6 to 3.8), illness duration in 15/18 studies (rang from 2.5 years to 4.8 years), Mini-Mental State Examination (MMSE) score in 13/18 studies (rang from 21 to 28), and Frontal Assessment Battery (FAB) examination in 6/18 studies (rang from 7.81 to 12.9) were reported. Nine out of the 18 studies were conducted on 1.5T MRI systems and 8/18 studies were on 3.0T systems. One study used either a 1.5T or a 3.0T MRI system. 13 out of the 18 studies reported the corrected results and the remaining 5 studies used the uncorrected thresholds. All the studies included used Statistical Parametric Mapping (SPM) softwares for imaging analyses. + Flowchart to identify the eligible studies for the meta-analysis + Key: PSP, Progressive Supranuclear Palsy; GM, Gray Matter; VBM, Voxel-Based Morphometry; ROI, Region Of Interest. + +The quality score of each study included in this meta-analysis was not less than 8 (a maximum score of 10 for each study), which indicates the high rigour of these studies. The list, demographic, clinical and imaging characteristics, and scores of quality assessment of the included studies are presented in Table . + Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis +Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; , a maximum score of 10 for each study. + + +### Main voxel-wise meta-analysis + +As shown in Figure , the voxel-wise meta-analysis identified significant GM reductions in the left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and orbitofrontal cortex (OFC), in the bilateral thalamus extending to the midbrain and caudate nucleus, in the bilateral anterior cingulate cortex (ACC) extending to the supplementary motor areas (SMA) and pre-SMA, superior medial frontal cortex, and medial OFC, in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex), and in the left anterior cerebellum (lobule III/IV/V) in patients with PSP compared with healthy controls. In contrast, no significant GM increases were observed in patients with PSP relative to healthy controls. Details of the results are summarized in Table . + Meta-analytic results of gray matter reductions in patients with PSP compared to healthy controls +Key: (A) , Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/ orbitofrontal cortex; (B) , Right/Left thalamus/midbrain/caudate nucleus; (C) , Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; (D) , Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); (E) , Left anterior cerebellum (lobule III/IV/V); PSP, Progressive Supranuclear Palsy; HC, healthy controls; SDM, Seed-based d Mapping. The color bar indicates the maximum and the minimum SDM-Z values. + GM reductions in patients with PSP compared to healthy controls +Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area. + +The subgroup analysis of 14 VBM studies that patients were suggestive of PSP-RS showed that the results remained largely unchanged. + + +### Supplemental analyses + +The jackknife sensitivity analysis revealed that regions of GM reductions in left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and OFC, in the bilateral thalami extending to the midbrain and caudate nucleus, in the bilateral ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex) in patients with PSP relative to healthy controls were replicable in all 18 studies. The region of GM reductions in the left anterior cerebellum (lobule III/IV/V) in patients with PSP relative to healthy controls was replicable in 15 studies (Table ). + Jackknife sensitivity analysis +Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported. + +The heterogeneity analysis revealed that there is significant between-study variability of GM differences in patients with PSP relative to healthy controls in the right inferior frontal gyrus extending to the insula and superior temporal gyrus, in the left insula extending to the superior temporal gyrus, in the bilateral ACC extending to the medial OFC, in the bilateral thalamus, in the left inferior frontal gyrus, in the left cerebellum (lobule III), and in the right caudate nucleus (Table ). + Regions of GM heterogeneity from the SDM analysis +Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex. + +No publication biases were detected in the regions obtained from the main voxel-wise meta-analysis as revealed by the symmetrical funnel plots (Figure ) and statistically non-significant Egger's tests (Table ). + Funnel plots of the peak coordinates of gray matter abnormalities in progressive supranuclear palsy +Meta-regression analysis revealed that the PSP group with older mean age (available from 17 studies) exhibited more GM reductions in the bilateral thalamus extending to the midbrain (Figure ) and in the left insula extending to the inferior frontal gyrus (Figure ). Higher male ratio of patients in the PSP group (available from 17 studies) was associated with more GM reductions in the left caudate nucleus extending to the thalamus (Figure ). Higher average UPDRS-III score (Figure ) or lower mean MMSE score (Figure ) in the PSP group (available from 11 and 13 studies, respectively) correlated with more GM reductions in the left insula. Meta-regression analysis indicated that longer mean illness duration of the PSP group (available from 15 studies) was associated with more GM reductions in the Left caudate nucleus extending to bilateral thalami (Figure ). Higher scanner field-strength in VBM studies tended to detect more GM atrophy in the right inferior frontal gyrus (Figure ) and in the left SMA (Figure ). Findings of these meta-regression analyses are presented in Table and the regression lines are shown in Figure . + Results of the meta-regression analyses + Key: UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. Each study is represented as a dot, with a larger dot indicating a larger sample size. (A) and (B) , meta-regression with mean age; (C) , meta-regression with male ratio of patients; (D) , meta-regression with mean UPDRS-III score; (E) , meta-regression with mean MMSE score; (F) , meta-regression with illness duration; (G) and (H) , meta-regression with scanner field-strength. + Meta-regression analyses +Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. + + + +## DISCUSSION + +Using a modified SDM approach, the present quantitative meta-analysis is timely given with a sufficient number of VBM studies that have recently become available. This comprehensive study synthesized the findings from 18 VBM studies comprising 284 patients with PSP and 367 healthy controls. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen, precentral gyrus (premotor cortex) and OFC, the thalamus extending to the midbrain and caudate nucleus, the ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and the anterior cerebellum (lobule III/IV/V). These GM changes in PSP were highly robust as verified by jackknife sensitivity analyses. In addition, no publication biases in these regions were observed. However, the heterogeneity analysis revealed a significant between-study variability of GM atrophy differences in some of these regions. Further meta-regression analyses indicated that these variations in GM alterations across VBM studies were correlated with the mean age, male ratio, UPDRS-III score, MMSE score and illness duration of PSP patients, as well as scanner field-strength employed. + +The pattern of GM atrophy in PSP identified in our meta-analysis is consistent with the histopathological distribution of neuronal loss, gliosis, and accumulation of tau proteins in the midbrain, diencephalon, basal ganglia, cerebellum, frontal and temporal cortices [ ]. Midbrain atrophy, which is consistently validated by many imaging modalities, is the most characteristic alteration of PSP [ ]. The hallmark of the disease, vertical supranuclear gaze palsy, is considered to correlate with the neurodegeneration of the rostral interstitial nucleus of the medial longitudinal fasciculus (riMLF and the interstitial nucleus of Cajal located in the midbrain, and the central mesencephalic reticular formation [ – ]. In addition, a recent study by Amtage and colleagues employing 18F-Fluorodeoxyglucose (FDG) positron-emission tomography (PET) suggests that the ACC (cingulate eye field), which connections the supplementary eye field, frontal eye field and midbrain regions [ ], plays an important role in downward gaze palsy in PSP [ ]. The substantia nigra in the midbrain, coupled with the basal ganglia, thalamus, motor cortices, and anterior cerebellum are hubs of a motor control network [ – ]. GM atrophy in these brain regions identified in the current meta-analysis, probably indicative for damage of this network, contributes to the pathophysiology of parkinsonism, such as rigidity, bradykinesia, and postural instability in patients with PSP [ , , ]. Recent evidence suggests that gait disturbance with early falls, one of the characteristic clinical features of PSP, are closely associated with the thalamic dysfunction, which influences the mesencephalic brainstem-thalamus loop [ ]. + +Beyond the motor control, the subcortical structures such as the the substantia nigra, basal ganglia and thalamus are also implicated in mediating cognition and behavior via the frontal-subcortical circuits [ – ]. In addition to the motor symptoms, patients with PSP are frequently accompanied by cognitive-behavioral disturbances, such as executive dysfunction, apathy, and disinhibition, which are prevalent and may occur early in the disease course affecting their quality of daily life [ , ]. Early cognitive impairment in PSP is shown to be an independent predictor of shorter survival [ – ]. PSP is typically considered a “subcortical dementia” with the impairment of the frontal-subcortical circuits, prominently attributed to the subcortical pathology [ , , ]. Resting-state functional MRI studies have demonstrated a widespread disruption of cortical-subcortical connectivity involved in cognitive and motor dysfunction in PSP [ – ]. Previous studies using manual ROI approaches for the frontal lobe, demonstrated that the severity of behavioral and cognitive disturbances was associated with the degree of frontal atrophy in PSP patients [ – ]. In addition, a longitudinal ROI study further showed that the progression of executive dysfunction correlated with increased rates of frontal atrophy in patients with PSP [ ]. A VBM study demonstrated that the severity of behavioral disturbances in mid-stage PSP correlated with atrophy of the OFC surrounding the inferior frontal sulcus and the midbrain [ ]. In accordance with these data, our voxel-wise meta-analysis identified frontal GM atrophy noted in the lateral (inferior frontal cortex extending to OFC) and medial (ACC extending to superior medial frontal cortex and medial OFC) frontal cortices, apart from the frontal motor associated cortices including the (pre-) SMA and the premotor cortex. In addition, we identified extra-frontal GM atrophy in the insular cortex and superior temporal cortex. The insula has rich connections with the frontal and subcortical structures acting as a hub for integrating cognitive-affective, sensorimotor, and autonomic information [ , ]. Our meta-regression analyses showed that the severity of the motor disabilities and cognitive impairment as well as the illness duration of PSP had notable effects on GM atrophy in the insula. Atrophy of the superior temporal cortex along with the OFC, parts of the OFC-subcortical circuit may be associated with disinhibition, one of the frequently observed behavioral symptoms in PSP [ – , ]. The ACC is engaged in the medial frontal-subcortical circuit and in the dorsolateral prefrontal-subcortical circuit, dysfunction of which are responsible for apathy and executive dysfunction, respectively [ – , , ]. In contrast to previous meta-analyses [ – ], cortical atrophy in the current study was more prominent, which may be attributed to the methodological improvement and sufficient statistical power with enough studies as discussed in the introduction [ – ]. Taken together, GM matter atrophy in these cortical and subcortical regions identified in the meta-analysis may shed light on the pathophysiology of the cognitive-behavioral disturbances in PSP. + +Notably in the current meta-analysis, we observed heterogeneity of brain GM alterations in some of the regions across studies, which are attributed to the confounding factors, such as age, male ratio, motor severity, MMSE score, and illness duration of PSP patients, and scanner field-strength that were not analyzed by previous meta-analysis [ – ]. For example, meta-regression analysis revealed that older mean age in PSP patients was associated with more GM atrophy in the bilateral thalamus extending to the midbrain and the left insula extending to the inferior frontal gyrus. Age is an important risk factor for PSP [ ]. Severer motor disabilities and cognitive-behavioral disturbances in PSP are associated with more GM atrophy in the left insula. The human insula is strongly interconnected with the basal ganglia and cortical regions, which is implicated in cognitive/affective and sensorimotor processing [ , ]. These brain structure-behavior correlations provided additional insight into the neurobiology of PSP. The epidemiologic data shows that PSP affects men more frequently than women [ , ]. In the current meta-analysis, we noted that samples with PSP with a higher male ratio tended to have more GM atrophy in the left caudate nucleus extending to the thalamus, which may provide a neuroanatomical basis of such gender susceptibility. However, we could not find the factors that contribute to the heterogeneity of GM changes in the bilateral ACC extending to the medial OFC. As mentioned above, these regions are implicated in cognitive-behavioral disturbances. Due to the limited data of frontal assessment battery and frontal behavioral inventory available from the original studies, we could not further explore the source of GM heterogeneity in these regions. More studies are warranted to assess cognitive-behavioral disturbances and to conduct clinical-neuroanatomical correlations in PSP. + +### Limitations + +Some limitations of this meta-analysis warrant consideration. First, an intrinsic limitation for coordinate-based meta-analytic approaches is that they are based on coordinate data rather than raw imaging data, which may bias the results [ , ]. Second, due to that fact that most of the samples were not pathologically confirmed, the clinically heterogeneous nature of PSP might limit specificity of the findings, although the subgroup analysis indicates that this pattern of GM atrophy is specific for classic PSP-RS patients. Further studies with large homogenous samples both by clinically diagnosed and pathologically confirmed are warranted to validate these findings. + + + +## MATERIALS AND METHODS + +### Literature search and selection + +As the VBM method was introduced in the year of 2000 [ ], we systematically searched PubMed, Embase, and Web of Science databases between January 1, 2000 and September 17, 2016 using the Medical Subject Heading (MeSH) term “progressive supranuclear palsy” and its corresponding free terms, and the keywords “voxel-based morphometry” or “vbm” or “gray matter” or “grey matter” or “voxel*”. Furthermore, we checked the bibliographies of relevant review papers and retrieved articles by hand for additional studies. One study was considered for inclusion in the meta-analysis if it (1) was published in an English-language peer-reviewed journal as an original article; (2) reported regional GM changes using a whole-brain VBM analysis for direct comparison between patients with PSP and healthy controls; (3) reported three-dimensional coordinates of maxima (x, y, z) in a standardized stereotaxic space (i.e., Montreal Neurological Institute [MNI] or Talairach); (4) reported significant results of regional GM differences within one study using a constant threshold. Only the baseline dataset was included if the study was longitudinal. Studies were excluded if they limited their analyses to specific regions of interest (ROIs) or volume of interest (VOI). A study was excluded if its sample overlapped with another publication. The quality of each study included in this meta-analysis was evaluated using a 10point checklist that integrated both the clinical and demographic information and the imaging-specific methodology ( ), which was based on previous meta-analytic studies [ , ]. Recorded data were extracted from original studies, including the first author's name, year of publication, age, gender and number of patients and controls, clinical variables (e.g., illness duration, UPDRS-III score, H&Y stage, MMSE score, and FAB score), and the imaging characteristics (e.g., scanner field-strength, processing software, full width half maximum [FWHM] and statistical threshold). In addition, peak coordinates and effect sizes (e.g., t-values) of GM differences between patients with PSP and healthy controls from each VBM study were extracted for the following voxel-wise meta-analysis. Two investigators independently performed literature search and selection, assessment of study quality, and data extraction. Any discrepancies were discussed with another investigator until they were resolved. This study followed the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [ ]. + + +### Data analysis + + +### Main voxel-wise meta-analysis + +Voxel-wise meta-analysis of regional GM differences between patient with PSP and healthy controls was conducted using the modified SDM software package available at . The details of the approach have been described in other publications [ , , – ] and the tutorial available at An effect-size signed map and an effect-size variance map of the GM differences was first separately recreated for each study. The mean map was then created by voxel-wise calculation of the random-effects mean of the study maps, which was weighted by the sample size, intra- study variability, and additional between-study heterogeneity. Statistical significance was set at a default un-normalised Gaussian kernel kernel size and threshold (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels), which provided the optimal balance of false positives and negatives [ , ]. It must be noted that this un-normalised kernel is not designed to smooth any image but to assign indicators of proximity to reported coordinates [ , ]. + +In addition, we conduct a subgroup analysis of VBM studies that patients met the NINDS-SPSP (NINDS-SPSP, National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy) criteria suggestive of PSP-RS [ , ]. + + +### Supplemental analyses + +A leave-one-out and whole-brain voxel-based jackknife analysis was performed to assess the sensitivity of the results by iteratively repeating the same analysis, discarding one study each time [ , ]. + +A heterogeneity analysis was carried out using a random effects model with Q statistics in order to explore which brain regions are more heterogeneous between studies. Jackknife and heterogeneity analyses were thresholded with the same default settings (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels) [ , ]. + +In addition, the Stata/SE 12.0 software (Stata Corp LP, College Station, TX, USA) was used to examine possible publication bias. Funnel plots and Egger's test was performed by extracting the values from the meta-analytic peaks [ ]. An asymmetry of funnel plots and a p-value less than 0.05 of Egger's test were considered significant. + +Meta-regression analyses were further conducted to explore the effects of age, gender, UPDRS-III score, MMSE score, illness duration, and scanner field-strength that could potentially influence the meta-analytic results. Statistical significance was thresholded at a more conservative p-value less than 0.0005 and cluster extent more than 10 voxels [ , ]. Variables, such as H&Y stage, FAB, frontal behavioral inventory (FBI), and the Progressive Supranuclear Palsy Rating Scale (PSPRS), could not be explored by meta-regression analyses due to limited information that was available from less than 10 original studies. + + + +## CONCLUSIONS + +In summary, our comprehensive meta-analysis demonstrates a specific pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries in the pathophysiology of the supranuclear gaze palsy, motor disabilities, and cognitive-behavioral disturbances. These morphological findings may have implications for neuroanatomical diagnostic biomarkers of PSP. In addition, our study indicates that many confounding factors contribute to the heterogeneity of GM alterations in PSP across studies, which merits much attention in further studies. + + +## SUPPLEMENTARY MATERIALS TABLE + + \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml new file mode 100644 index 0000000..30cabf1 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml @@ -0,0 +1,4829 @@ + +
+ + + + Oncotarget + Oncotarget + Oncotarget + ImpactJ + + Oncotarget + + 1949-2553 + + Impact Journals LLC + + + + 29113357 + 5655252 + 20895 + 10.18632/oncotarget.20895 + + + Research Paper + + + + Brain gray matter abnormalities in progressive supranuclear palsy revisited + + + + + Pan + PingLei + + + 1 + + + 2 + + + + + Liu + Yi + + + 1 + + + 3 + + + 4 + + + 5 + + + 6 + + + + + Zhang + Yang + + + 1 + + + 3 + + + 4 + + + 5 + + + 6 + + + + + Zhao + Hui + + + 1 + + + 3 + + + 4 + + + 5 + + + 6 + + + + + Ye + Xing + + + 1 + + + 3 + + + 4 + + + 5 + + + 6 + + + + + Xu + Yun + + + 1 + + + 3 + + + 4 + + + 5 + + + 6 + + + + 1 Department of Neurology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, PR China + 2 Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China + 3 The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, PR China + 4 Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, PR China + 5 Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, PR China + 6 Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, PR China + + + + Correspondence to: + + Yun Xu, + xuyun20042001@aliyun.com + + + + 6 + 10 + 2017 + + + 15 + 9 + 2017 + + 8 + 46 + 80941 + 80955 + + + 21 + 2 + 2017 + + + 26 + 8 + 2017 + + + + Copyright: © 2017 Pan et al. + 2017 + + This article is distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. + + + +

Whole-brain voxel-based morphometry (VBM) studies of progressive supranuclear palsy (PSP) have demonstrated heterogeneous findings regarding gray matter (GM) abnormalities. Here, we used Seed-based d Mapping, a coordinate-based meta-analytic approach to identify consistent regions of GM anomalies across studies of PSP. Totally, 18 original VBM studies, comprising 284 patients with PSP and 367 healthy controls were included. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the frontal motor cortices, medial (including anterior cingulate cortex) and lateral frontal cortices, insula, superior temporal gyrus, striatum (putamen and caudate nucleus), thalamus, midbrain, and anterior cerebellum. Our study further suggests that many confounding factors, such as age, male ratio, motor severity, cognitive impairment severity, and illness duration of PSP patients, and scanner field-strength, could contribute to the heterogeneity of GM alterations in PSP across studies. Our comprehensive meta-analysis demonstrates a specific neuroanatomical pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries that mediate vertical supranuclear gaze palsy, motor disabilities (postural instability with falls and parkinsonism), and cognitive-behavioral disturbances. Confounding factors merit attention in future studies.

+
+ + progressive supranuclear palsy + voxel-based morphometry + meta-analysis + cortical-subcortical circuitries + seed-based d mapping + +
+
+ + + INTRODUCTION +

Progressive supranuclear palsy (PSP) is a clinical syndrome characterized mainly by early postural instability with falls, vertical supranuclear gaze palsy, parkinsonism, and cognitive-behavioral disturbances that lead to significant disabilities with a mean survival of 6.38 years [15]. PSP is a rapidly progressive neurodegenerative disorder, pathologically confirmed by the accumulation of tau protein and neuropil threads in cortical and subcortical structures [3, 6]. Several clinical subtypes of PSP have been identified, of which the classic Richardson's syndrome (PSP-RS) and the PSP-parkinsonism variant (PSP-P) are the most common [3]. No effective treatments are available for PSP [7]. Its differential diagnosis from other parkinsonian disorders is critical but presents challenges in clinical practice, especially in the early disease stages [3, 8]. Despite impressive advances in understanding its pathophysiology, the reliably validated biomarkers for the ante-mortem diagnosis and the prognosis of PSP have not yet been established [3, 4, 9, 10].

+

Major improvements in modern magnetic resonance imaging (MRI) techniques increase our ability to identify brain structural alterations in vivo that shed light on the neuroanatomical basis of PSP and hold promise for its diagnosis [4, 1113]. Midbrain atrophy is a hallmark of PSP [12, 13]. Recent evidence from whole-brain voxel-based morphometry (VBM) studies in PSP has additionally demonstrated gray matter (GM) atrophy in a number of brain regions, such as the thalamus, basal ganglia, insula, frontal cortices, temporal cortices, parietal cortices, and cerebellum [1331]. Compared with conventional MRI investigations that draw regions of interest (ROIs) for morphometric comparisons, VBM is a hypothesis-free analytic tool to quantify regional structural differences between groups at a whole-brain level [32]. VBM has been widely used in neurodegenerative disorders [3335]. Despite the strengths, inconsistent results across different VBM studies were reported [13]. Shi, et al. in 2012, Shao, et al. in 2013, and Yu, et al. in 2014 thus conducted three coordinate-based meta-analyses to test the consistency of GM changes in PSP, which included nine, nine, and 12 VBM studies, respectively [3638]. However, these meta-analyses had several limitations. First, these meta-analyses did not examine the confounding variables, such as age, gender, disease duration, and symptom severity that potentially lead to heterogeneity of the structural alterations associated with PSP [13]. Second, the quantitative voxel-based meta-analytic tools have been modified [3941]. Several complementary analyses, such as jackknife sensitivity, heterogeneity, and publication bias analyses could be further performed to explore the robustness of the findings [42, 43]. Third, the numbers of VBM studies included in these meta-analyses were limited. To achieve sufficient power for moderate effects, Eickhoff and co-workers recently recommended that 17 or more experiments were needed for a coordinate-based meta-analysis to detect moderately sized effects [39]. In recent two years, we identified six more VBM studies of PSP eligibly for the meta-analysis. As such, results from previous meta-analyses cannot be considered conclusive and will need replication in a more exhaustive meta-analysis.

+

Against this background, we aimed to conduct an updated meta-analysis based on 18 whole-brain VBM studies in PSP using a modified Seed-based d Mapping (SDM) approach to obtain more accurate results. In addition, analyses of jackknife sensitivity, heterogeneity, publication bias and meta-regression were comprehensively performed to examine the robustness and replicability of GM abnormalities associated with PSP.

+
+ + RESULTS + + Characteristics of included studies +

Figure 1 presents the flow diagram for inclusion/exclusion of studies in the meta-analysis. Totally, 18 original studies reporting GM differences between 284 patients with PSP and 367 healthy controls were included in this meta-analysis [1431]. Across the 18 studies, one study included all pathologically confirmed patients with nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) and PSP (nfvPPA-PSP) [30] and another two studies included some pathologically confirmed patients with PSP in the samples [21, 31]. Patients with PSP in the remaining 15 studies were clinically diagnosed. Three of the 17 studies explicitly indicated that the samples included two subtypes of PSP, PSP-RS and PSP-P [18, 27, 29]. Patients in the remaining 14 of 17 studies were clinically diagnosed based on the NINDS-SPSP clinical criteria [44, 45], which has over 95% sensitivity and specificity in diagnosing PSP-RS [45]. No significant differences between patients with PSP and healthy controls regarding mean age (standardized mean difference = 0.17; 95% confidence interval [CI] = -0.003 to 0.344, z = 0.61, p = 0.055) or gender distribution (relative risk = 1.12, 95% CI = 0.974 to 1.289, z = 0.159, p = 0.112) were observed. Mean Unified Parkinson's Disease Rating Scale-motor examination (UPDRS-III) score in 11/18 studies (rang from 20.4 to 52.9), Hoehn and Yahr disability scale (H&Y) in 7/18 studies (rang from 2.6 to 3.8), illness duration in 15/18 studies (rang from 2.5 years to 4.8 years), Mini-Mental State Examination (MMSE) score in 13/18 studies (rang from 21 to 28), and Frontal Assessment Battery (FAB) examination in 6/18 studies (rang from 7.81 to 12.9) were reported. Nine out of the 18 studies were conducted on 1.5T MRI systems and 8/18 studies were on 3.0T systems. One study used either a 1.5T or a 3.0T MRI system. 13 out of the 18 studies reported the corrected results and the remaining 5 studies used the uncorrected thresholds. All the studies included used Statistical Parametric Mapping (SPM) softwares for imaging analyses.

+ + + + Flowchart to identify the eligible studies for the meta-analysis +

Key: PSP, Progressive Supranuclear Palsy; GM, Gray Matter; VBM, Voxel-Based Morphometry; ROI, Region Of Interest.

+ + +
+

The quality score of each study included in this meta-analysis was not less than 8 (a maximum score of 10 for each study), which indicates the high rigour of these studies. The list, demographic, clinical and imaging characteristics, and scores of quality assessment of the included studies are presented in Table 1.

+ + + + Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
+ Brenneis et al. (2004) + PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
+ Price et al. (2004) + PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
+ Cordato et al. (2005) + PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
+ Boxer et al. (2006) + PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
+ Padovani et al. (2006) + PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
+ Agosta et al. (2010) + PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
+ Lehericy et al. (2010) + PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
+ Takahashi et al. (2011) + PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
+ Ghosh et al. (2012) + PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
+ Giordano et al. (2013) + PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
+ Kamiya et al. (2013) + PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
+ Lagarde et al. (2013) + PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
+ Whitwell et al. (2013) + PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
+ Sandhya et al. (2014) + PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
+ Burciu et al. (2015) + PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
+ Piattella et al. (2015) + PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
+ Wang et al. (2015) + PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
+ Santos-Santos et al. (2016) + PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0
+ +

Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.

+
+
+
+ + Main voxel-wise meta-analysis +

As shown in Figure 2, the voxel-wise meta-analysis identified significant GM reductions in the left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and orbitofrontal cortex (OFC), in the bilateral thalamus extending to the midbrain and caudate nucleus, in the bilateral anterior cingulate cortex (ACC) extending to the supplementary motor areas (SMA) and pre-SMA, superior medial frontal cortex, and medial OFC, in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex), and in the left anterior cerebellum (lobule III/IV/V) in patients with PSP compared with healthy controls. In contrast, no significant GM increases were observed in patients with PSP relative to healthy controls. Details of the results are summarized in Table 2.

+ + + + Meta-analytic results of gray matter reductions in patients with PSP compared to healthy controls +

Key: (A), Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/ orbitofrontal cortex; (B), Right/Left thalamus/midbrain/caudate nucleus; (C), Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; (D), Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); (E), Left anterior cerebellum (lobule III/IV/V); PSP, Progressive Supranuclear Palsy; HC, healthy controls; SDM, Seed-based d Mapping. The color bar indicates the maximum and the minimum SDM-Z values.

+ + +
+ + + + GM reductions in patients with PSP compared to healthy controls + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
+ A + Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
+ B + Right/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
+ C + Right/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
+ D + Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
+ E + Left anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83
+ +

Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.

+
+
+

The subgroup analysis of 14 VBM studies that patients were suggestive of PSP-RS showed that the results remained largely unchanged.

+
+ + Supplemental analyses +

The jackknife sensitivity analysis revealed that regions of GM reductions in left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and OFC, in the bilateral thalami extending to the midbrain and caudate nucleus, in the bilateral ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex) in patients with PSP relative to healthy controls were replicable in all 18 studies. The region of GM reductions in the left anterior cerebellum (lobule III/IV/V) in patients with PSP relative to healthy controls was replicable in 15 studies (Table 3).

+ + + + Jackknife sensitivity analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
All studies but …ABCDE
+ Brenneis et al. (2004) + YesYesYesYesYes
+ Price et al. (2004) + YesYesYesYesYes
+ Cordato et al. (2005) + YesYesYesYesYes
+ Boxer et al. (2006) + YesYesYesYesYes
+ Padovani et al. (2006) + YesYesYesYesYes
+ Agosta et al. (2010) + YesYesYesYesYes
+ Lehericy et al. (2010) + YesYesYesYesYes
+ Takahashi et al. (2011) + YesYesYesYesYes
+ Ghosh et al. (2012) + YesYesYesYesNo
+ Giordano et al. (2013) + YesYesYesYesYes
+ Kamiya et al. (2013) + YesYesYesYesNo
+ Lagarde et al. (2013) + YesYesYesYesYes
+ Whitwell et al. (2013) + YesYesYesYesYes
+ Sandhya et al. (2014) + YesYesYesYesYes
+ Burciu et al. (2015) + YesYesYesYesYes
+ Piattella et al. (2015) + YesYesYesYesYes
+ Wang et al. (2015) + YesYesYesYesNo
+ Santos-Santos et al. (2016) + YesYesYesYesYes
+ Total + + 18 out of 18 + + 18 out of 18 + + 18 out of 18 + + 18 out of 18 + + 15 out of 18 +
+ +

Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.

+
+
+

The heterogeneity analysis revealed that there is significant between-study variability of GM differences in patients with PSP relative to healthy controls in the right inferior frontal gyrus extending to the insula and superior temporal gyrus, in the left insula extending to the superior temporal gyrus, in the bilateral ACC extending to the medial OFC, in the bilateral thalamus, in the left inferior frontal gyrus, in the left cerebellum (lobule III), and in the right caudate nucleus (Table 4).

+ + + + Regions of GM heterogeneity from the SDM analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
+ Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48) + 46, 14, 215234.800.0000046
+ Left insula/superior temporal gyrus (BAs 47, 13, and 48) + -40, 0, -29844.590.0000077
+ Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24) + 2, 50, 89624.260.000034
+ Right/Left thalamus + 2, -18, -83065.17∼0
+ Left inferior frontal gyrus (BAs 45, and 44) + -50, 20, 221783.490.00044
+ Left cerebellum (lobule III) + -8, -34, -18373.190.0011
+ Right caudate nucleus + 12, -2, 20112.770.0030
+ +

Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.

+
+
+

No publication biases were detected in the regions obtained from the main voxel-wise meta-analysis as revealed by the symmetrical funnel plots (Figure 3) and statistically non-significant Egger's tests (Table 2).

+ + + + Funnel plots of the peak coordinates of gray matter abnormalities in progressive supranuclear palsy + + + +

Meta-regression analysis revealed that the PSP group with older mean age (available from 17 studies) exhibited more GM reductions in the bilateral thalamus extending to the midbrain (Figure 4A) and in the left insula extending to the inferior frontal gyrus (Figure 4B). Higher male ratio of patients in the PSP group (available from 17 studies) was associated with more GM reductions in the left caudate nucleus extending to the thalamus (Figure 4C). Higher average UPDRS-III score (Figure 4D) or lower mean MMSE score (Figure 4E) in the PSP group (available from 11 and 13 studies, respectively) correlated with more GM reductions in the left insula. Meta-regression analysis indicated that longer mean illness duration of the PSP group (available from 15 studies) was associated with more GM reductions in the Left caudate nucleus extending to bilateral thalami (Figure 4F). Higher scanner field-strength in VBM studies tended to detect more GM atrophy in the right inferior frontal gyrus (Figure 4G) and in the left SMA (Figure 4H). Findings of these meta-regression analyses are presented in Table 5 and the regression lines are shown in Figure 4.

+ + + + Results of the meta-regression analyses +

Key: UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. Each study is represented as a dot, with a larger dot indicating a larger sample size. (A) and (B), meta-regression with mean age; (C), meta-regression with male ratio of patients; (D), meta-regression with mean UPDRS-III score; (E), meta-regression with mean MMSE score; (F), meta-regression with illness duration; (G) and (H), meta-regression with scanner field-strength.

+ + +
+ + + + Meta-regression analyses + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
+ Effect of age: GM changes in studies with older patients compared to younger patients +
+ a. Right/Left thalamus/midbrain + 2, -14, 6655-4.98∼0
+ b. Left insula/inferior frontal gyrus + -44, 12, 4641-4.150.0000026
+ Effect of gender: GM changes in studies with a higher male ratio of patients +
+ c. Left caudate nucleus/thalamus + -6, -4, 14155-3.710.000014
+ Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score +
+ d. Left insula + -32, 12, 1015-3.380.00015
+ Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score +
+ e. Left insula + -32, 22, 041-3.660.00015
+ Effect of illness duration: GM changes in studies of patients with longer average illness duration +
+ f. Left caudate nucleus/thalamus/Right thalamus + 0, -8, 121447-6.00∼0
+ Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength +
+ g. Right inferior frontal gyrus + 54, 12, 16127-4.020.000037
+ h. Left supplementary motor area + -2, 24, 5611-3.490.00032
+ +

Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.

+
+
+
+
+ + DISCUSSION +

Using a modified SDM approach, the present quantitative meta-analysis is timely given with a sufficient number of VBM studies that have recently become available. This comprehensive study synthesized the findings from 18 VBM studies comprising 284 patients with PSP and 367 healthy controls. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen, precentral gyrus (premotor cortex) and OFC, the thalamus extending to the midbrain and caudate nucleus, the ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and the anterior cerebellum (lobule III/IV/V). These GM changes in PSP were highly robust as verified by jackknife sensitivity analyses. In addition, no publication biases in these regions were observed. However, the heterogeneity analysis revealed a significant between-study variability of GM atrophy differences in some of these regions. Further meta-regression analyses indicated that these variations in GM alterations across VBM studies were correlated with the mean age, male ratio, UPDRS-III score, MMSE score and illness duration of PSP patients, as well as scanner field-strength employed.

+

The pattern of GM atrophy in PSP identified in our meta-analysis is consistent with the histopathological distribution of neuronal loss, gliosis, and accumulation of tau proteins in the midbrain, diencephalon, basal ganglia, cerebellum, frontal and temporal cortices [46]. Midbrain atrophy, which is consistently validated by many imaging modalities, is the most characteristic alteration of PSP [47]. The hallmark of the disease, vertical supranuclear gaze palsy, is considered to correlate with the neurodegeneration of the rostral interstitial nucleus of the medial longitudinal fasciculus (riMLF and the interstitial nucleus of Cajal located in the midbrain, and the central mesencephalic reticular formation [4850]. In addition, a recent study by Amtage and colleagues employing 18F-Fluorodeoxyglucose (FDG) positron-emission tomography (PET) suggests that the ACC (cingulate eye field), which connections the supplementary eye field, frontal eye field and midbrain regions [51], plays an important role in downward gaze palsy in PSP [52]. The substantia nigra in the midbrain, coupled with the basal ganglia, thalamus, motor cortices, and anterior cerebellum are hubs of a motor control network [5355]. GM atrophy in these brain regions identified in the current meta-analysis, probably indicative for damage of this network, contributes to the pathophysiology of parkinsonism, such as rigidity, bradykinesia, and postural instability in patients with PSP [3, 55, 56]. Recent evidence suggests that gait disturbance with early falls, one of the characteristic clinical features of PSP, are closely associated with the thalamic dysfunction, which influences the mesencephalic brainstem-thalamus loop [56].

+

Beyond the motor control, the subcortical structures such as the the substantia nigra, basal ganglia and thalamus are also implicated in mediating cognition and behavior via the frontal-subcortical circuits [5759]. In addition to the motor symptoms, patients with PSP are frequently accompanied by cognitive-behavioral disturbances, such as executive dysfunction, apathy, and disinhibition, which are prevalent and may occur early in the disease course affecting their quality of daily life [57, 60]. Early cognitive impairment in PSP is shown to be an independent predictor of shorter survival [6163]. PSP is typically considered a “subcortical dementia” with the impairment of the frontal-subcortical circuits, prominently attributed to the subcortical pathology [59, 63, 64]. Resting-state functional MRI studies have demonstrated a widespread disruption of cortical-subcortical connectivity involved in cognitive and motor dysfunction in PSP [6567]. Previous studies using manual ROI approaches for the frontal lobe, demonstrated that the severity of behavioral and cognitive disturbances was associated with the degree of frontal atrophy in PSP patients [6870]. In addition, a longitudinal ROI study further showed that the progression of executive dysfunction correlated with increased rates of frontal atrophy in patients with PSP [71]. A VBM study demonstrated that the severity of behavioral disturbances in mid-stage PSP correlated with atrophy of the OFC surrounding the inferior frontal sulcus and the midbrain [31]. In accordance with these data, our voxel-wise meta-analysis identified frontal GM atrophy noted in the lateral (inferior frontal cortex extending to OFC) and medial (ACC extending to superior medial frontal cortex and medial OFC) frontal cortices, apart from the frontal motor associated cortices including the (pre-) SMA and the premotor cortex. In addition, we identified extra-frontal GM atrophy in the insular cortex and superior temporal cortex. The insula has rich connections with the frontal and subcortical structures acting as a hub for integrating cognitive-affective, sensorimotor, and autonomic information [72, 73]. Our meta-regression analyses showed that the severity of the motor disabilities and cognitive impairment as well as the illness duration of PSP had notable effects on GM atrophy in the insula. Atrophy of the superior temporal cortex along with the OFC, parts of the OFC-subcortical circuit may be associated with disinhibition, one of the frequently observed behavioral symptoms in PSP [5860, 74]. The ACC is engaged in the medial frontal-subcortical circuit and in the dorsolateral prefrontal-subcortical circuit, dysfunction of which are responsible for apathy and executive dysfunction, respectively [5759, 74, 75]. In contrast to previous meta-analyses [3335], cortical atrophy in the current study was more prominent, which may be attributed to the methodological improvement and sufficient statistical power with enough studies as discussed in the introduction [3941]. Taken together, GM matter atrophy in these cortical and subcortical regions identified in the meta-analysis may shed light on the pathophysiology of the cognitive-behavioral disturbances in PSP.

+

Notably in the current meta-analysis, we observed heterogeneity of brain GM alterations in some of the regions across studies, which are attributed to the confounding factors, such as age, male ratio, motor severity, MMSE score, and illness duration of PSP patients, and scanner field-strength that were not analyzed by previous meta-analysis [3638]. For example, meta-regression analysis revealed that older mean age in PSP patients was associated with more GM atrophy in the bilateral thalamus extending to the midbrain and the left insula extending to the inferior frontal gyrus. Age is an important risk factor for PSP [76]. Severer motor disabilities and cognitive-behavioral disturbances in PSP are associated with more GM atrophy in the left insula. The human insula is strongly interconnected with the basal ganglia and cortical regions, which is implicated in cognitive/affective and sensorimotor processing [77, 78]. These brain structure-behavior correlations provided additional insight into the neurobiology of PSP. The epidemiologic data shows that PSP affects men more frequently than women [76, 79]. In the current meta-analysis, we noted that samples with PSP with a higher male ratio tended to have more GM atrophy in the left caudate nucleus extending to the thalamus, which may provide a neuroanatomical basis of such gender susceptibility. However, we could not find the factors that contribute to the heterogeneity of GM changes in the bilateral ACC extending to the medial OFC. As mentioned above, these regions are implicated in cognitive-behavioral disturbances. Due to the limited data of frontal assessment battery and frontal behavioral inventory available from the original studies, we could not further explore the source of GM heterogeneity in these regions. More studies are warranted to assess cognitive-behavioral disturbances and to conduct clinical-neuroanatomical correlations in PSP.

+ + Limitations +

Some limitations of this meta-analysis warrant consideration. First, an intrinsic limitation for coordinate-based meta-analytic approaches is that they are based on coordinate data rather than raw imaging data, which may bias the results [40, 80]. Second, due to that fact that most of the samples were not pathologically confirmed, the clinically heterogeneous nature of PSP might limit specificity of the findings, although the subgroup analysis indicates that this pattern of GM atrophy is specific for classic PSP-RS patients. Further studies with large homogenous samples both by clinically diagnosed and pathologically confirmed are warranted to validate these findings.

+
+
+ + MATERIALS AND METHODS + + Literature search and selection +

As the VBM method was introduced in the year of 2000 [32], we systematically searched PubMed, Embase, and Web of Science databases between January 1, 2000 and September 17, 2016 using the Medical Subject Heading (MeSH) term “progressive supranuclear palsy” and its corresponding free terms, and the keywords “voxel-based morphometry” or “vbm” or “gray matter” or “grey matter” or “voxel*”. Furthermore, we checked the bibliographies of relevant review papers and retrieved articles by hand for additional studies. One study was considered for inclusion in the meta-analysis if it (1) was published in an English-language peer-reviewed journal as an original article; (2) reported regional GM changes using a whole-brain VBM analysis for direct comparison between patients with PSP and healthy controls; (3) reported three-dimensional coordinates of maxima (x, y, z) in a standardized stereotaxic space (i.e., Montreal Neurological Institute [MNI] or Talairach); (4) reported significant results of regional GM differences within one study using a constant threshold. Only the baseline dataset was included if the study was longitudinal. Studies were excluded if they limited their analyses to specific regions of interest (ROIs) or volume of interest (VOI). A study was excluded if its sample overlapped with another publication. The quality of each study included in this meta-analysis was evaluated using a 10point checklist that integrated both the clinical and demographic information and the imaging-specific methodology (Supplementary Table 1), which was based on previous meta-analytic studies [81, 82]. Recorded data were extracted from original studies, including the first author's name, year of publication, age, gender and number of patients and controls, clinical variables (e.g., illness duration, UPDRS-III score, H&Y stage, MMSE score, and FAB score), and the imaging characteristics (e.g., scanner field-strength, processing software, full width half maximum [FWHM] and statistical threshold). In addition, peak coordinates and effect sizes (e.g., t-values) of GM differences between patients with PSP and healthy controls from each VBM study were extracted for the following voxel-wise meta-analysis. Two investigators independently performed literature search and selection, assessment of study quality, and data extraction. Any discrepancies were discussed with another investigator until they were resolved. This study followed the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [83].

+
+ + Data analysis + + + Main voxel-wise meta-analysis +

Voxel-wise meta-analysis of regional GM differences between patient with PSP and healthy controls was conducted using the modified SDM software package available at http://www.sdmproject.com. The details of the approach have been described in other publications [40, 80, 8486] and the tutorial available at http://www.sdmproject.com/software/tutorial.pdf An effect-size signed map and an effect-size variance map of the GM differences was first separately recreated for each study. The mean map was then created by voxel-wise calculation of the random-effects mean of the study maps, which was weighted by the sample size, intra- study variability, and additional between-study heterogeneity. Statistical significance was set at a default un-normalised Gaussian kernel kernel size and threshold (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels), which provided the optimal balance of false positives and negatives [40, 80]. It must be noted that this un-normalised kernel is not designed to smooth any image but to assign indicators of proximity to reported coordinates [80, 84].

+

In addition, we conduct a subgroup analysis of VBM studies that patients met the NINDS-SPSP (NINDS-SPSP, National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy) criteria suggestive of PSP-RS [44, 45].

+
+ + Supplemental analyses +

A leave-one-out and whole-brain voxel-based jackknife analysis was performed to assess the sensitivity of the results by iteratively repeating the same analysis, discarding one study each time [80, 84].

+

A heterogeneity analysis was carried out using a random effects model with Q statistics in order to explore which brain regions are more heterogeneous between studies. Jackknife and heterogeneity analyses were thresholded with the same default settings (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels) [40, 80].

+

In addition, the Stata/SE 12.0 software (Stata Corp LP, College Station, TX, USA) was used to examine possible publication bias. Funnel plots and Egger's test was performed by extracting the values from the meta-analytic peaks [42]. An asymmetry of funnel plots and a p-value less than 0.05 of Egger's test were considered significant.

+

Meta-regression analyses were further conducted to explore the effects of age, gender, UPDRS-III score, MMSE score, illness duration, and scanner field-strength that could potentially influence the meta-analytic results. Statistical significance was thresholded at a more conservative p-value less than 0.0005 and cluster extent more than 10 voxels [80, 85]. Variables, such as H&Y stage, FAB, frontal behavioral inventory (FBI), and the Progressive Supranuclear Palsy Rating Scale (PSPRS), could not be explored by meta-regression analyses due to limited information that was available from less than 10 original studies.

+
+
+ + CONCLUSIONS +

In summary, our comprehensive meta-analysis demonstrates a specific pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries in the pathophysiology of the supranuclear gaze palsy, motor disabilities, and cognitive-behavioral disturbances. These morphological findings may have implications for neuroanatomical diagnostic biomarkers of PSP. In addition, our study indicates that many confounding factors contribute to the heterogeneity of GM alterations in PSP across studies, which merits much attention in further studies.

+
+ + SUPPLEMENTARY MATERIALS TABLE + + + + + + + +

We are greatly indebted to the authors of the included studies.

+
+ + +

+ Author contributions +

+

YX, PLP and YL designed the protocol. PLP and YL wrote the main manuscript. YZ and HZ obtained the data. PLP and XY analyzed the results. YX revised the manuscript. All authors reviewed the manuscript.

+
+ +

+ CONFLICTS OF INTEREST +

+

The authors declare no conflicts of interest.

+
+ +

+ FUNDING +

+

This research was supported by the National Natural Science Foundation of China (81230026, 81630028, 81171085, 81601161), the Natural Science Foundation (BE2016610) of Jiangsu Province of China, the Ministry of Science and Technology in China (2016YFC0901004).

+
+
+ + Abbreviations + + + VBM + +

voxel-based morphometry

+
+
+ + PSP + +

progressive supranuclear palsy

+
+
+ + GM + +

gray matter

+
+
+ + PSP-RS + +

PSP Richardson's syndrome

+
+
+ + PSP-P + +

PSP-parkinsonism variant

+
+
+ + MRI + +

magnetic resonance imaging

+
+
+ + ROIs + +

regions of interest

+
+
+ + SDM + +

Seed-based d Mapping

+
+
+ + nfvPPA-PSP + +

nonfluent/agrammatic variant of primary progressive aphasia and PSP

+
+
+ + MeSH + +

Medical Subject Heading

+
+
+ + MNI + +

Montreal Neurological Institute [MNI]

+
+
+ + VOI + +

volume of interest

+
+
+ + UPDRS-III + +

Unified Parkinson's Disease Rating Scale-motor examination

+
+
+ + H&Y + +

Hoehn and Yahr

+
+
+ + MMSE + +

Mini-Mental State Examination

+
+
+ + FAB + +

frontal assessment battery

+
+
+ + FWHM + +

full width half maximum

+
+
+ + MOOSE + +

Meta-analysis Of Observational Studies in Epidemiology

+
+
+ + NINDS-SPSP + +

National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy

+
+
+ + PSPRS + +

Progressive Supranuclear Palsy Rating Scale

+
+
+ + CI + +

confidence interval

+
+
+ + OFC + +

orbitofrontal cortex

+
+
+ + ACC + +

anterior cingulate cortex

+
+
+ + SMA + +

supplementary motor areas

+
+
+ + BA + +

Brodmann Area

+
+
+
+
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+
diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv new file mode 100644 index 0000000..3b19e5b --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv @@ -0,0 +1,19 @@ +Study,Sample (male),Age (SD),UPDRS-III (SD),H&Y stage (SD),Duration (SD),MMSE (SD),FAB (SD),Scanner,Software,FWHM,Threshold,Quality# +Brenneis et al. (2004),PSP 12 (NA)HC 12 (NA),67.5 (6.6)60 (5.8),38.9 (10.9),,2.7 (0.9),,,1.5T,SPM99,10.0,p < 0.05corrected,8.5 +Price et al. (2004),PSP 12 (7)HC 12 (8),65.3 (5.8)67.4 (4.6),20.4 (8.7),,4.8 (1.7),27 (3.3),12.4 (3.1),1.5T,SPM99,8.0,p < 0.05corrected,9.5 +Cordato et al. (2005),PSP 21 (14)HC 23 (14),70.3 (6.4)71.5 (7.2),23.1 (10.1),3.8 (1.1),4.0 (2.8),25.4 (3.2),,1.5T,SPM99,12.0,p < 0.05corrected,9.5 +Boxer et al. (2006),PSP 15 (9)HC 80 (37),70.9 (6.9)67.9 (8.6),,3.3 (0.5),4.8 (1.7),24.0 (3.2),,1.5T,SPM2,12.0,p < 0.05corrected,8.5 +Padovani et al. (2006),PSP 14 (7)HC 14 (7),73 (5.6)65.6 (4.1),22.1 (8.9),,3.1 (1.0),25.8 (2.7),,1.5T,SPM2,10.0,p < 0.005corrected,9.0 +Agosta et al. (2010),PSP 20 (14)HC 24 (13),64.9 (NA)63.8 (NA),32.8 (NA),3.0 (NA),4.5 (NA),27.0 (NA),,1.5T,SPM5,8.0,p < 0.001uncorrected,9.0 +Lehericy et al. (2010),PSP 10 (6)HC 9 (5),66.9 (6.4)66.5 (4.8),30 (NA),,4.3 (1.0),27 (NA),11.5 (NA),1.5T,SPM5,8.0,p < 0.05corrected,8.5 +Takahashi et al. (2011),PSP 16 (11)HC 20 (16),64.6 (6.4)64.8 (6.4),,,,21.0 (4.4),,1.5T,SPM8,8.0,p < 0.001uncorrected,8.5 +Ghosh et al. (2012),PSP 23 (14)HC 22 (15),71.1 (8.6)71.4 (7.6),33.8 (15.7),,2.5 (NA),,,3.0T,SPM5,,p < 0.05corrected,9.0 +Giordano et al. (2013),PSP 15 (8)HC 15 (8),68.91 (1.2)65.5 (6.1),38.33 (4),3.80 (1.1),3.16 (1.3),21.23 (1.2),7.81 (0.9),3.0T,SPM8,8.0,p < 0.05corrected,9.5 +Kamiya et al. (2013),PSP 16 (10)HC 21 (12),71.4 (6.0)70.9 (8.0),,,,,,1.5T,SPM5,8.0,p < 0.001uncorrected,9.0 +Lagarde et al. (2013),PSP 19 (7)HC 18 (7),65.9 (6.5)67.8 (5.2),,,4.5 (1.8),25.5 (2.7),11.3 (2),3.0T,SPM8,8.0,p < 0.05corrected,9.0 +Whitwell et al. (2013),PSP 16 (8)HC 20 (4),72.1 (4.6)73.9 (6.3),52.9 (12.6),,4.0 (1.1),25.8 (2.7),12.9 (2.2),3.0T,SPM5,8.0,p < 0.05corrected,9.0 +Sandhya et al. (2014),PSP 10 (9)HC 8 (5),NANA,,,,,,3.0T,SPM8,,p < 0.001uncorrected,8.0 +Burciu et al. (2015),PSP 20 (10)HC 20 (10),67.8 (7.1)64.8 (8.8),39.0 (14.5),2.6 (0.9),2.6 (2.6),,,3.0T,SPM8,,p < 0.05corrected,9.0 +Piattella et al. (2015),PSP 16 (9)HC 16 (6),68.08 (5.9)69.4 (0.4),27.0 (17.4),2.9 (1.0),3.1 (NA),24.3 (3.9),11.1 (3.8),3.0T,SPM8,12.0,p < 0.05corrected,9.5 +Wang et al. (2015),PSP 24 (8)HC 23 (14),64.17 (6.72)60.52 (6.47),,3.1 (NA),3.87 (2.62),23.54 (4.28),,3.0T,SPM8,6.0,p < 0.001corrected,8.5 +Santos-Santos et al. (2016),PSP 5 (1) HC 10 (3),71.5 (NA)74 (NA),,,4 (NA),28 (NA),,,SPM12,,p<0.001uncorrected,8.0 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json new file mode 100644 index 0000000..533c435 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json @@ -0,0 +1 @@ +{"table_id": "T1", "table_label": "Table 1", "table_caption": "Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis", "table_foot": "Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.", "n_header_rows": 1, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv new file mode 100644 index 0000000..77bb4ba --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv @@ -0,0 +1,6 @@ +Cluster,Anatomical label,"Peak MNI coordinate (x, y, z)",No. of voxels,SDM-Z value,SDM-p value,Egger's test (p value) +A,"Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)","-48, 18, 0",5063,-4.8,∼0,0.56 +B,Right/Left thalamus/midbrain/caudate nucleus,"4, -14, 6",3916,-4.7,∼0,0.29 +C,"Right/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)","-4, 12, 44",3457,-3.32,0.000067,0.27 +D,"Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)","54, 16, 16",3186,-4.27,0.027743R254,0.78 +E,Left anterior cerebellum (lobule III/IV/V),"-14, -44, -24",108,-2.74,0.0014,0.83 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json new file mode 100644 index 0000000..488dc0d --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json @@ -0,0 +1 @@ +{"table_id": "T2", "table_label": "Table 2", "table_caption": "GM reductions in patients with PSP compared to healthy controls", "table_foot": "Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.", "n_header_rows": 1, "table_data_file": "table_001.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv new file mode 100644 index 0000000..2ec4d07 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv @@ -0,0 +1,20 @@ +All studies but …,A,B,C,D,E +Brenneis et al. (2004),Yes,Yes,Yes,Yes,Yes +Price et al. (2004),Yes,Yes,Yes,Yes,Yes +Cordato et al. (2005),Yes,Yes,Yes,Yes,Yes +Boxer et al. (2006),Yes,Yes,Yes,Yes,Yes +Padovani et al. (2006),Yes,Yes,Yes,Yes,Yes +Agosta et al. (2010),Yes,Yes,Yes,Yes,Yes +Lehericy et al. (2010),Yes,Yes,Yes,Yes,Yes +Takahashi et al. (2011),Yes,Yes,Yes,Yes,Yes +Ghosh et al. (2012),Yes,Yes,Yes,Yes,No +Giordano et al. (2013),Yes,Yes,Yes,Yes,Yes +Kamiya et al. (2013),Yes,Yes,Yes,Yes,No +Lagarde et al. (2013),Yes,Yes,Yes,Yes,Yes +Whitwell et al. (2013),Yes,Yes,Yes,Yes,Yes +Sandhya et al. (2014),Yes,Yes,Yes,Yes,Yes +Burciu et al. (2015),Yes,Yes,Yes,Yes,Yes +Piattella et al. (2015),Yes,Yes,Yes,Yes,Yes +Wang et al. (2015),Yes,Yes,Yes,Yes,No +Santos-Santos et al. (2016),Yes,Yes,Yes,Yes,Yes +Total,18 out of 18,18 out of 18,18 out of 18,18 out of 18,15 out of 18 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json new file mode 100644 index 0000000..b4ea01a --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json @@ -0,0 +1 @@ +{"table_id": "T3", "table_label": "Table 3", "table_caption": "Jackknife sensitivity analysis", "table_foot": "Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.", "n_header_rows": 1, "table_data_file": "table_002.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv new file mode 100644 index 0000000..b4f3c21 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv @@ -0,0 +1,8 @@ +Anatomical regions,Maximum MNI coordinate,No. of Voxels,SDM-Z,p +"Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)","46, 14, 2",1523,4.8,0.0000046 +"Left insula/superior temporal gyrus (BAs 47, 13, and 48)","-40, 0, -2",984,4.59,0.0000077 +"Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)","2, 50, 8",962,4.26,0.000034 +Right/Left thalamus,"2, -18, -8",306,5.17,∼0 +"Left inferior frontal gyrus (BAs 45, and 44)","-50, 20, 22",178,3.49,0.00044 +Left cerebellum (lobule III),"-8, -34, -18",37,3.19,0.0011 +Right caudate nucleus,"12, -2, 20",11,2.77,0.0030 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json new file mode 100644 index 0000000..0e34441 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json @@ -0,0 +1 @@ +{"table_id": "T4", "table_label": "Table 4", "table_caption": "Regions of GM heterogeneity from the SDM analysis", "table_foot": "Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.", "n_header_rows": 1, "table_data_file": "table_003.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv new file mode 100644 index 0000000..e98f66b --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv @@ -0,0 +1,15 @@ +Anatomical label,"Peak MNI coordinate (x, y, z)",No. of voxels,SDM-Z value,p value +Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients +a. Right/Left thalamus/midbrain,"2, -14, 6",655,-4.98,∼0 +b. Left insula/inferior frontal gyrus,"-44, 12, 4",641,-4.15,0.0000026 +Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients +c. Left caudate nucleus/thalamus,"-6, -4, 14",155,-3.71,0.000014 +Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score +d. Left insula,"-32, 12, 10",15,-3.38,0.00015 +Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score +e. Left insula,"-32, 22, 0",41,-3.66,0.00015 +Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration +f. Left caudate nucleus/thalamus/Right thalamus,"0, -8, 12",1447,-6.00,∼0 +Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength +g. Right inferior frontal gyrus,"54, 12, 16",127,-4.02,0.000037 +h. Left supplementary motor area,"-2, 24, 56",11,-3.49,0.00032 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json new file mode 100644 index 0000000..3aa7558 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json @@ -0,0 +1 @@ +{"table_id": "T5", "table_label": "Table 5", "table_caption": "Meta-regression analyses", "table_foot": "Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.", "n_header_rows": 1, "table_data_file": "table_004.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml new file mode 100644 index 0000000..3fc39d9 --- /dev/null +++ b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml @@ -0,0 +1,2 @@ + +56552522911335756552522089510.18632/oncotarget.20895T1Table 1Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysisKey: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
Brenneis et al. (2004)PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
Price et al. (2004)PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
Cordato et al. (2005)PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
Boxer et al. (2006)PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
Padovani et al. (2006)PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
Agosta et al. (2010)PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
Lehericy et al. (2010)PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
Takahashi et al. (2011)PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
Ghosh et al. (2012)PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
Giordano et al. (2013)PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
Kamiya et al. (2013)PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
Lagarde et al. (2013)PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
Whitwell et al. (2013)PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
Sandhya et al. (2014)PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
Burciu et al. (2015)PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
Piattella et al. (2015)PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
Wang et al. (2015)PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
Santos-Santos et al. (2016)PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0

Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.

Table 1
Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
Brenneis et al. (2004)PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
Price et al. (2004)PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
Cordato et al. (2005)PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
Boxer et al. (2006)PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
Padovani et al. (2006)PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
Agosta et al. (2010)PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
Lehericy et al. (2010)PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
Takahashi et al. (2011)PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
Ghosh et al. (2012)PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
Giordano et al. (2013)PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
Kamiya et al. (2013)PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
Lagarde et al. (2013)PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
Whitwell et al. (2013)PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
Sandhya et al. (2014)PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
Burciu et al. (2015)PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
Piattella et al. (2015)PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
Wang et al. (2015)PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
Santos-Santos et al. (2016)PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0
Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.
T2Table 2GM reductions in patients with PSP compared to healthy controlsKey: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.GM reductions in patients with PSP compared to healthy controls
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
ALeft inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
BRight/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
CRight/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
DRight inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
ELeft anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83

Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.

Table 2
GM reductions in patients with PSP compared to healthy controls
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
ALeft inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
BRight/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
CRight/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
DRight inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
ELeft anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83
Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.
T3Table 3Jackknife sensitivity analysisKey: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.Jackknife sensitivity analysis
All studies but …ABCDE
Brenneis et al. (2004)YesYesYesYesYes
Price et al. (2004)YesYesYesYesYes
Cordato et al. (2005)YesYesYesYesYes
Boxer et al. (2006)YesYesYesYesYes
Padovani et al. (2006)YesYesYesYesYes
Agosta et al. (2010)YesYesYesYesYes
Lehericy et al. (2010)YesYesYesYesYes
Takahashi et al. (2011)YesYesYesYesYes
Ghosh et al. (2012)YesYesYesYesNo
Giordano et al. (2013)YesYesYesYesYes
Kamiya et al. (2013)YesYesYesYesNo
Lagarde et al. (2013)YesYesYesYesYes
Whitwell et al. (2013)YesYesYesYesYes
Sandhya et al. (2014)YesYesYesYesYes
Burciu et al. (2015)YesYesYesYesYes
Piattella et al. (2015)YesYesYesYesYes
Wang et al. (2015)YesYesYesYesNo
Santos-Santos et al. (2016)YesYesYesYesYes
Total18 out of 1818 out of 1818 out of 1818 out of 1815 out of 18

Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.

Table 3
Jackknife sensitivity analysis
All studies but …ABCDE
Brenneis et al. (2004)YesYesYesYesYes
Price et al. (2004)YesYesYesYesYes
Cordato et al. (2005)YesYesYesYesYes
Boxer et al. (2006)YesYesYesYesYes
Padovani et al. (2006)YesYesYesYesYes
Agosta et al. (2010)YesYesYesYesYes
Lehericy et al. (2010)YesYesYesYesYes
Takahashi et al. (2011)YesYesYesYesYes
Ghosh et al. (2012)YesYesYesYesNo
Giordano et al. (2013)YesYesYesYesYes
Kamiya et al. (2013)YesYesYesYesNo
Lagarde et al. (2013)YesYesYesYesYes
Whitwell et al. (2013)YesYesYesYesYes
Sandhya et al. (2014)YesYesYesYesYes
Burciu et al. (2015)YesYesYesYesYes
Piattella et al. (2015)YesYesYesYesYes
Wang et al. (2015)YesYesYesYesNo
Santos-Santos et al. (2016)YesYesYesYesYes
Total18 out of 1818 out of 1818 out of 1818 out of 1815 out of 18
Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.
T4Table 4Regions of GM heterogeneity from the SDM analysisKey: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.Regions of GM heterogeneity from the SDM analysis
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)46, 14, 215234.800.0000046
Left insula/superior temporal gyrus (BAs 47, 13, and 48)-40, 0, -29844.590.0000077
Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)2, 50, 89624.260.000034
Right/Left thalamus2, -18, -83065.17∼0
Left inferior frontal gyrus (BAs 45, and 44)-50, 20, 221783.490.00044
Left cerebellum (lobule III)-8, -34, -18373.190.0011
Right caudate nucleus12, -2, 20112.770.0030

Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.

Table 4
Regions of GM heterogeneity from the SDM analysis
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)46, 14, 215234.800.0000046
Left insula/superior temporal gyrus (BAs 47, 13, and 48)-40, 0, -29844.590.0000077
Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)2, 50, 89624.260.000034
Right/Left thalamus2, -18, -83065.17∼0
Left inferior frontal gyrus (BAs 45, and 44)-50, 20, 221783.490.00044
Left cerebellum (lobule III)-8, -34, -18373.190.0011
Right caudate nucleus12, -2, 20112.770.0030
Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.
T5Table 5Meta-regression analysesKey: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.Meta-regression analyses
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
Effect of age: GM changes in studies with older patients compared to younger patients
a. Right/Left thalamus/midbrain2, -14, 6655-4.98∼0
b. Left insula/inferior frontal gyrus-44, 12, 4641-4.150.0000026
Effect of gender: GM changes in studies with a higher male ratio of patients
c. Left caudate nucleus/thalamus-6, -4, 14155-3.710.000014
Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score
d. Left insula-32, 12, 1015-3.380.00015
Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score
e. Left insula-32, 22, 041-3.660.00015
Effect of illness duration: GM changes in studies of patients with longer average illness duration
f. Left caudate nucleus/thalamus/Right thalamus0, -8, 121447-6.00∼0
Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength
g. Right inferior frontal gyrus54, 12, 16127-4.020.000037
h. Left supplementary motor area-2, 24, 5611-3.490.00032

Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.

Table 5
Meta-regression analyses
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
Effect of age: GM changes in studies with older patients compared to younger patients
a. Right/Left thalamus/midbrain2, -14, 6655-4.98∼0
b. Left insula/inferior frontal gyrus-44, 12, 4641-4.150.0000026
Effect of gender: GM changes in studies with a higher male ratio of patients
c. Left caudate nucleus/thalamus-6, -4, 14155-3.710.000014
Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score
d. Left insula-32, 12, 1015-3.380.00015
Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score
e. Left insula-32, 22, 041-3.660.00015
Effect of illness duration: GM changes in studies of patients with longer average illness duration
f. Left caudate nucleus/thalamus/Right thalamus0, -8, 121447-6.00∼0
Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength
g. Right inferior frontal gyrus54, 12, 16127-4.020.000037
h. Left supplementary motor area-2, 24, 5611-3.490.00032
Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.
\ No newline at end of file diff --git a/tests/test_dataset.py b/tests/test_dataset.py new file mode 100644 index 0000000..1393cd5 --- /dev/null +++ b/tests/test_dataset.py @@ -0,0 +1,7 @@ +from pipelines.dataset import Dataset + + +def test_dataset(sample_data): + dataset = Dataset(sample_data) + + assert len(dataset) == 3 diff --git a/tests/test_word_count.py b/tests/test_word_count.py new file mode 100644 index 0000000..cae7853 --- /dev/null +++ b/tests/test_word_count.py @@ -0,0 +1,21 @@ +from pathlib import Path +from pipelines.word_count.run import WordCountExtraction +from pipelines.dataset import Dataset + + +def test_word_count_extraction(sample_data, tmp_path): + """Test the word count extraction pipeline.""" + wce = WordCountExtraction(prefer="pubget") + dataset = Dataset(sample_data) + output_dir = tmp_path / "word_count" + wce.run(dataset, output_dir) + assert True + + +def test_word_count_extraction_ace(sample_data, tmp_path): + """Test the word count extraction pipeline.""" + wce = WordCountExtraction(prefer="ace") + dataset = Dataset(sample_data) + output_dir = tmp_path / "word_count" + wce.run(dataset, output_dir) + assert True From bbbfc9d4f498219a2e77645eb07b8a461b216e19 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 24 Oct 2024 16:08:57 -0500 Subject: [PATCH 02/42] add dependent pipeline --- pipelines/dataset.py | 61 ++++---- pipelines/pipeline.py | 241 ++++++++++++++++++++++++++++++- pipelines/word_count/__init__.py | 6 + pipelines/word_count/run.py | 115 ++++++--------- tests/test_word_count.py | 28 +++- 5 files changed, 342 insertions(+), 109 deletions(-) diff --git a/pipelines/dataset.py b/pipelines/dataset.py index cf95d10..3038adc 100644 --- a/pipelines/dataset.py +++ b/pipelines/dataset.py @@ -1,11 +1,21 @@ """Dataset creation for processing inputs.""" -from dataclasses import dataclass +from copy import deepcopy +from dataclasses import dataclass, field from pathlib import Path import re import json +from typing import Union, Optional + +INPUTS = [ + "text", + "coordinates", + "metadata", + "html", + "xml", + "tables", + "tables_xml", +] - - @dataclass class AceRaw: html: Path @@ -18,9 +28,10 @@ class PubgetRaw: @dataclass class ProcessedData: - coordinates: Path - text: Path - metadata: Path + coordinates: Path = None + text: Path = None + metadata: Path = None + raw: Optional[Union['PubgetRaw', 'AceRaw']] = field(default=None) @dataclass class Study: @@ -28,10 +39,8 @@ class Study: doi: str = None pmid: str = None pmcid: str = None - ace: ProcessedData = None - pubget: ProcessedData = None - ace_raw: AceRaw = None - pubget_raw: PubgetRaw = None + ace: ProcessedData = field(default_factory=ProcessedData) + pubget: ProcessedData = field(default_factory=ProcessedData) class Dataset: @@ -41,6 +50,12 @@ def __init__(self, input_directory): """Initialize the dataset.""" self.data = self.load_directory(input_directory) + def slice(self, ids): + """Slice the dataset.""" + deepcopy_obj = deepcopy(self) + deepcopy_obj.data = {k: v for k, v in deepcopy_obj.data.items() if k in ids} + return deepcopy_obj + def load_directory(self, input_directory): """Load the input directory. input_directory (str): The input directory containing the text. @@ -75,20 +90,20 @@ def load_directory(self, input_directory): # check if the source ace directory exists and load appropriate files if (source_dir / "ace").exists(): - study_obj.ace_raw = AceRaw(html=source_dir / "ace" / f"{study_obj.pmid}.html") + study_obj.ace.raw = AceRaw(html=source_dir / "ace" / f"{study_obj.pmid}.html") # check if the source pubget directory exists and load appropriate files if (source_dir / "pubget").exists(): - study_obj.pubget_raw = PubgetRaw( + study_obj.pubget.raw = PubgetRaw( xml=source_dir / "pubget" / f"{study_obj.pmcid}.xml", ) - study_obj.pubget_raw.tables_xml = source_dir / "pubget" / "tables" / "tables.xml" + study_obj.pubget.raw.tables_xml = source_dir / "pubget" / "tables" / "tables.xml" tables_files = (source_dir / "pubget" / "tables").glob("*.xml") tables_files = [t for t in tables_files if t.name != "tables.xml"] num_tables = len(tables_files) // 2 - study_obj.pubget_raw.tables = { + study_obj.pubget.raw.tables = { '{0:03}'.format(t): {"metadata": None, "contents": None} for t in range(num_tables) } @@ -100,23 +115,19 @@ def load_directory(self, input_directory): else: key = "contents" - study_obj.pubget_raw.tables[table_number][key] = tf + study_obj.pubget.raw.tables[table_number][key] = tf # processed directory processed_dir = study_dir / "processed" if (processed_dir / "ace").exists(): - study_obj.ace = ProcessedData( - coordinates=processed_dir / "ace" / "coordinates.csv", - text=processed_dir / "ace" / "text.txt", - metadata=processed_dir / "ace" / "metadata.json" - ) + study_obj.ace.coordinates = processed_dir / "ace" / "coordinates.csv" + study_obj.ace.text = processed_dir / "ace" / "text.txt" + study_obj.ace.metadata = processed_dir / "ace" / "metadata.json" if (processed_dir / "pubget").exists(): - study_obj.pubget = ProcessedData( - coordinates=processed_dir / "pubget" / "coordinates.csv", - text=processed_dir / "pubget" / "text.txt", - metadata=processed_dir / "pubget" / "metadata.json" - ) + study_obj.pubget.coordinates = processed_dir / "pubget" / "coordinates.csv" + study_obj.pubget.text = processed_dir / "pubget" / "text.txt" + study_obj.pubget.metadata = processed_dir / "pubget" / "metadata.json" dset_data[study_id] = study_obj diff --git a/pipelines/pipeline.py b/pipelines/pipeline.py index 6f980ca..095a159 100644 --- a/pipelines/pipeline.py +++ b/pipelines/pipeline.py @@ -1,9 +1,236 @@ -class Node: - """Processing step for the data. +from datetime import datetime +import json +import hashlib +from abc import ABC, abstractmethod +from functools import reduce +from pathlib import Path +from typing import Dict, Any, List, Union - input is a dataset object - output is a dataset object - """ - def hash(self, dataset): - """Return a hash of the input dataset.""" + +INPUTS = [ + "text", + "coordinates", + "metadata", +] + +RAW_INPUTS = [ + "raw.html", + "raw.xml", + "raw.tables", + "raw.tables_xml", +] + + +def deep_getattr(obj: Any, attr_path: str, default: Any = None) -> Any: + try: + return reduce(getattr, attr_path.split('.'), obj) + except AttributeError: + return default + + +class FileManager: + """Utility class for file handling operations.""" + + @staticmethod + def calculate_md5(file_path: Path) -> str: + """Calculate MD5 hash of a file.""" + with file_path.open('r') as f: + file_contents = f.read() + return hashlib.md5(file_contents.encode()).hexdigest() + + @staticmethod + def load_json(file_path: Path) -> Dict: + """Load JSON from a file.""" + with file_path.open('r') as f: + return json.load(f) + + @staticmethod + def write_json(file_path: Path, data: Dict): + """Write JSON to a file.""" + with file_path.open('w') as f: + json.dump(data, f) + + @staticmethod + def get_next_available_dir(base_path: Path) -> Path: + """Find the next available directory by appending numbers (-1, -2, etc.) if necessary.""" + counter = 1 + new_path = base_path + while new_path.exists(): + new_path = base_path.with_name(f"{base_path.name}-{counter}") + counter += 1 + return new_path + + +class Pipeline(ABC): + """Abstract pipeline class for processing data.""" + + _hash_args: List[str] = [] + _pipeline_type: str = None # independent or dependent + _version: str = None + + def __init__(self, inputs: Union[tuple, list] = ("text",), input_sources: tuple = ("pubget", "ace")): + self._inputs = inputs + self._input_sources = input_sources + + @abstractmethod + def function(self, study_inputs: Dict[str, Any]) -> Dict: + """Run the pipeline function on a single study. Returns a dictionary of results.""" pass + + @abstractmethod + def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: + """Run the pipeline function on a group of studies. Returns a dictionary of results.""" + pass + + def serialize_dataset_keys(self, dataset: Any) -> str: + """Return a hashable string of the input dataset.""" + return "_".join(list(dataset.data.keys())) + + def serialize_pipeline_args(self) -> str: + """Return a hashable string of the arguments.""" + return '_'.join([str(getattr(self, arg)) for arg in self._hash_args]) + + def full_output_hash(self, dataset_str: str, arg_str: str) -> str: + """Return the full hash.""" + return hashlib.shake_256(f"{dataset_str}_{arg_str}".encode()).hexdigest(6) + + def create_directory_hash(self, dataset: Any) -> str: + """Create a hash for the dataset.""" + dataset_str = self.serialize_dataset_keys(dataset) + arg_str = self.serialize_pipeline_args() + return self.full_output_hash(dataset_str, arg_str) + + def filter_existing_results(self, output_dir: Path, dataset: Any) -> Dict[str, Dict]: + """Find the most recent result for an existing study.""" + existing_results = {} + for d in output_dir.glob(f"{self._version}/**/*"): + if d.is_dir() and d.name in set(dataset.data.keys()): + info_file = d / "info.json" + if info_file.exists(): + info = FileManager.load_json(info_file) + found_info = { + "date": info["date"], + "inputs": info["inputs"], + "hash": d.parents[0].name + } + if ( + existing_results.get(d.name) is None + or datetime.strptime(info["date"], '%Y-%m-%d') > + datetime.strptime(existing_results[d.name]["date"], '%Y-%m-%d') + ): + existing_results[d.name] = found_info + return existing_results + + def are_file_hashes_identical(self, study_inputs: Dict[str, Path], existing_inputs: Dict[str, str]) -> bool: + """Compare file hashes to determine if the inputs have changed.""" + if set(str(p) for p in study_inputs.values()) != set(existing_inputs.keys()): + return False + + for existing_file, hash_val in existing_inputs.items(): + if FileManager.calculate_md5(Path(existing_file)) != hash_val: + return False + + return True + + def collect_study_inputs(self, study: Any) -> Dict[str, Path]: + """Collect inputs for a study.""" + study_inputs = {} + for source in self._input_sources: + source_obj = getattr(study, source, None) + if source_obj: + for input_type in self._inputs: + input_obj = deep_getattr(source_obj, input_type, None) + if input_obj and study_inputs.get(input_type) is None: + study_inputs[input_type] = input_obj + return study_inputs + + def gather_all_study_inputs(self, dataset: Any) -> Dict[str, Dict[str, Path]]: + """Collect all inputs for the dataset.""" + return {db_id: self.collect_study_inputs(study) for db_id, study in dataset.data.items()} + + def identify_matching_results(self, dataset: Any, existing_results: Dict[str, Dict]) -> Dict[str, bool]: + """Compare dataset inputs with existing results.""" + dataset_inputs = self.gather_all_study_inputs(dataset) + return { + db_id: self.are_file_hashes_identical(study_inputs, existing_results.get(db_id, {}).get("inputs", {})) + for db_id, study_inputs in dataset_inputs.items() + } + + def check_for_changes(self, output_directory: Path, dataset: Any) -> bool: + """Check if any study inputs have changed or if there are new studies.""" + existing_results = self.filter_existing_results(output_directory, dataset) + matching_results = self.identify_matching_results(dataset, existing_results) + # Return True if any of the studies' inputs have changed or if new studies exist + return any(not match for match in matching_results.values()) + + def write_output_info(self, hash_outdir: Path, db_id: str, study_inputs: Dict[str, Path]): + """Write information about the current run to an info.json file.""" + output_info = { + "date": datetime.now().isoformat(), + "inputs": {str(input_file): FileManager.calculate_md5(input_file) for input_file in study_inputs.values()} + } + FileManager.write_json(hash_outdir / db_id / "info.json", output_info) + + +class IndependentPipeline(Pipeline): + """Pipeline that processes each study independently.""" + + @abstractmethod + def function(self, study_inputs: Dict[str, Any]) -> Dict: + """Run the pipeline function on a single study. Returns a dictionary of results.""" + pass + + def run(self, dataset: Any, output_directory: Path): + """Run the pipeline for independent studies.""" + hash_str = self.create_directory_hash(dataset) + hash_outdir = output_directory / self._version / hash_str + + # If the directory exists, find the next available directory with a suffix like "-1", "-2", etc. + if hash_outdir.exists(): + hash_outdir = FileManager.get_next_available_dir(hash_outdir) + hash_outdir.mkdir(parents=True, exist_ok=True) + + # Process each study individually + filtered_dataset = self.filter_inputs(output_directory, dataset) + for db_id, study in filtered_dataset.data.items(): + study_inputs = self.collect_study_inputs(study) + study_outdir = hash_outdir / db_id + study_outdir.mkdir(parents=True, exist_ok=True) + + results = self.function(study_inputs) + FileManager.write_json(study_outdir / "results.json", results) + + self.write_output_info(hash_outdir, db_id, study_inputs) + + +class DependentPipeline(Pipeline): + """Pipeline that processes all studies as a group.""" + + @abstractmethod + def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: + """Run the pipeline function on a group of studies. Returns a dictionary of results.""" + pass + + def run(self, dataset: Any, output_directory: Path): + """Run the pipeline for dependent studies.""" + hash_str = self.create_directory_hash(dataset) + hash_outdir = output_directory / self._version / hash_str + + # Check if there are any changes for dependent mode + if not self.check_for_changes(output_directory, dataset): + print("No changes detected, skipping pipeline execution.") + return # No changes, so we skip the pipeline + + # If the directory exists, find the next available directory with a suffix like "-1", "-2", etc. + if hash_outdir.exists(): + hash_outdir = FileManager.get_next_available_dir(hash_outdir) + hash_outdir.mkdir(parents=True, exist_ok=True) + + # Collect all inputs and run the group function at once + all_study_inputs = self.gather_all_study_inputs(dataset) + grouped_results = self.group_function(all_study_inputs) + for db_id, results in grouped_results.items(): + study_outdir = hash_outdir / db_id + study_outdir.mkdir(parents=True, exist_ok=True) + FileManager.write_json(study_outdir / "results.json", results) + self.write_output_info(hash_outdir, db_id, all_study_inputs[db_id]) diff --git a/pipelines/word_count/__init__.py b/pipelines/word_count/__init__.py index e69de29..59f4a60 100644 --- a/pipelines/word_count/__init__.py +++ b/pipelines/word_count/__init__.py @@ -0,0 +1,6 @@ +from .run import WordCountExtraction + +__all__ = [ + "WordCountExtraction", + "WordDevianceExtraction", +] diff --git a/pipelines/word_count/run.py b/pipelines/word_count/run.py index 65d5dc6..b48d936 100644 --- a/pipelines/word_count/run.py +++ b/pipelines/word_count/run.py @@ -1,82 +1,59 @@ from datetime import datetime import hashlib import json -# from neurostore_text_extraction.pipelines.dataset import Dataset -# from neurostore_text_extraction.pipelines.pipeline import Node -class WordCountExtraction: - """Extract word count from the documents. +from pipelines.pipeline import IndependentPipeline, DependentPipeline - Extract word counts from the text data. + +class WordCountExtraction(IndependentPipeline): + """Word count extraction pipeline.""" + + _version = "1.0.0" + _hash_args = ["_inputs", "_input_sources"] + _pipeline_type = "independent" + + def function(self, study_inputs): + """Run the word count extraction pipeline.""" + text_file = study_inputs["text"] + + with open(text_file, "r") as f: + text = f.read() + + return {"word_count": len(text.split())} + + +class WordDevianceExtraction(DependentPipeline): + """Word deviance pipeline. + + Count the deviance of each study from the average word count. """ + _version = "1.0.0" + _hash_args = ["_inputs", "_input_sources"] + _pipeline_type = "dependent" - _pipeline_type = "independent" + def group_function(self, all_study_inputs): + """Run the word count extraction pipeline.""" - _hash_args = ['prefer'] - - def __init__(self, prefer="pubget"): - """Initialize the word count extraction pipeline.""" - self.prefer = prefer - - def run(self, dataset, output_dir): - """Run the word count extraction pipeline. - Output metadata: - - version - - date - - source - """ - - # dataset hash - dataset_str = "_".join(list(dataset.data.keys())) - # argument hash - arg_str = '_'.join([str(getattr(self, arg)) for arg in self._hash_args]) - - # full hash - hash_str = hashlib.shake_256(f"{dataset_str}_{arg_str}".encode()).hexdigest(6) - - # find the most recent restults across all directories - existing_results = {} # main key is study id, contains dates, inputs, and hash where the file came from - # extract the hash from the directory - for d in output_dir.glob(f"{self._version}/**/*"): - if d.is_dir() and d.name in set(dataset.data.keys()): - if (d / "info.json").exists(): - with open(d / "info.json", "r") as f: - info = json.load(f) - found_info = {"date": info["date"], "inputs": info["inputs"], "hash": hashlib.md5(str(info).encode()).hexdigest()} - if existing_results.get(d.name) is None or info["date"] > existing_results[d.name]["date"]: - existing_results[d.name] = found_info - - hash_outdir = (output_dir / self._version / hash_str) - hash_outdir.mkdir(parents=True, exist_ok=True) - - for db_id, study in dataset.data.items(): - if self.prefer == "pubget" and getattr(study.pubget, "text", None) is not None: - text_file = study.pubget.text - elif self.prefer == "ace" and getattr(study.ace, "text", None) is not None: - text_file = study.ace.text - else: - text_file = getattr(study.pubget, "text", None) or getattr(study.ace, "text", None) - - if text_file is None: - raise ValueError(f"No text found for {db_id}") + # Calculate the average word count + total_word_count = 0 + total_studies = len(all_study_inputs) + study_word_counts = {} + for study_id, study_inputs in all_study_inputs.items(): + text_file = study_inputs["text"] with open(text_file, "r") as f: text = f.read() - text_len = len(text.split()) - - # make the directory if it doesn't exist - study_outdir = (hash_outdir / db_id) - study_outdir.mkdir(parents=True, exist_ok=True) - with open(study_outdir / "results.json", "w") as f: - json.dump({"word_count": text_len}, f) - - with open(study_outdir / "info.json", "w") as f: - json.dump({ - "date": datetime.now().isoformat(), - "inputs": { - str(text_file): hashlib.md5(text.encode()).hexdigest() - } - }, f - ) + num_words = len(text.split()) + total_word_count += num_words + study_word_counts[study_id] = num_words + + average_word_count = total_word_count // total_studies + study_word_deviances = { + study_id: {"word_deviance": abs(num_words - average_word_count)} + for study_id, num_words in study_word_counts.items() + } + + # key is study_id, value is deviance from average word count + return study_word_deviances diff --git a/tests/test_word_count.py b/tests/test_word_count.py index cae7853..3c7ba10 100644 --- a/tests/test_word_count.py +++ b/tests/test_word_count.py @@ -1,21 +1,33 @@ from pathlib import Path -from pipelines.word_count.run import WordCountExtraction +from pipelines.word_count.run import WordCountExtraction, WordDevianceExtraction from pipelines.dataset import Dataset -def test_word_count_extraction(sample_data, tmp_path): +def test_WordCountExtraction(sample_data, tmp_path): """Test the word count extraction pipeline.""" - wce = WordCountExtraction(prefer="pubget") + wce = WordCountExtraction() dataset = Dataset(sample_data) output_dir = tmp_path / "word_count" wce.run(dataset, output_dir) + # rerun where the output directory already exists + # no ouputs generated + wce.run(dataset, output_dir) + # rerun with preference of ace + wce_ace = WordCountExtraction(input_sources=("ace", "pubget")) + wce_ace.run(dataset, output_dir) assert True -def test_word_count_extraction_ace(sample_data, tmp_path): - """Test the word count extraction pipeline.""" - wce = WordCountExtraction(prefer="ace") +def test_WordDevianceExtraction(sample_data, tmp_path): + """Test the word deviance extraction pipeline.""" + wde = WordDevianceExtraction() dataset = Dataset(sample_data) - output_dir = tmp_path / "word_count" - wce.run(dataset, output_dir) + output_dir = tmp_path / "word_deviance" + wde.run(dataset, output_dir) + # rerun where the output directory already exists + # no ouputs generated + wde.run(dataset, output_dir) + # rerun with preference of ace + wde_ace = WordDevianceExtraction(input_sources=("ace", "pubget")) + wde_ace.run(dataset, output_dir) assert True From 60c09789404a8a5e419e96b5ee53a29cc765b77f Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 24 Oct 2024 16:27:07 -0500 Subject: [PATCH 03/42] mark pipeline as (in)dependent --- pipelines/pipeline.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pipelines/pipeline.py b/pipelines/pipeline.py index 095a159..fb5c8eb 100644 --- a/pipelines/pipeline.py +++ b/pipelines/pipeline.py @@ -175,6 +175,8 @@ def write_output_info(self, hash_outdir: Path, db_id: str, study_inputs: Dict[st class IndependentPipeline(Pipeline): """Pipeline that processes each study independently.""" + _pipeline_type = "independent" + @abstractmethod def function(self, study_inputs: Dict[str, Any]) -> Dict: """Run the pipeline function on a single study. Returns a dictionary of results.""" @@ -206,6 +208,8 @@ def run(self, dataset: Any, output_directory: Path): class DependentPipeline(Pipeline): """Pipeline that processes all studies as a group.""" + _pipeline_type = "dependent" + @abstractmethod def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: """Run the pipeline function on a group of studies. Returns a dictionary of results.""" From 89d45f3e3c42c0813bd94240f26c72d957320049 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 24 Oct 2024 16:27:28 -0500 Subject: [PATCH 04/42] wip: start modifying the existing pipeline --- pipelines/participant_demographics/run.py | 44 ++++++++++++++++++++++- 1 file changed, 43 insertions(+), 1 deletion(-) diff --git a/pipelines/participant_demographics/run.py b/pipelines/participant_demographics/run.py index 5b4e690..af87d0e 100644 --- a/pipelines/participant_demographics/run.py +++ b/pipelines/participant_demographics/run.py @@ -9,6 +9,8 @@ import prompts from .clean import clean_predictions +from pipelines.pipeline import IndependentPipeline + def extract(extraction_model, extraction_client, docs, output_dir, prompt_set='', **extract_kwargs): extract_kwargs.pop('search_query', None) @@ -80,4 +82,44 @@ def run(extraction_model, docs_path, prompt_set, output_dir=None, **kwargs): ) # Save predictions - _save_predictions(predictions, clean_preds, output_dir) \ No newline at end of file + _save_predictions(predictions, clean_preds, output_dir) + + +def ParticipantDemographics(IndependentPipeline): + """Participant demographics extraction pipeline.""" + + _version = "1.0.0" + _hash_args = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] + + def __init__( + self, + extraction_model, + prompt_set, inputs=("text",), + input_sources=("pubget", "ace"), + **kwargs + ): + super().__init__(inputs=inputs, input_sources=input_sources) + self.extraction_model = extraction_model + self.prompt_set = prompt_set + self.kwargs = kwargs + + def function(self, study_inputs): + """Run the participant demographics extraction pipeline.""" + extraction_client = _load_client(self.extraction_model) + + prompt_config = _load_prompt_config(self.prompt_set) + if self.kwargs is not None: + prompt_config.update(self.kwargs) + + + predictions, clean_preds = extract( + self.extraction_model, + extraction_client, + study_inputs["text"], + prompt_set=self.prompt_set, + **prompt_config + ) + + # Save predictions + + return {"predictions": predictions, "clean_predictions": clean_preds} From 90bee39b312dd727f41a01a6ed3bff9e064d7d31 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 13:14:38 -0500 Subject: [PATCH 05/42] Restructure package --- README.md | 13 ++++++------- {pipelines => ns_pipelines}/__init__.py | 0 {pipelines => ns_pipelines}/dataset.py | 0 .../participant_demographics/__init__.py | 0 .../participant_demographics/clean.py | 0 .../participant_demographics/prompts.py | 0 .../participant_demographics/run.py | 6 +----- .../participant_demographics/schemas.py | 0 {pipelines => ns_pipelines}/pipeline.py | 2 -- .../umls_disease/__init__.py | 0 {pipelines => ns_pipelines}/umls_disease/run.py | 0 {pipelines => ns_pipelines}/word_count/__init__.py | 0 {pipelines => ns_pipelines}/word_count/run.py | 2 +- pyproject.toml | 2 +- tests/test_dataset.py | 2 +- tests/test_word_count.py | 4 ++-- 16 files changed, 12 insertions(+), 19 deletions(-) rename {pipelines => ns_pipelines}/__init__.py (100%) rename {pipelines => ns_pipelines}/dataset.py (100%) rename {pipelines => ns_pipelines}/participant_demographics/__init__.py (100%) rename {pipelines => ns_pipelines}/participant_demographics/clean.py (100%) rename {pipelines => ns_pipelines}/participant_demographics/prompts.py (100%) rename {pipelines => ns_pipelines}/participant_demographics/run.py (95%) rename {pipelines => ns_pipelines}/participant_demographics/schemas.py (100%) rename {pipelines => ns_pipelines}/pipeline.py (99%) rename {pipelines => ns_pipelines}/umls_disease/__init__.py (100%) rename {pipelines => ns_pipelines}/umls_disease/run.py (100%) rename {pipelines => ns_pipelines}/word_count/__init__.py (100%) rename {pipelines => ns_pipelines}/word_count/run.py (95%) diff --git a/README.md b/README.md index 279b329..39630ab 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ To install the necessary dependencies, run: ## Usage ### Running pipelines -Executable workflows in `pipelines/{pipeline_name}/run.py` will take as input standardized pubget-style text inputs (row row per article). +Executable workflows in `pipelines/{pipeline_name}/run.py` will take as input standardized pubget-style text inputs (1 row per article). Run all available pipelines and harmonize outputs using CLI (todo) @@ -28,11 +28,11 @@ See `ns-text-extraction-outputs` sub repository. ### Types of pipelines -#### Each study is indepentently processed +#### Each study is independently processed -1) scenerio 1: nothing changed -2) scenerio 2: a study was added -3) scenerio 3: a study was changed +1) scenario 1: nothing changed +2) scenario 2: a study was added +3) scenario 3: a study was changed `info.json` in the output directory increment (value): 0 @@ -45,9 +45,8 @@ have a place for the raw output of the API/external service. raw.json and clean.json clean function for a pipeline output, that can be used to clean the output of a pipeline -#### Each study is processed in the context of all other studies - +#### Each study is processed in the context of all other studies Have a dev version only include openaccess papers diff --git a/pipelines/__init__.py b/ns_pipelines/__init__.py similarity index 100% rename from pipelines/__init__.py rename to ns_pipelines/__init__.py diff --git a/pipelines/dataset.py b/ns_pipelines/dataset.py similarity index 100% rename from pipelines/dataset.py rename to ns_pipelines/dataset.py diff --git a/pipelines/participant_demographics/__init__.py b/ns_pipelines/participant_demographics/__init__.py similarity index 100% rename from pipelines/participant_demographics/__init__.py rename to ns_pipelines/participant_demographics/__init__.py diff --git a/pipelines/participant_demographics/clean.py b/ns_pipelines/participant_demographics/clean.py similarity index 100% rename from pipelines/participant_demographics/clean.py rename to ns_pipelines/participant_demographics/clean.py diff --git a/pipelines/participant_demographics/prompts.py b/ns_pipelines/participant_demographics/prompts.py similarity index 100% rename from pipelines/participant_demographics/prompts.py rename to ns_pipelines/participant_demographics/prompts.py diff --git a/pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py similarity index 95% rename from pipelines/participant_demographics/run.py rename to ns_pipelines/participant_demographics/run.py index 0012376..816e6e5 100644 --- a/pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -10,13 +10,9 @@ from . import prompts from .clean import clean_predictions -<<<<<<< HEAD -from pipelines.pipeline import IndependentPipeline +from ns_pipelines.pipeline import IndependentPipeline def extract(extraction_model, extraction_client, docs, output_dir, prompt_set='', **extract_kwargs): -======= -def extract(extraction_model, extraction_client, docs, **extract_kwargs): ->>>>>>> f3ce9c24f4aaea67df4a5fb3b7d6a53e892ac747 extract_kwargs.pop('search_query', None) # Extract diff --git a/pipelines/participant_demographics/schemas.py b/ns_pipelines/participant_demographics/schemas.py similarity index 100% rename from pipelines/participant_demographics/schemas.py rename to ns_pipelines/participant_demographics/schemas.py diff --git a/pipelines/pipeline.py b/ns_pipelines/pipeline.py similarity index 99% rename from pipelines/pipeline.py rename to ns_pipelines/pipeline.py index fb5c8eb..9830a16 100644 --- a/pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -72,12 +72,10 @@ def __init__(self, inputs: Union[tuple, list] = ("text",), input_sources: tuple self._inputs = inputs self._input_sources = input_sources - @abstractmethod def function(self, study_inputs: Dict[str, Any]) -> Dict: """Run the pipeline function on a single study. Returns a dictionary of results.""" pass - @abstractmethod def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: """Run the pipeline function on a group of studies. Returns a dictionary of results.""" pass diff --git a/pipelines/umls_disease/__init__.py b/ns_pipelines/umls_disease/__init__.py similarity index 100% rename from pipelines/umls_disease/__init__.py rename to ns_pipelines/umls_disease/__init__.py diff --git a/pipelines/umls_disease/run.py b/ns_pipelines/umls_disease/run.py similarity index 100% rename from pipelines/umls_disease/run.py rename to ns_pipelines/umls_disease/run.py diff --git a/pipelines/word_count/__init__.py b/ns_pipelines/word_count/__init__.py similarity index 100% rename from pipelines/word_count/__init__.py rename to ns_pipelines/word_count/__init__.py diff --git a/pipelines/word_count/run.py b/ns_pipelines/word_count/run.py similarity index 95% rename from pipelines/word_count/run.py rename to ns_pipelines/word_count/run.py index b48d936..7bcdc23 100644 --- a/pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -2,7 +2,7 @@ import hashlib import json -from pipelines.pipeline import IndependentPipeline, DependentPipeline +from ns_pipelines.pipeline import IndependentPipeline, DependentPipeline class WordCountExtraction(IndependentPipeline): diff --git a/pyproject.toml b/pyproject.toml index c59e2ec..5960202 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,4 +41,4 @@ word_count = [ source = "vcs" [tool.hatch.build.hooks.vcs] -version-file = "pipelines/_version.py" +version-file = "ns_pipelines/_version.py" diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 1393cd5..783e894 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,4 +1,4 @@ -from pipelines.dataset import Dataset +from ns_pipelines.dataset import Dataset def test_dataset(sample_data): diff --git a/tests/test_word_count.py b/tests/test_word_count.py index 3c7ba10..b9def54 100644 --- a/tests/test_word_count.py +++ b/tests/test_word_count.py @@ -1,6 +1,6 @@ from pathlib import Path -from pipelines.word_count.run import WordCountExtraction, WordDevianceExtraction -from pipelines.dataset import Dataset +from ns_pipelines.word_count.run import WordCountExtraction, WordDevianceExtraction +from ns_pipelines.dataset import Dataset def test_WordCountExtraction(sample_data, tmp_path): From cb16fc98b3acdb80bde314d70b341058642d0475 Mon Sep 17 00:00:00 2001 From: James Kent Date: Wed, 30 Oct 2024 13:58:22 -0500 Subject: [PATCH 06/42] add filter_inputs function --- ns_pipelines/pipeline.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index 9830a16..e57b7b4 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -72,14 +72,6 @@ def __init__(self, inputs: Union[tuple, list] = ("text",), input_sources: tuple self._inputs = inputs self._input_sources = input_sources - def function(self, study_inputs: Dict[str, Any]) -> Dict: - """Run the pipeline function on a single study. Returns a dictionary of results.""" - pass - - def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: - """Run the pipeline function on a group of studies. Returns a dictionary of results.""" - pass - def serialize_dataset_keys(self, dataset: Any) -> str: """Return a hashable string of the input dataset.""" return "_".join(list(dataset.data.keys())) @@ -154,6 +146,14 @@ def identify_matching_results(self, dataset: Any, existing_results: Dict[str, Di for db_id, study_inputs in dataset_inputs.items() } + def filter_inputs(self, output_directory: Path, dataset: Any) -> bool: + """Filter inputs based on the pipeline type.""" + existing_results = self.filter_existing_results(output_directory, dataset) + matching_results = self.identify_matching_results(dataset, existing_results) + # Return True if any of the studies' inputs have changed or if new studies exist + keep_ids = set(dataset.data.keys()) - {db_id for db_id, match in matching_results.items() if match} + return dataset.slice(keep_ids) + def check_for_changes(self, output_directory: Path, dataset: Any) -> bool: """Check if any study inputs have changed or if there are new studies.""" existing_results = self.filter_existing_results(output_directory, dataset) From 58bc72759a3b6bb60bac427de072c6d61eacb78f Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 14:48:13 -0500 Subject: [PATCH 07/42] Refactor init logic to dataclasses --- ns_pipelines/dataset.py | 151 ++++++++++++++++++++++------------------ 1 file changed, 85 insertions(+), 66 deletions(-) diff --git a/ns_pipelines/dataset.py b/ns_pipelines/dataset.py index 3038adc..5e0331f 100644 --- a/ns_pipelines/dataset.py +++ b/ns_pipelines/dataset.py @@ -20,12 +20,40 @@ class AceRaw: html: Path + def __post_init__(self): + # Preprocessing logic for AceRaw can be added here if needed + if not self.html.exists(): + raise ValueError(f"HTML file {self.html} does not exist.") + @dataclass class PubgetRaw: xml: Path - tables: dict = None + tables: dict = field(default_factory=dict) tables_xml: Path = None + def __post_init__(self): + # Load tables and assign file paths + if not self.xml.exists(): + raise ValueError(f"XML file {self.xml} does not exist.") + + if self.tables_xml and not self.tables_xml.exists(): + raise ValueError(f"Tables XML file {self.tables_xml} does not exist.") + + if self.tables_xml: + tables_files = list(self.tables_xml.parent.glob("*.xml")) + tables_files = [t for t in tables_files if t.name != self.tables_xml.name] + + num_tables = len(tables_files) // 2 + self.tables = {f'{t:03}': {"metadata": None, "contents": None} for t in range(num_tables)} + + for tf in tables_files: + table_number = tf.stem.split("_")[1] + if tf.suffix == ".json": + key = "metadata" + else: + key = "contents" + self.tables[table_number][key] = tf + @dataclass class ProcessedData: coordinates: Path = None @@ -33,14 +61,65 @@ class ProcessedData: metadata: Path = None raw: Optional[Union['PubgetRaw', 'AceRaw']] = field(default=None) + def __post_init__(self): + # Ensure the processed data files exist + if self.coordinates and not self.coordinates.exists(): + raise ValueError(f"Coordinates file {self.coordinates} does not exist.") + if self.text and not self.text.exists(): + raise ValueError(f"Text file {self.text} does not exist.") + if self.metadata and not self.metadata.exists(): + raise ValueError(f"Metadata file {self.metadata} does not exist.") + @dataclass class Study: dbid: str + study_dir: Path doi: str = None pmid: str = None pmcid: str = None - ace: ProcessedData = field(default_factory=ProcessedData) - pubget: ProcessedData = field(default_factory=ProcessedData) + ace: ProcessedData = None + pubget: ProcessedData = None + + def __post_init__(self): + self.dbid = self.study_dir.name + + # Load identifiers + with open((self.study_dir / "identifiers.json"), "r") as ident_fp: + ids = json.load(ident_fp) + + # Setup the processed data objects + # Load AceRaw if available + source_dir = self.study_dir / "source" + ace_raw = None + pubget_raw = None + + # Load AceRaw if available + ace_path = source_dir / "ace" / f"{self.pmid}.html" + if ace_path.exists(): + ace_raw = AceRaw(html=ace_path) + + # Load PubgetRaw if available + pubget_dir = source_dir / "pubget" + pubget_xml_path = pubget_dir / f"{self.pmcid}.xml" + tables_xml_path = pubget_dir / "tables" / "tables.xml" + if pubget_xml_path.exists(): + pubget_raw = PubgetRaw( + xml=pubget_xml_path, + tables_xml=tables_xml_path + ) + + # Load processed data + for t in ["ace", "pubget"]: + processed_dir = self.study_dir / "processed" / t + if processed_dir.exists(): + processed = ProcessedData( + coordinates=processed_dir / "coordinates.csv", + text=processed_dir / "text.txt", + metadata=processed_dir / "metadata.json", + raw = ace_raw if t == "ace" else pubget_raw + ) + + setattr(self, t, processed) class Dataset: @@ -57,16 +136,9 @@ def slice(self, ids): return deepcopy_obj def load_directory(self, input_directory): - """Load the input directory. - input_directory (str): The input directory containing the text. - processed (bool): Whether the input text is already processed. - source (str): The source of the input text. - (ace or pubget, if None, tries to find both) - """ + """Load the input directory.""" pattern = re.compile(r'^[a-zA-Z0-9]{12}$') - sub_directories = input_directory.glob("[0-9A-Za-z]*") - study_directories = [ dir_ for dir_ in sub_directories if dir_.is_dir() and pattern.match(dir_.name) @@ -75,64 +147,11 @@ def load_directory(self, input_directory): dset_data = {} for study_dir in study_directories: + study_obj = Study(study_dir=study_dir) - study_id = study_dir.name - study_obj = Study(dbid=study_id) - # associate IDs with study object - with open((study_dir / "identifiers.json"), "r") as ident_fp: - ids = json.load(ident_fp) - - study_obj.doi = ids["doi"] or None - study_obj.pmid = ids["pmid"] or None - study_obj.pmcid = ids["pmcid"] or None - - source_dir = study_dir / "source" - - # check if the source ace directory exists and load appropriate files - if (source_dir / "ace").exists(): - study_obj.ace.raw = AceRaw(html=source_dir / "ace" / f"{study_obj.pmid}.html") - - # check if the source pubget directory exists and load appropriate files - if (source_dir / "pubget").exists(): - study_obj.pubget.raw = PubgetRaw( - xml=source_dir / "pubget" / f"{study_obj.pmcid}.xml", - ) - study_obj.pubget.raw.tables_xml = source_dir / "pubget" / "tables" / "tables.xml" - - tables_files = (source_dir / "pubget" / "tables").glob("*.xml") - tables_files = [t for t in tables_files if t.name != "tables.xml"] - - num_tables = len(tables_files) // 2 - study_obj.pubget.raw.tables = { - '{0:03}'.format(t): {"metadata": None, "contents": None} - for t in range(num_tables) - } - - for tf in tables_files: - table_number = tf.stem.split("_")[1] - if tf.suffix == ".json": - key = "metadata" - else: - key = "contents" - - study_obj.pubget.raw.tables[table_number][key] = tf - - # processed directory - processed_dir = study_dir / "processed" - if (processed_dir / "ace").exists(): - study_obj.ace.coordinates = processed_dir / "ace" / "coordinates.csv" - study_obj.ace.text = processed_dir / "ace" / "text.txt" - study_obj.ace.metadata = processed_dir / "ace" / "metadata.json" - - if (processed_dir / "pubget").exists(): - study_obj.pubget.coordinates = processed_dir / "pubget" / "coordinates.csv" - study_obj.pubget.text = processed_dir / "pubget" / "text.txt" - study_obj.pubget.metadata = processed_dir / "pubget" / "metadata.json" - - dset_data[study_id] = study_obj + dset_data[study_obj.dbid] = study_obj return dset_data - def __len__(self): """Return the length of the dataset.""" return len(self.data) From cfff8bb5951688ccd8b1cb7c78fc2e25bf604eaa Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 14:53:57 -0500 Subject: [PATCH 08/42] Both group and independent can use the same function name ('function') which we know will operate differently absed on '_pipeline_type' --- ns_pipelines/pipeline.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index e57b7b4..33dabea 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -169,17 +169,22 @@ def write_output_info(self, hash_outdir: Path, db_id: str, study_inputs: Dict[st } FileManager.write_json(hash_outdir / db_id / "info.json", output_info) + @abstractmethod + def run(self, dataset: Any, output_directory: Path): + """Run the pipeline.""" + pass + + @abstractmethod + def function(self, study_inputs: Dict[str, Any]) -> Dict: + """Run the pipeline function.""" + pass + class IndependentPipeline(Pipeline): """Pipeline that processes each study independently.""" _pipeline_type = "independent" - @abstractmethod - def function(self, study_inputs: Dict[str, Any]) -> Dict: - """Run the pipeline function on a single study. Returns a dictionary of results.""" - pass - def run(self, dataset: Any, output_directory: Path): """Run the pipeline for independent studies.""" hash_str = self.create_directory_hash(dataset) @@ -208,11 +213,6 @@ class DependentPipeline(Pipeline): _pipeline_type = "dependent" - @abstractmethod - def group_function(self, dataset_inputs: Dict[str, Any]) -> Dict: - """Run the pipeline function on a group of studies. Returns a dictionary of results.""" - pass - def run(self, dataset: Any, output_directory: Path): """Run the pipeline for dependent studies.""" hash_str = self.create_directory_hash(dataset) @@ -230,7 +230,7 @@ def run(self, dataset: Any, output_directory: Path): # Collect all inputs and run the group function at once all_study_inputs = self.gather_all_study_inputs(dataset) - grouped_results = self.group_function(all_study_inputs) + grouped_results = self.function(all_study_inputs) for db_id, results in grouped_results.items(): study_outdir = hash_outdir / db_id study_outdir.mkdir(parents=True, exist_ok=True) From 79bfdcc6f621990faabe65bbe46d5ce4a152e89c Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 14:55:59 -0500 Subject: [PATCH 09/42] group_function to function --- ns_pipelines/word_count/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 7bcdc23..213198a 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -32,7 +32,7 @@ class WordDevianceExtraction(DependentPipeline): _hash_args = ["_inputs", "_input_sources"] _pipeline_type = "dependent" - def group_function(self, all_study_inputs): + def function(self, all_study_inputs): """Run the word count extraction pipeline.""" # Calculate the average word count From 0625124611ed63e760851993f05f245afd6cd552 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 15:00:29 -0500 Subject: [PATCH 10/42] _hash_attrs instead --- ns_pipelines/participant_demographics/run.py | 2 +- ns_pipelines/pipeline.py | 4 ++-- ns_pipelines/word_count/run.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index 816e6e5..f24f3e3 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -94,7 +94,7 @@ def ParticipantDemographics(IndependentPipeline): """Participant demographics extraction pipeline.""" _version = "1.0.0" - _hash_args = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] + _hash_attrs = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] def __init__( self, diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index 33dabea..339bb09 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -64,7 +64,7 @@ def get_next_available_dir(base_path: Path) -> Path: class Pipeline(ABC): """Abstract pipeline class for processing data.""" - _hash_args: List[str] = [] + _hash_attrs: List[str] = [] _pipeline_type: str = None # independent or dependent _version: str = None @@ -78,7 +78,7 @@ def serialize_dataset_keys(self, dataset: Any) -> str: def serialize_pipeline_args(self) -> str: """Return a hashable string of the arguments.""" - return '_'.join([str(getattr(self, arg)) for arg in self._hash_args]) + return '_'.join([str(getattr(self, arg)) for arg in self._hash_attrs]) def full_output_hash(self, dataset_str: str, arg_str: str) -> str: """Return the full hash.""" diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 213198a..5d14c32 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -9,7 +9,7 @@ class WordCountExtraction(IndependentPipeline): """Word count extraction pipeline.""" _version = "1.0.0" - _hash_args = ["_inputs", "_input_sources"] + _hash_attrs = ["_inputs", "_input_sources"] _pipeline_type = "independent" def function(self, study_inputs): @@ -29,7 +29,7 @@ class WordDevianceExtraction(DependentPipeline): """ _version = "1.0.0" - _hash_args = ["_inputs", "_input_sources"] + _hash_attrs = ["_inputs", "_input_sources"] _pipeline_type = "dependent" def function(self, all_study_inputs): From c26ef1b7faca7b3a066d0cb75c7fdb2a847e8b1f Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 30 Oct 2024 15:00:57 -0500 Subject: [PATCH 11/42] Set default _hash_attrs --- ns_pipelines/pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index 339bb09..ce51c0f 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -64,7 +64,7 @@ def get_next_available_dir(base_path: Path) -> Path: class Pipeline(ABC): """Abstract pipeline class for processing data.""" - _hash_attrs: List[str] = [] + _hash_attrs: List[str] = ['_inputs', '_input_sources'] _pipeline_type: str = None # independent or dependent _version: str = None From e9076244926fa38973fbb006044bae7211166159 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 31 Oct 2024 14:11:36 -0500 Subject: [PATCH 12/42] refactor based on feedback --- ns_pipelines/dataset.py | 4 +- ns_pipelines/participant_demographics/run.py | 5 +- ns_pipelines/pipeline.py | 167 ++++++++++--------- ns_pipelines/word_count/run.py | 25 ++- 4 files changed, 111 insertions(+), 90 deletions(-) diff --git a/ns_pipelines/dataset.py b/ns_pipelines/dataset.py index 5e0331f..656741b 100644 --- a/ns_pipelines/dataset.py +++ b/ns_pipelines/dataset.py @@ -35,7 +35,7 @@ def __post_init__(self): # Load tables and assign file paths if not self.xml.exists(): raise ValueError(f"XML file {self.xml} does not exist.") - + if self.tables_xml and not self.tables_xml.exists(): raise ValueError(f"Tables XML file {self.tables_xml} does not exist.") @@ -72,8 +72,8 @@ def __post_init__(self): @dataclass class Study: - dbid: str study_dir: Path + dbid: str = None doi: str = None pmid: str = None pmcid: str = None diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index f24f3e3..b889e58 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -94,7 +94,8 @@ def ParticipantDemographics(IndependentPipeline): """Participant demographics extraction pipeline.""" _version = "1.0.0" - _hash_attrs = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] + # _hash_attrs = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] + # everything in __init__ is hashed with _inputs and _input_sources def __init__( self, @@ -108,7 +109,7 @@ def __init__( self.prompt_set = prompt_set self.kwargs = kwargs - def function(self, study_inputs): + def _run(self, study_inputs, n_cpus=1): """Run the participant demographics extraction pipeline.""" extraction_client = _load_client(self.extraction_model) diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index ce51c0f..1d59096 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -1,4 +1,5 @@ from datetime import datetime +import inspect import json import hashlib from abc import ABC, abstractmethod @@ -64,33 +65,83 @@ def get_next_available_dir(base_path: Path) -> Path: class Pipeline(ABC): """Abstract pipeline class for processing data.""" - _hash_attrs: List[str] = ['_inputs', '_input_sources'] - _pipeline_type: str = None # independent or dependent _version: str = None def __init__(self, inputs: Union[tuple, list] = ("text",), input_sources: tuple = ("pubget", "ace")): - self._inputs = inputs - self._input_sources = input_sources + self.inputs = inputs + self.input_sources = input_sources + self._pipeline_type = inspect.getmro(self.__class__)[1].__name__.lower().rstrip("pipeline") - def serialize_dataset_keys(self, dataset: Any) -> str: - """Return a hashable string of the input dataset.""" - return "_".join(list(dataset.data.keys())) - - def serialize_pipeline_args(self) -> str: - """Return a hashable string of the arguments.""" - return '_'.join([str(getattr(self, arg)) for arg in self._hash_attrs]) + @abstractmethod + def run(self, dataset: Any, output_directory: Path, **kwargs): + """Run the pipeline.""" + pass - def full_output_hash(self, dataset_str: str, arg_str: str) -> str: - """Return the full hash.""" - return hashlib.shake_256(f"{dataset_str}_{arg_str}".encode()).hexdigest(6) + @abstractmethod + def _run(self, study_inputs: Dict[str, Any], **kwargs) -> Dict: + """Run the pipeline function.""" + pass def create_directory_hash(self, dataset: Any) -> str: """Create a hash for the dataset.""" - dataset_str = self.serialize_dataset_keys(dataset) - arg_str = self.serialize_pipeline_args() - return self.full_output_hash(dataset_str, arg_str) + dataset_str = self._serialize_dataset_keys(dataset) + arg_str = self._serialize_pipeline_args() + return hashlib.shake_256(f"{dataset_str}_{arg_str}".encode()).hexdigest(6) + + def filter_inputs(self, output_directory: Path, dataset: Any) -> bool: + """Filter inputs based on the pipeline type.""" + existing_results = self._filter_existing_results(output_directory, dataset) + matching_results = self._identify_matching_results(dataset, existing_results) + # Return True if any of the studies' inputs have changed or if new studies exist + keep_ids = set(dataset.data.keys()) - {db_id for db_id, match in matching_results.items() if match} + return dataset.slice(keep_ids) + + def gather_all_study_inputs(self, dataset: Any) -> Dict[str, Dict[str, Path]]: + """Collect all inputs for the dataset.""" + return {db_id: self.collect_study_inputs(study) for db_id, study in dataset.data.items()} + + def collect_study_inputs(self, study: Any) -> Dict[str, Path]: + """Collect inputs for a study.""" + study_inputs = {} + for source in self.input_sources: + source_obj = getattr(study, source, None) + if source_obj: + for input_type in self.inputs: + input_obj = deep_getattr(source_obj, input_type, None) + if input_obj and study_inputs.get(input_type) is None: + study_inputs[input_type] = input_obj + return study_inputs + + def write_pipeline_info(self, hash_outdir: Path): + """Write information about the pipeline to a pipeline_info.json file.""" + pipeline_info = { + "date": datetime.now().isoformat(), + "version": self._version, + "type": self._pipeline_type, + "arguments": { + arg: getattr(self, arg) for arg in inspect.signature(self.__init__).parameters.keys() + }, + } + FileManager.write_json(hash_outdir / "pipeline_info.json", pipeline_info) + + def write_study_info(self, hash_outdir: Path, db_id: str, study_inputs: Dict[str, Path]): + """Write information about the current run to an info.json file.""" + output_info = { + "date": datetime.now().isoformat(), + "inputs": {str(input_file): FileManager.calculate_md5(input_file) for input_file in study_inputs.values()} + } + FileManager.write_json(hash_outdir / db_id / "info.json", output_info) + + def _serialize_dataset_keys(self, dataset: Any) -> str: + """Return a hashable string of the input dataset.""" + return "_".join(list(dataset.data.keys())) + + def _serialize_pipeline_args(self) -> str: + """Return a hashable string of the arguments.""" + args = list(inspect.signature(self.__init__).parameters.keys()) + return '_'.join([f"{arg}_{str(getattr(self, arg))}" for arg in args]) - def filter_existing_results(self, output_dir: Path, dataset: Any) -> Dict[str, Dict]: + def _filter_existing_results(self, output_dir: Path, dataset: Any) -> Dict[str, Dict]: """Find the most recent result for an existing study.""" existing_results = {} for d in output_dir.glob(f"{self._version}/**/*"): @@ -111,7 +162,7 @@ def filter_existing_results(self, output_dir: Path, dataset: Any) -> Dict[str, D existing_results[d.name] = found_info return existing_results - def are_file_hashes_identical(self, study_inputs: Dict[str, Path], existing_inputs: Dict[str, str]) -> bool: + def _are_file_hashes_identical(self, study_inputs: Dict[str, Path], existing_inputs: Dict[str, str]) -> bool: """Compare file hashes to determine if the inputs have changed.""" if set(str(p) for p in study_inputs.values()) != set(existing_inputs.keys()): return False @@ -122,71 +173,20 @@ def are_file_hashes_identical(self, study_inputs: Dict[str, Path], existing_inpu return True - def collect_study_inputs(self, study: Any) -> Dict[str, Path]: - """Collect inputs for a study.""" - study_inputs = {} - for source in self._input_sources: - source_obj = getattr(study, source, None) - if source_obj: - for input_type in self._inputs: - input_obj = deep_getattr(source_obj, input_type, None) - if input_obj and study_inputs.get(input_type) is None: - study_inputs[input_type] = input_obj - return study_inputs - - def gather_all_study_inputs(self, dataset: Any) -> Dict[str, Dict[str, Path]]: - """Collect all inputs for the dataset.""" - return {db_id: self.collect_study_inputs(study) for db_id, study in dataset.data.items()} - - def identify_matching_results(self, dataset: Any, existing_results: Dict[str, Dict]) -> Dict[str, bool]: + def _identify_matching_results(self, dataset: Any, existing_results: Dict[str, Dict]) -> Dict[str, bool]: """Compare dataset inputs with existing results.""" dataset_inputs = self.gather_all_study_inputs(dataset) return { - db_id: self.are_file_hashes_identical(study_inputs, existing_results.get(db_id, {}).get("inputs", {})) + db_id: self._are_file_hashes_identical(study_inputs, existing_results.get(db_id, {}).get("inputs", {})) for db_id, study_inputs in dataset_inputs.items() } - def filter_inputs(self, output_directory: Path, dataset: Any) -> bool: - """Filter inputs based on the pipeline type.""" - existing_results = self.filter_existing_results(output_directory, dataset) - matching_results = self.identify_matching_results(dataset, existing_results) - # Return True if any of the studies' inputs have changed or if new studies exist - keep_ids = set(dataset.data.keys()) - {db_id for db_id, match in matching_results.items() if match} - return dataset.slice(keep_ids) - - def check_for_changes(self, output_directory: Path, dataset: Any) -> bool: - """Check if any study inputs have changed or if there are new studies.""" - existing_results = self.filter_existing_results(output_directory, dataset) - matching_results = self.identify_matching_results(dataset, existing_results) - # Return True if any of the studies' inputs have changed or if new studies exist - return any(not match for match in matching_results.values()) - - def write_output_info(self, hash_outdir: Path, db_id: str, study_inputs: Dict[str, Path]): - """Write information about the current run to an info.json file.""" - output_info = { - "date": datetime.now().isoformat(), - "inputs": {str(input_file): FileManager.calculate_md5(input_file) for input_file in study_inputs.values()} - } - FileManager.write_json(hash_outdir / db_id / "info.json", output_info) - - @abstractmethod - def run(self, dataset: Any, output_directory: Path): - """Run the pipeline.""" - pass - - @abstractmethod - def function(self, study_inputs: Dict[str, Any]) -> Dict: - """Run the pipeline function.""" - pass - class IndependentPipeline(Pipeline): """Pipeline that processes each study independently.""" - _pipeline_type = "independent" - - def run(self, dataset: Any, output_directory: Path): - """Run the pipeline for independent studies.""" + def run(self, dataset: Any, output_directory: Path, **kwargs): + """Run the pipeline for studies that are independent of eachother.""" hash_str = self.create_directory_hash(dataset) hash_outdir = output_directory / self._version / hash_str @@ -195,6 +195,7 @@ def run(self, dataset: Any, output_directory: Path): hash_outdir = FileManager.get_next_available_dir(hash_outdir) hash_outdir.mkdir(parents=True, exist_ok=True) + self.write_pipeline_info(hash_outdir) # Process each study individually filtered_dataset = self.filter_inputs(output_directory, dataset) for db_id, study in filtered_dataset.data.items(): @@ -202,18 +203,23 @@ def run(self, dataset: Any, output_directory: Path): study_outdir = hash_outdir / db_id study_outdir.mkdir(parents=True, exist_ok=True) - results = self.function(study_inputs) + results = self._run(study_inputs, **kwargs) FileManager.write_json(study_outdir / "results.json", results) - self.write_output_info(hash_outdir, db_id, study_inputs) + self.write_study_info(hash_outdir, db_id, study_inputs) class DependentPipeline(Pipeline): """Pipeline that processes all studies as a group.""" - _pipeline_type = "dependent" + def check_for_changes(self, output_directory: Path, dataset: Any) -> bool: + """Check if any study inputs have changed or if there are new studies.""" + existing_results = self._filter_existing_results(output_directory, dataset) + matching_results = self._identify_matching_results(dataset, existing_results) + # Return True if any of the studies' inputs have changed or if new studies exist + return any(not match for match in matching_results.values()) - def run(self, dataset: Any, output_directory: Path): + def run(self, dataset: Any, output_directory: Path, **kwargs): """Run the pipeline for dependent studies.""" hash_str = self.create_directory_hash(dataset) hash_outdir = output_directory / self._version / hash_str @@ -228,11 +234,12 @@ def run(self, dataset: Any, output_directory: Path): hash_outdir = FileManager.get_next_available_dir(hash_outdir) hash_outdir.mkdir(parents=True, exist_ok=True) + self.write_pipeline_info(hash_outdir) # Collect all inputs and run the group function at once all_study_inputs = self.gather_all_study_inputs(dataset) - grouped_results = self.function(all_study_inputs) + grouped_results = self._run(all_study_inputs, **kwargs) for db_id, results in grouped_results.items(): study_outdir = hash_outdir / db_id study_outdir.mkdir(parents=True, exist_ok=True) FileManager.write_json(study_outdir / "results.json", results) - self.write_output_info(hash_outdir, db_id, all_study_inputs[db_id]) + self.write_study_info(hash_outdir, db_id, all_study_inputs[db_id]) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 5d14c32..0d40e05 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -9,13 +9,19 @@ class WordCountExtraction(IndependentPipeline): """Word count extraction pipeline.""" _version = "1.0.0" - _hash_attrs = ["_inputs", "_input_sources"] - _pipeline_type = "independent" - def function(self, study_inputs): + def __init__(self, inputs=("text",), input_sources=("pubget", "ace"), square_root=False): + """add any pipeline configuration here (as opposed to runtime arguments like n_cpus or n_cores)""" + self.square_root = square_root + super().__init__(inputs=inputs, input_sources=input_sources) + + def _run(self, study_inputs, debug=False): """Run the word count extraction pipeline.""" text_file = study_inputs["text"] + if debug: + print(f"Processing {text_file}") + with open(text_file, "r") as f: text = f.read() @@ -29,16 +35,23 @@ class WordDevianceExtraction(DependentPipeline): """ _version = "1.0.0" - _hash_attrs = ["_inputs", "_input_sources"] - _pipeline_type = "dependent" - def function(self, all_study_inputs): + def __init__(self, inputs=("text",), input_sources=("pubget", "ace"), square_root=False): + """add any pipeline configuration here (as opposed to runtime arguments like n_cpus or n_cores)""" + self.square_root = square_root + super().__init__(inputs=inputs, input_sources=input_sources) + + def _run(self, all_study_inputs, debug=False): """Run the word count extraction pipeline.""" # Calculate the average word count total_word_count = 0 total_studies = len(all_study_inputs) study_word_counts = {} + + if debug: + print(f"Processing {total_studies} studies") + for study_id, study_inputs in all_study_inputs.items(): text_file = study_inputs["text"] From 85b20dd01afbc02b5a4a60625250b291100387ff Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 31 Oct 2024 14:42:54 -0500 Subject: [PATCH 13/42] add pipeline name to output path --- ns_pipelines/pipeline.py | 35 ++++++++++++++++++++++++++--------- 1 file changed, 26 insertions(+), 9 deletions(-) diff --git a/ns_pipelines/pipeline.py b/ns_pipelines/pipeline.py index 1d59096..04f0c20 100644 --- a/ns_pipelines/pipeline.py +++ b/ns_pipelines/pipeline.py @@ -144,22 +144,39 @@ def _serialize_pipeline_args(self) -> str: def _filter_existing_results(self, output_dir: Path, dataset: Any) -> Dict[str, Dict]: """Find the most recent result for an existing study.""" existing_results = {} - for d in output_dir.glob(f"{self._version}/**/*"): - if d.is_dir() and d.name in set(dataset.data.keys()): - info_file = d / "info.json" + result_directory = output_dir / self.__class__.__name__ / self._version + current_args = { + arg: getattr(self, arg) for arg in inspect.signature(self.__init__).parameters.keys() + } + + for d in result_directory.glob(f"*"): + if not d.is_dir(): + continue + + pipeline_info = FileManager.load_json(d / "pipeline_info.json") + pipeline_args = pipeline_info.get("arguments", {}) + + if pipeline_args != current_args: + continue + + for sub_d in d.glob("*"): + if not sub_d.is_dir(): + continue + + info_file = sub_d / "info.json" if info_file.exists(): info = FileManager.load_json(info_file) found_info = { "date": info["date"], "inputs": info["inputs"], - "hash": d.parents[0].name + "hash": sub_d.name } if ( - existing_results.get(d.name) is None + existing_results.get(sub_d.name) is None or datetime.strptime(info["date"], '%Y-%m-%d') > - datetime.strptime(existing_results[d.name]["date"], '%Y-%m-%d') + datetime.strptime(existing_results[sub_d.name]["date"], '%Y-%m-%d') ): - existing_results[d.name] = found_info + existing_results[sub_d.name] = found_info return existing_results def _are_file_hashes_identical(self, study_inputs: Dict[str, Path], existing_inputs: Dict[str, str]) -> bool: @@ -188,7 +205,7 @@ class IndependentPipeline(Pipeline): def run(self, dataset: Any, output_directory: Path, **kwargs): """Run the pipeline for studies that are independent of eachother.""" hash_str = self.create_directory_hash(dataset) - hash_outdir = output_directory / self._version / hash_str + hash_outdir = output_directory / self.__class__.__name__ / self._version / hash_str # If the directory exists, find the next available directory with a suffix like "-1", "-2", etc. if hash_outdir.exists(): @@ -222,7 +239,7 @@ def check_for_changes(self, output_directory: Path, dataset: Any) -> bool: def run(self, dataset: Any, output_directory: Path, **kwargs): """Run the pipeline for dependent studies.""" hash_str = self.create_directory_hash(dataset) - hash_outdir = output_directory / self._version / hash_str + hash_outdir = output_directory / self.__class__.__name__ / self._version / hash_str # Check if there are any changes for dependent mode if not self.check_for_changes(output_directory, dataset): From ddd5b6734841c56caccce496484072e82c5b1800 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 31 Oct 2024 14:43:08 -0500 Subject: [PATCH 14/42] wip: modify readme --- README.md | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 39630ab..2f86280 100644 --- a/README.md +++ b/README.md @@ -11,22 +11,28 @@ To install the necessary dependencies, run: ## Usage -### Running pipelines -Executable workflows in `pipelines/{pipeline_name}/run.py` will take as input standardized pubget-style text inputs (1 row per article). +### Overview -Run all available pipelines and harmonize outputs using CLI (todo) +Executable workflows in `pipelines/{pipeline_name}/run.py` will have a specific class that implements the `run` method. +The `run` method will take a `Dataset` object and an output directory as input, and will output extracted features to the output directory in the following format: + # the pipeline info file contains configuration information about the pipeline + output_dir/{pipeline_name}/{pipeline_version}/{input_hash}/pipeline_info.json + # the study results file contains whatever extracted features from the study by the pipeline + output_dir/{pipeline_name}/{pipeline_version}/{input_hash}/{study_id}/results.json + # the study info file contains metadata about the inputs to the pipeline + output_dir/{pipeline_name}/{pipeline_version}/{input_hash}/{study_id}/info.json -### Pipeline outputs -Pipeline results are output to `data/outputs/{input_hash}/{pipeline_name}. -Outputs include extracted features `features.csv`, feature descriptions `descriptions.json`, and extraction information `info.json`. +You will need to create a dataset object that contains the studies you want to process, and then pass that dataset object to the `run` method of the pipeline class. -Pipeline outputs are not stored as part of this repository. -See `ns-text-extraction-outputs` sub repository. +Run all available pipelines and harmonize outputs using CLI (todo) -### Types of pipelines +Pipelines can either be "dependent" or "independent". +Dependent pipelines are those whose outputs for each individual study depend on the outputs of other studies. +Independent pipelines are those whose outputs for each individual study do not depend on the outputs of other studies. +## Note(s) for self #### Each study is independently processed From c99c83f4b1fcc01a34b5fa282058026180dfac17 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 31 Oct 2024 14:53:07 -0500 Subject: [PATCH 15/42] add tests dependencies --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 5960202..4c5467a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,10 @@ word_count = [ "pandas", ] +tests = [ + "pytest", +] + [tool.hatch.version] source = "vcs" From a78f24115cab5f20ec7f158a94cc15824896ca4e Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 14 Nov 2024 17:00:29 -0600 Subject: [PATCH 16/42] add test for participant demographics --- .../participant_demographics/clean.py | 47 +++++++++---------- ns_pipelines/participant_demographics/run.py | 33 +++++++------ pyproject.toml | 2 + tests/test_participant_demographics.py | 17 +++++++ 4 files changed, 60 insertions(+), 39 deletions(-) create mode 100644 tests/test_participant_demographics.py diff --git a/ns_pipelines/participant_demographics/clean.py b/ns_pipelines/participant_demographics/clean.py index f00525d..3b11988 100644 --- a/ns_pipelines/participant_demographics/clean.py +++ b/ns_pipelines/participant_demographics/clean.py @@ -2,49 +2,48 @@ import numpy as np -def clean_predictions(predictions): - # Clean known issues with GPT demographics predictions - predictions = [p for p in predictions if p and "groups" in p] +def clean_prediction(prediction): + # Clean known issues with GPT demographics prediction meta_keys = ["pmid", "rank", "start_char", "end_char", "id"] - meta_keys = [k for k in meta_keys if k in predictions[0]] - + meta_keys = [k for k in meta_keys if k in prediction] + # Convert JSON to DataFrame - predictions = pd.json_normalize( - predictions, record_path=["groups"], + prediction = pd.json_normalize( + prediction, record_path=["groups"], meta=meta_keys ) - predictions.columns = predictions.columns.str.replace(' ', '_') + prediction.columns = prediction.columns.str.replace(' ', '_') - predictions = predictions.fillna(value=np.nan) - predictions["group_name"] = predictions["group_name"].fillna("healthy") + prediction = prediction.fillna(value=np.nan) + prediction["group_name"] = prediction["group_name"].fillna("healthy") # Drop rows where count is NA - predictions = predictions[~pd.isna(predictions["count"])] + prediction = prediction[~pd.isna(prediction["count"])] # Set group_name to healthy if no diagnosis - predictions.loc[ - (predictions["group_name"] != "healthy") & (pd.isna(predictions["diagnosis"])), + prediction.loc[ + (prediction["group_name"] != "healthy") & (pd.isna(prediction["diagnosis"])), "group_name", ] = "healthy" # If no male count, substract count from female count columns - ix_male_miss = (pd.isna(predictions["male_count"])) & ~( - pd.isna(predictions["female_count"]) + ix_male_miss = (pd.isna(prediction["male_count"])) & ~( + pd.isna(prediction["female_count"]) ) - predictions.loc[ix_male_miss, "male_count"] = ( - predictions.loc[ix_male_miss, "count"] - - predictions.loc[ix_male_miss, "female_count"] + prediction.loc[ix_male_miss, "male_count"] = ( + prediction.loc[ix_male_miss, "count"] + - prediction.loc[ix_male_miss, "female_count"] ) # Same for female count - ix_female_miss = (pd.isna(predictions["female_count"])) & ~( - pd.isna(predictions["male_count"]) + ix_female_miss = (pd.isna(prediction["female_count"])) & ~( + pd.isna(prediction["male_count"]) ) - predictions.loc[ix_female_miss, "female_count"] = ( - predictions.loc[ix_female_miss, "count"] - - predictions.loc[ix_female_miss, "male_count"] + prediction.loc[ix_female_miss, "female_count"] = ( + prediction.loc[ix_female_miss, "count"] + - prediction.loc[ix_female_miss, "male_count"] ) - return predictions \ No newline at end of file + return {"groups": prediction.to_dict(orient="records")} diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index b889e58..dcc3f41 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -1,5 +1,6 @@ """ Extract participant demographics from HTML files. """ import os + from publang.extract import extract_from_text from openai import OpenAI from pathlib import Path @@ -8,35 +9,34 @@ import logging from . import prompts -from .clean import clean_predictions +from .clean import clean_prediction from ns_pipelines.pipeline import IndependentPipeline -def extract(extraction_model, extraction_client, docs, output_dir, prompt_set='', **extract_kwargs): + +def extract(extraction_model, extraction_client, text, prompt_set='', **extract_kwargs): extract_kwargs.pop('search_query', None) # Extract predictions = extract_from_text( - docs['body'].to_list(), - model=extraction_model, client=extraction_client, + text, + model=extraction_model, + client=extraction_client, **extract_kwargs ) - # Add PMCID to predictions - for i, pred in enumerate(predictions): - if not pred: - logging.warning(f"No prediction for document {docs['pmid'].iloc[i]}") - continue - pred['pmid'] = int(docs['pmid'].iloc[i]) + if not predictions: + logging.warning("No predictions found.") + return None, None - clean_preds = clean_predictions(predictions) + clean_preds = clean_prediction(predictions) return predictions, clean_preds def _load_client(model_name): if 'gpt' in model_name: - client = OpenAI(api_key=os.getenv('MYOPENAI_API_KEY')) + client = OpenAI(api_key=os.getenv('OPENAI_API_KEY')) else: raise ValueError(f"Model {model_name} not supported") @@ -90,7 +90,7 @@ def __main__(extraction_model, docs_path, prompt_set, output_dir=None, **kwargs) return predictions, clean_preds -def ParticipantDemographics(IndependentPipeline): +class ParticipantDemographicsExtraction(IndependentPipeline): """Participant demographics extraction pipeline.""" _version = "1.0.0" @@ -100,7 +100,8 @@ def ParticipantDemographics(IndependentPipeline): def __init__( self, extraction_model, - prompt_set, inputs=("text",), + prompt_set, + inputs=("text",), input_sources=("pubget", "ace"), **kwargs ): @@ -117,11 +118,13 @@ def _run(self, study_inputs, n_cpus=1): if self.kwargs is not None: prompt_config.update(self.kwargs) + with open(study_inputs["text"]) as f: + text = f.read() predictions, clean_preds = extract( self.extraction_model, extraction_client, - study_inputs["text"], + text, prompt_set=self.prompt_set, **prompt_config ) diff --git a/pyproject.toml b/pyproject.toml index 4c5467a..720bfde 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,8 @@ word_count = [ tests = [ "pytest", + "pytest-recording", + "vcrpy", ] [tool.hatch.version] diff --git a/tests/test_participant_demographics.py b/tests/test_participant_demographics.py new file mode 100644 index 0000000..1205615 --- /dev/null +++ b/tests/test_participant_demographics.py @@ -0,0 +1,17 @@ +import pytest + +from ns_pipelines.participant_demographics.run import ParticipantDemographicsExtraction +from ns_pipelines.dataset import Dataset + + +@pytest.mark.vcr(record_mode="once", filter_headers=["authorization"]) +def test_ParticipantDemographicsExtraction(sample_data, tmp_path): + """Test the word count extraction pipeline.""" + pde = ParticipantDemographicsExtraction( + extraction_model="gpt-4o-2024-08-06", + prompt_set="ZERO_SHOT_MULTI_GROUP_FTSTRICT_FC" + ) + dataset = Dataset(sample_data) + output_dir = tmp_path / "participant_demographics" + pde.run(dataset, output_dir) + assert True From cdbdec2467ab7f23a5b9c707fc2cfc4e85ba8f28 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 09:55:28 -0600 Subject: [PATCH 17/42] opensource data --- .../39ua7DtHYLoW/identifiers.json | 1 - .../processed/ace/coordinates.csv | 15 - .../39ua7DtHYLoW/processed/ace/metadata.json | 11 - .../39ua7DtHYLoW/processed/ace/text.txt | 724 --- .../processed/pubget/coordinates.csv | 15 - .../processed/pubget/metadata.json | 11 - .../39ua7DtHYLoW/processed/pubget/text.txt | 120 - .../39ua7DtHYLoW/source/ace/28527986.html | 239 - .../39ua7DtHYLoW/source/pubget/28527986.xml | 1613 ------ .../source/pubget/tables/table_000.csv | 17 - .../source/pubget/tables/table_000_info.json | 1 - 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end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv deleted file mode 100644 index 5be8f6d..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/coordinates.csv +++ /dev/null @@ -1,15 +0,0 @@ -table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,-94.0,6.0,0.004,Middle OG,,2.82,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,54.0,-56.0,20.0,0.003,Angular G,,2.76,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,32.0,22.0,-14.0,0.003,Posterior OFG,,2.73,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,28.0,52.0,-4.0,0.005,Anterior OFG,,2.54,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,20.0,-16.0,0.001,Posterior OFG,,3.17,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,32.0,50.0,-8.0,0.002,Anterior OFG,,2.93,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,26.0,20.0,-18.0,0.001,Posterior OFG,,3.04,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,34.0,50.0,-10.0,0.004,Anterior OFG,,2.63,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-46.0,-18.0,-32.0,0.003,Anterior ITG,,2.73,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-8.0,-94.0,-18.0,0.004,Occipital G,,2.67,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-44.0,-18.0,-32.0,0.003,Anterior ITG,,2.72,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-8.0,-94.0,-18.0,0.003,Occipital G,,2.63,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-44.0,-20.0,-34.0,0.004,Anterior ITG,,2.64,INHIBITION Angry > Happy -8837,Table 1,Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).,1,-34.0,-4.0,-32.0,0.005,Hippocampus,,2.64,INHIBITION Angry > Happy diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json deleted file mode 100644 index 459df10..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/metadata.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "title": "The temporal and spatial brain dynamics of automatic emotion regulation in children.", - "authors": "Urbain, Charline;Sato, Julie;Pang, Elizabeth W;Taylor, Margot J", - "journal": "Developmental cognitive neuroscience", - "keywords": null, - "abstract": "Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (\u223c10 years old) who performed a go/no-go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425ms. These activations involved the right angular and occipital gyri from 100 to175ms, the right orbito-frontal gyrus (OFG) from 250 to 325ms (p<0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.", - "publication_year": 2017, - "coordinate_space": "MNI", - "license": null, - "text": true -} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt b/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt deleted file mode 100644 index 636c866..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/ace/text.txt +++ /dev/null @@ -1,724 +0,0 @@ - - - - - - - - - - - - - - - - - - -The temporal and spatial brain dynamics of automatic emotion regulation in children - PMC - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Back to Top - -Skip to main content - - - - - - -An official website of the United States government - -Here's how you know - - - - - - - - -The .gov means it’s official. - - Federal government websites often end in .gov or .mil. 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Inclusion in an NLM database does not imply endorsement of, or agreement with, - the contents by NLM or the National Institutes of Health. - Learn more: - PMC Disclaimer - | - - PMC Copyright Notice - - - - - - -Dev Cogn Neurosci. 2017 Aug; 26: 62-68. Published online 2017 May 17. doi: 10.1016/j.dcn.2017.05.004PMCID: PMC6987902PMID: 28527986The temporal and spatial brain dynamics of automatic emotion regulation in childrenCharline Urbain,a,⁎ Julie Sato,b,c Elizabeth W. Pang,c,e and Margot J. Taylorb,c,d,eCharline UrbainaUR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, BelgiumFind articles by Charline UrbainJulie SatobDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadaFind articles by Julie SatoElizabeth W. PangcNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaFind articles by Elizabeth W. PangMargot J. TaylorbDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadadDepartment of Psychology, University of Toronto, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaFind articles by Margot J. TaylorAuthor information Article notes Copyright and License information PMC DisclaimeraUR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, BelgiumbDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, CanadacNeuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, CanadadDepartment of Psychology, University of Toronto, Toronto, CanadaeDivision of Neurology, The Hospital for Sick Children, Toronto, CanadaCharline Urbain: eb.ca.blu@niabruc ⁎Corresponding author. eb.ca.blu@niabrucReceived 2016 Nov 29; Revised 2017 May 8; Accepted 2017 May 8.Copyright © 2017 The AuthorsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).This article has been corrected. See Dev Cogn Neurosci. 2020 October; 45: 100843.Associated DataSupplementary Materialsmmc1.doc (34K)GUID: E42B178D-0AA0-4256-9F73-D312A046CE43AbstractMechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.Keywords: MEG, Social cognition, Cognitive control, Orbito-frontal cortex, Temporal pole1. IntroductionDuring development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002, Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007, Todd et al., 2008, Hum et al., 2013a, Hum et al., 2013b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007, Hum et al., 2013a, Hum et al., 2013b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011, Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007, Todd et al., 2008, Todd et al., 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004, Gross and Muñoz, 1995, Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004, Hopp et al., 2011).Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.2. Material and methods2.1. ParticipantsParticipants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.2.2. Experimental MEG task and procedureThe children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.Open in a separate windowFig. 1(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (p = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (p < 0.002) but no differences between emotions were found in the vigilance condition (p > 0.88).Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.2.3. MEG data acquisitionMEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.2.4. MRI data acquisitionEach child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.2.5. Behavioural analysesAt the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.2.6. MEG analysesWith MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).2.6.1. Preprocessing steps MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.2.6.2. Source reconstruction Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005, Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).Table 1Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).INHIBITION Angry > HappyBrain regionsTime window (ms)z-valuep-valueMNI coordinatesxyzRMiddle OG100–1752.820.00426−946RAngular G125–1752.760.00354−5620RPosterior OFG225–2752.730.0033222−14RAnterior OFG225–2752.540.0052852−4RPosterior OFG250–3003.170.0012620−16RAnterior OFG250–3002.930.0023250−8RPosterior OFG275–3253.040.0012620−18RAnterior OFG275–3252.630.0043450−10LAnterior ITG325–3752.730.003−46−18−32LOccipital G325–3752.670.004−8−94−18LAnterior ITG350–4002.720.003−44−18−32LOccipital G350–4002.630.003−8−94−18LAnterior ITG375–4252.640.004−44−20−34LHippocampus375–4252.640.005−34−4−32Open in a separate windowNote: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.3. Results3.1. Behavioural resultsBehavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p > 0.88).3.2. MEG resultsAnalyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).Open in a separate windowFig. 23D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).Open in a separate windowFig. 3Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (p = 0.023) with the Inhibition Angry condition driving the interaction.4. DiscussionIn this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010, Cohen Kadosh et al., 2014).At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a, Hum et al., 2013b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998, Hillyard et al., 1998, Corbetta and Shulman, 2002, Singh-Curry and Husain, 2009, Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007, Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006, Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009, Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999, Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, Todd et al., 2012).Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010, Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002, Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.Conflict of Interest:None.AcknowledgementsThe authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.FootnotesAppendix ASupplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2017.05.004.Appendix A. Supplementary dataThe following is Supplementary data to this article:Click here to view.(34K, doc)ReferencesAlbert J., Lopez-Martin S., Carretie L. 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[PMC free article] [PubMed] [Google Scholar]Articles from Developmental Cognitive Neuroscience are provided here courtesy of Elsevier - - - - - -Other Formats - -PDF (2.6M) - - - -Actions - - - -Cite - - - - - - -Collections - - - -Add to Collections - - - - - - - -Create a new collection - - - -Add to an existing collection - - - - - - - Name your collection: - - - - Name must be less than characters - - - - - Choose a collection: - - - - - Unable to load your collection due to an error -Please try again - - - - - - Add - - - Cancel - - - - - - - - - - - -Share - -  -  - - - - - - - - - - Permalink - - - -Copy - - - - - - - - -RESOURCES - - - - - Similar articles - - - - - - - - - Cited by other articles - - - - - - - - - Links to NCBI Databases - - - - - - - - - - - -[x] -Cite - - - - - Copy - - -Download .nbib -.nbib - - -Format: - - - AMA - - - APA - - - MLA - - - NLM - - - - - - - - - - -Follow NCBI - - - - - - -Twitter - - - - -Facebook - - - - -LinkedIn - - - - - - - -GitHub - - - - - - - - - - - - - - - - - - - - - - - - - -Connect with NLM - - - -SM-Twitter - - - - - - - - - - - - -SM-Facebook - - - - - - - - - -SM-Youtube - - - - - - - - - -National Library of Medicine -8600 Rockville Pike - Bethesda, MD 20894 - - -Web Policies -FOIA -HHS Vulnerability Disclosure - - -Help -Accessibility -Careers - - - - - - - -NLM - - -NIH - - -HHS - - -USA.gov - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/coordinates.csv b/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/coordinates.csv deleted file mode 100644 index 9be8613..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/coordinates.csv +++ /dev/null @@ -1,15 +0,0 @@ -table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups -tbl0005,Table 1,,,26.0,-94.0,6.0,,,,, -tbl0005,Table 1,,,54.0,-56.0,20.0,,,,, -tbl0005,Table 1,,,32.0,22.0,-14.0,,,,, -tbl0005,Table 1,,,28.0,52.0,-4.0,,,,, -tbl0005,Table 1,,,26.0,20.0,-16.0,,,,, -tbl0005,Table 1,,,32.0,50.0,-8.0,,,,, -tbl0005,Table 1,,,26.0,20.0,-18.0,,,,, -tbl0005,Table 1,,,34.0,50.0,-10.0,,,,, -tbl0005,Table 1,,,-46.0,-18.0,-32.0,,,,, -tbl0005,Table 1,,,-8.0,-94.0,-18.0,,,,, -tbl0005,Table 1,,,-44.0,-18.0,-32.0,,,,, -tbl0005,Table 1,,,-8.0,-94.0,-18.0,,,,, -tbl0005,Table 1,,,-44.0,-20.0,-34.0,,,,, -tbl0005,Table 1,,,-34.0,-4.0,-32.0,,,,, diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/metadata.json b/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/metadata.json deleted file mode 100644 index ca05cff..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/metadata.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "title": "The temporal and spatial brain dynamics of automatic emotion regulation in children", - "authors": "Urbain, Charline; Sato, Julie; Pang, Elizabeth W.; Taylor, Margot J.", - "journal": "Dev Cogn Neurosci", - "keywords": "MEG\nSocial cognition\nCognitive control\nOrbito-frontal cortex\nTemporal pole\n", - "abstract": " \nMechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (\u223c10 years old) who performed a go/no\u2013go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425\u202fms. These activations involved the right angular and occipital gyri from 100 to175\u202fms, the right orbito-frontal gyrus (OFG) from 250 to 325\u202fms (p \u202f< \u202f0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425\u202fms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context. \n ", - "publication_year": 2017, - "coordinate_space": "MNI", - "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", - "text": true -} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt b/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt deleted file mode 100644 index c38857b..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/processed/pubget/text.txt +++ /dev/null @@ -1,120 +0,0 @@ - -## Introduction - -During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions ( ). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly ( , ). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus ( ). - -Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces ( ), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces ( ). - -Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues ( , , , ). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in ). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; ( )) or gender ( , , ) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process ( ), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels ( , ; see , for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental ( , , ). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not ( and see for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth ( , , ). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms ( , ). - -Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children ( ). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; ). Although fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction. - -The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition. - - -## Material and methods - -### Participants - -Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs). - -Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task. - - -### Experimental MEG task and procedure - -The children completed an emotional go/no–go task (see a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing. -(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task ( p  = . 000001; Inhibition < Vigilance) and Emotion ( p  = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition ( p  = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition ( p  < 0.002) but no differences between emotions were found in the vigilance condition ( p  > 0.88). - Fig. 1 - -Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants. - -The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions ( ). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used. - -All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials. - - -### MEG data acquisition - -MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain. - - -### MRI data acquisition - -Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil. - - -### Behavioural analyses - -At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press). - -To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors. - - -### MEG analyses - -With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014). - -#### Preprocessing steps - -MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip ( ). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature ( ). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected. - - -#### Source reconstruction - -Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms). - -Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise ( ). Head modeling was computed using a single shell head model ( ) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design ( ). - -Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t- statistics [SPM(T)], reported significant at p  < 0.005. A family-wise error correction (p  < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction ( ) which adjusted the p -value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM ( , ), and adapted to MEG ( ), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix ( ). The correction corresponded effectively to a significance level of P < 0.002 (see in bold). -Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset). - Table 1 - - - - -## Results - -### Behavioural results - -Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p  = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p  = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p  = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p  < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p  > 0.88). - - -### MEG results - -Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see and ) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms). -3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms). - Fig. 2 - -These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see for an example of these results in the right OFG). -Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion ( p  = 0.023) with the Inhibition Angry condition driving the interaction. - Fig. 3 - - - -## Discussion - -In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces). - -Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues ( ), consistent with adolescent and adult studies ( , ). - -At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition. - -Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces. - -The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing ( ), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies ( , ). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures ( ) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks ( , , , , ). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context. - -The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., , ). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see in bold). - -The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children ( ) and adults ( , ). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions ( ) and, thus plays an important role in flexible social behaviour [for reviews see, , ]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children ( , ). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see ) or avoidance behaviour ( ) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity ( , ). - -Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone ( , ). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes ( , ). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes ( and see for a review of temporal pole functions). - -In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children. - - -## Conflict of Interest: - -None. - - \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html b/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html deleted file mode 100644 index ba86267..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/source/ace/28527986.html +++ /dev/null @@ -1,239 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - The temporal and spatial brain dynamics of automatic emotion regulation in children - ScienceDirect - - - - - - - - - - - - - - - - - Skip to main content - -
 
Elsevier

Developmental Cognitive Neuroscience

Volume 26, August 2017, Pages 62-68
open access
Developmental Cognitive Neuroscience

The temporal and spatial brain dynamics of automatic emotion regulation in children

Under a Creative Commons license

Abstract

Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.

Keywords

MEG
Social cognition
Cognitive control
Orbito-frontal cortex
Temporal pole

1. Introduction

During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002; Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).

Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).

Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007; Todd et al., 2008; Hum et al., 2013a,b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007; Hum et al., 2013a,b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011; Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007; Todd et al., 2008, 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004; Gross and Muñoz, 1995; Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004; Hopp et al., 2011).

Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.

The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

2. Material and methods

2.1. Participants

Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).

Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.

2.2. Experimental MEG task and procedure

The children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.

Fig. 1

Fig. 1. (A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (< 0.002) but no differences between emotions were found in the vigilance condition (> 0.88).

Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.

The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.

All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.

2.3. MEG data acquisition

MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.

2.4. MRI data acquisition

Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.

2.5. Behavioural analyses

At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).

To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.

2.6. MEG analyses

With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).

2.6.1. Preprocessing steps

MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.

2.6.2. Source reconstruction

Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).

Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).

Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005; Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).

Table 1. Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

3. Results

3.1. Behavioural results

Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, > 0.88).

3.2. MEG results

Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).

Fig. 2

Fig. 2. 3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).

These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).

Fig. 3

Fig. 3. Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (= 0.023) with the Inhibition Angry condition driving the interaction.

4. Discussion

In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).

Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010; Cohen Kadosh et al., 2014).

At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.

The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a,b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998; Hillyard et al., 1998; Corbetta and Shulman, 2002; Singh-Curry and Husain, 2009; Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.

The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007; Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).

The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006; Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009; Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999; Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, 2012).

Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010; Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002; Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).

In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.

Acknowledgements

The authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.

Appendix A. Supplementary data

The following is Supplementary data to this article:

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- - - - Dev Cogn Neurosci - Dev Cogn Neurosci - - Developmental Cognitive Neuroscience - - 1878-9293 - 1878-9307 - - Elsevier - - - - 28527986 - 6987902 - S1878-9293(16)30233-X - 10.1016/j.dcn.2017.05.004 - - - Original Research - - - - The temporal and spatial brain dynamics of automatic emotion regulation in children - - - - - Urbain - Charline - - curbain@ulb.ac.be - a - - - - - Sato - Julie - - b - c - - - - Pang - Elizabeth W. - - c - e - - - - Taylor - Margot J. - - b - c - d - e - - - UR2NF—Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium - Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada - Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada - Department of Psychology, University of Toronto, Toronto, Canada - Division of Neurology, The Hospital for Sick Children, Toronto, Canada - - Corresponding author. curbain@ulb.ac.be - - - 17 - 5 - 2017 - - - - 8 - 2017 - - - 17 - 5 - 2017 - - 26 - 62 - 68 - - - 29 - 11 - 2016 - - - 8 - 5 - 2017 - - - 8 - 5 - 2017 - - - - © 2017 The Authors - 2017 - - This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). - - - -

Mechanisms for automatic emotion regulation (AER) are essential during childhood as they offset the impact of unwanted or negative emotional responses without drawing on limited attentional resources. Despite the importance of AER in improving the efficiency and flexibility of self-regulation, few research studies have investigated the underlying neurophysiological mechanisms. To fill this gap, we used magnetoencephalography (MEG) to investigate AER-related brain processes in 25 children (∼10 years old) who performed a go/no–go task that included an incidental exposure to faces containing socio-emotional cues. Whole brain results revealed that the inhibition of angry faces (compared with happy faces) was associated with a stronger recruitment of several brain regions from 100 to 425 ms. These activations involved the right angular and occipital gyri from 100 to175 ms, the right orbito-frontal gyrus (OFG) from 250 to 325 ms (pcorr < 0.05), and finally, the left anterior temporal lobe (ATL) from 325 to 425 ms. Our results suggest a specific involvement of these regions in the automatic regulation of negative emotional stimuli in children. In the future, this knowledge may help understand developmental conditions where inhibition impairments are exacerbated by an emotional context.

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- - Keywords - MEG - Social cognition - Cognitive control - Orbito-frontal cortex - Temporal pole - -
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- - - - Introduction -

During development, children learn how to adapt, or inhibit, their behaviour in accordance with exposure to various types of emotions (Cole et al., 2004). Particularly in the context of peer interactions and social activities, children rapidly detect implicit socio-emotional cues (e.g., facial expressions) and use appropriate strategies to regulate their emotions accordingly (Gross, 2002, Cole et al., 2004). For instance, whereas smiling faces will encourage answers and approach, a negative countenance will trigger behavioural regulation (e.g., inhibition) to avoid a potentially disturbing situation. This suggests that the impact of emotion on cognition depends on the arousal and valence of the stimulus (Pessoa, 2009).

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Although the development of emotion regulation strategies has important affective, cognitive and social consequences in children, behavioural and neuroimaging studies investigating this process are few and their results are discrepant. For instance, at the behavioural level, whereas Cohen Kadosh et al. (2014) reported that children (11–12 years old) encounter more attentional control difficulties in the context of fearful compared to happy faces (Cohen Kadosh et al., 2014), others have shown that emotional context alters response inhibition ability in children; however, this inhibition is equal to both happy and sad faces (Urben et al., 2012).

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Knowledge about the inhibitory brain mechanisms, in children, that trigger emotion regulation, particularly those that allow adaptive functioning in the presence of socio-emotional cues (face expressions), is also limited. Thus far, a few ERP studies in children have highlighted the functional role of the N2, an inhibitory-related frontal component occurring 200–400 ms after stimulus onset, in the regulation of socio-emotional cues (Lewis et al., 2007, Todd et al., 2008, Hum et al., 2013a, Hum et al., 2013b). These studies used an emotional go/no–go task where participants responded to ‘go’ stimuli and withheld responses to ‘no–go’ stimuli in the context of happy, angry or fearful faces. Although the results are of interest, the protocols could be improved in several ways. Firstly, these studies compared go trials (containing a motor response) with no–go trials (containing no motor response), thus integrating a motor confound into the analysis (see discussion in Vidal et al., 2012). Secondly, previous ERP studies have used explicit socio-emotional cues during the emotional go/no–go task which required participants to directly respond to emotion (i.e., Happy/Angry/Fearful; (Hare et al., 2008)) or gender (Lewis et al., 2007, Hum et al., 2013a, Hum et al., 2013b) of the stimulus. However, although emotion regulation is usually portrayed as a deliberate and explicit process (Gross, 2014), a growing body of research has shown that emotion regulation often operates on more implicit or automatic levels (Gyurak et al., 2011, Koole and Rothermund, 2011; see Koole et al., 2015, for a review). According to these models, automatic emotion regulation (AER) processes operate almost constantly in daily life and represent a powerful aid in keeping emotional context from interfering with one’s ongoing activities. Hence, investigating the impact of an incidental exposure to emotional stimuli on controlled behaviour provides a more a realistic measure of socio-behavioural interactions, where emotional cues are often incidental (Goldstein et al., 2007, Todd et al., 2008, Todd et al., 2012). The AER assists children in developing adaptive emotion regulation strategies by facilitating an implicit and rapid monitoring of whether an emotional response is appropriate or not (Hopp et al., 2011 and see Koole et al., 2015 for a recent review). For instance, by efficiently offsetting the impact of unwanted or negative emotional responses without drawing on limited attentional resources, the AER crucially contributes to resilience to stressful life events and to personal growth (Bonanno, 2004, Gross and Muñoz, 1995, Moore et al., 2008). Moreover, implicit emotion regulation has been associated with improved well being or social adjustment and reduced depressive symptoms (Bonanno, 2004, Hopp et al., 2011).

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Despite the importance of the AER in improving self-regulation in children, a clear understanding of AER-related neurophysiological mechanisms is still missing. To our knowledge, only one functional magnetic resonance imaging (fMRI) study has characterized the brain regions involved in AER regulation (i.e., incidental exposure to happy or angry faces during a go/no–go task) in children (Todd et al., 2012). Results showed that inhibition-related activity in the orbito-frontal cortex (OFC) was modulated by the emotional valence of the faces. In particular, whereas Happy faces triggered more activity in the left OFC, compared to Angry faces, in younger children (4.4–6.5 years), the emotion-related modulation of the OFC shifted to greater activation for Angry faces in older children (6.5–9.0 years; Todd et al., 2012). Although Todd et al. (2012)’s fMRI study showed the specific contribution of the OFC in socio-emotional regulation processes in children, and possibly its crucial importance during development, the poor temporal resolution of fMRI precludes an understanding of the brain dynamics that regulate inhibition and emotion interaction.

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The goal of the present study was to characterise precisely the spatio-temporal brain dynamics of AER in children. To do so, we used magnetoencephalography (MEG) which offers a unique opportunity to investigate both the spatial and temporal brain patterns that underlie inhibitory brain mechanisms. We determined how these brain processes were modulated by an incidental exposure to negative (angry faces) vs. positive (happy faces) emotions, thus, allowing adaptive functioning in children. As MEG provides excellent time resolution and better spatial localisation than ERPs, it represents a remarkable tool for studying such complex cognitive processes (e.g., see Hari et al., 2010 for a review). The MEG analyses compared the timing and localisation of inhibition-related brain activity which occurred with incidental exposure to positive vs. negative emotional faces. Moreover, to prevent the usual confound of movement-related activity (when go and no–go trials are contrasted), we compared no–go trials associated with stimuli in an inhibitory condition to no–go trials occurring within a vigilance condition (same no–go stimuli in a non-inhibitory context) to ensure the specificity of the inhibition task effect. We hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

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- - - Material and methods - - - Participants -

Participants were selected from a larger series of 40 children [age range: 7–13 yrs]. All children had normal vision and no history or existing diagnosis of psychiatric, neurological disorders or learning disability. One child was excluded due to high IQ (>140), three were excluded due to excessive movement in the MRI and MEG scanners and 11 were excluded due to poor performance on the task (high false alarm (FAs) rate of no–go trials, <10% difference between HITS and FAs).

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Thus, the final sample of this study included 25 children (17 males: 8 females, mean ± SD: 10.23 ± 1.79yrs), 21 were right handed and 4 left–handed. All children provided informed assent and parents gave informed written consent. The study was approved by the Research Ethics Board at the Hospital for Sick Children and is in accordance with the declaration of Helsinki. All children were in the appropriate grade level in school and were recruited through fliers, advertisements and word of mouth. Prior to MEG testing, all participants received instructions and completed practice trials to ensure full understanding of the task.

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- - - Experimental MEG task and procedure -

The children completed an emotional go/no–go task (see Fig. 1a) in the MEG scanner. During this task, children were instructed to respond as fast as possible to ‘go’ stimuli by pressing a button, and to withhold a response to ‘no–go’ stimuli. The go and no–go trials were identified by a coloured frame around either an Angry or a Happy face. Participants were instructed to ignore the faces and only attend to the colour of the frame (e.g., go trials were identified by a blue frame and no–go stimuli by a purple frame). Children were thus incidentally exposed to two different emotional valences of faces which allowed us to investigate how emotional context (Happy vs. Angry) affects inhibition processing.

(A) Task Design: Two conditions were used, an inhibition (with 25% no–go trials) and vigilance (with 75% no–go trials) condition. This was done to ensure that the no–go trials from both conditions could be compared without a motor confound associated with go trials. Participants were required to respond (button press) to ‘go’ stimuli as fast as possible and to withhold a response (no button press) to ‘no–go’ stimuli (randomized target: either blue or purple frame). Happy or Angry faces were incidentally presented within the frames as emotional distractors. (B) Inhibition-related behavioural results revealed a main effect of Task (p = . 000001; Inhibition < Vigilance) and Emotion (p = 0.03; Angry < Happy) as well as a main interaction between emotional valence and task condition (p = 0.023). LSD Fisher post-hoc analyses showed that children had greater no–go accuracy in the presence of Happy faces compared to Angry faces within the inhibition condition (p < 0.002) but no differences between emotions were found in the vigilance condition (p > 0.88).

Fig. 1

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Inhibition performance and the associated brain activity were compared to a go/no–go vigilance (control) task. In the Inhibition (I) condition, the majority of stimuli were go trials (75%) so the prepotent tendency to respond was established, and thus it was difficult to inhibit to no–go trials (25%). In contrast, the Vigilance (V) condition included 75% no–go trials, with only 25% go trials, and can thus be seen as a classic vigilance task. The two MEG tasks were presented in randomized order across participants.

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The go or no–go stimuli were randomized to be either a blue or purple frame, within which emotional distracter faces were presented. There were 52 emotional faces (26 females: 26 males) that were selected from the NimStim Set of Facial Expressions (Tottenham et al., 2009). Only images that were correctly classified as Happy or Angry with ≥80% accuracy were used.

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All stimuli appeared on a projection screen located 80 cm from the children’s eyes; the visual angle of the stimuli subtended approximately 4° of visual field. Trials began with a stimulus duration of 700 ms, which was adjusted between 300 and 700 ms, followed by a fixation cross in the inter-stimulus interval (ISI), which varied between 650 and 1300 ms, based on response accuracy. The paradigm was designed to maintain a steady error rate (≥95% accuracy for go trials, ≥80% accuracy for no–go trials). Therefore, the stimulus duration and ISI were adjusted in real time based on global go and no–go accuracies (calculated from the start of the run) as well as recent accuracy rates (calculated from the last 5 trials of each stimulus type). ISI duration always contained a random jitter of ±200 ms from the adjusted value. For all the details of the titration of the timing in the task, please see Supplemental materials.

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- - - MEG data acquisition -

MEG data were recorded continuously (600 Hz sampling rate, 0–150 Hz band pass, third-order spatial gradient noise cancellation) using a 151 channel CTF system (Coquitlam, BC, Canada). Before testing, three localization coils were placed at the nasion and the left and right pre-auricular points to ascertain head position, and allow continuous head motion correction. MEG data were co-registered with anatomical magnetic resonance images (MRI) for each participant to estimate activation at each location in the brain.

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- - - MRI data acquisition -

Each child had a T1-weighted MRI (3D SAG MPRAGE: PAT, GRAPPA = 2, TR/TE/FA = 2300 ms/2.96 ms/90°, FOV = 28.8 × 19.2 cm, 240 × 256 matrix, 192 slices, slice thickness = 1.0 mm isotropic voxels) from a 3T MR scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-channel head coil.

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- - - Behavioural analyses -

At the behavioural level, accuracy scores (percentage of correct responses) [Acc] were calculated both for the go (Button press) and the no–go trials (no button press).

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To ensure adequate quality of behavioural results for the no–go trials prior to source analysis, children’s data were excluded if they did not perform above chance, meaning that the percentage of HITS (accuracy) was always higher (>10%) than the percentage of false alarms (FAs; the opposite of the intended action, e.g., a button press to no–go stimuli) across tasks (I and V) and the emotional context (Happy and Angry faces). Mean reaction times [RT], and RT coefficient of variation [CV] (calculated for each subject as the standard deviation of the mean RT divided by mean RT) associated with the go trials were also recorded. Performance on the go and the no–go trials were submitted to repeated measures ANOVA (performed using Statistica version 7.0; Statsoft Inc., Tulsa, OK, USA) with Task type (Inhibition vs. Vigilance) and Emotional context (Happy vs. Angry) as the within-subject factors.

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- - - MEG analyses -

With MEG we investigated how the incidental exposure to Happy or Angry faces impacted inhibition-related brain processes involved in the processing of no–go trials in children. To ensure the specificity of the inhibition task effect, functional brain activity associated with the processing of no–go trials in the Vigilance (control) condition (75% no–go) was also included in the factorial analysis (see below) and directly compared to the functional brain activity associated with the processing of no–go trials in the Inhibition condition (25% no–go). Worth noting, the vigilance condition (no–go 75%) may still involve some subtle inhibitory processes that may be generated by the absence of a response required to the visual stimuli. Hence, the contrast (Inhibition > Vigilance) allows the identification of brain regions that are specifically associated with processes generated by the inhibition of the prepotent response, while also excluding common brain activity shared between the Inhibition and Vigilance conditions (e.g., visual processing, etc.), as well as the motor confound which would be seen in the more conventional go vs. no–go comparison. Preprocessing and brain functional analyses described below were performed using SPM12 (Wellcome Trust Centre of Neuroimaging, London) implemented in MATLAB 2014b (The MathWorks, 2014).

- - - Preprocessing steps -

MEG data were band-pass filtered at 1–40 Hz and time-locked to each go/no–go trial onset using a photodiode. Baseline-corrected epochs associated with correct go/no–go trials were then extracted from −200 ms pre-stimulus to 500 ms post–stimulus. Data were then corrected for head motion, removing any epochs with motion greater than 5 mm. Ocular and muscle artefacts were detected and subtracted from the trials on a subject-by-subject basis using ICA (Independent Component Analysis) as implemented by FieldTrip (Oostenveld et al., 2011). ICA decomposition was performed simultaneously across all conditions and all subjects as recommended in the literature (Kovacevic and McIntosh, 2007). For each participant, 30 components were examined for artefacts and a minimum of two and a maximum of four components were removed per participant based on visual analysis of the component performed by an ICA expert. Epochs during which an MEG sensor signal exceeded the level of 2500fT/cm were also rejected.

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- - - Source reconstruction -

Functional images of whole-head activity were generated for Happy and Angry no–go trials in both task conditions (Inhibition and Vigilance) by applying vector beamformer weights on 50 ms sliding time windows, overlapping 25 ms each over the task epoch of interest (0–500 ms).

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Weights were derived using both a forward field (a model of the fields measured in response to a unit current with known location/orientation) and an estimated channel-level covariance matrix. Beamforming is a spatial filtering approach to MEG inverse source modeling that relies on a minimization of total brain power while constraining the gain in the voxel of interest, resulting in the suppression of background noise (Brookes et al., 2011). Head modeling was computed using a single shell head model (Nolte, 2003) fitted to the inner skull surface derived from each child’s MRI. Resulting individual contrast images (for Happy and Angry conditions in the Inhibition and Vigilance tasks) were spatially smoothed using a Gaussian kernel of 12 mm full-width at half maximum, then entered in a factorial design (Penny and Holmes, 2003).

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Emotion-dependent changes in inhibition-related brain processes were then tested as changes in event-related magnetic fields (ERFs) between Happy and Angry contexts using a factorial design model including two within-subject factors: Tasks (Inhibition vs. Vigilance) and Emotion (Happy vs. Angry). The resulting set of voxel values constituted a map of t-statistics [SPM(T)], reported significant at puncorr < 0.005. A family-wise error correction (pcorr < 0.05) was also applied to the results. The family-wise error was controlled using a Bonferroni correction (Wens et al., 2015) which adjusted the p-value for the number of spatial degrees of freedom involved in beamformer reconstructions. This technique is somewhat analogous to the random field theory approach considered in SPM (Kilner et al., 2005, Litvak et al., 2011), and adapted to MEG (Barnes et al., 2011), wherein the number of independent voxels is estimated from the smoothness of the images. The smoothness of source activity is essentially controlled by the forward model and the number of spatial degrees of freedom can be estimated as the rank of the lead field matrix (Wens et al., 2015). The correction corresponded effectively to a significance level of P < 0.002 (see Table 1 in bold).

Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

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- - - Results - - - Behavioural results -

Behavioural analysis performed on no–go trials showed a main effect of task [F(1, 24) = 185.68, p = 0.000001 (Inhibition < Vigilance)] and Emotion [F(1, 24) = 4.79, p = 0.03 (Angry < Happy)]. We also identified an interaction between emotional valence and task condition [F(1, 24) = 5.89, p = 0.023]. LSD Fisher post–hoc analyses showed that children had greater accuracy at withholding responses to no–go stimuli in the context of Happy faces compared to Angry faces during the inhibition task (25% No–Go; Angry < Happy, p < 0.002) whereas emotional context did not impact performance during the vigilance task (75% no–go, Happy = Angry, p > 0.88).

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- - - MEG results -

Analyses performed at the source space level on no–go trials showed a main interaction between our two factors of interest Task and Emotion (see Table 1 and Fig. 2) where Inhibition–related brain processes significantly differed when no–go trials occurred in the context of Happy or Angry faces whereas similar effects were not present in the Vigilance condition. Inhibition processes occurring in the context of Angry faces elicited stronger brain activations than similar inhibition processes in the context of Happy faces. Angry-related inhibition processes encompassed a distributed parieto–fronto–temporal network from 125 ms to 425 ms. This sequence of activation involved the right angular and occipital gyri (100–175 ms), then the right orbito-frontal gyrus (OFG, 225–325 ms) and, finally, the left occipital and ventral aspect of the anterior temporal lobe (ATL, 325–425 ms).

3D brain representations of the significant sources associated with the main interaction of Task and Emotion for no–go (Inhibition > Vigilance condition: Angry > Happy) from 125 to 425 ms. This sequence of activation involved progressively the right angular and occipital gyri (100–175 ms), the right orbito-frontal gyrus (OFG, 225–325 ms) and finally the left occipital and ventral part of the anterior temporal lobe (ATL, 325–425 ms).

Fig. 2

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These brain regions were more active in the inhibition condition in the Angry trials, whereas no activations were found to be stronger in the Happy condition regardless of task condition (see Fig. 3 for an example of these results in the right OFG).

Right orbitofrontal gyrus (OFG) activation during the time window 275–325 ms. (A) Grand-averaged amplitude of right frontal sensors for no–go trials associated with Inhibition Angry (in red) vs. Inhibition Happy (in blue). The dotted rectangle represents the time window of interest, 275–325 ms, where the Inhibition Angry trials had greater magnitude compared to the Inhibition Happy condition. (B) Analyses performed in source space on no-go trials revealed a significant main interaction between Task × Emotion (Inhibition: Angry > Happy) in the right anterior OFG. (C) A repeated measures ANOVA was performed on the mean activation (% signal change) for the right anterior OFG (x: 26, y: 20, z: −18), with Emotion (Happy/Angry) and Task (Vigilance/Inhibition) as the within-subject variables. Results revealed a main interaction of Task × Emotion (p = 0.023) with the Inhibition Angry condition driving the interaction.

Fig. 3

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- - - Discussion -

In this study, we used MEG to characterize the precise timing and localisation of brain activity involved in automatic emotional regulation (AER) processes in children. To eliminate the motor confound present in earlier studies which compared go with no–go trials, we compared brain activity associated with two no–go experimental conditions (Inhibition vs. Vigilance) both containing incidental exposures to a positive vs. negative socio-emotional context (Happy vs. Angry faces).

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Behavioural results showed that children had more difficulty inhibiting their responses in the context of Angry than Happy faces, suggesting greater attentional regulation in the presence of aversive socio-emotional cues (Lamm and Lewis, 2010), consistent with adolescent and adult studies (Albert et al., 2010, Cohen Kadosh et al., 2014).

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At the neurophysiological level, we hypothesised that the emotional context, particularly the presence of angry faces, would affect inhibitory brain processes and this would be expressed by greater activation in brain areas classically linked to inhibition.

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Whole brain analyses revealed a significant interaction between task conditions (no–go Inhibition vs. no–go Vigilance) and socio-emotional cues (Angry vs. Happy): specific brain regions were more active in the inhibition condition (Inhibition no–go trials) with the incidental exposure to Angry but not Happy faces.

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The early sensitivity of the angular gyrus and occipital cortex to emotion regulation mechanisms (Inhibition: Angry > Happy) was unanticipated. While previous ERP studies reported a crucial role of the P100, located over posterior occipito-parietal sites, for inhibition processes or face processing (Batty and Taylor, 2006), the sensitivity of this early component to emotion regulation was not systematically reported in other EEG studies (Hum et al., 2013a, Hum et al., 2013b). However, adult fMRI studies found stronger activity in the right angular gyrus when inhibition processes occurred with the incidental exposure to aversive compared to neutral pictures (Brown et al., 2012) but fMRI cannot provide information on the timing of activation. Both the P1 and the angular gyrus have been related to processes of visual attention and conflict monitoring during go/no–go tasks (Corbetta, 1998, Hillyard et al., 1998, Corbetta and Shulman, 2002, Singh-Curry and Husain, 2009, Seghier, 2013). Thus, our results suggest that the early activity in the right angular gyrus may reflect neural recruitment to meet the higher visual attention demands required to mediate impulse control due to the emotional context.

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The combination of high temporal and spatial resolution provided by our MEG study, also helped clarify the N2 generators, reported as a key component of inhibition and emotion regulation processes by several ERP studies (e.g., Lewis et al., 2007, Todd et al., 2008). In particular, our results showed that the incidental exposure to Angry compared to Happy faces was associated with stronger, longer-lasting brain inhibition-related activations in the right OFG from 225 to 325 ms. Worth noticing, activations reported in the orbito-frontal gyrus were the only ones surviving the correction for multiple comparisons threshold (p < 0.05 corrected), other activations discussed here were significant at p < 0.005 uncorrected (see Table 1 in bold).

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The OFG is known to be modulated by interactions between response inhibition (go/no–go task) and stimulus valence, both in children (Todd et al., 2012) and adults (Shafritz et al., 2006, Goldstein et al., 2007). Moreover, convergent research studies indicate that the orbitofrontal region evaluates and regulates how emotion influences control mechanisms which guide ongoing actions (Pessoa, 2009) and, thus plays an important role in flexible social behaviour [for reviews see, Schoenbaum et al., 2009, Nelson and Guyer, 2011]. For instance, it has been shown that the OFG is implicated in evaluating the degree of control required to modify or inhibit actions elicited by the facial expression (e.g., unpleasant or discouraging) in children (Blair et al., 1999, Todd et al., 2012). Our results suggest that OFG-related changes in amplitude associated with inhibition processes occurring from 225 to 325 ms after no-go (inhibitory) trials would help guide further behaviour in response to facial expressions (i.e., towards action vs. inaction). In addition, our results showed that automatic emotion regulation processes related to the OFG were right lateralized. This is consistent with adult studies showing that negative affect (see Davidson, 2004) or avoidance behaviour (Harmon-Jones, 2004) will trigger greater right-lateralized frontal responses. As well, earlier studies of facial affect in children which also showed right-lateralised OFG activity (Todd et al., 2008, Todd et al., 2012).

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Immediately after the recruitment of the right OFG, inhibitory-related brain processes to Angry faces activated the ventral part of the left anterior temporal pole (ATL) from 325 to 425 ms, although this did not survive correction for multiple comparisons. However, this region of the ATL is known to be involved in the encoding and retrieval of individual faces, and also in the rapid binding of faces with other pieces of information including affective tone (Eifuku et al., 2010, Olson et al., 2013). Prior studies have shown that the non-conscious perception of facial expressions can activate learned associations and/or modulate cognitive processes (Ohman, 2002, Tottenham et al., 2011). Therefore, it may be that the recruitment of the right OFG, which analyzes the control required for decision making regarding an aversive stimulus, may trigger the retrieval of learned associations stored in the ventral left ATL. This hypothesis is supported by a recent meta-analysis suggesting that socially and emotionally tagged knowledge stored in the ATL would guide orbitofrontal-based decision processes (Olson et al., 2013 and see Olson et al., 2007 for a review of temporal pole functions).

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In conclusion, this study is the first to characterize the precise spatiotemporal brain dynamics underlying automatic emotion regulation in children using MEG. Our results showed that inhibition processes which occurred with the incidental exposure to aversive socio-emotional stimuli (Angry compared to Happy faces) which produced brain activity which helped children regulate their responses in an emotional context. Our results suggest that 125 ms after the no–go stimulus, the angular gyrus may participate in the recruitment of extra visual attention needed to mediate impulse control, after which, the right OFG and the ventral section of the left ATL would work in concert, from 225 to 425 ms, to analyze the degree of control required to guide appropriate decision drawing upon learned associations related to the aversive situation. These findings align with previous studies which have shown that these regions, and in particular connectivity patterns including the OFG and the ATL, are critical in high-level social behaviours and should be further investigated in various psycho-affective or neurodevelopmental disorders known to have difficulties with emotion regulation. Finally, future investigations such as connectivity analyses involving causality measures, could clarify the functional contribution of the spatio-temporal dynamics observed between the fronto-temporo-parietal regions and the relation between behavioural processes and automatic emotion regulation in children.

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- - Conflict of Interest: -

None.

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The following is Supplementary data to this article:

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- - Acknowledgements -

The authors thank MEG and MRI technologists, Marc Lalancette, Ruth Weiss and Tammy Rayner, for all their support in data acquisition. Many thanks to Anne Keller for her work and help in the MEG analyses. This work was supported by Canadian Institutes of Health Research (grant number: MOP-119541) to MJT.

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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2017.05.004.

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diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv deleted file mode 100644 index 6c600c8..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000.csv +++ /dev/null @@ -1,17 +0,0 @@ -INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy,INHIBITION Angry > Happy -Unnamed: 0_level_1,Brain regions,Time window (ms),z-value,p-value,MNI coordinates,MNI coordinates,MNI coordinates -Unnamed: 0_level_2,Unnamed: 1_level_2,Unnamed: 2_level_2,Unnamed: 3_level_2,Unnamed: 4_level_2,x,y,z -R,Middle OG,100–175,2.82,0.004,26,−94,6 -R,Angular G,125–175,2.76,0.003,54,−56,20 -R,Posterior OFG,225–275,2.73,0.003,32,22,−14 -R,Anterior OFG,225–275,2.54,0.005,28,52,−4 -R,Posterior OFG,250–300,3.17,0.001,26,20,−16 -R,Anterior OFG,250–300,2.93,0.002,32,50,−8 -R,Posterior OFG,275–325,3.04,0.001,26,20,−18 -R,Anterior OFG,275–325,2.63,0.004,34,50,−10 -L,Anterior ITG,325–375,2.73,0.003,−46,−18,−32 -L,Occipital G,325–375,2.67,0.004,−8,−94,−18 -L,Anterior ITG,350–400,2.72,0.003,−44,−18,−32 -L,Occipital G,350–400,2.63,0.003,−8,−94,−18 -L,Anterior ITG,375–425,2.64,0.004,−44,−20,−34 -L,Hippocampus,375–425,2.64,0.005,−34,−4,−32 diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json deleted file mode 100644 index 6bfe18f..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/table_000_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "tbl0005", "table_label": "Table 1", "table_caption": "Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0\u2013500\u202fms post-stimulus onset).", "table_foot": "Note: Statistical significance was set for the significant voxels of activations at puncorr\u202f<\u202f0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr\u202f<\u202f0.05.", "n_header_rows": 3, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml b/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml deleted file mode 100644 index 7311d1f..0000000 --- a/tests/data/sample_inputs/39ua7DtHYLoW/source/pubget/tables/tables.xml +++ /dev/null @@ -1,2 +0,0 @@ - -6987902285279866987902S1878-9293(16)30233-X10.1016/j.dcn.2017.05.004tbl0005Table 1Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).

Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32

Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.

Table 1
Interaction effect between the emotion (angry vs. happy) and task condition (inhibition vs. valance) factors on whole brain processes associated with the processing of no-go stimuli (0–500 ms post-stimulus onset).
Table 1
INHIBITION Angry > Happy
Brain regionsTime window (ms)z-valuep-valueMNI coordinates
xyz
RMiddle OG100–1752.820.00426−946
RAngular G125–1752.760.00354−5620
RPosterior OFG225–2752.730.0033222−14
RAnterior OFG225–2752.540.0052852−4
RPosterior OFG250–3003.170.0012620−16
RAnterior OFG250–3002.930.0023250−8
RPosterior OFG275–3253.040.0012620−18
RAnterior OFG275–3252.630.0043450−10
LAnterior ITG325–3752.730.003−46−18−32
LOccipital G325–3752.670.004−8−94−18
LAnterior ITG350–4002.720.003−44−18−32
LOccipital G350–4002.630.003−8−94−18
LAnterior ITG375–4252.640.004−44−20−34
LHippocampus375–4252.640.005−34−4−32
Note: Statistical significance was set for the significant voxels of activations at puncorr < 0.005. Activations highlighted in bold are corrected for multiple comparisons at pcorr < 0.05.
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GWR, gray/white matter border ratio; LGI, local gyrification index.",3,19.0,-69.0,-7.0,,,0.063,"127,927", +8971,Table 3,"Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.",3,-23.0,53.0,10.0,,,1.969,"42,505", +8971,Table 3,"Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.",3,39.0,30.0,18.0,,,0.744,"94,896", +8971,Table 3,"Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.",3,6.0,47.0,-20.0,,,0.796,"84,773", +8971,Table 3,"Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.",3,-44.0,-62.0,44.0,,,0.131,"122,169", diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/metadata.json b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/metadata.json new file mode 100644 index 0000000..d9f3be0 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/metadata.json @@ -0,0 +1,11 @@ +{ + "title": "Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture.", + "authors": "Filbey, Francesca M;McQueeny, Tim;DeWitt, Samuel J;Mishra, Virendra", + "journal": "Developmental cognitive neuroscience", + "keywords": null, + "abstract": "As the most commonly used illicit substance during early adolescence, long-term or latent effects of early adolescent marijuana use across adolescent developmental processes remain to be determined. | We examined cortical thickness, gray/white matter border contrast (GWR) and local gyrification index (LGI) in 42 marijuana (MJ) users. Voxelwise regressions assessed early-onset (age <16) vs. late-onset (\u226516 years-old) differences and relationships to continued use while controlling for current age and alcohol use. | Although groups did not differ by onset status, groups diverged in their correlations between cannabis use and cortical architecture. Among early-onset users, continued years of MJ use and current MJ consumption were associated with thicker cortex, increased GWR and decreased LGI. Late-onset users exhibited the opposite pattern. This divergence was observed in all three morphological measures in the anterior dorsolateral frontal cortex (p<.05, FWE-corrected). | Divergent patterns between current MJ use and elements of cortical architecture were associated with early MJ use onset. Considering brain development in early adolescence, findings are consistent with disruptions in pruning. However, divergence with continued use for many years thereafter suggests altered trajectories of brain maturation during late adolescence and beyond.", + "publication_year": 2015, + "coordinate_space": "TAL", + "license": null, + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/text.txt b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/text.txt new file mode 100644 index 0000000..2052226 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/text.txt @@ -0,0 +1,724 @@ + + + + + + + + + + + + + + + + + + +Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture - PMC + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Back to Top + +Skip to main content + + + + + + +An official website of the United States government + +Here's how you know + + + + + + + + +The .gov means it’s official. + + Federal government websites often end in .gov or .mil. 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Inclusion in an NLM database does not imply endorsement of, or agreement with, + the contents by NLM or the National Institutes of Health. + Learn more: + PMC Disclaimer + | + + PMC Copyright Notice + + + + + + +Dev Cogn Neurosci. 2015 Dec; 16: 16-22. Published online 2015 Oct 9. doi: 10.1016/j.dcn.2015.10.001PMCID: PMC4691364NIHMSID: NIHMS733302PMID: 26507433Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architectureFrancesca M. Filbey,a,⁎ Tim McQueeny,a Samuel J. DeWitt,a and Virendra MishrabFrancesca M. FilbeyaCenter for BrainHealth, School of Behavioral and Brain Sciences, The University of Texas at Dallas, United StatesFind articles by Francesca M. FilbeyTim McQueenyaCenter for BrainHealth, School of Behavioral and Brain Sciences, The University of Texas at Dallas, United StatesFind articles by Tim McQueenySamuel J. DeWittaCenter for BrainHealth, School of Behavioral and Brain Sciences, The University of Texas at Dallas, United StatesFind articles by Samuel J. DeWittVirendra MishrabAdvance MRI, LLC, Frisco, TX, United StatesFind articles by Virendra MishraAuthor information Article notes Copyright and License information PMC DisclaimeraCenter for BrainHealth, School of Behavioral and Brain Sciences, The University of Texas at Dallas, United StatesbAdvance MRI, LLC, Frisco, TX, United StatesFrancesca M. Filbey: ude.salladtu@yebliF.acsecnarF ⁎Corresponding author at: Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, 2200 West Mockingbird, Dallas, TX 75235, United States. ude.salladtu@yebliF.acsecnarFReceived 2014 Dec 12; Revised 2015 Sep 9; Accepted 2015 Oct 2.Copyright © 2015 The AuthorsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Associated DataSupplementary Materialsmmc1.pptx (2.1M)GUID: E6C77A81-D133-4EBD-83BB-A2005D84A0C0Highlights•Early onset MJ use was associated with different patterns of cortical architecture.•Early vs. late onset divergence was in brain regions underlying higher-order cognition.•Findings were above and beyond effects of alcohol and current age.Keywords: Adolescence, Marijuana, Cortical thickness, Gyrification, Morphology, FreeSurferAbstractBackgroundAs the most commonly used illicit substance during early adolescence, long-term or latent effects of early adolescent marijuana use across adolescent developmental processes remain to be determined.MethodsWe examined cortical thickness, gray/white matter border contrast (GWR) and local gyrification index (LGI) in 42 marijuana (MJ) users. Voxelwise regressions assessed early-onset (age <16) vs. late-onset (≥16 years-old) differences and relationships to continued use while controlling for current age and alcohol use.ResultsAlthough groups did not differ by onset status, groups diverged in their correlations between cannabis use and cortical architecture. Among early-onset users, continued years of MJ use and current MJ consumption were associated with thicker cortex, increased GWR and decreased LGI. Late-onset users exhibited the opposite pattern. This divergence was observed in all three morphological measures in the anterior dorsolateral frontal cortex (p < .05, FWE-corrected).ConclusionsDivergent patterns between current MJ use and elements of cortical architecture were associated with early MJ use onset. Considering brain development in early adolescence, findings are consistent with disruptions in pruning. However, divergence with continued use for many years thereafter suggests altered trajectories of brain maturation during late adolescence and beyond.Keywords: Adolescence, Marijuana, Cortical thickness, Gyrification, Morphology, FreeSurfer1. IntroductionWith more than 25% of high school seniors reporting recent use and 6.5% of 12th graders being daily users (Johnston et al., 2014), marijuana (MJ) is the most frequently used illicit substance among adolescents. Across all age groups over 70% of new drug initiates start with using MJ at an average age of 18 years (SAMHSA, 2014). Indeed, the scope of MJ use prevalence is of great public interest, as MJ use in early adolescence is associated with increased risk of greater substance use, legal problems, disrupting education, injuries/medical problems, developing psychopathology, cognitive changes and chronic psychosocial struggles (CASA, 2011, Fergusson and Horwood, 1997, Fergusson et al., 1996, Patton et al., 2002). Taken together, rates of MJ use are suggestive of an epidemic based in adolescence, which is concerning not just due to societal cost, but also due to the potential to offset sensitive brain development during this period.Despite its prevalence, the impact of MJ use on adolescent brain development is not fully known. Important neuromaturational processes during adolescence through young adulthood are believed to bring about improved higher-order cognition by refining neural systems locally and globally through white and gray matter development (Casey et al., 2005, Giedd, 2008, Paus, 2005). In general, gray matter reductions and cortical thinning coincide with increased white matter volume and organization through adolescence and young adulthood, suggestive of synaptic pruning and axonal myelination (Giorgio et al., 2010, Gogtay et al., 2004, Hasan et al., 2007, Lebel et al., 2010, Shaw et al., 2008). The endogenous cannabinoid (CB) system is also immature during adolescence (Anavi-Goffer and Mulder, 2009, Verdurand et al., 2011). In an animal model (Verdurand et al., 2011) imaged CB1 receptor binding using PET and found relatively lower activation of CB1 receptors in adolescent rats compared to adult rats in brain areas including those in the frontal cortex, temporal lobe (hippocampus and amygdala) and sub-cortical regions including striatal regions, thalamus, hypothalamus, superior colliculus. Thus, adolescence represents a developmental period with vulnerability to structural and functional changes due to exogenous MJ exposure.Adolescent MJ use has the potential to cause structural and functional changes in the brain by altering cannabinoid signaling. One possible mechanism would be blunt neurotoxic influence. For example, delta9-tetrahydrocannabinol (THC), the primary psychoactive component in MJ that binds CB1 receptors, is reported to cause cell shrinkage and damage DNA strands in THC-treated neuron cultures (Chan et al., 1998). This may be the mechanism by which smaller volumes have been observed in individuals exposed to cannabis during adolescence (Battistella et al., 2014). However, it is more likely that MJ exerts its influence on brain development indirectly. The cannabinoid system plays a role in modulating other neurotransmitters, including gamma-aminobutyric acid (GABA), glutamate and monoamines (Lopez-Moreno et al., 2008). Specifically, activation of CB1 receptors is associated with down-regulating inhibitory GABAergic transmission in cortical interneurons during adolescence (Caballero and Tseng, 2012, Cass et al., 2014). In addition, CB signaling inhibits microglia function (Walter et al., 2003). These two points are important because cortical pruning processes involve glial-mediated synaptic elimination and altering the excitatory/inhibitory balance is liable to disrupt the selective tagging and preserving synapses (Selemon, 2013). The impact of this indirect influence on the developing brain may be in the observations of abnormal connectivity in those who began MJ use in adolescence (Jacobus et al., 2009). Evidence from human neuroimaging studies lends greater support to MJ-related disruptions to brain development.Structural neuroimaging studies have indicated that volumes of several brain areas are smaller in heavy adult MJ users especially in areas enriched with cannabinoid 1 (CB1) receptors, such as medial temporal lobe, and prefrontal cortex (Lorenzetti et al., 2010). Studies of adult chronic MJ users note brain volume reductions in temporal lobe, insula, and prefrontal cortex, amygdala and hippocampus (Battistella et al., 2014, Cousijn et al., 2012, Filbey et al., 2014, Matochik et al., 2005, Yucel et al., 2008). Among different characteristics of MJ involvement (e.g., dependence symptoms, use frequency, consumption), the age of initial MJ use is a robust factor that has been associated with smaller brain volumes in users. For example, Battistella et al. (2014) observed left parahippocampal gyrus and right temporal pole structural differences in 25 regular MJ users compared to 22 occasional users, however, even the occasional users who began smoking MJ during adolescence (before age 18) demonstrated similar brain changes as the regular users. Our group has also found links with early MJ use onset (Bava et al., 2009) and structural connectivity with orbitofrontal cortex in a cohort of daily MJ users, suggesting complex neuroadaptive processes related to MJ use in the context of adolescent brain development (Filbey et al., 2014). These findings underscore the potential for significant heterogeneity in brain changes among adult MJ users, especially those who began using MJ during neurodevelopment.Studies comparing early adolescent MJ use to users initiating MJ use in later adolescence provide further evidence for the potential of MJ to cause enduring change. The few studies that have directly investigated the timing of the effects of MJ during adolescence have noted divergent neurodevelopment effects. For example, in an fMRI study by Gruber and colleagues, functional and behavioral differences during an interference task were reported between early (before age 16) and late (after age 16) MJ users (Gruber et al., 2012) (Sagar et al., 2015). The same group also reported decreased white matter integrity in early onset vs. late onset MJ users (mean age 14.46 vs. 17.93) (Gruber et al., 2014). Similar differential effects have also been noted in parietal lobe activation between early and late adolescent binge drinkers during a spatial working memory task (Tapert et al., 2004). These studies highlight the importance of clarifying the differential neural effects of early- and late-adolescent onset use.To that end, in the current study, we compared daily MJ users who were early onset users (<16 years old) versus late onset users (≥16 years old) on measures of cortical morphology that are sensitive to developmental changes. We aimed to characterize both the effect of early onset status on cortical morphology as well as assess for morphological patterns linked to the continued use of MJ after early and late adolescent MJ initiation. We expected early onset users to show a morphological pattern consistent with disruption of early adolescent brain development (e.g., increased cortical thickness, greater gray/white definition of the cortical ribbon via disruptions to adolescent pruning processes) that may be more consistent with indirect impact of MJ of brain development. While gray matter decline has been shown to be associated with marijuana use, particularly in areas rich in CB1 receptors, increased cortical thickness and greater gray/white definition in the cortical ribbon point to potential disruption in neurodevelopment (i.e. synaptic pruning) that may result from MJ use at key developmental stages (i.e. earlier as opposed to later in adolescent neuronal development). Such disruptions may extend to gyrification as well. While this process begins in utero, there is evidence that gyrification is ongoing into adolescence (Armstrong et al., 1995, Alemán-Gómez et al., 2013, Klein et al., 2014) and may also display aberrant developmental patterns in the presence of MJ use.2. MethodsThis study was approved by the University of Texas at Dallas (UTD) and University of Texas Southwestern Medical Center (UTSW) Institutional Review Boards. All participants were recruited from the Dallas-Ft.Worth metro area via flyers and advertisements. Following informed consent, MJ users completed two sessions – a baseline appointment for collecting demographic, psychosocial and behavioral measures and a thorough substance use history. Three days later the participants returned for a neuroimaging appointment. Prior to their scanning session, participants were asked to be abstinent from MJ use for 72 h, from alcohol for 24 h, and from caffeine and cigarettes for the preceding 2 h. These were confirmed by self-report (MJ, alcohol, caffeine and cigarettes), quantitative THC urinalysis (MJ), and by breath alcohol level of .000 (alcohol) at the start of their session.2.1. ParticipantsWe scanned 45 regular heavy MJ users as part of the parent project. Inclusion criteria were: right-handedness, English as the primary language and no histories of psychosis, traumatic brain injury, and MRI contraindications (e.g., pregnancy, non-removal metallic implants, claustrophobia). One subject reported a history of anxiety and depression and one other reported a history of ADHD as a child. Additional exclusions for the current study included: Axis I diagnosis (via SCID) other than cannabis use disorder, unusable sMRI due to motion artifact or poor signal-to-noise ratio that precluded accurate tissue segmentation (n = 1) and incomplete drug use histories (n = 2). Of the 42 remaining cases, 22 were early onset users (onset of first use before age 16). Group categorization using onset of regular use as opposed to onset of first use maintained the same grouping (mean early onset of regular use = 16.5, mean late onset of regular use = 19.0). Regular use was defined as at least one time per week. To determine how age of onset of regular MJ use influenced our reported effects, we performed these analyses while covarying for age of onset of regular use (see Supplement). Table 1 summarizes demographic and substance use information according to onset status. Table 2 summarizes the correlation between age and identified marijuana use variables. Only MJ years of use and current age showed a statistically significant correlation. Participants were recruited based on self-reported daily MJ use and a positive urinalysis for THC metabolites at their baseline visit. All of the participants were screened via urinalysis for other drugs of abuse and were excluded if drugs (other than MJ) were detected. Participants were required to have used MJ for a minimum of 5000 lifetime occasions and self-report daily use (without >24 h abstinence) for the last 60 days.Table 1Sample characteristics. MJ, marijuana.MeasureEarly onset (n = 20)Late onset (n = 22)p-ValueEffect size***StatisticMean(SD)Min–MaxMean(SD)Min–Max|t/UAge32.50(8.01)21–5030.25(7.19)21–470.3160.302t = 1.01Education (years)12.91(2.54)8–1813.26(2.40)10–190.6510.144t = 0.456Gender (male)55%73%0.2410.034χ2 = 1.41Ethnicity (% Caucasian)50%50%0.5660.008χ2 = 0.336IQ*108(9.99)88–124105(13.54)83–1290.3510.298t = 0.94Age of first MJ use**13.18(1.89)9–1516.90(1.48)16–21<0.0010.866U = 0Age of regular MJ use**16.50(3.57)9–2519.00(4.29)16–360.0040.439U = 108Substance use in the last 60 days MJ grams (daily)2.14(1.79)0.50–7.501.65(1.21)0.46–4.230.3380.083U = 182 # EtOH drinks44.09(76.30)0–31038.85(58.61)0–1830.5880.039U = 198.05 Max # EtOH drinks6.62(7.21)0–316.25(5.80)0–210.80.095U = 210 # EtOH drinking days11.09(16.69)0–599.84(13.51)0–600.5370.006U = 185.5 # Binge EtOH drinking days4.36(12.50)0–592.90(5.53)0–190.9680.035U = 218.5 # EtOH drinks per day2.99(2.27)0–7.403.56(3.29)0–14.000.820.289U = 211 # Cigarette days1.18(3.72)0–172.95(1.17)0–210.060.294U = 159 # Cigarettes per day0.22(0.55)0–2.000.78(1.23)0–4.500.0570.296U = 158 Max # cigarettes0.25(0.61)0–20.96(1.60)0–60.0540.290U = 157.5Illicit drug use/past 90 days14%5%Lifetime illicit drug use73%75%Open in a separate window*IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.**p < .05; SS, standard score; |t|, absolute value of student's t, U is the Mann–Whitney U's score.***The effect sizes of the above table were calculated either based on mean differences if normally distributed, correlation coefficient or F-value score using the default Cohen's effect size formula for respective metrics.Table 2The correlations between current age and all MJ use variables.MeasureEarly onsetLate onsetFirst MJ user = 0.038r = 0.189Regular MJ user = 0.289r = 0.203MJ years of user = 0.898*r = 0.623**MJ gramsr = 0.123r = 0.206Open in a separate window*p < 0.001.**p < 0.005.2.2. MRI acquisition and analysis2.2.1. Image acquisition Scanning sessions took place at the Advanced Imaging Research Center at the University of Texas, Southwestern Medical Center three days following their initial visit. Another verification of THC metabolites via urinalysis was also performed before the scan. MRI images were collected using a 3T Philips whole-body scanner equipped with Quasar gradient subsystem (40 mT/m amplitude, a slew rate of 220 mT/m/ms). High-resolution T1-weighted anatomical scans were collected using a MPRAGE sequence: TR/TE/TI = 2100/3.70/1100 ms; flip angle = 12°; field of view = 256 mm × 256 mm; slab thickness = 160 mm (along left-right direction); voxel size = 1 mm × 1 mm × 1 mm, Total scan time = 3 m 57 s.2.2.2. Image processing MPRAGE anatomical scans were pre-processed for surface-based analyses using FreeSurfer v5.3 semi-automated pipeline (http://surfer.nmr.mgh.harvard.edu). This semi-automated pipeline included spatial (Talairach) and signal intensity normalization of images, volumetric segmentation and subcortical labeling (Dale et al., 1999, Fischl et al., 2002). Outer gray matter and white matter boundaries were then identified and reconstructed into a mesh of over 150,000 tessellated vertices to allow point-to-point surface measures (Fischl et al., 1999). Next, gyral anatomy is aligned to a standard spherical template using surface convexity and curvature measures. Resulting surfaces were inspected, blind to MJ onset status, to identify and correct any errors made during cortical reconstruction. Modifications to the volumes were made as necessary to correct for tissue misclassifications according to FreeSurfer's wiki manual (Schmansky et al., 2010). In preparation for analysis, each morphological measure for each case was co-registered to a standard template (fsaverage). Anatomical labels in FreeSurfer (Desikan et al., 2006) were used for interpretation of results.2.3. Morphological measures2.3.1. Cortical thickness The width of the cortical ribbon was measured as the distance between corresponding vertices of the white matter and gray matter surfaces at each vertex in the cortical mantel (Fischl and Dale, 2000).2.3.2. Gray–white matter ratio (GWR) To assess the quality of cortical ribbon definition, a tissue contrast between gray and white matter signal intensities was computed as a percent ratio (W − G)/(.5*(W + G)) (from pctsurfcon v1.11.2.1, inbuilt component of FreeSurfer pipeline v5.3, 2011). White matter signal intensities were measured at an absolute length of 1 mm below the gray–white border surface and gray matter signal was measured 30% into the cortical ribbon (Salat et al., 2009).2.3.3. Local gyrification index The cortical surface from FreeSurfer's main pipeline is further processed to create an outer surface that encapsulates the gyral and sulcal curvature for each hemisphere, which serves as a basis for calculating a local gyrification index (Schaer et al., 2012). LGI is measured as the amount of cortex within the sulcal folds beneath the outer surface compared to the amount of visible cortex that touches the outer surface. Cortical maps are generated from repeated iterations of delineating a 25 mm radius sphere on the outer surface and its corresponding point on the cortical surface using a matching algorithm.2.4. Background and premorbid characteristics2.4.1. Sample characteristics Age, gender, education level, ethnicity, along with other background information, was obtained using a standard demographics questionnaire. The two-subtest administration of the Wechsler Abbreviated Scale of Intelligence (Vocabulary and Matrix Reasoning) provided estimates of intellect (Wechsler, 1999).2.4.2. Substance use The Substance Use Disorder modules of the Structured Clinical Interview for DSM-IV (SCID) (First et al., 2002) were administered by a trained research assistant to assess for lifetime and current symptoms of abuse and dependence for alcohol, nicotine, MJ and other substances. The SCID interview also provided the onset of use information. A Time Line Follow-Back (TLFB) approach was used to quantify alcohol, nicotine, and MJ use patterns for 90 days prior to study participation (Sobell and Sobell, 1992). Marijuana use in grams was obtained via self-report in response to probes aimed at quantifying their regular use.2.5. Statistical analysesStatistical analyses were conducted in SPSS 18.0 for behavioral and psychosocial measures whereas general linear model group comparisons on surfaced-based morphology measures were carried out FreeSurfer's built-in application QDEC (v1.5). Independent samples t-tests, Mann–Whitney U-tests or chi-square tests, compared groups on background and demographic variables (see Table 1). Before statistical analysis was conducted, the dependent measures of cortical thickness, GWR and LGI were smoothed using a FWHM Gaussian filter with a width of either 10 or 15 mm. Separate univariate general linear model (GLM) was then used to model cortical thickness, GWR and LGI with onset status of MJ use as a between groups factor. The dependent variables were thickness, gray–white ratio or local gyrification index and the independent variables were either recent monthly MJ use in grams (MJ grams) or duration of MJ use (MJ years). Age and total drinks in the past 2 months were treated as nuisance covariates in the model. Using MJ years of use and MJ grams as independent predictors of interest allowed us to characterize and differentiate the latent developmental effects from cumulative and current effects of MJ use. The variable “marijuana years of use” was based on the participants’ response to the question “For how many years have you been using marijuana regularly?” Of note, an outlier in the early onset group was removed before the statistical comparisons were performed.3. Results3.1. Cortical thicknessThere were no regions of group differences in cortical thickness by early onset status alone, controlled for age and alcohol use. However, MJ use characteristics were correlated with anterior dorsolateral prefrontal cortex thickness based on onset status. Early onset users showed increased thickness with increased MJ grams while late onset users showed thinner cortex with increased MJ grams (p < 0.05 uncorrected) (Table 3). The same pattern emerged with more years of MJ use being associated with thicker region of the right medial temporal lobe in the early onset users and the reverse for the late onset users (p < 0.05 uncorrected) (Fig. 1).Table 3Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.MeasureLabel@Max, Extended coverageSideMax-log(p)VtxMaxSize (mm2)xyzCorrelateP (corr)F-valueEffect Size**ThicknessLingualR−2.488127,927111019−69−7MJ Years0.01610.070.063GWRRostral middle frontal,L−2.66842,5051730−235310MJ Grams0.00111.091.969Rostral middle frontalR−3.56594,8962661393018MJ Years0.000216.60.744Medial orbitofrontalR−3.30484,7731368647−20MJ Years0.01314.920.796LGIInferior parietalL3.456122,1692565−44−6244MJ Grams0.01515.890.131Open in a separate windowp(corr), family-wise error fully corrected.**The effect sizes were derived from Freesurfer's tool in the significant region of interest using mri_segstats. This was also confirmed manually by using the F-value reported by Freesurfer.Open in a separate windowFig. 1Early vs. late onset marijuana users show divergent morphological patterns based on current marijuana use (measured in grams; MJ grams) in overlapping areas of anterior prefrontal cortex. GWR, gray/white matter border ratio; LGI, local gyrification index.3.2. Gray–white matter contrastThere were no regions of group differences in gray–white matter contrast by early onset status alone, controlled for age and alcohol use. However, current MJ consumption (grams) and onset status were differentially correlated with gray–white matter contrast in a left anterior dorsal frontal region (p < 0.05, FWE corrected). Increased gray–white contrast with heavier MJ use was seen in the early onset users and the opposite was seen in later onset users (heavier current use linked to decreasing GWR). The same pattern was seen between duration of MJ use in two prefrontal cortex clusters of the right dorsal frontal and medial orbitofrontal area p < 0.05, FWE corrected – more years of MJ use were linked to greater GWR among early users (Fig. 1).3.3. GyrificationMJ use onset status alone showed no significant main effects above age and alcohol covariates. However, onset status was correlated with divergent patterns between local gyrification and MJ use, whereby early onset users showed decreasing LGI with increasing MJ consumption and longer duration of use in prefrontal cortex regions p < 0.05, FWE corrected. The left hemisphere clusters encompassed the majority of the length of the middle lateral surface of the left cortex, including motor cortices, parietal lobe and multimodal integration areas (Fig. 1).4. DiscussionThe present study was designed to characterize the cortical architecture in adolescent onset MJ users by comparing early adolescent onset users to late adolescent onset in MJ use on measures of cortical thickness, gray/white matter contrast and gyrification. The primary finding was that early versus late onset MJ users showed a divergent pattern in cortical thickness, definition of the cortical ribbon and local gyrification with continued use through and beyond adolescent years. Specifically, early onset users showed cortical thickening, enhanced gray/white matter contrast, and decreased gyrification in association with more years of MJ use and current consumption of MJ in grams in frontal and temporal regions – areas that underlie higher order cognition including executive functioning, learning and memory. Findings were above and beyond effects of alcohol and current age, therefore, results are less likely to reflect morphological trends due to aging.Our findings did not find the expected age of onset differences previously reported in marijuana users (Gruber et al., 2012, Gruber et al., 2014). This inconsistency suggests that the age of onset effects may be more robust in brain white matter connectivity (Gruber et al., 2014) and function (Gruber et al., 2012) than brain surface morphometry. To date, the few studies that have described altered cortical morphology in MJ users have led to mixed findings. Mata et al. (2010) identified brain regions with decreased sulcal depth suggestive of lower gyrification in a study of adult MJ users. Jacobus and Tapert (2014) recently reported increased cortical thickness in the entorhinal cortex among 24 adolescent MJ users (mean age = 17.7, mean MJ onset age = 15.4) relative to peer controls. However, the authors also reported a negative relationship between cortical thickness and total MJ use in the right paracentral gyrus, and they observed consistent positive relationships in various brain regions between age of MJ onset and thickness. In the only other known adolescent study of cortical thickness and MJ, Lopez-Larson and colleagues studied 18 adolescent heavy MJ users (similar in age and MJ onset as Jacobus and Tapert, 2014) and reported mixed findings of increased thickness in prefrontal/insula regions and decreased thickness in posterior/temporal lobe areas in the MJ users compared to controls. In contrast to Jacobus and Tapert, 2014, Lopez-Larson et al., 2011 found areas of the frontal lobe and insula that were thinner with increased urine THC metabolites and thicker with earlier age of onset. Select findings from the current study align with aspects of both of these studies, with a consensus supporting findings of a negative dose-dependent relationship between MJ use and cortical thickness. Given the low availability of studies to compare, this consensus is very limited. Although Jacobus et al. and Lopez-Larson et al. found the opposite effect of age of onset on thickness, the pattern of divergence among early vs. late onset users in the current study is more consistent with the latter study, whereby we saw early onset users exhibit thicker cortex with continued MJ use. Taken together, findings of increased thickness related to early MJ onset accompanied by negative dose-dependent relationships with MJ exposure may reflect two distinct processes. One process may be specific to the interactions with cortical development during early adolescence, likely leading to a disruption in pruning, and, the other, specific to the pharmacological effect with heavy chronic MJ use.In the only known study to examine the curvature-morphology of the cortex in adult MJ users, Mata et al. (2010) identified decreased sulcal concavity and thinner sulci in 23 MJ users compared to controls (n = 44), also in prefrontal areas. However, they did not observe significant relationships with age, MJ onset age, or cumulative MJ use. It is interesting that the authors detected group level differences (MJ vs. controls) but no correlations with MJ use characteristics such as dose or age of onset, whereas our primary findings are the consistent effects of continued MJ use differing after early or late adolescent onset. There are substantial methodological explanations for this disparity. For example, the current study did not compare morphology in MJ users to a normative control sample, therefore, it is feasible that group-level differences may emerge with such a comparison. Likewise, we deliberately covaried for current age in order to control for brain changes with aging and thus optimize our interrogation of developmental effects of early onset age and of aspects of continued use.The heterogeneity of MJ effects clearly suggests a multifactorial system of neurobiological processes involved. The primary results uphold that age of onset is a robust variable that differentiates heavy MJ users based on early versus late MJ onset. However, this group distinction relied on current use characteristics. Therefore, in the absence of group-level differences, the interactions between onset age and current use indicates that continued cannabis exposure and early adolescent developmental factors both contribute to a dynamic and sustained departure from what is expected based on developmental studies.Typical synaptic refinement processes during early adolescence are in the context of long-term depression and potentiation of cortical neurons in order to facilitate neuronal remodeling. Thus, the normal course of early adolescent development is uniquely vulnerable to disruption by MJ due to the electrochemical conditions and maturity of brain processes that would not present together again. Cass and colleagues tested the sensitivity of early adolescence cannabinoid exposure in an animal model (Cass et al., 2014). They found that acute administration of cannabinoid agonists in early, middle and late adolescent rats led to a state of frequency-dependent disinhibition of neurons in the frontal cortex in the early-to-middle adolescent rats, but not in the late adolescent rats. Moreover, the authors also noted that adult rats previously exposed to cannabinoid agonists in adolescence displayed comparable neuronal disinhibition. Thus, by changing the inhibitory/excitatory landscape during adolescence, MJ can influence lasting changes to typical cortical remodeling during sensitive early adolescent years.The sequence of pruning and myelination likely plays a formative role in lasting changes from early adolescent onset MJ use. With decreased synaptic elimination, our findings of greater GW border contrast may reflect greater proliferation of myelin at the boundary of the cortical ribbon where non-pruned synapses remained with linked axons. Findings of altered white matter tissue qualities are mixed in adolescent and adult MJ user samples. Some report both increases and decreases in fractional anisotropy (FA) and average water diffusion (Bava et al., 2009) whereas others report consistent decreases in FA among adolescent MJ users (Ashtari et al., 2009, Jacobus et al., 2009) or null findings (Delisi et al., 2006). Two studies of diffusion tensor imaging in adult MJ users reported reduced FA in users compared to controls (Gruber et al., 2011, Gruber et al., 2014). In addition to equivocal findings, research is needed to address the microstructural changes that could result in altered definition of the cortical ribbon. For example, rather than whole brain techniques that assess diffusion measures along major white matter tracts, indices assessing axonal organization along radial and interneuron association fibers along the cortical ribbon are needed. This scenario played out could result in increased gray matter (thicker cortex from disrupted pruning) and the myelination of connections to these spared terminals would result in increased density of white matter at the cortical boundary. Without any known studies of adolescent development of the gray/white tissue contrast at the cortical border to serve as a point of comparison, we speculate that early adolescent disruption of pruning and subsequent myelination of connections at the cortical boundary would be reflected by increased GWR as we saw in the current study.5. Limitations and conclusionsThe cross-sectional nature of this study limits causal attributions in terms of what we can infer to be directly related to the effects of MJ. Although a longitudinal design is optimal for addressing brain changes directly due to MJ, cross-sectional studies facilitate data-driven hypotheses that can be assessed directly in prospective studies.It is important to keep in mind that the participants were not explicitly asked for possible years of abstinence during their period of regular use, which may have created possible inflation in reported duration of regular use. However, because the participants provided number of years of “regular” marijuana use, this inherently suggests continued, uninterrupted years of use. Concurrent nicotine use could have also influenced our reported results. But in the absence of a larger sample size and the presence of huge variance in nicotine use in the current sample, we were unable to verify the effect of nicotine use in the reported results.Interpretation of these findings is also limited by the lack of behavioral anchors for the observed morphological effects and lack of information on other aspects of developmental history that could further characterize the effects of marijuana during neurodevelopment. This is further limited by the absence of “expected” patterns based on normative data. Given the varied directions of effects and the small sample size, these findings should be replicated and be viewed as preliminary.To conclude, early MJ use was linked to altered neurodevelopmental patterns in brain regions sub-serving higher-order cognitive process. Clinical implications include need for early, targeted intervention. Given that the most robust results were related to interactions between onset age and continued use through emerging adulthood, harm reduction approaches may be effective in moderating adolescent MJ use to levels that are less likely to cause long-term developmental changes.Conflict of interestThe authors report no conflicts of interest.AcknowledgementsThis research was funded by the National Institute on Drug Abuse (R01 DA030344, Filbey). We would like to thank all the participants who volunteered for this study. We are also very grateful to Talha Alvi, Sina Aslan, Jessica Baine, Collette Bice, Vicki Germer, Ariel Ketcherside, Alison King, Brittany Kuhn, Tyler Rhinehardt, Wing Ting To and the team of lab interns for their assistance with recruitment, running participants and data management.FootnotesAppendix ASupplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2015.10.001.Appendix A. Supplementary dataThe following are Supplementary data to this article:Click here to view.(2.1M, pptx)ReferencesAlemán-Gómez Y. The human cerebral cortex flattens during adolescence. J. Neurosci. 2013;33(38):15004-15010. [PMC free article] [PubMed] [Google Scholar]Anavi-Goffer S., Mulder J. The polarised life of the endocannabinoid system in CNS development. Chembiochem. 2009;10(10):1591-1598. [PubMed] [Google Scholar]Armstrong E. The ontogeny of human gyrification. Cereb. Cortex. 1995;5(1):56-63. 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[PubMed] [Google Scholar]Articles from Developmental Cognitive Neuroscience are provided here courtesy of Elsevier + + + + + +Other Formats + +PDF (753K) + + + +Actions + + + +Cite + + + + + + +Collections + + + +Add to Collections + + + + + + + +Create a new collection + + + +Add to an existing collection + + + + + + + Name your collection: + + + + Name must be less than characters + + + + + Choose a collection: + + + + + Unable to load your collection due to an error +Please try again + + + + + + Add + + + Cancel + + + + + + + + + + + +Share + +  +  + + + + + + + + + + Permalink + + + +Copy + + + + + + + + +RESOURCES + + + + + Similar articles + + + + + + + + + Cited by other articles + + + + + + + + + Links to NCBI Databases + + + + + + + + + + + +[x] +Cite + + + + + Copy + + +Download .nbib +.nbib + + +Format: + + + AMA + + + APA + + + MLA + + + NLM + + + + + + + + + + +Follow NCBI + + + + + + +Twitter + + + + +Facebook + + + + +LinkedIn + + + + + + + +GitHub + + + + + + + + + + + + + + + + + + + + + + + + + +Connect with NLM + + + +SM-Twitter + + + + + + + + + + + + +SM-Facebook + + + + + + + + + +SM-Youtube + + + + + + + + + +National Library of Medicine +8600 Rockville Pike + Bethesda, MD 20894 + + +Web Policies +FOIA +HHS Vulnerability Disclosure + + +Help +Accessibility +Careers + + + + + + + +NLM + + +NIH + + +HHS + + +USA.gov + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/coordinates.csv b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/coordinates.csv new file mode 100644 index 0000000..5da262b --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/coordinates.csv @@ -0,0 +1,6 @@ +table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups +tbl0015,Table 3,,,19.0,-69.0,-7.0,,,,, +tbl0015,Table 3,,,-23.0,53.0,10.0,,,,, +tbl0015,Table 3,,,39.0,30.0,18.0,,,,, +tbl0015,Table 3,,,6.0,47.0,-20.0,,,,, +tbl0015,Table 3,,,-44.0,-62.0,44.0,,,,, diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/metadata.json b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/metadata.json new file mode 100644 index 0000000..7675db6 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/metadata.json @@ -0,0 +1,11 @@ +{ + "title": "Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture", + "authors": "Filbey, Francesca M.; McQueeny, Tim; DeWitt, Samuel J.; Mishra, Virendra", + "journal": "Dev Cogn Neurosci", + "keywords": "Adolescence\nMarijuana\nCortical thickness\nGyrification\nMorphology\nFreeSurfer\n", + "abstract": " Highlights \n \nEarly onset MJ use was associated with different patterns of cortical architecture. \n \nEarly vs. late onset divergence was in brain regions underlying higher-order cognition. \n \nFindings were above and beyond effects of alcohol and current age. \n \n \n## Background \n \nAs the most commonly used illicit substance during early adolescence, long-term or latent effects of early adolescent marijuana use across adolescent developmental processes remain to be determined. \n\n\n## Methods \n \nWe examined cortical thickness, gray/white matter border contrast (GWR) and local gyrification index (LGI) in 42 marijuana (MJ) users. Voxelwise regressions assessed early-onset (age <16) vs. late-onset (\u226516 years-old) differences and relationships to continued use while controlling for current age and alcohol use. \n\n\n## Results \n \nAlthough groups did not differ by onset status, groups diverged in their correlations between cannabis use and cortical architecture. Among early-onset users, continued years of MJ use and current MJ consumption were associated with thicker cortex, increased GWR and decreased LGI. Late-onset users exhibited the opposite pattern. This divergence was observed in all three morphological measures in the anterior dorsolateral frontal cortex ( p \u00a0<\u00a0.05, FWE-corrected). \n\n\n## Conclusions \n \nDivergent patterns between current MJ use and elements of cortical architecture were associated with early MJ use onset. Considering brain development in early adolescence, findings are consistent with disruptions in pruning. However, divergence with continued use for many years thereafter suggests altered trajectories of brain maturation during late adolescence and beyond. \n\n ", + "publication_year": 2015, + "coordinate_space": "TAL", + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/text.txt b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/text.txt new file mode 100644 index 0000000..4f82ff2 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/text.txt @@ -0,0 +1,132 @@ + +## Introduction + +With more than 25% of high school seniors reporting recent use and 6.5% of 12th graders being daily users ( ), marijuana (MJ) is the most frequently used illicit substance among adolescents. Across all age groups over 70% of new drug initiates start with using MJ at an average age of 18 years ( ). Indeed, the scope of MJ use prevalence is of great public interest, as MJ use in early adolescence is associated with increased risk of greater substance use, legal problems, disrupting education, injuries/medical problems, developing psychopathology, cognitive changes and chronic psychosocial struggles ( , , , ). Taken together, rates of MJ use are suggestive of an epidemic based in adolescence, which is concerning not just due to societal cost, but also due to the potential to offset sensitive brain development during this period. + +Despite its prevalence, the impact of MJ use on adolescent brain development is not fully known. Important neuromaturational processes during adolescence through young adulthood are believed to bring about improved higher-order cognition by refining neural systems locally and globally through white and gray matter development ( , , ). In general, gray matter reductions and cortical thinning coincide with increased white matter volume and organization through adolescence and young adulthood, suggestive of synaptic pruning and axonal myelination ( , , , , ). The endogenous cannabinoid (CB) system is also immature during adolescence ( , ). In an animal model ( ) imaged CB1 receptor binding using PET and found relatively lower activation of CB1 receptors in adolescent rats compared to adult rats in brain areas including those in the frontal cortex, temporal lobe (hippocampus and amygdala) and sub-cortical regions including striatal regions, thalamus, hypothalamus, superior colliculus. Thus, adolescence represents a developmental period with vulnerability to structural and functional changes due to exogenous MJ exposure. + +Adolescent MJ use has the potential to cause structural and functional changes in the brain by altering cannabinoid signaling. One possible mechanism would be blunt neurotoxic influence. For example, delta9-tetrahydrocannabinol (THC), the primary psychoactive component in MJ that binds CB1 receptors, is reported to cause cell shrinkage and damage DNA strands in THC-treated neuron cultures ( ). This may be the mechanism by which smaller volumes have been observed in individuals exposed to cannabis during adolescence ( ). However, it is more likely that MJ exerts its influence on brain development indirectly. The cannabinoid system plays a role in modulating other neurotransmitters, including gamma-aminobutyric acid (GABA), glutamate and monoamines ( ). Specifically, activation of CB1 receptors is associated with down-regulating inhibitory GABAergic transmission in cortical interneurons during adolescence ( , ). In addition, CB signaling inhibits microglia function ( ). These two points are important because cortical pruning processes involve glial-mediated synaptic elimination and altering the excitatory/inhibitory balance is liable to disrupt the selective tagging and preserving synapses ( ). The impact of this indirect influence on the developing brain may be in the observations of abnormal connectivity in those who began MJ use in adolescence ( ). Evidence from human neuroimaging studies lends greater support to MJ-related disruptions to brain development. + +Structural neuroimaging studies have indicated that volumes of several brain areas are smaller in heavy adult MJ users especially in areas enriched with cannabinoid 1 (CB1) receptors, such as medial temporal lobe, and prefrontal cortex ( ). Studies of adult chronic MJ users note brain volume reductions in temporal lobe, insula, and prefrontal cortex, amygdala and hippocampus ( , , , , ). Among different characteristics of MJ involvement (e.g., dependence symptoms, use frequency, consumption), the age of initial MJ use is a robust factor that has been associated with smaller brain volumes in users. For example, observed left parahippocampal gyrus and right temporal pole structural differences in 25 regular MJ users compared to 22 occasional users, however, even the occasional users who began smoking MJ during adolescence (before age 18) demonstrated similar brain changes as the regular users. Our group has also found links with early MJ use onset ( ) and structural connectivity with orbitofrontal cortex in a cohort of daily MJ users, suggesting complex neuroadaptive processes related to MJ use in the context of adolescent brain development ( ). These findings underscore the potential for significant heterogeneity in brain changes among adult MJ users, especially those who began using MJ during neurodevelopment. + +Studies comparing early adolescent MJ use to users initiating MJ use in later adolescence provide further evidence for the potential of MJ to cause enduring change. The few studies that have directly investigated the timing of the effects of MJ during adolescence have noted divergent neurodevelopment effects. For example, in an fMRI study by Gruber and colleagues, functional and behavioral differences during an interference task were reported between early (before age 16) and late (after age 16) MJ users ( ) ( ). The same group also reported decreased white matter integrity in early onset vs. late onset MJ users (mean age 14.46 vs. 17.93) ( ). Similar differential effects have also been noted in parietal lobe activation between early and late adolescent binge drinkers during a spatial working memory task ( ). These studies highlight the importance of clarifying the differential neural effects of early- and late-adolescent onset use. + +To that end, in the current study, we compared daily MJ users who were early onset users (<16 years old) versus late onset users (≥16 years old) on measures of cortical morphology that are sensitive to developmental changes. We aimed to characterize both the effect of early onset status on cortical morphology as well as assess for morphological patterns linked to the continued use of MJ after early and late adolescent MJ initiation. We expected early onset users to show a morphological pattern consistent with disruption of early adolescent brain development (e.g., increased cortical thickness, greater gray/white definition of the cortical ribbon via disruptions to adolescent pruning processes) that may be more consistent with indirect impact of MJ of brain development. While gray matter decline has been shown to be associated with marijuana use, particularly in areas rich in CB1 receptors, increased cortical thickness and greater gray/white definition in the cortical ribbon point to potential disruption in neurodevelopment (i.e. synaptic pruning) that may result from MJ use at key developmental stages (i.e. earlier as opposed to later in adolescent neuronal development). Such disruptions may extend to gyrification as well. While this process begins in utero, there is evidence that gyrification is ongoing into adolescence ( , , ) and may also display aberrant developmental patterns in the presence of MJ use. + + +## Methods + +This study was approved by the University of Texas at Dallas (UTD) and University of Texas Southwestern Medical Center (UTSW) Institutional Review Boards. All participants were recruited from the Dallas-Ft.Worth metro area via flyers and advertisements. Following informed consent, MJ users completed two sessions – a baseline appointment for collecting demographic, psychosocial and behavioral measures and a thorough substance use history. Three days later the participants returned for a neuroimaging appointment. Prior to their scanning session, participants were asked to be abstinent from MJ use for 72 h, from alcohol for 24 h, and from caffeine and cigarettes for the preceding 2 h. These were confirmed by self-report (MJ, alcohol, caffeine and cigarettes), quantitative THC urinalysis (MJ), and by breath alcohol level of .000 (alcohol) at the start of their session. + +### Participants + +We scanned 45 regular heavy MJ users as part of the parent project. Inclusion criteria were: right-handedness, English as the primary language and no histories of psychosis, traumatic brain injury, and MRI contraindications (e.g., pregnancy, non-removal metallic implants, claustrophobia). One subject reported a history of anxiety and depression and one other reported a history of ADHD as a child. Additional exclusions for the current study included: Axis I diagnosis (via SCID) other than cannabis use disorder, unusable sMRI due to motion artifact or poor signal-to-noise ratio that precluded accurate tissue segmentation ( n  = 1) and incomplete drug use histories ( n  = 2). Of the 42 remaining cases, 22 were early onset users (onset of first use before age 16). Group categorization using onset of regular use as opposed to onset of first use maintained the same grouping (mean early onset of regular use = 16.5, mean late onset of regular use = 19.0). Regular use was defined as at least one time per week. To determine how age of onset of regular MJ use influenced our reported effects, we performed these analyses while covarying for age of onset of regular use (see ). summarizes demographic and substance use information according to onset status. summarizes the correlation between age and identified marijuana use variables. Only MJ years of use and current age showed a statistically significant correlation. Participants were recruited based on self-reported daily MJ use and a positive urinalysis for THC metabolites at their baseline visit. All of the participants were screened via urinalysis for other drugs of abuse and were excluded if drugs (other than MJ) were detected. Participants were required to have used MJ for a minimum of 5000 lifetime occasions and self-report daily use (without >24 h abstinence) for the last 60 days. +Sample characteristics. MJ, marijuana. + +The correlations between current age and all MJ use variables. + + + +### MRI acquisition and analysis + +#### Image acquisition + +Scanning sessions took place at the Advanced Imaging Research Center at the University of Texas, Southwestern Medical Center three days following their initial visit. Another verification of THC metabolites via urinalysis was also performed before the scan. MRI images were collected using a 3T Philips whole-body scanner equipped with Quasar gradient subsystem (40 mT/m amplitude, a slew rate of 220 mT/m/ms). High-resolution T1-weighted anatomical scans were collected using a MPRAGE sequence: TR/TE/TI = 2100/3.70/1100 ms; flip angle = 12°; field of view = 256 mm × 256 mm; slab thickness = 160 mm (along left-right direction); voxel size = 1 mm × 1 mm × 1 mm, Total scan time = 3 m 57 s. + + +#### Image processing + +MPRAGE anatomical scans were pre-processed for surface-based analyses using FreeSurfer v5.3 semi-automated pipeline ( ). This semi-automated pipeline included spatial (Talairach) and signal intensity normalization of images, volumetric segmentation and subcortical labeling ( , ). Outer gray matter and white matter boundaries were then identified and reconstructed into a mesh of over 150,000 tessellated vertices to allow point-to-point surface measures ( ). Next, gyral anatomy is aligned to a standard spherical template using surface convexity and curvature measures. Resulting surfaces were inspected, blind to MJ onset status, to identify and correct any errors made during cortical reconstruction. Modifications to the volumes were made as necessary to correct for tissue misclassifications according to FreeSurfer's wiki manual ( ). In preparation for analysis, each morphological measure for each case was co-registered to a standard template (fsaverage). Anatomical labels in FreeSurfer ( ) were used for interpretation of results. + + + +### Morphological measures + +#### Cortical thickness + +The width of the cortical ribbon was measured as the distance between corresponding vertices of the white matter and gray matter surfaces at each vertex in the cortical mantel ( ). + + +#### Gray–white matter ratio (GWR) + +To assess the quality of cortical ribbon definition, a tissue contrast between gray and white matter signal intensities was computed as a percent ratio (W − G)/(.5*(W + G)) (from pctsurfcon v1.11.2.1, inbuilt component of FreeSurfer pipeline v5.3, 2011). White matter signal intensities were measured at an absolute length of 1 mm below the gray–white border surface and gray matter signal was measured 30% into the cortical ribbon ( ). + + +#### Local gyrification index + +The cortical surface from FreeSurfer's main pipeline is further processed to create an outer surface that encapsulates the gyral and sulcal curvature for each hemisphere, which serves as a basis for calculating a local gyrification index ( ). LGI is measured as the amount of cortex within the sulcal folds beneath the outer surface compared to the amount of visible cortex that touches the outer surface. Cortical maps are generated from repeated iterations of delineating a 25 mm radius sphere on the outer surface and its corresponding point on the cortical surface using a matching algorithm. + + + +### Background and premorbid characteristics + +#### Sample characteristics + +Age, gender, education level, ethnicity, along with other background information, was obtained using a standard demographics questionnaire. The two-subtest administration of the Wechsler Abbreviated Scale of Intelligence (Vocabulary and Matrix Reasoning) provided estimates of intellect ( ). + + +#### Substance use + +The Substance Use Disorder modules of the Structured Clinical Interview for DSM-IV (SCID) ( ) were administered by a trained research assistant to assess for lifetime and current symptoms of abuse and dependence for alcohol, nicotine, MJ and other substances. The SCID interview also provided the onset of use information. A Time Line Follow-Back (TLFB) approach was used to quantify alcohol, nicotine, and MJ use patterns for 90 days prior to study participation ( ). Marijuana use in grams was obtained via self-report in response to probes aimed at quantifying their regular use. + + + +### Statistical analyses + +Statistical analyses were conducted in SPSS 18.0 for behavioral and psychosocial measures whereas general linear model group comparisons on surfaced-based morphology measures were carried out FreeSurfer's built-in application QDEC (v1.5). Independent samples t -tests, Mann–Whitney U -tests or chi-square tests, compared groups on background and demographic variables (see ). Before statistical analysis was conducted, the dependent measures of cortical thickness, GWR and LGI were smoothed using a FWHM Gaussian filter with a width of either 10 or 15 mm. Separate univariate general linear model (GLM) was then used to model cortical thickness, GWR and LGI with onset status of MJ use as a between groups factor. The dependent variables were thickness, gray–white ratio or local gyrification index and the independent variables were either recent monthly MJ use in grams (MJ grams) or duration of MJ use (MJ years). Age and total drinks in the past 2 months were treated as nuisance covariates in the model. Using MJ years of use and MJ grams as independent predictors of interest allowed us to characterize and differentiate the latent developmental effects from cumulative and current effects of MJ use. The variable “marijuana years of use” was based on the participants’ response to the question “For how many years have you been using marijuana regularly?” Of note, an outlier in the early onset group was removed before the statistical comparisons were performed. + + + +## Results + +### Cortical thickness + +There were no regions of group differences in cortical thickness by early onset status alone, controlled for age and alcohol use. However, MJ use characteristics were correlated with anterior dorsolateral prefrontal cortex thickness based on onset status. Early onset users showed increased thickness with increased MJ grams while late onset users showed thinner cortex with increased MJ grams ( p  < 0.05 uncorrected) ( ). The same pattern emerged with more years of MJ use being associated with thicker region of the right medial temporal lobe in the early onset users and the reverse for the late onset users ( p  < 0.05 uncorrected) ( ). +Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index. + +Early vs. late onset marijuana users show divergent morphological patterns based on current marijuana use (measured in grams; MJ grams) in overlapping areas of anterior prefrontal cortex. GWR, gray/white matter border ratio; LGI, local gyrification index. + + + +### Gray–white matter contrast + +There were no regions of group differences in gray–white matter contrast by early onset status alone, controlled for age and alcohol use. However, current MJ consumption (grams) and onset status were differentially correlated with gray–white matter contrast in a left anterior dorsal frontal region ( p  < 0.05, FWE corrected). Increased gray–white contrast with heavier MJ use was seen in the early onset users and the opposite was seen in later onset users (heavier current use linked to decreasing GWR). The same pattern was seen between duration of MJ use in two prefrontal cortex clusters of the right dorsal frontal and medial orbitofrontal area p  < 0.05, FWE corrected – more years of MJ use were linked to greater GWR among early users ( ). + + +### Gyrification + +MJ use onset status alone showed no significant main effects above age and alcohol covariates. However, onset status was correlated with divergent patterns between local gyrification and MJ use, whereby early onset users showed decreasing LGI with increasing MJ consumption and longer duration of use in prefrontal cortex regions p  < 0.05, FWE corrected. The left hemisphere clusters encompassed the majority of the length of the middle lateral surface of the left cortex, including motor cortices, parietal lobe and multimodal integration areas ( ). + + + +## Discussion + +The present study was designed to characterize the cortical architecture in adolescent onset MJ users by comparing early adolescent onset users to late adolescent onset in MJ use on measures of cortical thickness, gray/white matter contrast and gyrification. The primary finding was that early versus late onset MJ users showed a divergent pattern in cortical thickness, definition of the cortical ribbon and local gyrification with continued use through and beyond adolescent years. Specifically, early onset users showed cortical thickening, enhanced gray/white matter contrast, and decreased gyrification in association with more years of MJ use and current consumption of MJ in grams in frontal and temporal regions – areas that underlie higher order cognition including executive functioning, learning and memory. Findings were above and beyond effects of alcohol and current age, therefore, results are less likely to reflect morphological trends due to aging. + +Our findings did not find the expected age of onset differences previously reported in marijuana users ( , ). This inconsistency suggests that the age of onset effects may be more robust in brain white matter connectivity ( ) and function ( ) than brain surface morphometry. To date, the few studies that have described altered cortical morphology in MJ users have led to mixed findings. identified brain regions with decreased sulcal depth suggestive of lower gyrification in a study of adult MJ users. recently reported increased cortical thickness in the entorhinal cortex among 24 adolescent MJ users (mean age = 17.7, mean MJ onset age = 15.4) relative to peer controls. However, the authors also reported a negative relationship between cortical thickness and total MJ use in the right paracentral gyrus, and they observed consistent positive relationships in various brain regions between age of MJ onset and thickness. In the only other known adolescent study of cortical thickness and MJ, Lopez-Larson and colleagues studied 18 adolescent heavy MJ users (similar in age and MJ onset as ) and reported mixed findings of increased thickness in prefrontal/insula regions and decreased thickness in posterior/temporal lobe areas in the MJ users compared to controls. In contrast to , found areas of the frontal lobe and insula that were thinner with increased urine THC metabolites and thicker with earlier age of onset. Select findings from the current study align with aspects of both of these studies, with a consensus supporting findings of a negative dose-dependent relationship between MJ use and cortical thickness. Given the low availability of studies to compare, this consensus is very limited. Although Jacobus et al. and Lopez-Larson et al. found the opposite effect of age of onset on thickness, the pattern of divergence among early vs. late onset users in the current study is more consistent with the latter study, whereby we saw early onset users exhibit thicker cortex with continued MJ use. Taken together, findings of increased thickness related to early MJ onset accompanied by negative dose-dependent relationships with MJ exposure may reflect two distinct processes. One process may be specific to the interactions with cortical development during early adolescence, likely leading to a disruption in pruning, and, the other, specific to the pharmacological effect with heavy chronic MJ use. + +In the only known study to examine the curvature-morphology of the cortex in adult MJ users, identified decreased sulcal concavity and thinner sulci in 23 MJ users compared to controls ( n  = 44), also in prefrontal areas. However, they did not observe significant relationships with age, MJ onset age, or cumulative MJ use. It is interesting that the authors detected group level differences (MJ vs. controls) but no correlations with MJ use characteristics such as dose or age of onset, whereas our primary findings are the consistent effects of continued MJ use differing after early or late adolescent onset. There are substantial methodological explanations for this disparity. For example, the current study did not compare morphology in MJ users to a normative control sample, therefore, it is feasible that group-level differences may emerge with such a comparison. Likewise, we deliberately covaried for current age in order to control for brain changes with aging and thus optimize our interrogation of developmental effects of early onset age and of aspects of continued use. + +The heterogeneity of MJ effects clearly suggests a multifactorial system of neurobiological processes involved. The primary results uphold that age of onset is a robust variable that differentiates heavy MJ users based on early versus late MJ onset. However, this group distinction relied on current use characteristics. Therefore, in the absence of group-level differences, the interactions between onset age and current use indicates that continued cannabis exposure and early adolescent developmental factors both contribute to a dynamic and sustained departure from what is expected based on developmental studies. + +Typical synaptic refinement processes during early adolescence are in the context of long-term depression and potentiation of cortical neurons in order to facilitate neuronal remodeling. Thus, the normal course of early adolescent development is uniquely vulnerable to disruption by MJ due to the electrochemical conditions and maturity of brain processes that would not present together again. Cass and colleagues tested the sensitivity of early adolescence cannabinoid exposure in an animal model ( ). They found that acute administration of cannabinoid agonists in early, middle and late adolescent rats led to a state of frequency-dependent disinhibition of neurons in the frontal cortex in the early-to-middle adolescent rats, but not in the late adolescent rats. Moreover, the authors also noted that adult rats previously exposed to cannabinoid agonists in adolescence displayed comparable neuronal disinhibition. Thus, by changing the inhibitory/excitatory landscape during adolescence, MJ can influence lasting changes to typical cortical remodeling during sensitive early adolescent years. + +The sequence of pruning and myelination likely plays a formative role in lasting changes from early adolescent onset MJ use. With decreased synaptic elimination, our findings of greater GW border contrast may reflect greater proliferation of myelin at the boundary of the cortical ribbon where non-pruned synapses remained with linked axons. Findings of altered white matter tissue qualities are mixed in adolescent and adult MJ user samples. Some report both increases and decreases in fractional anisotropy (FA) and average water diffusion ( ) whereas others report consistent decreases in FA among adolescent MJ users ( , ) or null findings ( ). Two studies of diffusion tensor imaging in adult MJ users reported reduced FA in users compared to controls ( , ). In addition to equivocal findings, research is needed to address the microstructural changes that could result in altered definition of the cortical ribbon. For example, rather than whole brain techniques that assess diffusion measures along major white matter tracts, indices assessing axonal organization along radial and interneuron association fibers along the cortical ribbon are needed. This scenario played out could result in increased gray matter (thicker cortex from disrupted pruning) and the myelination of connections to these spared terminals would result in increased density of white matter at the cortical boundary. Without any known studies of adolescent development of the gray/white tissue contrast at the cortical border to serve as a point of comparison, we speculate that early adolescent disruption of pruning and subsequent myelination of connections at the cortical boundary would be reflected by increased GWR as we saw in the current study. + + +## Limitations and conclusions + +The cross-sectional nature of this study limits causal attributions in terms of what we can infer to be directly related to the effects of MJ. Although a longitudinal design is optimal for addressing brain changes directly due to MJ, cross-sectional studies facilitate data-driven hypotheses that can be assessed directly in prospective studies. + +It is important to keep in mind that the participants were not explicitly asked for possible years of abstinence during their period of regular use, which may have created possible inflation in reported duration of regular use. However, because the participants provided number of years of “regular” marijuana use, this inherently suggests continued, uninterrupted years of use. Concurrent nicotine use could have also influenced our reported results. But in the absence of a larger sample size and the presence of huge variance in nicotine use in the current sample, we were unable to verify the effect of nicotine use in the reported results. + +Interpretation of these findings is also limited by the lack of behavioral anchors for the observed morphological effects and lack of information on other aspects of developmental history that could further characterize the effects of marijuana during neurodevelopment. This is further limited by the absence of “expected” patterns based on normative data. Given the varied directions of effects and the small sample size, these findings should be replicated and be viewed as preliminary. + +To conclude, early MJ use was linked to altered neurodevelopmental patterns in brain regions sub-serving higher-order cognitive process. Clinical implications include need for early, targeted intervention. Given that the most robust results were related to interactions between onset age and continued use through emerging adulthood, harm reduction approaches may be effective in moderating adolescent MJ use to levels that are less likely to cause long-term developmental changes. + + +## Conflict of interest + +The authors report no conflicts of interest. + + \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/ace/26507433.html b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/ace/26507433.html new file mode 100644 index 0000000..37f56a8 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/ace/26507433.html @@ -0,0 +1,239 @@ + + + + + + + + + + + + + + + + + + + + + Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture - ScienceDirect + + + + + + + + + + + + + + + + + Skip to main content + +
 
Elsevier

Developmental Cognitive Neuroscience

Volume 16, December 2015, Pages 16-22
open access
Developmental Cognitive Neuroscience

Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture

Under a Creative Commons license

Highlights

Early onset MJ use was associated with different patterns of cortical architecture.

Early vs. late onset divergence was in brain regions underlying higher-order cognition.

Findings were above and beyond effects of alcohol and current age.

Abstract

Background

As the most commonly used illicit substance during early adolescence, long-term or latent effects of early adolescent marijuana use across adolescent developmental processes remain to be determined.

Methods

We examined cortical thickness, gray/white matter border contrast (GWR) and local gyrification index (LGI) in 42 marijuana (MJ) users. Voxelwise regressions assessed early-onset (age <16) vs. late-onset (≥16 years-old) differences and relationships to continued use while controlling for current age and alcohol use.

Results

Although groups did not differ by onset status, groups diverged in their correlations between cannabis use and cortical architecture. Among early-onset users, continued years of MJ use and current MJ consumption were associated with thicker cortex, increased GWR and decreased LGI. Late-onset users exhibited the opposite pattern. This divergence was observed in all three morphological measures in the anterior dorsolateral frontal cortex (p < .05, FWE-corrected).

Conclusions

Divergent patterns between current MJ use and elements of cortical architecture were associated with early MJ use onset. Considering brain development in early adolescence, findings are consistent with disruptions in pruning. However, divergence with continued use for many years thereafter suggests altered trajectories of brain maturation during late adolescence and beyond.

Keywords

Adolescence
Marijuana
Cortical thickness
Gyrification
Morphology
FreeSurfer

1. Introduction

With more than 25% of high school seniors reporting recent use and 6.5% of 12th graders being daily users (Johnston et al., 2014), marijuana (MJ) is the most frequently used illicit substance among adolescents. Across all age groups over 70% of new drug initiates start with using MJ at an average age of 18 years (SAMHSA, 2014). Indeed, the scope of MJ use prevalence is of great public interest, as MJ use in early adolescence is associated with increased risk of greater substance use, legal problems, disrupting education, injuries/medical problems, developing psychopathology, cognitive changes and chronic psychosocial struggles (CASA, 2011; Fergusson and Horwood, 1997; Fergusson et al., 1996; Patton et al., 2002). Taken together, rates of MJ use are suggestive of an epidemic based in adolescence, which is concerning not just due to societal cost, but also due to the potential to offset sensitive brain development during this period.

Despite its prevalence, the impact of MJ use on adolescent brain development is not fully known. Important neuromaturational processes during adolescence through young adulthood are believed to bring about improved higher-order cognition by refining neural systems locally and globally through white and gray matter development (Casey et al., 2005; Giedd, 2008; Paus, 2005). In general, gray matter reductions and cortical thinning coincide with increased white matter volume and organization through adolescence and young adulthood, suggestive of synaptic pruning and axonal myelination (Giorgio et al., 2010; Gogtay et al., 2004; Hasan et al., 2007; Lebel et al., 2010; Shaw et al., 2008). The endogenous cannabinoid (CB) system is also immature during adolescence (Anavi-Goffer and Mulder, 2009; Verdurand et al., 2011). In an animal model (Verdurand et al., 2011) imaged CB1 receptor binding using PET and found relatively lower activation of CB1 receptors in adolescent rats compared to adult rats in brain areas including those in the frontal cortex, temporal lobe (hippocampus and amygdala) and sub-cortical regions including striatal regions, thalamus, hypothalamus, superior colliculus. Thus, adolescence represents a developmental period with vulnerability to structural and functional changes due to exogenous MJ exposure.

Adolescent MJ use has the potential to cause structural and functional changes in the brain by altering cannabinoid signaling. One possible mechanism would be blunt neurotoxic influence. For example, delta9-tetrahydrocannabinol (THC), the primary psychoactive component in MJ that binds CB1 receptors, is reported to cause cell shrinkage and damage DNA strands in THC-treated neuron cultures (Chan et al., 1998). This may be the mechanism by which smaller volumes have been observed in individuals exposed to cannabis during adolescence (Battistella et al., 2014). However, it is more likely that MJ exerts its influence on brain development indirectly. The cannabinoid system plays a role in modulating other neurotransmitters, including gamma-aminobutyric acid (GABA), glutamate and monoamines (Lopez-Moreno et al., 2008). Specifically, activation of CB1 receptors is associated with down-regulating inhibitory GABAergic transmission in cortical interneurons during adolescence (Caballero and Tseng, 2012; Cass et al., 2014). In addition, CB signaling inhibits microglia function (Walter et al., 2003). These two points are important because cortical pruning processes involve glial-mediated synaptic elimination and altering the excitatory/inhibitory balance is liable to disrupt the selective tagging and preserving synapses (Selemon, 2013). The impact of this indirect influence on the developing brain may be in the observations of abnormal connectivity in those who began MJ use in adolescence (Jacobus et al., 2009). Evidence from human neuroimaging studies lends greater support to MJ-related disruptions to brain development.

Structural neuroimaging studies have indicated that volumes of several brain areas are smaller in heavy adult MJ users especially in areas enriched with cannabinoid 1 (CB1) receptors, such as medial temporal lobe, and prefrontal cortex (Lorenzetti et al., 2010). Studies of adult chronic MJ users note brain volume reductions in temporal lobe, insula, and prefrontal cortex, amygdala and hippocampus (Battistella et al., 2014; Cousijn et al., 2012; Filbey et al., 2014; Matochik et al., 2005; Yucel et al., 2008). Among different characteristics of MJ involvement (e.g., dependence symptoms, use frequency, consumption), the age of initial MJ use is a robust factor that has been associated with smaller brain volumes in users. For example, Battistella et al. (2014) observed left parahippocampal gyrus and right temporal pole structural differences in 25 regular MJ users compared to 22 occasional users, however, even the occasional users who began smoking MJ during adolescence (before age 18) demonstrated similar brain changes as the regular users. Our group has also found links with early MJ use onset (Bava et al., 2009) and structural connectivity with orbitofrontal cortex in a cohort of daily MJ users, suggesting complex neuroadaptive processes related to MJ use in the context of adolescent brain development (Filbey et al., 2014). These findings underscore the potential for significant heterogeneity in brain changes among adult MJ users, especially those who began using MJ during neurodevelopment.

Studies comparing early adolescent MJ use to users initiating MJ use in later adolescence provide further evidence for the potential of MJ to cause enduring change. The few studies that have directly investigated the timing of the effects of MJ during adolescence have noted divergent neurodevelopment effects. For example, in an fMRI study by Gruber and colleagues, functional and behavioral differences during an interference task were reported between early (before age 16) and late (after age 16) MJ users (Gruber et al., 2012) (Sagar et al., 2015). The same group also reported decreased white matter integrity in early onset vs. late onset MJ users (mean age 14.46 vs. 17.93) (Gruber et al., 2014). Similar differential effects have also been noted in parietal lobe activation between early and late adolescent binge drinkers during a spatial working memory task (Tapert et al., 2004). These studies highlight the importance of clarifying the differential neural effects of early- and late-adolescent onset use.

To that end, in the current study, we compared daily MJ users who were early onset users (<16 years old) versus late onset users (≥16 years old) on measures of cortical morphology that are sensitive to developmental changes. We aimed to characterize both the effect of early onset status on cortical morphology as well as assess for morphological patterns linked to the continued use of MJ after early and late adolescent MJ initiation. We expected early onset users to show a morphological pattern consistent with disruption of early adolescent brain development (e.g., increased cortical thickness, greater gray/white definition of the cortical ribbon via disruptions to adolescent pruning processes) that may be more consistent with indirect impact of MJ of brain development. While gray matter decline has been shown to be associated with marijuana use, particularly in areas rich in CB1 receptors, increased cortical thickness and greater gray/white definition in the cortical ribbon point to potential disruption in neurodevelopment (i.e. synaptic pruning) that may result from MJ use at key developmental stages (i.e. earlier as opposed to later in adolescent neuronal development). Such disruptions may extend to gyrification as well. While this process begins in utero, there is evidence that gyrification is ongoing into adolescence (Armstrong et al., 1995; Alemán-Gómez et al., 2013; Klein et al., 2014) and may also display aberrant developmental patterns in the presence of MJ use.

2. Methods

This study was approved by the University of Texas at Dallas (UTD) and University of Texas Southwestern Medical Center (UTSW) Institutional Review Boards. All participants were recruited from the Dallas-Ft.Worth metro area via flyers and advertisements. Following informed consent, MJ users completed two sessions – a baseline appointment for collecting demographic, psychosocial and behavioral measures and a thorough substance use history. Three days later the participants returned for a neuroimaging appointment. Prior to their scanning session, participants were asked to be abstinent from MJ use for 72 h, from alcohol for 24 h, and from caffeine and cigarettes for the preceding 2 h. These were confirmed by self-report (MJ, alcohol, caffeine and cigarettes), quantitative THC urinalysis (MJ), and by breath alcohol level of .000 (alcohol) at the start of their session.

2.1. Participants

We scanned 45 regular heavy MJ users as part of the parent project. Inclusion criteria were: right-handedness, English as the primary language and no histories of psychosis, traumatic brain injury, and MRI contraindications (e.g., pregnancy, non-removal metallic implants, claustrophobia). One subject reported a history of anxiety and depression and one other reported a history of ADHD as a child. Additional exclusions for the current study included: Axis I diagnosis (via SCID) other than cannabis use disorder, unusable sMRI due to motion artifact or poor signal-to-noise ratio that precluded accurate tissue segmentation (n = 1) and incomplete drug use histories (n = 2). Of the 42 remaining cases, 22 were early onset users (onset of first use before age 16). Group categorization using onset of regular use as opposed to onset of first use maintained the same grouping (mean early onset of regular use = 16.5, mean late onset of regular use = 19.0). Regular use was defined as at least one time per week. To determine how age of onset of regular MJ use influenced our reported effects, we performed these analyses while covarying for age of onset of regular use (see Supplement). Table 1 summarizes demographic and substance use information according to onset status. Table 2 summarizes the correlation between age and identified marijuana use variables. Only MJ years of use and current age showed a statistically significant correlation. Participants were recruited based on self-reported daily MJ use and a positive urinalysis for THC metabolites at their baseline visit. All of the participants were screened via urinalysis for other drugs of abuse and were excluded if drugs (other than MJ) were detected. Participants were required to have used MJ for a minimum of 5000 lifetime occasions and self-report daily use (without >24 h abstinence) for the last 60 days.

2.2. MRI acquisition and analysis

2.2.1. Image acquisition

Scanning sessions took place at the Advanced Imaging Research Center at the University of Texas, Southwestern Medical Center three days following their initial visit. Another verification of THC metabolites via urinalysis was also performed before the scan. MRI images were collected using a 3T Philips whole-body scanner equipped with Quasar gradient subsystem (40 mT/m amplitude, a slew rate of 220 mT/m/ms). High-resolution T1-weighted anatomical scans were collected using a MPRAGE sequence: TR/TE/TI = 2100/3.70/1100 ms; flip angle = 12°; field of view = 256 mm × 256 mm; slab thickness = 160 mm (along left-right direction); voxel size = 1 mm × 1 mm × 1 mm, Total scan time = 3 m 57 s.

2.2.2. Image processing

MPRAGE anatomical scans were pre-processed for surface-based analyses using FreeSurfer v5.3 semi-automated pipeline (http://surfer.nmr.mgh.harvard.edu). This semi-automated pipeline included spatial (Talairach) and signal intensity normalization of images, volumetric segmentation and subcortical labeling (Dale et al., 1999; Fischl et al., 2002). Outer gray matter and white matter boundaries were then identified and reconstructed into a mesh of over 150,000 tessellated vertices to allow point-to-point surface measures (Fischl et al., 1999). Next, gyral anatomy is aligned to a standard spherical template using surface convexity and curvature measures. Resulting surfaces were inspected, blind to MJ onset status, to identify and correct any errors made during cortical reconstruction. Modifications to the volumes were made as necessary to correct for tissue misclassifications according to FreeSurfer's wiki manual (Schmansky et al., 2010). In preparation for analysis, each morphological measure for each case was co-registered to a standard template (fsaverage). Anatomical labels in FreeSurfer (Desikan et al., 2006) were used for interpretation of results.

2.3. Morphological measures

2.3.1. Cortical thickness

The width of the cortical ribbon was measured as the distance between corresponding vertices of the white matter and gray matter surfaces at each vertex in the cortical mantel (Fischl and Dale, 2000).

2.3.2. Gray–white matter ratio (GWR)

To assess the quality of cortical ribbon definition, a tissue contrast between gray and white matter signal intensities was computed as a percent ratio (W − G)/(.5*(W + G)) (from pctsurfcon v1.11.2.1, inbuilt component of FreeSurfer pipeline v5.3, 2011). White matter signal intensities were measured at an absolute length of 1 mm below the gray–white border surface and gray matter signal was measured 30% into the cortical ribbon (Salat et al., 2009).

2.3.3. Local gyrification index

The cortical surface from FreeSurfer's main pipeline is further processed to create an outer surface that encapsulates the gyral and sulcal curvature for each hemisphere, which serves as a basis for calculating a local gyrification index (Schaer et al., 2012). LGI is measured as the amount of cortex within the sulcal folds beneath the outer surface compared to the amount of visible cortex that touches the outer surface. Cortical maps are generated from repeated iterations of delineating a 25 mm radius sphere on the outer surface and its corresponding point on the cortical surface using a matching algorithm.

2.4. Background and premorbid characteristics

2.4.1. Sample characteristics

Age, gender, education level, ethnicity, along with other background information, was obtained using a standard demographics questionnaire. The two-subtest administration of the Wechsler Abbreviated Scale of Intelligence (Vocabulary and Matrix Reasoning) provided estimates of intellect (Wechsler, 1999).

2.4.2. Substance use

The Substance Use Disorder modules of the Structured Clinical Interview for DSM-IV (SCID) (First et al., 2002) were administered by a trained research assistant to assess for lifetime and current symptoms of abuse and dependence for alcohol, nicotine, MJ and other substances. The SCID interview also provided the onset of use information. A Time Line Follow-Back (TLFB) approach was used to quantify alcohol, nicotine, and MJ use patterns for 90 days prior to study participation (Sobell and Sobell, 1992). Marijuana use in grams was obtained via self-report in response to probes aimed at quantifying their regular use.

2.5. Statistical analyses

Statistical analyses were conducted in SPSS 18.0 for behavioral and psychosocial measures whereas general linear model group comparisons on surfaced-based morphology measures were carried out FreeSurfer's built-in application QDEC (v1.5). Independent samples t-tests, Mann–Whitney U-tests or chi-square tests, compared groups on background and demographic variables (see Table 1). Before statistical analysis was conducted, the dependent measures of cortical thickness, GWR and LGI were smoothed using a FWHM Gaussian filter with a width of either 10 or 15 mm. Separate univariate general linear model (GLM) was then used to model cortical thickness, GWR and LGI with onset status of MJ use as a between groups factor. The dependent variables were thickness, gray–white ratio or local gyrification index and the independent variables were either recent monthly MJ use in grams (MJ grams) or duration of MJ use (MJ years). Age and total drinks in the past 2 months were treated as nuisance covariates in the model. Using MJ years of use and MJ grams as independent predictors of interest allowed us to characterize and differentiate the latent developmental effects from cumulative and current effects of MJ use. The variable “marijuana years of use” was based on the participants’ response to the question “For how many years have you been using marijuana regularly?” Of note, an outlier in the early onset group was removed before the statistical comparisons were performed.

3. Results

3.1. Cortical thickness

There were no regions of group differences in cortical thickness by early onset status alone, controlled for age and alcohol use. However, MJ use characteristics were correlated with anterior dorsolateral prefrontal cortex thickness based on onset status. Early onset users showed increased thickness with increased MJ grams while late onset users showed thinner cortex with increased MJ grams (p < 0.05 uncorrected) (Table 3). The same pattern emerged with more years of MJ use being associated with thicker region of the right medial temporal lobe in the early onset users and the reverse for the late onset users (p < 0.05 uncorrected) (Fig. 1).

Table 3. Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.

MeasureLabel@Max, Extended coverageSideMax-log(p)VtxMaxSize (mm2)xyzCorrelateP (corr)F-valueEffect Size**
ThicknessLingualR−2.488127,927111019−69−7MJ Years0.01610.070.063
GWRRostral middle frontal,L−2.66842,5051730−235310MJ Grams0.00111.091.969
Rostral middle frontalR−3.56594,8962661393018MJ Years0.000216.60.744
Medial orbitofrontalR−3.30484,7731368647−20MJ Years0.01314.920.796
LGIInferior parietalL3.456122,1692565−44−6244MJ Grams0.01515.890.131

p(corr), family-wise error fully corrected.

**

The effect sizes were derived from Freesurfer's tool in the significant region of interest using mri_segstats. This was also confirmed manually by using the F-value reported by Freesurfer.

Fig. 1. Early vs. late onset marijuana users show divergent morphological patterns based on current marijuana use (measured in grams; MJ grams) in overlapping areas of anterior prefrontal cortex. GWR, gray/white matter border ratio; LGI, local gyrification index.

3.2. Gray–white matter contrast

There were no regions of group differences in gray–white matter contrast by early onset status alone, controlled for age and alcohol use. However, current MJ consumption (grams) and onset status were differentially correlated with gray–white matter contrast in a left anterior dorsal frontal region (p < 0.05, FWE corrected). Increased gray–white contrast with heavier MJ use was seen in the early onset users and the opposite was seen in later onset users (heavier current use linked to decreasing GWR). The same pattern was seen between duration of MJ use in two prefrontal cortex clusters of the right dorsal frontal and medial orbitofrontal area p < 0.05, FWE corrected – more years of MJ use were linked to greater GWR among early users (Fig. 1).

3.3. Gyrification

MJ use onset status alone showed no significant main effects above age and alcohol covariates. However, onset status was correlated with divergent patterns between local gyrification and MJ use, whereby early onset users showed decreasing LGI with increasing MJ consumption and longer duration of use in prefrontal cortex regions p < 0.05, FWE corrected. The left hemisphere clusters encompassed the majority of the length of the middle lateral surface of the left cortex, including motor cortices, parietal lobe and multimodal integration areas (Fig. 1).

4. Discussion

The present study was designed to characterize the cortical architecture in adolescent onset MJ users by comparing early adolescent onset users to late adolescent onset in MJ use on measures of cortical thickness, gray/white matter contrast and gyrification. The primary finding was that early versus late onset MJ users showed a divergent pattern in cortical thickness, definition of the cortical ribbon and local gyrification with continued use through and beyond adolescent years. Specifically, early onset users showed cortical thickening, enhanced gray/white matter contrast, and decreased gyrification in association with more years of MJ use and current consumption of MJ in grams in frontal and temporal regions – areas that underlie higher order cognition including executive functioning, learning and memory. Findings were above and beyond effects of alcohol and current age, therefore, results are less likely to reflect morphological trends due to aging.

Our findings did not find the expected age of onset differences previously reported in marijuana users (Gruber et al., 2012, 2014). This inconsistency suggests that the age of onset effects may be more robust in brain white matter connectivity (Gruber et al., 2014) and function (Gruber et al., 2012) than brain surface morphometry. To date, the few studies that have described altered cortical morphology in MJ users have led to mixed findings. Mata et al. (2010) identified brain regions with decreased sulcal depth suggestive of lower gyrification in a study of adult MJ users. Jacobus and Tapert (2014) recently reported increased cortical thickness in the entorhinal cortex among 24 adolescent MJ users (mean age = 17.7, mean MJ onset age = 15.4) relative to peer controls. However, the authors also reported a negative relationship between cortical thickness and total MJ use in the right paracentral gyrus, and they observed consistent positive relationships in various brain regions between age of MJ onset and thickness. In the only other known adolescent study of cortical thickness and MJ, Lopez-Larson and colleagues studied 18 adolescent heavy MJ users (similar in age and MJ onset as Jacobus and Tapert, 2014) and reported mixed findings of increased thickness in prefrontal/insula regions and decreased thickness in posterior/temporal lobe areas in the MJ users compared to controls. In contrast to Jacobus and Tapert (2014), Lopez-Larson et al. (2011) found areas of the frontal lobe and insula that were thinner with increased urine THC metabolites and thicker with earlier age of onset. Select findings from the current study align with aspects of both of these studies, with a consensus supporting findings of a negative dose-dependent relationship between MJ use and cortical thickness. Given the low availability of studies to compare, this consensus is very limited. Although Jacobus et al. and Lopez-Larson et al. found the opposite effect of age of onset on thickness, the pattern of divergence among early vs. late onset users in the current study is more consistent with the latter study, whereby we saw early onset users exhibit thicker cortex with continued MJ use. Taken together, findings of increased thickness related to early MJ onset accompanied by negative dose-dependent relationships with MJ exposure may reflect two distinct processes. One process may be specific to the interactions with cortical development during early adolescence, likely leading to a disruption in pruning, and, the other, specific to the pharmacological effect with heavy chronic MJ use.

In the only known study to examine the curvature-morphology of the cortex in adult MJ users, Mata et al. (2010) identified decreased sulcal concavity and thinner sulci in 23 MJ users compared to controls (n = 44), also in prefrontal areas. However, they did not observe significant relationships with age, MJ onset age, or cumulative MJ use. It is interesting that the authors detected group level differences (MJ vs. controls) but no correlations with MJ use characteristics such as dose or age of onset, whereas our primary findings are the consistent effects of continued MJ use differing after early or late adolescent onset. There are substantial methodological explanations for this disparity. For example, the current study did not compare morphology in MJ users to a normative control sample, therefore, it is feasible that group-level differences may emerge with such a comparison. Likewise, we deliberately covaried for current age in order to control for brain changes with aging and thus optimize our interrogation of developmental effects of early onset age and of aspects of continued use.

The heterogeneity of MJ effects clearly suggests a multifactorial system of neurobiological processes involved. The primary results uphold that age of onset is a robust variable that differentiates heavy MJ users based on early versus late MJ onset. However, this group distinction relied on current use characteristics. Therefore, in the absence of group-level differences, the interactions between onset age and current use indicates that continued cannabis exposure and early adolescent developmental factors both contribute to a dynamic and sustained departure from what is expected based on developmental studies.

Typical synaptic refinement processes during early adolescence are in the context of long-term depression and potentiation of cortical neurons in order to facilitate neuronal remodeling. Thus, the normal course of early adolescent development is uniquely vulnerable to disruption by MJ due to the electrochemical conditions and maturity of brain processes that would not present together again. Cass and colleagues tested the sensitivity of early adolescence cannabinoid exposure in an animal model (Cass et al., 2014). They found that acute administration of cannabinoid agonists in early, middle and late adolescent rats led to a state of frequency-dependent disinhibition of neurons in the frontal cortex in the early-to-middle adolescent rats, but not in the late adolescent rats. Moreover, the authors also noted that adult rats previously exposed to cannabinoid agonists in adolescence displayed comparable neuronal disinhibition. Thus, by changing the inhibitory/excitatory landscape during adolescence, MJ can influence lasting changes to typical cortical remodeling during sensitive early adolescent years.

The sequence of pruning and myelination likely plays a formative role in lasting changes from early adolescent onset MJ use. With decreased synaptic elimination, our findings of greater GW border contrast may reflect greater proliferation of myelin at the boundary of the cortical ribbon where non-pruned synapses remained with linked axons. Findings of altered white matter tissue qualities are mixed in adolescent and adult MJ user samples. Some report both increases and decreases in fractional anisotropy (FA) and average water diffusion (Bava et al., 2009) whereas others report consistent decreases in FA among adolescent MJ users (Ashtari et al., 2009; Jacobus et al., 2009) or null findings (Delisi et al., 2006). Two studies of diffusion tensor imaging in adult MJ users reported reduced FA in users compared to controls (Gruber et al., 2011, 2014). In addition to equivocal findings, research is needed to address the microstructural changes that could result in altered definition of the cortical ribbon. For example, rather than whole brain techniques that assess diffusion measures along major white matter tracts, indices assessing axonal organization along radial and interneuron association fibers along the cortical ribbon are needed. This scenario played out could result in increased gray matter (thicker cortex from disrupted pruning) and the myelination of connections to these spared terminals would result in increased density of white matter at the cortical boundary. Without any known studies of adolescent development of the gray/white tissue contrast at the cortical border to serve as a point of comparison, we speculate that early adolescent disruption of pruning and subsequent myelination of connections at the cortical boundary would be reflected by increased GWR as we saw in the current study.

5. Limitations and conclusions

The cross-sectional nature of this study limits causal attributions in terms of what we can infer to be directly related to the effects of MJ. Although a longitudinal design is optimal for addressing brain changes directly due to MJ, cross-sectional studies facilitate data-driven hypotheses that can be assessed directly in prospective studies.

It is important to keep in mind that the participants were not explicitly asked for possible years of abstinence during their period of regular use, which may have created possible inflation in reported duration of regular use. However, because the participants provided number of years of “regular” marijuana use, this inherently suggests continued, uninterrupted years of use. Concurrent nicotine use could have also influenced our reported results. But in the absence of a larger sample size and the presence of huge variance in nicotine use in the current sample, we were unable to verify the effect of nicotine use in the reported results.

Interpretation of these findings is also limited by the lack of behavioral anchors for the observed morphological effects and lack of information on other aspects of developmental history that could further characterize the effects of marijuana during neurodevelopment. This is further limited by the absence of “expected” patterns based on normative data. Given the varied directions of effects and the small sample size, these findings should be replicated and be viewed as preliminary.

To conclude, early MJ use was linked to altered neurodevelopmental patterns in brain regions sub-serving higher-order cognitive process. Clinical implications include need for early, targeted intervention. Given that the most robust results were related to interactions between onset age and continued use through emerging adulthood, harm reduction approaches may be effective in moderating adolescent MJ use to levels that are less likely to cause long-term developmental changes.

Conflict of interest

The authors report no conflicts of interest.

Acknowledgements

This research was funded by the National Institute on Drug Abuse (R01 DA030344, Filbey). We would like to thank all the participants who volunteered for this study. We are also very grateful to Talha Alvi, Sina Aslan, Jessica Baine, Collette Bice, Vicki Germer, Ariel Ketcherside, Alison King, Brittany Kuhn, Tyler Rhinehardt, Wing Ting To and the team of lab interns for their assistance with recruitment, running participants and data management.

Appendix A. Supplementary data

The following are Supplementary data to this article:

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+ + + + Dev Cogn Neurosci + Dev Cogn Neurosci + + Developmental Cognitive Neuroscience + + 1878-9293 + 1878-9307 + + Elsevier + + + + 26507433 + 4691364 + S1878-9293(15)00091-2 + 10.1016/j.dcn.2015.10.001 + + + Original Research + + + + Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture + + + + + Filbey + Francesca M. + + Francesca.Filbey@utdallas.edu + a + + + + + McQueeny + Tim + + a + + + + DeWitt + Samuel J. + + a + + + + Mishra + Virendra + + b + + + Center for BrainHealth, School of Behavioral and Brain Sciences, The University of Texas at Dallas, United States + Advance MRI, LLC, Frisco, TX, United States + + Corresponding author at: Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, 2200 West Mockingbird, Dallas, TX 75235, United States. Francesca.Filbey@utdallas.edu + + + 09 + 10 + 2015 + + + + 12 + 2015 + + + 09 + 10 + 2015 + + 16 + 16 + 22 + + + 12 + 12 + 2014 + + + 9 + 9 + 2015 + + + 2 + 10 + 2015 + + + + © 2015 The Authors + 2015 + + This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). + + + + Highlights +

+ + + +

Early onset MJ use was associated with different patterns of cortical architecture.

+ + + +

Early vs. late onset divergence was in brain regions underlying higher-order cognition.

+
+ + +

Findings were above and beyond effects of alcohol and current age.

+
+ +

+
+ + + Background +

As the most commonly used illicit substance during early adolescence, long-term or latent effects of early adolescent marijuana use across adolescent developmental processes remain to be determined.

+
+ + Methods +

We examined cortical thickness, gray/white matter border contrast (GWR) and local gyrification index (LGI) in 42 marijuana (MJ) users. Voxelwise regressions assessed early-onset (age <16) vs. late-onset (≥16 years-old) differences and relationships to continued use while controlling for current age and alcohol use.

+
+ + Results +

Although groups did not differ by onset status, groups diverged in their correlations between cannabis use and cortical architecture. Among early-onset users, continued years of MJ use and current MJ consumption were associated with thicker cortex, increased GWR and decreased LGI. Late-onset users exhibited the opposite pattern. This divergence was observed in all three morphological measures in the anterior dorsolateral frontal cortex (p < .05, FWE-corrected).

+
+ + Conclusions +

Divergent patterns between current MJ use and elements of cortical architecture were associated with early MJ use onset. Considering brain development in early adolescence, findings are consistent with disruptions in pruning. However, divergence with continued use for many years thereafter suggests altered trajectories of brain maturation during late adolescence and beyond.

+
+
+ + Keywords + Adolescence + Marijuana + Cortical thickness + Gyrification + Morphology + FreeSurfer + +
+
+ + + + Introduction +

With more than 25% of high school seniors reporting recent use and 6.5% of 12th graders being daily users (Johnston et al., 2014), marijuana (MJ) is the most frequently used illicit substance among adolescents. Across all age groups over 70% of new drug initiates start with using MJ at an average age of 18 years (SAMHSA, 2014). Indeed, the scope of MJ use prevalence is of great public interest, as MJ use in early adolescence is associated with increased risk of greater substance use, legal problems, disrupting education, injuries/medical problems, developing psychopathology, cognitive changes and chronic psychosocial struggles (CASA, 2011, Fergusson and Horwood, 1997, Fergusson et al., 1996, Patton et al., 2002). Taken together, rates of MJ use are suggestive of an epidemic based in adolescence, which is concerning not just due to societal cost, but also due to the potential to offset sensitive brain development during this period.

+

Despite its prevalence, the impact of MJ use on adolescent brain development is not fully known. Important neuromaturational processes during adolescence through young adulthood are believed to bring about improved higher-order cognition by refining neural systems locally and globally through white and gray matter development (Casey et al., 2005, Giedd, 2008, Paus, 2005). In general, gray matter reductions and cortical thinning coincide with increased white matter volume and organization through adolescence and young adulthood, suggestive of synaptic pruning and axonal myelination (Giorgio et al., 2010, Gogtay et al., 2004, Hasan et al., 2007, Lebel et al., 2010, Shaw et al., 2008). The endogenous cannabinoid (CB) system is also immature during adolescence (Anavi-Goffer and Mulder, 2009, Verdurand et al., 2011). In an animal model (Verdurand et al., 2011) imaged CB1 receptor binding using PET and found relatively lower activation of CB1 receptors in adolescent rats compared to adult rats in brain areas including those in the frontal cortex, temporal lobe (hippocampus and amygdala) and sub-cortical regions including striatal regions, thalamus, hypothalamus, superior colliculus. Thus, adolescence represents a developmental period with vulnerability to structural and functional changes due to exogenous MJ exposure.

+

Adolescent MJ use has the potential to cause structural and functional changes in the brain by altering cannabinoid signaling. One possible mechanism would be blunt neurotoxic influence. For example, delta9-tetrahydrocannabinol (THC), the primary psychoactive component in MJ that binds CB1 receptors, is reported to cause cell shrinkage and damage DNA strands in THC-treated neuron cultures (Chan et al., 1998). This may be the mechanism by which smaller volumes have been observed in individuals exposed to cannabis during adolescence (Battistella et al., 2014). However, it is more likely that MJ exerts its influence on brain development indirectly. The cannabinoid system plays a role in modulating other neurotransmitters, including gamma-aminobutyric acid (GABA), glutamate and monoamines (Lopez-Moreno et al., 2008). Specifically, activation of CB1 receptors is associated with down-regulating inhibitory GABAergic transmission in cortical interneurons during adolescence (Caballero and Tseng, 2012, Cass et al., 2014). In addition, CB signaling inhibits microglia function (Walter et al., 2003). These two points are important because cortical pruning processes involve glial-mediated synaptic elimination and altering the excitatory/inhibitory balance is liable to disrupt the selective tagging and preserving synapses (Selemon, 2013). The impact of this indirect influence on the developing brain may be in the observations of abnormal connectivity in those who began MJ use in adolescence (Jacobus et al., 2009). Evidence from human neuroimaging studies lends greater support to MJ-related disruptions to brain development.

+

Structural neuroimaging studies have indicated that volumes of several brain areas are smaller in heavy adult MJ users especially in areas enriched with cannabinoid 1 (CB1) receptors, such as medial temporal lobe, and prefrontal cortex (Lorenzetti et al., 2010). Studies of adult chronic MJ users note brain volume reductions in temporal lobe, insula, and prefrontal cortex, amygdala and hippocampus (Battistella et al., 2014, Cousijn et al., 2012, Filbey et al., 2014, Matochik et al., 2005, Yucel et al., 2008). Among different characteristics of MJ involvement (e.g., dependence symptoms, use frequency, consumption), the age of initial MJ use is a robust factor that has been associated with smaller brain volumes in users. For example, Battistella et al. (2014) observed left parahippocampal gyrus and right temporal pole structural differences in 25 regular MJ users compared to 22 occasional users, however, even the occasional users who began smoking MJ during adolescence (before age 18) demonstrated similar brain changes as the regular users. Our group has also found links with early MJ use onset (Bava et al., 2009) and structural connectivity with orbitofrontal cortex in a cohort of daily MJ users, suggesting complex neuroadaptive processes related to MJ use in the context of adolescent brain development (Filbey et al., 2014). These findings underscore the potential for significant heterogeneity in brain changes among adult MJ users, especially those who began using MJ during neurodevelopment.

+

Studies comparing early adolescent MJ use to users initiating MJ use in later adolescence provide further evidence for the potential of MJ to cause enduring change. The few studies that have directly investigated the timing of the effects of MJ during adolescence have noted divergent neurodevelopment effects. For example, in an fMRI study by Gruber and colleagues, functional and behavioral differences during an interference task were reported between early (before age 16) and late (after age 16) MJ users (Gruber et al., 2012) (Sagar et al., 2015). The same group also reported decreased white matter integrity in early onset vs. late onset MJ users (mean age 14.46 vs. 17.93) (Gruber et al., 2014). Similar differential effects have also been noted in parietal lobe activation between early and late adolescent binge drinkers during a spatial working memory task (Tapert et al., 2004). These studies highlight the importance of clarifying the differential neural effects of early- and late-adolescent onset use.

+

To that end, in the current study, we compared daily MJ users who were early onset users (<16 years old) versus late onset users (≥16 years old) on measures of cortical morphology that are sensitive to developmental changes. We aimed to characterize both the effect of early onset status on cortical morphology as well as assess for morphological patterns linked to the continued use of MJ after early and late adolescent MJ initiation. We expected early onset users to show a morphological pattern consistent with disruption of early adolescent brain development (e.g., increased cortical thickness, greater gray/white definition of the cortical ribbon via disruptions to adolescent pruning processes) that may be more consistent with indirect impact of MJ of brain development. While gray matter decline has been shown to be associated with marijuana use, particularly in areas rich in CB1 receptors, increased cortical thickness and greater gray/white definition in the cortical ribbon point to potential disruption in neurodevelopment (i.e. synaptic pruning) that may result from MJ use at key developmental stages (i.e. earlier as opposed to later in adolescent neuronal development). Such disruptions may extend to gyrification as well. While this process begins in utero, there is evidence that gyrification is ongoing into adolescence (Armstrong et al., 1995, Alemán-Gómez et al., 2013, Klein et al., 2014) and may also display aberrant developmental patterns in the presence of MJ use.

+
+ + + Methods +

This study was approved by the University of Texas at Dallas (UTD) and University of Texas Southwestern Medical Center (UTSW) Institutional Review Boards. All participants were recruited from the Dallas-Ft.Worth metro area via flyers and advertisements. Following informed consent, MJ users completed two sessions – a baseline appointment for collecting demographic, psychosocial and behavioral measures and a thorough substance use history. Three days later the participants returned for a neuroimaging appointment. Prior to their scanning session, participants were asked to be abstinent from MJ use for 72 h, from alcohol for 24 h, and from caffeine and cigarettes for the preceding 2 h. These were confirmed by self-report (MJ, alcohol, caffeine and cigarettes), quantitative THC urinalysis (MJ), and by breath alcohol level of .000 (alcohol) at the start of their session.

+ + + Participants +

We scanned 45 regular heavy MJ users as part of the parent project. Inclusion criteria were: right-handedness, English as the primary language and no histories of psychosis, traumatic brain injury, and MRI contraindications (e.g., pregnancy, non-removal metallic implants, claustrophobia). One subject reported a history of anxiety and depression and one other reported a history of ADHD as a child. Additional exclusions for the current study included: Axis I diagnosis (via SCID) other than cannabis use disorder, unusable sMRI due to motion artifact or poor signal-to-noise ratio that precluded accurate tissue segmentation (n = 1) and incomplete drug use histories (n = 2). Of the 42 remaining cases, 22 were early onset users (onset of first use before age 16). Group categorization using onset of regular use as opposed to onset of first use maintained the same grouping (mean early onset of regular use = 16.5, mean late onset of regular use = 19.0). Regular use was defined as at least one time per week. To determine how age of onset of regular MJ use influenced our reported effects, we performed these analyses while covarying for age of onset of regular use (see Supplement). Table 1 summarizes demographic and substance use information according to onset status. Table 2 summarizes the correlation between age and identified marijuana use variables. Only MJ years of use and current age showed a statistically significant correlation. Participants were recruited based on self-reported daily MJ use and a positive urinalysis for THC metabolites at their baseline visit. All of the participants were screened via urinalysis for other drugs of abuse and were excluded if drugs (other than MJ) were detected. Participants were required to have used MJ for a minimum of 5000 lifetime occasions and self-report daily use (without >24 h abstinence) for the last 60 days.

Sample characteristics. MJ, marijuana.

MeasureEarly onset (n = 20)
Late onset (n = 22)
p-ValueEffect size***Statistic
Mean(SD)Min–MaxMean(SD)Min–Max|t/U
Age32.50(8.01)21–5030.25(7.19)21–470.3160.302t = 1.01
Education (years)12.91(2.54)8–1813.26(2.40)10–190.6510.144t = 0.456
Gender (male)55%73%0.2410.034χ2 = 1.41
Ethnicity (% Caucasian)50%50%0.5660.008χ2 = 0.336
IQ*108(9.99)88–124105(13.54)83–1290.3510.298t = 0.94
Age of first MJ use**13.18(1.89)9–1516.90(1.48)16–21<0.0010.866U = 0
Age of regular MJ use**16.50(3.57)9–2519.00(4.29)16–360.0040.439U = 108
Substance use in the last 60 days
 MJ grams (daily)2.14(1.79)0.50–7.501.65(1.21)0.46–4.230.3380.083U = 182
 # EtOH drinks44.09(76.30)0–31038.85(58.61)0–1830.5880.039U = 198.05
 Max # EtOH drinks6.62(7.21)0–316.25(5.80)0–210.80.095U = 210
 # EtOH drinking days11.09(16.69)0–599.84(13.51)0–600.5370.006U = 185.5
 # Binge EtOH drinking days4.36(12.50)0–592.90(5.53)0–190.9680.035U = 218.5
 # EtOH drinks per day2.99(2.27)0–7.403.56(3.29)0–14.000.820.289U = 211
 # Cigarette days1.18(3.72)0–172.95(1.17)0–210.060.294U = 159
 # Cigarettes per day0.22(0.55)0–2.000.78(1.23)0–4.500.0570.296U = 158
 Max # cigarettes0.25(0.61)0–20.96(1.60)0–60.0540.290U = 157.5
Illicit drug use/past 90 days14%5%
Lifetime illicit drug use73%75%

IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.

p < .05; SS, standard score; |t|, absolute value of student's t, U is the Mann–Whitney U's score.

The effect sizes of the above table were calculated either based on mean differences if normally distributed, correlation coefficient or F-value score using the default Cohen's effect size formula for respective metrics.

The correlations between current age and all MJ use variables.

MeasureEarly onsetLate onset
First MJ user = 0.038r = 0.189
Regular MJ user = 0.289r = 0.203
MJ years of user = 0.898*r = 0.623**
MJ gramsr = 0.123r = 0.206

p < 0.001.

p < 0.005.

+
+ + + MRI acquisition and analysis + + + Image acquisition +

Scanning sessions took place at the Advanced Imaging Research Center at the University of Texas, Southwestern Medical Center three days following their initial visit. Another verification of THC metabolites via urinalysis was also performed before the scan. MRI images were collected using a 3T Philips whole-body scanner equipped with Quasar gradient subsystem (40 mT/m amplitude, a slew rate of 220 mT/m/ms). High-resolution T1-weighted anatomical scans were collected using a MPRAGE sequence: TR/TE/TI = 2100/3.70/1100 ms; flip angle = 12°; field of view = 256 mm × 256 mm; slab thickness = 160 mm (along left-right direction); voxel size = 1 mm × 1 mm × 1 mm, Total scan time = 3 m 57 s.

+
+ + + Image processing +

MPRAGE anatomical scans were pre-processed for surface-based analyses using FreeSurfer v5.3 semi-automated pipeline (http://surfer.nmr.mgh.harvard.edu). This semi-automated pipeline included spatial (Talairach) and signal intensity normalization of images, volumetric segmentation and subcortical labeling (Dale et al., 1999, Fischl et al., 2002). Outer gray matter and white matter boundaries were then identified and reconstructed into a mesh of over 150,000 tessellated vertices to allow point-to-point surface measures (Fischl et al., 1999). Next, gyral anatomy is aligned to a standard spherical template using surface convexity and curvature measures. Resulting surfaces were inspected, blind to MJ onset status, to identify and correct any errors made during cortical reconstruction. Modifications to the volumes were made as necessary to correct for tissue misclassifications according to FreeSurfer's wiki manual (Schmansky et al., 2010). In preparation for analysis, each morphological measure for each case was co-registered to a standard template (fsaverage). Anatomical labels in FreeSurfer (Desikan et al., 2006) were used for interpretation of results.

+
+
+ + + Morphological measures + + + Cortical thickness +

The width of the cortical ribbon was measured as the distance between corresponding vertices of the white matter and gray matter surfaces at each vertex in the cortical mantel (Fischl and Dale, 2000).

+
+ + + Gray–white matter ratio (GWR) +

To assess the quality of cortical ribbon definition, a tissue contrast between gray and white matter signal intensities was computed as a percent ratio (W − G)/(.5*(W + G)) (from pctsurfcon v1.11.2.1, inbuilt component of FreeSurfer pipeline v5.3, 2011). White matter signal intensities were measured at an absolute length of 1 mm below the gray–white border surface and gray matter signal was measured 30% into the cortical ribbon (Salat et al., 2009).

+
+ + + Local gyrification index +

The cortical surface from FreeSurfer's main pipeline is further processed to create an outer surface that encapsulates the gyral and sulcal curvature for each hemisphere, which serves as a basis for calculating a local gyrification index (Schaer et al., 2012). LGI is measured as the amount of cortex within the sulcal folds beneath the outer surface compared to the amount of visible cortex that touches the outer surface. Cortical maps are generated from repeated iterations of delineating a 25 mm radius sphere on the outer surface and its corresponding point on the cortical surface using a matching algorithm.

+
+
+ + + Background and premorbid characteristics + + + Sample characteristics +

Age, gender, education level, ethnicity, along with other background information, was obtained using a standard demographics questionnaire. The two-subtest administration of the Wechsler Abbreviated Scale of Intelligence (Vocabulary and Matrix Reasoning) provided estimates of intellect (Wechsler, 1999).

+
+ + + Substance use +

The Substance Use Disorder modules of the Structured Clinical Interview for DSM-IV (SCID) (First et al., 2002) were administered by a trained research assistant to assess for lifetime and current symptoms of abuse and dependence for alcohol, nicotine, MJ and other substances. The SCID interview also provided the onset of use information. A Time Line Follow-Back (TLFB) approach was used to quantify alcohol, nicotine, and MJ use patterns for 90 days prior to study participation (Sobell and Sobell, 1992). Marijuana use in grams was obtained via self-report in response to probes aimed at quantifying their regular use.

+
+
+ + + Statistical analyses +

Statistical analyses were conducted in SPSS 18.0 for behavioral and psychosocial measures whereas general linear model group comparisons on surfaced-based morphology measures were carried out FreeSurfer's built-in application QDEC (v1.5). Independent samples t-tests, Mann–Whitney U-tests or chi-square tests, compared groups on background and demographic variables (see Table 1). Before statistical analysis was conducted, the dependent measures of cortical thickness, GWR and LGI were smoothed using a FWHM Gaussian filter with a width of either 10 or 15 mm. Separate univariate general linear model (GLM) was then used to model cortical thickness, GWR and LGI with onset status of MJ use as a between groups factor. The dependent variables were thickness, gray–white ratio or local gyrification index and the independent variables were either recent monthly MJ use in grams (MJ grams) or duration of MJ use (MJ years). Age and total drinks in the past 2 months were treated as nuisance covariates in the model. Using MJ years of use and MJ grams as independent predictors of interest allowed us to characterize and differentiate the latent developmental effects from cumulative and current effects of MJ use. The variable “marijuana years of use” was based on the participants’ response to the question “For how many years have you been using marijuana regularly?” Of note, an outlier in the early onset group was removed before the statistical comparisons were performed.

+
+
+ + + Results + + + Cortical thickness +

There were no regions of group differences in cortical thickness by early onset status alone, controlled for age and alcohol use. However, MJ use characteristics were correlated with anterior dorsolateral prefrontal cortex thickness based on onset status. Early onset users showed increased thickness with increased MJ grams while late onset users showed thinner cortex with increased MJ grams (p < 0.05 uncorrected) (Table 3). The same pattern emerged with more years of MJ use being associated with thicker region of the right medial temporal lobe in the early onset users and the reverse for the late onset users (p < 0.05 uncorrected) (Fig. 1).

Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.

MeasureLabel@Max, Extended coverageSideMax-log(p)VtxMaxSize (mm2)xyzCorrelateP (corr)F-valueEffect Size**
ThicknessLingualR−2.488127,927111019−69−7MJ Years0.01610.070.063
GWRRostral middle frontal,L−2.66842,5051730−235310MJ Grams0.00111.091.969
Rostral middle frontalR−3.56594,8962661393018MJ Years0.000216.60.744
Medial orbitofrontalR−3.30484,7731368647−20MJ Years0.01314.920.796
LGIInferior parietalL3.456122,1692565−44−6244MJ Grams0.01515.890.131

p(corr), family-wise error fully corrected.

The effect sizes were derived from Freesurfer's tool in the significant region of interest using mri_segstats. This was also confirmed manually by using the F-value reported by Freesurfer.

Early vs. late onset marijuana users show divergent morphological patterns based on current marijuana use (measured in grams; MJ grams) in overlapping areas of anterior prefrontal cortex. GWR, gray/white matter border ratio; LGI, local gyrification index.

+
+ + + Gray–white matter contrast +

There were no regions of group differences in gray–white matter contrast by early onset status alone, controlled for age and alcohol use. However, current MJ consumption (grams) and onset status were differentially correlated with gray–white matter contrast in a left anterior dorsal frontal region (p < 0.05, FWE corrected). Increased gray–white contrast with heavier MJ use was seen in the early onset users and the opposite was seen in later onset users (heavier current use linked to decreasing GWR). The same pattern was seen between duration of MJ use in two prefrontal cortex clusters of the right dorsal frontal and medial orbitofrontal area p < 0.05, FWE corrected – more years of MJ use were linked to greater GWR among early users (Fig. 1).

+
+ + + Gyrification +

MJ use onset status alone showed no significant main effects above age and alcohol covariates. However, onset status was correlated with divergent patterns between local gyrification and MJ use, whereby early onset users showed decreasing LGI with increasing MJ consumption and longer duration of use in prefrontal cortex regions p < 0.05, FWE corrected. The left hemisphere clusters encompassed the majority of the length of the middle lateral surface of the left cortex, including motor cortices, parietal lobe and multimodal integration areas (Fig. 1).

+
+
+ + + Discussion +

The present study was designed to characterize the cortical architecture in adolescent onset MJ users by comparing early adolescent onset users to late adolescent onset in MJ use on measures of cortical thickness, gray/white matter contrast and gyrification. The primary finding was that early versus late onset MJ users showed a divergent pattern in cortical thickness, definition of the cortical ribbon and local gyrification with continued use through and beyond adolescent years. Specifically, early onset users showed cortical thickening, enhanced gray/white matter contrast, and decreased gyrification in association with more years of MJ use and current consumption of MJ in grams in frontal and temporal regions – areas that underlie higher order cognition including executive functioning, learning and memory. Findings were above and beyond effects of alcohol and current age, therefore, results are less likely to reflect morphological trends due to aging.

+

Our findings did not find the expected age of onset differences previously reported in marijuana users (Gruber et al., 2012, Gruber et al., 2014). This inconsistency suggests that the age of onset effects may be more robust in brain white matter connectivity (Gruber et al., 2014) and function (Gruber et al., 2012) than brain surface morphometry. To date, the few studies that have described altered cortical morphology in MJ users have led to mixed findings. Mata et al. (2010) identified brain regions with decreased sulcal depth suggestive of lower gyrification in a study of adult MJ users. Jacobus and Tapert (2014) recently reported increased cortical thickness in the entorhinal cortex among 24 adolescent MJ users (mean age = 17.7, mean MJ onset age = 15.4) relative to peer controls. However, the authors also reported a negative relationship between cortical thickness and total MJ use in the right paracentral gyrus, and they observed consistent positive relationships in various brain regions between age of MJ onset and thickness. In the only other known adolescent study of cortical thickness and MJ, Lopez-Larson and colleagues studied 18 adolescent heavy MJ users (similar in age and MJ onset as Jacobus and Tapert, 2014) and reported mixed findings of increased thickness in prefrontal/insula regions and decreased thickness in posterior/temporal lobe areas in the MJ users compared to controls. In contrast to Jacobus and Tapert, 2014, Lopez-Larson et al., 2011 found areas of the frontal lobe and insula that were thinner with increased urine THC metabolites and thicker with earlier age of onset. Select findings from the current study align with aspects of both of these studies, with a consensus supporting findings of a negative dose-dependent relationship between MJ use and cortical thickness. Given the low availability of studies to compare, this consensus is very limited. Although Jacobus et al. and Lopez-Larson et al. found the opposite effect of age of onset on thickness, the pattern of divergence among early vs. late onset users in the current study is more consistent with the latter study, whereby we saw early onset users exhibit thicker cortex with continued MJ use. Taken together, findings of increased thickness related to early MJ onset accompanied by negative dose-dependent relationships with MJ exposure may reflect two distinct processes. One process may be specific to the interactions with cortical development during early adolescence, likely leading to a disruption in pruning, and, the other, specific to the pharmacological effect with heavy chronic MJ use.

+

In the only known study to examine the curvature-morphology of the cortex in adult MJ users, Mata et al. (2010) identified decreased sulcal concavity and thinner sulci in 23 MJ users compared to controls (n = 44), also in prefrontal areas. However, they did not observe significant relationships with age, MJ onset age, or cumulative MJ use. It is interesting that the authors detected group level differences (MJ vs. controls) but no correlations with MJ use characteristics such as dose or age of onset, whereas our primary findings are the consistent effects of continued MJ use differing after early or late adolescent onset. There are substantial methodological explanations for this disparity. For example, the current study did not compare morphology in MJ users to a normative control sample, therefore, it is feasible that group-level differences may emerge with such a comparison. Likewise, we deliberately covaried for current age in order to control for brain changes with aging and thus optimize our interrogation of developmental effects of early onset age and of aspects of continued use.

+

The heterogeneity of MJ effects clearly suggests a multifactorial system of neurobiological processes involved. The primary results uphold that age of onset is a robust variable that differentiates heavy MJ users based on early versus late MJ onset. However, this group distinction relied on current use characteristics. Therefore, in the absence of group-level differences, the interactions between onset age and current use indicates that continued cannabis exposure and early adolescent developmental factors both contribute to a dynamic and sustained departure from what is expected based on developmental studies.

+

Typical synaptic refinement processes during early adolescence are in the context of long-term depression and potentiation of cortical neurons in order to facilitate neuronal remodeling. Thus, the normal course of early adolescent development is uniquely vulnerable to disruption by MJ due to the electrochemical conditions and maturity of brain processes that would not present together again. Cass and colleagues tested the sensitivity of early adolescence cannabinoid exposure in an animal model (Cass et al., 2014). They found that acute administration of cannabinoid agonists in early, middle and late adolescent rats led to a state of frequency-dependent disinhibition of neurons in the frontal cortex in the early-to-middle adolescent rats, but not in the late adolescent rats. Moreover, the authors also noted that adult rats previously exposed to cannabinoid agonists in adolescence displayed comparable neuronal disinhibition. Thus, by changing the inhibitory/excitatory landscape during adolescence, MJ can influence lasting changes to typical cortical remodeling during sensitive early adolescent years.

+

The sequence of pruning and myelination likely plays a formative role in lasting changes from early adolescent onset MJ use. With decreased synaptic elimination, our findings of greater GW border contrast may reflect greater proliferation of myelin at the boundary of the cortical ribbon where non-pruned synapses remained with linked axons. Findings of altered white matter tissue qualities are mixed in adolescent and adult MJ user samples. Some report both increases and decreases in fractional anisotropy (FA) and average water diffusion (Bava et al., 2009) whereas others report consistent decreases in FA among adolescent MJ users (Ashtari et al., 2009, Jacobus et al., 2009) or null findings (Delisi et al., 2006). Two studies of diffusion tensor imaging in adult MJ users reported reduced FA in users compared to controls (Gruber et al., 2011, Gruber et al., 2014). In addition to equivocal findings, research is needed to address the microstructural changes that could result in altered definition of the cortical ribbon. For example, rather than whole brain techniques that assess diffusion measures along major white matter tracts, indices assessing axonal organization along radial and interneuron association fibers along the cortical ribbon are needed. This scenario played out could result in increased gray matter (thicker cortex from disrupted pruning) and the myelination of connections to these spared terminals would result in increased density of white matter at the cortical boundary. Without any known studies of adolescent development of the gray/white tissue contrast at the cortical border to serve as a point of comparison, we speculate that early adolescent disruption of pruning and subsequent myelination of connections at the cortical boundary would be reflected by increased GWR as we saw in the current study.

+
+ + + Limitations and conclusions +

The cross-sectional nature of this study limits causal attributions in terms of what we can infer to be directly related to the effects of MJ. Although a longitudinal design is optimal for addressing brain changes directly due to MJ, cross-sectional studies facilitate data-driven hypotheses that can be assessed directly in prospective studies.

+

It is important to keep in mind that the participants were not explicitly asked for possible years of abstinence during their period of regular use, which may have created possible inflation in reported duration of regular use. However, because the participants provided number of years of “regular” marijuana use, this inherently suggests continued, uninterrupted years of use. Concurrent nicotine use could have also influenced our reported results. But in the absence of a larger sample size and the presence of huge variance in nicotine use in the current sample, we were unable to verify the effect of nicotine use in the reported results.

+

Interpretation of these findings is also limited by the lack of behavioral anchors for the observed morphological effects and lack of information on other aspects of developmental history that could further characterize the effects of marijuana during neurodevelopment. This is further limited by the absence of “expected” patterns based on normative data. Given the varied directions of effects and the small sample size, these findings should be replicated and be viewed as preliminary.

+

To conclude, early MJ use was linked to altered neurodevelopmental patterns in brain regions sub-serving higher-order cognitive process. Clinical implications include need for early, targeted intervention. Given that the most robust results were related to interactions between onset age and continued use through emerging adulthood, harm reduction approaches may be effective in moderating adolescent MJ use to levels that are less likely to cause long-term developmental changes.

+
+ + Conflict of interest +

The authors report no conflicts of interest.

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The following are Supplementary data to this article:

+
+ + Acknowledgements +

This research was funded by the National Institute on Drug Abuse (R01 DA030344, Filbey). We would like to thank all the participants who volunteered for this study. We are also very grateful to Talha Alvi, Sina Aslan, Jessica Baine, Collette Bice, Vicki Germer, Ariel Ketcherside, Alison King, Brittany Kuhn, Tyler Rhinehardt, Wing Ting To and the team of lab interns for their assistance with recruitment, running participants and data management.

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+ + + +

Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2015.10.001.

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diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000.csv new file mode 100644 index 0000000..f023fbb --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000.csv @@ -0,0 +1,21 @@ +Measure,Early onset (n = 20),Early onset (n = 20),Early onset (n = 20),Late onset (n = 22),Late onset (n = 22),Late onset (n = 22),p-Value,Effect size???,Statistic +Unnamed: 0_level_1,Mean,(SD),Min–Max,Mean,(SD),Min–Max,Unnamed: 7_level_1,Unnamed: 8_level_1,|t/U +Age,32.50,(8.01),21–50,30.25,(7.19),21–47,0.316,0.302,t = 1.01 +Education (years),12.91,(2.54),8–18,13.26,(2.40),10–19,0.651,0.144,t = 0.456 +Gender (male),55%,,,73%,,,0.241,0.034,χ2 = 1.41 +Ethnicity (% Caucasian),50%,,,50%,,,0.566,0.008,χ2 = 0.336 +IQ???,108,(9.99),88–124,105,(13.54),83–129,0.351,0.298,t = 0.94 +Age of first MJ use???,13.18,(1.89),9–15,16.90,(1.48),16–21,<0.001,0.866,U = 0 +Age of regular MJ use???,16.50,(3.57),9–25,19.00,(4.29),16–36,0.004,0.439,U = 108 +Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days,Substance use in the last 60 days +MJ grams (daily),2.14,(1.79),0.50–7.50,1.65,(1.21),0.46–4.23,0.338,0.083,U = 182 +# EtOH drinks,44.09,(76.30),0–310,38.85,(58.61),0–183,0.588,0.039,U = 198.05 +Max # EtOH drinks,6.62,(7.21),0–31,6.25,(5.80),0–21,0.8,0.095,U = 210 +# EtOH drinking days,11.09,(16.69),0–59,9.84,(13.51),0–60,0.537,0.006,U = 185.5 +# Binge EtOH drinking days,4.36,(12.50),0–59,2.90,(5.53),0–19,0.968,0.035,U = 218.5 +# EtOH drinks per day,2.99,(2.27),0–7.40,3.56,(3.29),0–14.00,0.82,0.289,U = 211 +# Cigarette days,1.18,(3.72),0–17,2.95,(1.17),0–21,0.06,0.294,U = 159 +# Cigarettes per day,0.22,(0.55),0–2.00,0.78,(1.23),0–4.50,0.057,0.296,U = 158 +Max # cigarettes,0.25,(0.61),0–2,0.96,(1.60),0–6,0.054,0.290,U = 157.5 +Illicit drug use/past 90 days,14%,,,5%,,,,, +Lifetime illicit drug use,73%,,,75%,,,,, diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000_info.json new file mode 100644 index 0000000..950f279 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000_info.json @@ -0,0 +1 @@ +{"table_id": "tbl0005", "table_label": "Table 1", "table_caption": "Sample characteristics. MJ, marijuana.", "table_foot": "*IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.", "n_header_rows": 2, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001.csv b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001.csv new file mode 100644 index 0000000..abbbe2d --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001.csv @@ -0,0 +1,5 @@ +Measure,Early onset,Late onset +First MJ use,r = 0.038,r = 0.189 +Regular MJ use,r = 0.289,r = 0.203 +MJ years of use,r = 0.898???,r = 0.623??? +MJ grams,r = 0.123,r = 0.206 diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001_info.json b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001_info.json new file mode 100644 index 0000000..c6bb046 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001_info.json @@ -0,0 +1 @@ +{"table_id": "tbl0010", "table_label": "Table 2", "table_caption": "The correlations between current age and all MJ use variables.", "table_foot": "*p\u00a0<\u00a00.001.", "n_header_rows": 1, "table_data_file": "table_001.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002.csv b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002.csv new file mode 100644 index 0000000..7445b91 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002.csv @@ -0,0 +1,6 @@ +Measure,"Label@Max, Extended coverage",Side,Max-log(p),VtxMax,Size (mm2),x,y,z,Correlate,P (corr),F-value,Effect Size??? +Thickness,Lingual,R,−2.488,"127,927",1110,19,−69,−7,MJ Years,0.016,10.07,0.063 +GWR,"Rostral middle frontal,",L,−2.668,"42,505",1730,−23,53,10,MJ Grams,0.001,11.09,1.969 +,Rostral middle frontal,R,−3.565,"94,896",2661,39,30,18,MJ Years,0.0002,16.6,0.744 +,Medial orbitofrontal,R,−3.304,"84,773",1368,6,47,−20,MJ Years,0.013,14.92,0.796 +LGI,Inferior parietal,L,3.456,"122,169",2565,−44,−62,44,MJ Grams,0.015,15.89,0.131 diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002_info.json b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002_info.json new file mode 100644 index 0000000..bfd3e11 --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002_info.json @@ -0,0 +1 @@ +{"table_id": "tbl0015", "table_label": "Table 3", "table_caption": "Clusters of significant age of onset\u00a0\u00d7\u00a0marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.", "table_foot": "p(corr), family-wise error fully corrected.", "n_header_rows": 1, "table_data_file": "table_002.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/tables.xml b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/tables.xml new file mode 100644 index 0000000..35835ea --- /dev/null +++ b/tests/data/sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/tables.xml @@ -0,0 +1,2 @@ + +4691364265074334691364S1878-9293(15)00091-210.1016/j.dcn.2015.10.001tbl0005Table 1Sample characteristics. MJ, marijuana.*IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.

Sample characteristics. MJ, marijuana.

MeasureEarly onset (n = 20)
Late onset (n = 22)
p-ValueEffect size***Statistic
Mean(SD)Min–MaxMean(SD)Min–Max|t/U
Age32.50(8.01)21–5030.25(7.19)21–470.3160.302t = 1.01
Education (years)12.91(2.54)8–1813.26(2.40)10–190.6510.144t = 0.456
Gender (male)55%73%0.2410.034χ2 = 1.41
Ethnicity (% Caucasian)50%50%0.5660.008χ2 = 0.336
IQ*108(9.99)88–124105(13.54)83–1290.3510.298t = 0.94
Age of first MJ use**13.18(1.89)9–1516.90(1.48)16–21<0.0010.866U = 0
Age of regular MJ use**16.50(3.57)9–2519.00(4.29)16–360.0040.439U = 108
Substance use in the last 60 days
 MJ grams (daily)2.14(1.79)0.50–7.501.65(1.21)0.46–4.230.3380.083U = 182
 # EtOH drinks44.09(76.30)0–31038.85(58.61)0–1830.5880.039U = 198.05
 Max # EtOH drinks6.62(7.21)0–316.25(5.80)0–210.80.095U = 210
 # EtOH drinking days11.09(16.69)0–599.84(13.51)0–600.5370.006U = 185.5
 # Binge EtOH drinking days4.36(12.50)0–592.90(5.53)0–190.9680.035U = 218.5
 # EtOH drinks per day2.99(2.27)0–7.403.56(3.29)0–14.000.820.289U = 211
 # Cigarette days1.18(3.72)0–172.95(1.17)0–210.060.294U = 159
 # Cigarettes per day0.22(0.55)0–2.000.78(1.23)0–4.500.0570.296U = 158
 Max # cigarettes0.25(0.61)0–20.96(1.60)0–60.0540.290U = 157.5
Illicit drug use/past 90 days14%5%
Lifetime illicit drug use73%75%

IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.

p < .05; SS, standard score; |t|, absolute value of student's t, U is the Mann–Whitney U's score.

The effect sizes of the above table were calculated either based on mean differences if normally distributed, correlation coefficient or F-value score using the default Cohen's effect size formula for respective metrics.

Table 1
Sample characteristics. MJ, marijuana.
MeasureEarly onset (n = 20)Late onset (n = 22)p-ValueEffect size???Statistic
Mean(SD)Min–MaxMean(SD)Min–Max|t/U
Age32.50(8.01)21–5030.25(7.19)21–470.3160.302t = 1.01
Education (years)12.91(2.54)8–1813.26(2.40)10–190.6510.144t = 0.456
Gender (male)55%73%0.2410.034χ2 = 1.41
Ethnicity (% Caucasian)50%50%0.5660.008χ2 = 0.336
IQ???108(9.99)88–124105(13.54)83–1290.3510.298t = 0.94
Age of first MJ use???13.18(1.89)9–1516.90(1.48)16–21<0.0010.866U = 0
Age of regular MJ use???16.50(3.57)9–2519.00(4.29)16–360.0040.439U = 108
Substance use in the last 60 days
 MJ grams (daily)2.14(1.79)0.50–7.501.65(1.21)0.46–4.230.3380.083U = 182
 # EtOH drinks44.09(76.30)0–31038.85(58.61)0–1830.5880.039U = 198.05
 Max # EtOH drinks6.62(7.21)0–316.25(5.80)0–210.80.095U = 210
 # EtOH drinking days11.09(16.69)0–599.84(13.51)0–600.5370.006U = 185.5
 # Binge EtOH drinking days4.36(12.50)0–592.90(5.53)0–190.9680.035U = 218.5
 # EtOH drinks per day2.99(2.27)0–7.403.56(3.29)0–14.000.820.289U = 211
 # Cigarette days1.18(3.72)0–172.95(1.17)0–210.060.294U = 159
 # Cigarettes per day0.22(0.55)0–2.000.78(1.23)0–4.500.0570.296U = 158
 Max # cigarettes0.25(0.61)0–20.96(1.60)0–60.0540.290U = 157.5
Illicit drug use/past 90 days14%5%
Lifetime illicit drug use73%75%
*IQ scores derived from Wechsler Abbreviated Scale of Intelligence Vocabulary and Matrix Reasoning subtests.**p < .05; SS, standard score; |t|, absolute value of student's t, U is the Mann–Whitney U's score.***The effect sizes of the above table were calculated either based on mean differences if normally distributed, correlation coefficient or F-value score using the default Cohen's effect size formula for respective metrics.
tbl0010Table 2The correlations between current age and all MJ use variables.*p < 0.001.

The correlations between current age and all MJ use variables.

MeasureEarly onsetLate onset
First MJ user = 0.038r = 0.189
Regular MJ user = 0.289r = 0.203
MJ years of user = 0.898*r = 0.623**
MJ gramsr = 0.123r = 0.206

p < 0.001.

p < 0.005.

Table 2
The correlations between current age and all MJ use variables.
MeasureEarly onsetLate onset
First MJ user = 0.038r = 0.189
Regular MJ user = 0.289r = 0.203
MJ years of user = 0.898???r = 0.623???
MJ gramsr = 0.123r = 0.206
*p < 0.001.**p < 0.005.
tbl0015Table 3Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.p(corr), family-wise error fully corrected.

Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.

MeasureLabel@Max, Extended coverageSideMax-log(p)VtxMaxSize (mm2)xyzCorrelateP (corr)F-valueEffect Size**
ThicknessLingualR−2.488127,927111019−69−7MJ Years0.01610.070.063
GWRRostral middle frontal,L−2.66842,5051730−235310MJ Grams0.00111.091.969
Rostral middle frontalR−3.56594,8962661393018MJ Years0.000216.60.744
Medial orbitofrontalR−3.30484,7731368647−20MJ Years0.01314.920.796
LGIInferior parietalL3.456122,1692565−44−6244MJ Grams0.01515.890.131

p(corr), family-wise error fully corrected.

The effect sizes were derived from Freesurfer's tool in the significant region of interest using mri_segstats. This was also confirmed manually by using the F-value reported by Freesurfer.

Table 3
Clusters of significant age of onset × marijuana use interactions. GWR, gray/white matter border ratio; LGI, local gyrification index.
MeasureLabel@Max, Extended coverageSideMax-log(p)VtxMaxSize (mm2)xyzCorrelateP (corr)F-valueEffect Size???
ThicknessLingualR−2.488127,927111019−69−7MJ Years0.01610.070.063
GWRRostral middle frontal,L−2.66842,5051730−235310MJ Grams0.00111.091.969
Rostral middle frontalR−3.56594,8962661393018MJ Years0.000216.60.744
Medial orbitofrontalR−3.30484,7731368647−20MJ Years0.01314.920.796
LGIInferior parietalL3.456122,1692565−44−6244MJ Grams0.01515.890.131
p(corr), family-wise error fully corrected.**The effect sizes were derived from Freesurfer's tool in the significant region of interest using mri_segstats. This was also confirmed manually by using the F-value reported by Freesurfer.
\ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json b/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json deleted file mode 100644 index f72d946..0000000 --- a/tests/data/sample_inputs/6ZqhJVpfYDzu/identifiers.json +++ /dev/null @@ -1 +0,0 @@ -{"pmid": "16513147", "doi": "10.1016/j.neuropsychologia.2006.01.005", "pmcid": null} \ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv deleted file mode 100644 index 0e3dc1e..0000000 --- a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/coordinates.csv +++ /dev/null @@ -1,77 +0,0 @@ -table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-39.0,57.0,3.0,,Left middle frontal gyrus (BA 10),6,3.9,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,42.0,57.0,9.0,,Right middle frontal gyrus (BA 10),23,4.4,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,54.0,21.0,-6.0,,Right inferior frontal gyrus (BA 47),21,3.9,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-42.0,18.0,-6.0,,Left inferior frontal gyrus (BA 47),9,3.9,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,48.0,15.0,12.0,,Right insula (BA 13),8,3.7,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-39.0,0.0,27.0,,Left insula (BA 13),15,4.4,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,45.0,-42.0,51.0,,Right inferior parietal lobule (BA 40),37,4.1,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,33.0,-57.0,51.0,,Right superior parietal lobule (BA 7),25,4.4,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,42.0,-57.0,48.0,,Right angular gyrus (BA 39),8,3.7,Cue identification PM > uncontaminated ongoing -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,0.0,48.0,-6.0,,Medial anterior prefrontal cortex (BA 10),23,3.9,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,30.0,39.0,51.0,,Right superior frontal gyrus (BA 8),5,3.4,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,6.0,12.0,36.0,,Right cingulate gyrus (BA 24),7,3.9,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-42.0,-15.0,42.0,,Left precentral gyrus (BA 4/3),11,4.2,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-3.0,-21.0,57.0,,Left medial frontal gyrus (BA 6/31),17,3.7,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,6.0,-24.0,69.0,,Right medial frontal gyrus (BA 6),19,3.9,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,24.0,-24.0,72.0,,Right precentral gyrus (BA 4),11,3.6,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,3.0,-33.0,54.0,,Right precuneus (BA 7),13,3.7,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-3.0,-48.0,60.0,,Left precuneus (BA 7),14,3.5,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,12.0,-51.0,6.0,,Right lingual gyrus (BA 18/19),17,3.6,Uncontaminated ongoing > cue identification PM -3622,Table 2,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",2,-9.0,-63.0,12.0,,Left lingual gyrus/cuneus (BA 18/30),24,4.0,Uncontaminated ongoing > cue identification PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-39.0,57.0,3.0,,Left middle frontal gyrus (BA 10),8,3.6,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,39.0,54.0,15.0,,Right middle frontal gyrus (BA 10),203,5.1,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,27.0,54.0,-12.0,,Right middle/superior frontal gyrus (BA 11),16,3.7,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,21.0,-15.0,,Right inferior frontal gyrus (BA 47),22,4.1,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,9.0,21.0,48.0,,Right medial frontal gyrus (BA 8),15,3.6,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-39.0,18.0,-6.0,,Left insula (BA 13),20,3.9,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,12.0,39.0,,Right precentral gyrus (BA 9),24,3.7,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,51.0,12.0,9.0,,Right precentral gyrus/insula (BA 44),36,4.0,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-51.0,9.0,39.0,,Left inferior/middle central gyrus (BA 9),31,4.5,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-42.0,3.0,27.0,,Left precentral gyrus (BA 6),54,5.1,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,45.0,-42.0,51.0,,Right inferior parietal lobule (BA 40),199,5.2,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-48.0,-45.0,48.0,,Left inferior parietal lobule (BA 40),155,5.0,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,12.0,-66.0,39.0,,Right precuneus (BA 19),12,4.3,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,36.0,-90.0,-12.0,,Right inferior occipital gyrus (BA 18),46,4.0,Intention retrieval PM > uncontaminated ongoing -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,0.0,48.0,-6.0,,Medial anterior prefrontal cortex (BA 10),243,5.3,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,12.0,36.0,,Right cingulate gyrus (BA 24),5,3.7,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,54.0,-12.0,57.0,,Right lateral parietal cortex (BA 3),30,4.4,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-42.0,-15.0,45.0,,Left precentral gyrus (BA 4/3),13,3.6,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-3.0,-18.0,57.0,,Left medial frontal gyrus (BA 6/31),19,4.0,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,-30.0,72.0,,Right medial frontal gyrus (BA 6/4),13,3.7,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,3.0,-33.0,57.0,,Right precuneus (BA 7),8,3.6,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,12.0,-51.0,6.0,,Right lingual gyrus (BA 18),8,3.6,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,-6.0,-57.0,15.0,,Left lingual gyrus/cuneus (BA 30),48,4.7,Uncontaminated ongoing > intention retrieval PM -3623,Table 3,". Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks",3,6.0,-87.0,24.0,,Right cuneus (BA 18),6,3.7,Uncontaminated ongoing > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,3.0,36.0,-12.0,,Right anterior cingulate (BA 32/11),53,3.7,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-3.0,15.0,-6.0,,Left anterior cingulate (BA 25),31,4.5,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-24.0,0.0,-9.0,,Left putamen,51,5.0,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,57.0,-18.0,21.0,,Right insula (BA 40),24,3.6,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-54.0,-21.0,21.0,,Left postcentral gyrus/insula (BA 40),113,4.5,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-54.0,-72.0,24.0,,Left middle temporal gyrus (BA 39/19),7,3.4,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-21.0,-78.0,-6.0,,Left lingual gyrus (BA 18),17,3.8,Cue identification PM > intention retrieval PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-33.0,51.0,18.0,,Left middle frontal gyrus (BA 10),6,3.4,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,27.0,51.0,-3.0,,Right superior frontal gyrus (BA 10),12,3.8,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-18.0,42.0,21.0,,Left medial frontal gyrus (BA 9),5,3.4,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-12.0,27.0,27.0,,Left anterior cingulate (BA 32/24),11,3.9,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,36.0,24.0,-3.0,,Right insula (BA 13/47),13,4.2,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-3.0,21.0,45.0,,Left cingulate gyrus (BA 32),364,6.3,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,24.0,12.0,63.0,,Right lateral frontal cortex (BA 6),118,4.5,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,3.0,9.0,30.0,,Right cingulate gyrus (BA 24),5,4.0,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-27.0,6.0,66.0,,Left lateral frontal cortex (BA 6),46,5.1,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-42.0,0.0,57.0,,Left middle frontal gyrus (BA 6),5,3.4,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-45.0,-54.0,57.0,,Left inferior parietal lobule (BA 40),5,3.4,Intention retrieval PM > cue identification PM -3624,Table 4,". Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks",4,-12.0,-75.0,33.0,,Left cuneus (BA 18),246,5.8,Intention retrieval PM > cue identification PM -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,36.0,30.0,-6.0,,Right inferior frontal gyrus (BA 47),31,4.1,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-42.0,21.0,15.0,,Left insula (BA 13),9,4.3,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-51.0,9.0,33.0,,Left precentral gyrus (BA 6),11,3.5,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-27.0,6.0,-6.0,,Left putamen,30,3.9,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-42.0,-48.0,-9.0,,Left fusiform gyrus (BA 37),9,3.8,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-30.0,-93.0,21.0,,Left middle occipital gyrus (BA 19),9,4.3,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-15.0,-99.0,-3.0,,Left occipital lingual gyrus (BA 18),79,4.9,Cue identification ongoing > intention retrieval ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-21.0,54.0,6.0,,Left medial frontal gyrus (BA 10),15,4.2,Intention retrieval ongoing > cue identification ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,18.0,18.0,66.0,,Right middle frontal gyrus (BA 6),8,3.7,Intention retrieval ongoing > cue identification ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-12.0,-18.0,60.0,,Left paracentral lobule (BA 6),14,3.8,Intention retrieval ongoing > cue identification ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,12.0,-30.0,66.0,,Right paracentral lobule (BA 6),20,3.9,Intention retrieval ongoing > cue identification ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,-15.0,-30.0,66.0,,Left postcentral gyrus (BA 4),10,4.0,Intention retrieval ongoing > cue identification ongoing -3625,Table 5,". Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks",5,3.0,-75.0,36.0,,Left cuneus (BA 18),6,3.9,Intention retrieval ongoing > cue identification ongoing diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json deleted file mode 100644 index fc3e0ea..0000000 --- a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/metadata.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "title": "Differential components of prospective memory? Evidence from fMRI.", - "authors": "Simons, Jon S;Sch\u00f6lvinck, Marieke L;Gilbert, Sam J;Frith, Chris D;Burgess, Paul W", - "journal": "Neuropsychologia", - "keywords": null, - "abstract": "Two of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (\"cue identification\") and remembering the action to be performed (\"intention retrieval\"). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.", - "publication_year": 2006, - "coordinate_space": "MNI", - "license": null, - "text": true -} \ No newline at end of file diff --git a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt b/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt deleted file mode 100644 index 388975f..0000000 --- a/tests/data/sample_inputs/6ZqhJVpfYDzu/processed/ace/text.txt +++ /dev/null @@ -1,68 +0,0 @@ - - - - - - - - - - - - - - - - - - - - -Differential components of prospective memory?: Evidence from fMRI - ScienceDirect - - - - - - - - - - - - - - - - JavaScript is disabled on your browser. - Please enable JavaScript to use all the features on this page. - - -Skip to main contentSkip to article -ScienceDirectJournals & BooksHelpSearchMy accountThe University of Texas at AustinView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Methods3. Results4. DiscussionAcknowledgementsReferencesShow full outlineCited by (253)Figures (2)Tables (5)Table 1Table 2Table 3Table 4Table 5NeuropsychologiaVolume 44, Issue 8, 2006, Pages 1388-1397Differential components of prospective memory?: Evidence from fMRIAuthor links open overlay panelJon S. Simons a, Marieke L. Schölvinck a, Sam J. Gilbert a, Chris D. Frith b, Paul W. Burgess aShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.neuropsychologia.2006.01.005Get rights and contentAbstractTwo of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (“cue identification”) and remembering the action to be performed (“intention retrieval”). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.Previous article in issueNext article in issueKeywordsAnterior prefrontal cortexRostral prefrontal cortexFrontal lobesFunctional magnetic resonance imaging (fMRI)Executive function1. IntroductionProspective memory (PM1), remembering to perform an intended action after a delay (Meacham & Singer, 1977), may involve a number of processing stages: forming an intention, maintaining the intention in memory over an interval while being engaged in another (or ongoing) task, executing the intended action at the appropriate moment, and evaluating the outcome (Freud, 1901, Ellis, 1996). Much research into PM has focused on the third of these stages, involving recognition of the appropriate moment to act and remembering what action was to be performed (see Table 1 in Burgess, Scott, & Frith, 2003, for a list of cardinal properties of PM). The most often studied example is where an action needs to be performed when an external event occurs, such as remembering to stop and buy a loaf of bread when you drive past the grocery store (“event-based PM”; Einstein, Holland, McDaniel, & Guynn, 1992). McDaniel and Einstein (1992) proposed a division of event-based PM into two components: cue identification and intention retrieval. Cue identification involves the detection of the cue event (e.g., the grocery store) signaling that the intended action should be performed; intention retrieval involves the subsequent recovery of that intention (e.g., buying the bread) from memory. There are a considerable number of behavioral studies that have investigated these components (e.g., Brandimonte & Passolunghi, 1994; Cohen, West, & Craik, 2001; Marsh, Hicks, Cook, Hansen, & Pallos, 2003; Einstein et al., 1992; Einstein, McDaniel, Manzi, Cochran, & Baker, 2000; Ellis & Milne, 1996; West, Herndon, & Crewdson, 2001; West & Ross-Munroe, 2002; West, Wymbs, Jakubek, & Herndon, 2003).Much of this research has focused on one or other of the two components, such as on the effect of cue characteristics in triggering a response. It has been shown that cues that are particularly salient tend to be noticed more frequently (Einstein et al., 2000), that unfamiliar cues benefit prospective remembering (Brandimonte & Passolunghi, 1994), and that when an intention has been formed to respond to a particular category of cues, highly typical category members evoke the intention more often than less typical exemplars (Ellis & Milne, 1996). Studies of intention retrieval have concentrated primarily on the association between the cue and the stored intention. When this association is strong, retrieval may be relatively automatic, as opposed to more effortful processing when the association is weak (McDaniel & Einstein, 2000; McDaniel, Einstein, Guynn, & Breneiser, 2004).Despite the extensive research on PM processes, however, relatively few studies have investigated whether cue identification and intention retrieval might rely on separable cognitive processes. Cohen et al. (2001) evaluated in separate experiments the hypothesis that cue identification and intention retrieval are primarily supported by stimulus-driven and conceptually driven processes, respectively. Cue identification was manipulated by a change in format of the cue from study session to test session and, in a second experiment, intention retrieval was manipulated by a change in semantic relatedness between the cue and the intention from study session to test session. The authors found that a change in cue format reduced the number of PM cues detected, while semantically unrelated intentions were less often correctly recalled upon detection of the cue, consistent with their hypothesis. However, only accuracy data were reported, although many PM studies have reported an effect of prospective memory retrieval on reaction times (e.g., Burgess, Scott, & Frith, 2003; Marsh, Hicks, & Watson, 2002; Marsh et al., 2003; West et al., 2001). Marsh et al. (2003) examined reaction times while investigating the extent to which maintenance of a PM intention might affect cognitive processing of the ongoing task at the time a PM cue is encountered (see also Smith, 2003). Marsh et al. manipulated cue identification and intention retrieval demands separately and found differential effects on reaction times in the ongoing task. However, despite showing a differential effect of maintaining a PM intention on performance of the ongoing task, these authors did not study the effect of manipulating cue identification and intention retrieval on the PM task itself.The few available results thus suggest that cue identification and intention retrieval might be separable behaviorally, in that manipulating the demand on the two components may differentially affect error rates and/or reaction times. However, even if this is the case, it may not necessarily follow that the two components are supported by exclusively different brain regions. Previous neuroimaging studies of PM have used a variety of paradigms which, although not designed to manipulate cue identification and intention retrieval as experimental variables, have nevertheless involved cue identification and intention retrieval processes to differing extents. In all of these studies, a consistent pattern of activation has been observed, involving particularly anterior prefrontal cortex (approximating Brodmann area 10) (Burgess, Quayle, & Frith, 2001; Burgess et al., 2003; den Ouden, Frith, Frith, & Blakemore, 2005; Okuda et al., 1998). It is not clear, therefore, whether the anterior prefrontal cortex network is involved in PM function to a similar degree irrespective of the demands on cue identification and intention retrieval, or whether varying cue identification and intention retrieval as experimental variables will reveal that the key neural correlate of PM reflects processing relating to one of the hypothesized components more than the other.The experiment presented here examined these issues by scanning participants using fMRI while they were undertaking a task in which PM trials were embedded in an ongoing task in such a way as to prevent participants from actively rehearsing the intentions. Two PM conditions were used, one with high cue identification demand and low intention retrieval demand (the ‘cue identification PM condition’), and one with low cue identification demand and high intention retrieval demand (the ‘intention retrieval PM condition’). Cue identification was manipulated by altering the perceptual salience of the PM cues (Brandimonte & Passolunghi, 1994; Einstein et al., 2000). In the low cue identification demand condition, the cues were perceptually distinct from the ongoing trials, while in the high demand condition, the cues were perceptually similar but conceptually distinct. Intention retrieval demand was manipulated by varying the number of actions participants needed to perform in order to determine the appropriate response. If, as predicted by previous neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), an anterior prefrontal cortex network supports PM function regardless of the demands on cue identification and intention retrieval, then substantial overlap can be expected between the patterns of activation associated with each PM condition.The use of word and shape versions of the task enabled analysis involving conjunction contrasts across tasks to identify brain regions that are commonly activated across stimulus types, and might be considered to reflect “central”, task-independent PM processes, as opposed to those that might be specific to a particular stimulus type or task. In addition, to examine the effect of maintaining a PM intention on performance of the ongoing task (Marsh et al., 2003, Smith, 2003), a session consisting solely of ongoing trials was included at the beginning of the experiment, before participants had received any instructions concerning PM trials. Previous studies have shown that once instructed about a PM condition, the expectation that a PM trial will occur continues even if participants are subsequently instructed that there will be no PM trials in the upcoming block (Burgess et al., 2003; Einstein et al., 2005; Holbrook, Bost, & Cave, 2003). Ongoing trials presented before exposure to a PM condition should not be contaminated by the expectation of a PM trial, so were termed ‘uncontaminated’ ongoing trials, with ongoing trials occurring after presentation of PM instructions termed ‘contaminated’ ongoing trials. Burgess et al. (2001) have shown that not only the execution, but also the expectation, of a PM trial can be associated with lateral anterior prefrontal cortex activation. If this region is involved in maintenance of the PM intention, it should show greater activation in the present experiment during contaminated versus uncontaminated ongoing trials and, indeed, between contaminated ongoing trials in the intention retrieval versus cue identification PM conditions.2. Methods2.1. ParticipantsSixteen right-handed native speakers of English (eight males, eight females, mean age 23.4 years, range 18-30 years) volunteered to take part in the experiment. They were screened using a comprehensive medical questionnaire and written informed consent was obtained before participating.2.2. Design and materialsTwo PM tasks, a word and a shape task, were administered to each participant. Each task consisted of ongoing trials, prospective memory trials with high cue identification and low intention retrieval demands (termed cue identification PM trials), and prospective memory trials with low cue identification and high intention retrieval demands (termed intention retrieval PM trials). The two PM conditions occurred in separate sessions.Each trial in the word task consisted of two nouns presented in the middle of the screen, one of which was written in upper case and the other in lower case letters (see Fig. 1). Words were drawn from the MRC Psycholinguistic Database (Wilson, 1988), and were matched for written frequency, familiarity, and concreteness. For ongoing trials, participants were instructed to indicate using a keypad whether the left or the right of the two words contained more letters. In the cue identification PM condition, in which trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the words belonged to the same semantic category, for example cow and horse. Conversely, in the intention retrieval PM condition, in which trials were perceptually distinct from the ongoing condition, words were written in the same case and participants were required to retrieve a greater number of actions than in the cue identification PM condition: count up the syllables of both words and press one key if the total was four or less, or another key if the total was higher than four. To avoid interference between the instructions, words of the same semantic category were never written in the same case.Download : Download full-size imageFig. 1. The words (top) and shapes (bottom) experimental tasks. An ongoing trial, a cue identification PM trial, and an intention retrieval PM trial are shown for each task.The shape task consisted of a 4 × 4 grid, in which a colored triangle and a random other shape, such as a pentagon, were presented (see Fig. 1). Each shape was drawn in a different color, selected from six possible hues. Irregular shapes were used to avoid recognition at first glance. For ongoing trials, participants were instructed to indicate whether the shape other than the triangle was presented to the left or the right of the triangle (see Fig. 1). In the cue identification PM condition in which, as before, trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the two shapes were a chess knight's move away from each other. In the intention retrieval condition, in which trials were perceptually distinct from the ongoing condition, the two shapes were of the same color and participants were required to retrieve a greater number of actions than in the cue identification PM condition: determine the number of sides of the shape other than the triangle, and press one key if this number was five or less, and another key if this number was higher than five. Again, to avoid conflicting instructions, shapes were never both a knight's move away from each other and drawn in the same color.Word and shape tasks were administered in short blocks of approximately 35 s, interrupted by approximately 10 s of an unrelated task which was used as a baseline condition, common to all scanning sessions, against which hemodynamic activity relating to the different experimental conditions could be contrasted across sessions. In the unrelated task, participants were asked to press two keys alternately to make a row of Xs flip as quickly as possible between a horizontal and a vertical configuration. The inter-trial interval in the unrelated task was varied randomly between 300 and 700 ms to induce subjects to pay attention to the stimuli.2.3. ProcedureEach trial consisted of 500 ms of a fixation cross, followed by presentation of the stimulus (the two words or shapes) for a maximum of 3000 ms. The tasks were subject-paced to prevent rehearsal of the given instructions (cf. Burgess et al., 2003) and ensure variable onset times of the trials, which improves fMRI design estimation efficiency (Henson, 2003). There was an inter-trial interval of 250 ms.The tasks were administered in six sessions. The first two sessions (one words, one shapes, with task order counterbalanced between subjects) consisted of “uncontaminated” ongoing trials only without any expectation of a PM trial (no mention of PM conditions was made in the instructions for these first sessions). Two PM sessions then followed for each task, one session containing “contaminated” ongoing and cue identification PM trials, and one session containing “contaminated” ongoing and intention retrieval PM trials. The order of the PM conditions was also counterbalanced between subjects. Each session consisted of blocks of approximately 35 s of task (range 34-36 s, randomized) alternating with around 10 s of the unrelated task (range 9-11 s, randomized), with a 1 s pause between blocks. All sessions were preceded by instructions and a practice round. Each of the four PM sessions (two PM conditions for each of the two tasks) consisted of a number of ongoing trials interspersed with one group of four PM trials per 35 s block. This group of PM trials was always presented after approximately 20 s of ongoing task, to ensure that the participant would be fully engaged in the ongoing task and to control for the time between the last PM trial of a group and the first PM trial of the next group. To minimize the possibility that the appearance of the PM trials could be anticipated, two of the 35 s blocks in each condition did not contain any PM trials at all, and in two other 35 s blocks the PM trials were presented close to the beginning of the block. Anticipation of the PM task on a trial to trial basis was reduced further by placing an extra ongoing trial randomly somewhere in between the four PM trials and by varying the position of the group of PM trials within the block. In total, 32 PM trials were presented per session. The four PM sessions each lasted 517 s.2.4. Image acquisition and data analysisT2-weighted echo-planar functional images were acquired using a 3T Siemens Allegra system. For each subject, two time series of 21 followed by four time series of 227 whole-brain images were obtained (TR = 2.34 s, TE = 30 ms, 36 sequential axial slices aligned at approximately 10° to the AC-PC transverse plane, 2 mm thickness, 1 mm inter-slice skip). The first six images of each session were discarded to allow for T1 equilibration. Prior to the actual experiment, a magnetic fieldmap was acquired for each subject, which was used in the pre-processing of the functional images to reduce the distorting effects of the sinus area on the prefrontal cortex.FMRI data were pre-processed and analyzed using the statistical parametric mapping procedure as implemented in SPM2 (Wellcome Department of Imaging Neuroscience, London). All images were realigned to the first image to correct for motion (using 4th-degree B-spline interpolation), after which the magnetic fieldmap was used to create a mean undistorted image. After realignment, all images were resampled in time to match the middle slice, to correct for differences in slice acquisition timing. The images were then normalized to an EPI template in MNI stereotactic space (Cocosco, Kollokian, Kwan, & Evans, 1997). Normalized images were resampled into 3 mm cubic voxels and smoothed using a Gaussian filter (8 mm FWHM kernel). A high-pass filter of 1/128 Hz was used to remove low-frequency noise, and an AR(1) + white noise model corrected for temporal autocorrelation. Finally, the time series was scaled to a grand mean of 100 across voxels and scans within each session.Random effects statistical analysis was undertaken twice, once using a block design to estimate the main effects of interest, and once using an event-related design to allow investigation of effects that might be correlated with reaction time. Each analysis was conducted in two stages of a mixed effects model. In the first analysis, 16 conditions were defined: baseline and ongoing conditions for all six sessions, and a PM condition in the last four sessions. Blocks of PM condition lasted from the moment that the first PM trial which the subject responded correctly to appeared on the screen until the subject responded to the last PM trial in a group. Blocks of all conditions were modeled by convolving a boxcar that had a specific onset time and duration with a canonical haemodynamic response function. In the second analysis, the aforementioned conditions were re-defined as event-types for separate trials; a separate regressor coded for missed responses in each session. In both analyses, parameter values for each covariate were then estimated using a subject-specific fixed-effects model. Movement parameters in the three directions of motion and 3° of rotation were included as confounds, as well as a single covariate representing the mean session effect. In the event-related analysis, RTs were included as a parametric modulator of each event-type regressor.Subject-specific estimates for the contrasts of interest were obtained using linear contrasts across sessions. To control for between-session signal differences, the effects of interest within each session were contrasted against the baseline condition from that same session. These estimates were entered into the second stage of analysis treating subjects as a random effect, using a one-sample t-test across subjects. Since activations that were independent of the type of stimuli involved (words or shapes) were of principal theoretical interest, a cognitive conjunction analysis was applied to the data which identifies as significant only those brain regions that are commonly activated in both tasks. Statistical parametric maps of the independent word and shape contrasts of interest were constructed and a one-way ANOVA on the 16 subjects was used to reveal brain regions significantly activated across both tasks, at an uncorrected threshold of p < 0.001. The anatomical locations and approximate Brodmann areas of significant cluster maxima of at least five contiguous voxels were localized using the Talairach and Tournoux atlas (Talairach & Tournoux, 1988) after adjusting coordinates to allow for differences between the MNI and Talairach templates (Brett, Christoff, Cusack, & Lancaster, 2001). The activation in prefrontal cortex associated with both PM conditions was examined in more detail by extracting mean percentage signal change magnitude relative to the baseline conditions from the subject-specific parameter estimates of cluster maxima, and subjecting them to a repeated-measures analysis that included region (left lateral BA 10, right lateral BA 10, and medial BA 10) and PM condition as repeated factors.3. Results3.1. Behavioral resultsThe accuracy and reaction time data as a function of task and condition are displayed in Table 1. There was no main effect of task in terms of accuracy, F(1,15) = 0.67, n.s., but a significant main effect of condition, F(3,45) = 102.74, p < 0.0001, and a trend towards an interaction between the two factors, F(3,45) = 2.73, p = 0.055. Accuracy scores were lower on PM trials compared to both types of ongoing trials, F(1,15) = 53.57, p < 0.0001. The accuracy difference between the cue identification and intention retrieval PM conditions was also significant, participants being less accurate in the intention retrieval PM condition compared to the cue identification PM condition, F(1,15) = 15.44, p < 0.001.Table 1. Accuracy (%) and reaction time (ms) data per task (standard deviations in parentheses)Empty CellWords taskShapes taskUncontaminated ongoing Accuracy98.9 (1.7)97.4 (3.0) RT566 (151)720 (129)Contaminated ongoing Accuracy97.0 (1.7)96.8 (2.2) RT991 (260)839 (102)Cue identification PM Accuracy80.6 (12.0)83.3 (13.6) RT1133 (169)836 (113)Intention retrieval PM Accuracy75.9 (10.2)68.3 (7.9) RT1596 (190)1394 (207)Uncontaminated ongoing: ongoing trials where no PM trial was expected. Contaminated ongoing: ongoing trials where a PM trial was expected. Cue identification PM: prospective memory trials with high demand on cue identification and low demand on intention retrieval. Intention retrieval PM: prospective memory trials with high demand on intention retrieval and low demand on cue identification.Reaction times were significantly increased in the word compared to the shape task, F(1,15) = 23.34, p < 0.0001, and showed a main effect of condition, F(3,45) = 145.20, p < 0.0001, and an interaction, F(3,45) = 23.07, p < 0.0001. Both tasks showed an increase in reaction times between uncontaminated and contaminated ongoing trials, F(1,15) = 38.08, p < 0.0001, and between cue identification PM and intention retrieval PM trials, F(1,15) = 191.08, p < 0.0001. A comparison between the contaminated ongoing trials in the two PM conditions revealed that ongoing trials in the cue identification PM condition were associated with significantly longer RTs than in the intention retrieval PM condition, t(15) = 4.55, p < 0.001, though when separated per task this difference was only significant in the words task, t(15) = 4.93, p < 0.001.3.2. Neuroimaging resultsThe results from the block analysis are reported first.2 To identify the brain areas involved in prospective memory, each of the PM conditions in the word and shape tasks was contrasted with the uncontaminated ongoing condition, with between-session differences controlled for using the common baseline condition as described in the Methods section. Because we are interested in the brain regions associated with prospective memory that are not dependent on the kind of task used, a conjunction contrast was used to identify activations that were common to both word and shape tasks. The conjunction contrast revealed a remarkably consistent pattern of significant activation in both the cue identification and intention retrieval PM conditions in anterior prefrontal cortex (BA 10), with activation bilaterally in lateral BA 10 and deactivation in medial BA 10 (see Fig. 2A and B). Examination of the signal confirmed that there was no significant effect of PM condition on activation in the anterior prefrontal cortex clusters identified, F(1,15) = 3.22, n.s. Significant activation was also found in a number of other areas including ventrolateral prefrontal and lateral parietal cortex in both PM conditions, and dorsolateral prefrontal cortex and orbitofrontal cortex in the intention retrieval PM condition (see Table 2, Table 3).Download : Download full-size imageFig. 2. Group functional activation maps of percentage signal change, in a conjunction across the word and the shape task. Activations are shown in yellow/red, deactivations are shown in blue, with activations of particular interest circled. Z coordinates are shown in top left corner. (A) In the cue identification PM > uncontaminated ongoing contrast, bilateral BA 10 activation and medial BA 10 deactivation was observed. A highly similar pattern was observed in (B) the intention retrieval PM > uncontaminated ongoing contrast. Differences between conditions emerge in (C), the direct intention retrieval PM > cue identification PM contrast, with significantly greater activation in anterior prefrontal cortex bilaterally in the intention retrieval PM condition, and evidence of deactivation in medial anterior BA 10. Left lateral BA 10 activation was found in (D) the contaminated ongoing > uncontaminated ongoing condition (left: cue identification PM ongoing condition; right: intention retrieval PM ongoing condition) (for interpretation of the references to color in this figure legend, the reader is referred to the web version of the article).Table 2. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification PM > uncontaminated ongoing Left middle frontal gyrus (BA 10)-395733.96 Right middle frontal gyrus (BA 10)425794.423 Right inferior frontal gyrus (BA 47)5421-63.921 Left inferior frontal gyrus (BA 47)-4218-63.99 Right insula (BA 13)4815123.78 Left insula (BA 13)-390274.415 Right inferior parietal lobule (BA 40)45-42514.137 Right superior parietal lobule (BA 7)33-57514.425 Right angular gyrus (BA 39)42-57483.78Uncontaminated ongoing > cue identification PM Medial anterior prefrontal cortex (BA 10)048-63.923 Right superior frontal gyrus (BA 8)3039513.45 Right cingulate gyrus (BA 24)612363.97 Left precentral gyrus (BA 4/3)-42-15424.211 Left medial frontal gyrus (BA 6/31)-3-21573.717 Right medial frontal gyrus (BA 6)6-24693.919 Right precentral gyrus (BA 4)24-24723.611 Right precuneus (BA 7)3-33543.713 Left precuneus (BA 7)-3-48603.514 Right lingual gyrus (BA 18/19)12-5163.617 Left lingual gyrus/cuneus (BA 18/30)-9-63124.024Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Table 3. Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellIntention retrieval PM > uncontaminated ongoing Left middle frontal gyrus (BA 10)-395733.68 Right middle frontal gyrus (BA 10)3954155.1203 Right middle/superior frontal gyrus (BA 11)2754-123.716 Right inferior frontal gyrus (BA 47)4521-154.122 Right medial frontal gyrus (BA 8)921483.615 Left insula (BA 13)-3918-63.920 Right precentral gyrus (BA 9)4512393.724 Right precentral gyrus/insula (BA 44)511294.036 Left inferior/middle central gyrus (BA 9)-519394.531 Left precentral gyrus (BA 6)-423275.154 Right inferior parietal lobule (BA 40)45-42515.2199 Left inferior parietal lobule (BA 40)-48-45485.0155 Right precuneus (BA 19)12-66394.312 Right inferior occipital gyrus (BA 18)36-90-124.046Uncontaminated ongoing > intention retrieval PM Medial anterior prefrontal cortex (BA 10)048-65.3243 Right cingulate gyrus (BA 24)612363.75 Right lateral parietal cortex (BA 3)54-12574.430 Left precentral gyrus (BA 4/3)-42-15453.613 Left medial frontal gyrus (BA 6/31)-3-18574.019 Right medial frontal gyrus (BA 6/4)6-30723.713 Right precuneus (BA 7)3-33573.68 Right lingual gyrus (BA 18)12-5163.68 Left lingual gyrus/cuneus (BA 30)-6-57154.748 Right cuneus (BA 18)6-87243.76Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Separate examination of the word and shape task contrasts revealed additional, potentially task-dependent, activation associated with cue identification in left premotor cortex, precuneus, and occipital cortex for words, and parahippocampal cortex for shapes. Similarly, the intention retrieval PM condition was associated with additional activation in left cingulate gyrus, premotor cortex, and precuneus for words, and a more anterior part of cingulate gyrus and postcentral gyrus for shapes.Returning to the task-independent conjunction contrasts, despite the similarities in activation patterns associated with the two PM conditions versus the uncontaminated ongoing condition, a number of brain regions were identified in the direct task-independent contrast between cue identification PM and intention retrieval PM (see Fig. 2C). These results provide evidence of additional differential BA 10 involvement in the PM conditions. Significantly greater activation was found in the intention retrieval PM condition bilaterally in regions of BA 10 that peaked slightly less laterally than those observed in the contrasts above. Medial BA 10 appeared to be more active in the cue identification PM condition (see blue activation in Fig. 2C), although this effect did not exceed the whole-brain threshold of p < 0.001. At a threshold of p < 0.05 corrected for the voxels in an 8-mm sphere around the peak that was identified in both the cue identification PM versus ongoing and intention retrieval PM versus ongoing contrasts (0, 48, -6; see Table 2, Table 3), however, significant medial BA 10 activation did emerge in the cue identification versus intention retrieval PM contrast (0, 45, -9; Z = 3.5; voxels = 19). Other brain regions which were more active at the whole-brain threshold in the cue identification PM condition included the anterior cingulate, whereas in the intention retrieval PM condition more extensive areas of the cingulate gyri were among regions showing greater activation (see Table 4).Table 4. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification PM > intention retrieval PM Right anterior cingulate (BA 32/11)336-123.753 Left anterior cingulate (BA 25)-315-64.531 Left putamen-240-95.051 Right insula (BA 40)57-18213.624 Left postcentral gyrus/insula (BA 40)-54-21214.5113 Left middle temporal gyrus (BA 39/19)-54-72243.47 Left lingual gyrus (BA 18)-21-78-63.817Intention retrieval PM > cue identification PM Left middle frontal gyrus (BA 10)-3351183.46 Right superior frontal gyrus (BA 10)2751-33.812 Left medial frontal gyrus (BA 9)-1842213.45 Left anterior cingulate (BA 32/24)-1227273.911 Right insula (BA 13/47)3624-34.213 Left cingulate gyrus (BA 32)-321456.3364 Right lateral frontal cortex (BA 6)2412634.5118 Right cingulate gyrus (BA 24)39304.05 Left lateral frontal cortex (BA 6)-276665.146 Left middle frontal gyrus (BA 6)-420573.45 Left inferior parietal lobule (BA 40)-45-54573.45 Left cuneus (BA 18)-12-75335.8246Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.Since the behavioral data showed longer RTs in the intention retrieval PM than in the cue identification PM condition, it is possible that the greater bilateral BA 10 activation in the intention retrieval PM condition might be attributable to differences in time on task. To test this possibility, a second analysis was conducted including RT as a parametric modulator to identify brain regions where activation was positively or negatively correlated with RTs. No significant correlation was found between RT and activation in the regions of BA 10 identified in the previous contrasts, even when the threshold was dropped to p < 0.1 uncorrected. It thus seems safe to attribute the greater bilateral BA 10 activation during intention retrieval PM trials primarily to the demands on recovering the delayed intention.To examine the effect of maintaining a PM intention on performance of the ongoing task, the contaminated ongoing trials in both PM conditions were contrasted with the uncontaminated ongoing trials. In both PM conditions, contaminated ongoing trials were associated with greater activation in left lateral BA 10/47 (-48, 39, 0 for cue identification; -48, 42, -3 for intention retrieval; see Fig. 2D). Again, no correlation was found between RTs and activation in these regions in the analysis that included RT as a parametric modulator. Contrasting directly the contaminated ongoing trials in both PM conditions, significantly greater activation in left lateral BA 10 was observed during the intention retrieval PM condition than the cue identification PM condition (see Table 5). This is consistent with the idea that activation in this region reflects maintenance of the intended PM actions (Burgess et al., 2001).Table 5. Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasksBrain regionCoordinatesZVoxelsEmpty CellxyzEmpty CellEmpty CellCue identification ongoing > intention retrieval ongoing Right inferior frontal gyrus (BA 47)3630-64.131 Left insula (BA 13)-4221154.39 Left precentral gyrus (BA 6)-519333.511 Left putamen-276-63.930 Left fusiform gyrus (BA 37)-42-48-93.89 Left middle occipital gyrus (BA 19)-30-93214.39 Left occipital lingual gyrus (BA 18)-15-99-34.979 Right cuneus (BA 18)15-102124.458Intention retrieval ongoing > cue identification ongoing Left medial frontal gyrus (BA 10)-215464.215 Right middle frontal gyrus (BA 6)1818663.78 Left paracentral lobule (BA 6)-12-18603.814 Right paracentral lobule (BA 6)12-30663.920 Left postcentral gyrus (BA 4)-15-30664.010 Left cuneus (BA 18)3-75363.96Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.4. DiscussionThe main finding of the present experiment was that, despite behavioral differences between the cue identification and intention retrieval PM conditions, a strikingly similar pattern of hemodynamic changes, involving anterior prefrontal cortex (BA 10), was observed across both conditions. Finding such consistent results even when the demands on cue identification and intention retrieval were manipulated sufficiently to produce significant behavioral effects, confirms the view from previous neuroimaging studies that this region is likely to be of central importance to prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005).For example, Okuda et al. (1998) reported activation in the left frontal pole (BA 10), as well as right dorsolateral and ventrolateral prefrontal cortex (BA 8/9/47) and anterior cingulate (BA 24), when participants remembered and acted upon a list of target words relative to performing an ongoing routine activity (word repetition). Activation in the frontal pole (BA 10, bilaterally) was also found by Burgess et al. (2001) across several cognitive tasks. In that study, activation during an ongoing task was compared to activation in two PM conditions: one in which PM trials were expected but never actually occurred, and one in which PM trials were expected and acted upon. In both PM conditions, activation in bilateral frontal pole, right lateral prefrontal and parietal cortex, plus precuneus, was found. Burgess et al. (2003) extended these results by showing that the bilateral activation of lateral BA 10 associated with retrieving a delayed intention was accompanied by deactivation in medial BA 10. Recently, den Ouden et al. (2005) reported activation in lateral BA 10, lateral parietal cortex, and precuneus, associated with keeping an intention in mind while performing an ongoing task responding to questions about intentions and actions.In the present experiment, the reliability of the consistently observed lateral BA 10 activation was assessed by manipulating the demands placed on cue identification and intention retrieval processes as experimental variables. Of course, both PM conditions involved cue identification and intention retrieval to some degree; the experimental manipulation was not all-or-none. However, the demands on the two PM components were manipulated sufficiently for significant behavioral differences to emerge between them: accuracy was lower, and reaction times longer, in the intention retrieval condition than in the cue identification condition, although correlational analysis indicated that these behavioral differences could not explain the consistent hemodynamic changes observed across conditions. Significant lateral BA 10 activation was seen bilaterally in both PM conditions, confirming the characteristic nature of this activation, as reported by a number of neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005). Moreover, both PM conditions were additionally associated with medial BA 10 deactivation when contrasted with ongoing trials, consistent with the findings of Burgess et al. (2003), who also observed reduced medial BA 10 activation during PM trials versus ongoing trials. Of course, the limitations of fMRI do not allow us to speculate as to whether precisely the same neurons were recruited by both PM conditions, but it does seem clear that, at the macro level of Brodmann areas at least, similar patterns of activation in BA 10 were observed during both cue identification and intention retrieval PM conditions.Despite finding that similar regions of BA 10 were activated (and deactivated) in both PM conditions, there was evidence that the level of activation in lateral BA 10 was greater in the intention retrieval PM condition than in the cue identification PM condition. The correlational RT analysis showed this effect was not attributable to differences in reaction time as the activations observed did not correlate with RT durations, consistent with results observed by Burgess et al. (2003). This suggests that the results cannot be explained by a simple task difficulty account, since greater difficulty (in terms at least of the amount of effort required to perform the task) could be expected to be reflected in increased RTs. Left lateral BA 10 was also significantly activated in the contrast between contaminated and uncontaminated PM trials, suggesting that activation in the region is affected by prior exposure to a PM intention. This is consistent with the finding from Burgess et al. (2001) that BA 10 activation is seen when PM trials are expected, regardless of whether they actually occur, and perhaps indicates that the region contributes to the maintenance of the delayed intention, a conclusion supported by the finding in the present experiment that BA 10 activation during contaminated ongoing trials was greater in the intention retrieval than the cue identification PM conditions. Moreover, there are echoes in these results of data from a number of electrophysiology studies which found that intention retrieval was associated with a sustained frontal slow-wave modulation that appeared to be particularly evident when retrieval of the PM intention was made more demanding (West et al., 2001; West & Ross-Munroe, 2002). Increasing the number of intentions to be remembered resulted in a larger amplitude of this frontal slow-wave (West et al., 2003). Although it is problematic to compare electrophysiological and neuroimaging results directly, there are similarities between the ERP increase over frontal sites and the present finding of greater lateral anterior prefrontal cortex activation when intention retrieval is more demanding.The greater lateral BA 10 activation associated with intention retrieval can be conceived of in terms of differential attention towards external events and internally generated thought processes (Burgess, Simons, Dumontheil, & Gilbert, 2005; Gilbert, Frith, & Burgess, 2005; Gilbert, Simons, Frith, & Burgess, in press; Simons, Owen, Fletcher, & Burgess, 2005; Simons, Gilbert, Owen, Fletcher, & Burgess, 2005; see also Christoff & Gabrieli, 2000; Gusnard, Akbudak, Shulman, & Raichle, 2001). By this account, when one's primary aim is to detect a cue, attention must be turned towards the external world; once the cue has been detected, however, one must disengage from the external stimuli and attend to internal representations so that the relevant intention can be retrieved from memory. Thus, placing higher demands on cue identification implies biasing attention more towards external stimuli, whereas a higher load on intention retrieval implies an increase of attentional focus upon internally generated thought.A number of previous studies have shown that lateral BA 10 plays an important role in the recollection of details about the context in which previous events occurred (Ranganath, Johnson, & D’Esposito, 2000; Rugg, Fletcher, Chua, & Dolan, 1999; Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005), an ability that requires retrieval of internally represented mnemonic information (Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005). In contrast, medial BA 10 has been associated with performance of tasks that emphasize the processing of externally presented stimuli (Gilbert et al., 2005, Janata et al., 2002, Small et al., 2003). Thus, Burgess et al. (2005) have suggested that lateral areas of BA 10 may play a role in maintaining attention towards internal cognition and more medial areas in maintaining attention towards external stimuli. The results from the PM versus ongoing contrasts in the present experiment can be interpreted along these lines, with medial BA 10 deactivation reflecting disengagement from the external ongoing task stimuli and the lateral BA 10 activation being associated with the directing of attention towards the internally represented PM intention (see Burgess et al., 2003, for a similar suggestion). Consistent with this view, lateral BA 10 activation was greater when the demands on intention retrieval were higher, and there was evidence of greater medial BA 10 activation associated with the cue identification PM condition, in which external cue processing demands were greater. Taken as a whole, these results are in agreement with the hypothesis that BA 10 acts as a “gateway”, biasing attention between externally derived perceptual information used to detect the occurrence of a PM cue and internal thought processes relating to the stored PM intention (Burgess et al., 2005; Gilbert et al., 2005; Simons, Gilbert, et al., 2005).PM-related activation was also seen in a number of areas outside the anterior PFC region of interest. When contrasted against uncontaminated ongoing trials, the cue identification and intention retrieval PM conditions provoked activation in regions of lateral PFC and parietal cortex, among other areas. A number of regions showed differential activation in the two PM conditions. These included anterior cingulate cortex, which was activated more in the cue identification PM condition, and posterior cingulate and precuneus, which showed greater activation in the intention retrieval PM condition. Activation in all these regions has been observed in previous neuroimaging studies of prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), and has been linked with a number of processes relevant to PM retrieval. For example, lateral PFC, anterior cingulate and lateral parietal cortex have been proposed to constitute a cognitive control network involved in sustained attention and vigilance to particularly visual stimuli (Burgess et al., 2001; Coull, Frith, Frackowiak, & Grasby, 1996; Cabeza et al., 2003). Posterior cingulate, precuneus, and lateral parietal cortex have been linked in many studies with functions related to retrieval of stored mnemonic information such as recollection, retrieval confidence, and imagery (see Wagner, Shannon, Kahn, & Buckner, 2005, for a recent review).In conclusion, the present experiment demonstrated that it is possible to tease apart behaviorally cue identification and intention retrieval components of PM, but that they may reflect the operation of similar underlying processes supported by anterior prefrontal cortex (BA 10). Evidence suggests that lateral BA 10 may reflect the maintenance and/or retrieval of the stored PM intention, showing greater activation not only during intention retrieval PM trials, but also when maintaining an intention in memory during the ongoing task.AcknowledgementsWe are grateful to Daniel Glaser and Hanneke den Ouden for discussion and advice, and the staff of the Functional Imaging Laboratory for scanning assistance. 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Theta and alpha oscillations were compared between the two conditions within each modality using a cluster-based permutation approach.PM conditions were associated with clusters of decreased alpha and theta activity in both modalities. However, different spatiotemporal dynamics were elicited as a function of sensory modality: alpha decreases displayed an overlapping onset between modalities, but different durations, lasting longer in the auditory modality. Conversely, the clusters of decreased theta activity presented similar durations between modalities, but different temporal and spatial onsets, appearing at different moments over the respective sensory areas.The similar spatiotemporal properties of alpha suppression between modalities indicate that such oscillations may represent a supramodal, top-down process, presumably reflecting the external direction of attention to successfully detect the prospective cue (strategic monitoring). In theta, the clusters showed more modality-specific differences, which temporal and spatial properties correspond to the ones necessary to perform the ongoing task, suggesting a shift in resource allocation in favor of the PM task.Dynamic functional connectivity associated with prospective memory success in children2022, Neuroimage: ReportsShow abstractTo remember the prospective intention successfully, going back and forth between the background task and the intention, i.e., the dynamics of these multiple processes can be critical. An executive function like task switching has been associated with the success of prospective memory (PM) in children, but the neural mechanism of PM in children has not been investigated. The purpose of this study was to reveal the dynamic functional connectivity underlying the success of PM in children. Healthy 108 children, aged 7 to 15, were engaged in a single trial PM task, with a 30-min delay. Temporal variabilities in their resting-state functional connectivity were analyzed, using sliding windows with seed regions of interest ROIs of the PM network. About 70% of children successfully remembered the intention; they showed greater dynamics in neural connectivity between the right dorsolateral prefrontal cortex (DLPFC) and intraparietal sulcus, and between the right DLPFC and insula as compared to children with PM failure. Everyday activities and the usual attention to ongoing processes can be associated with alertness in the right frontoparietal network and internal-state monitoring in the insula network, and those dynamics might be associated with one-time event PM success in children.Prefrontal cortical activation associated with prospective memory while walking around a real-world street environment2022, NeuroImageShow abstractRostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in “real world” situations outside the laboratory in freely ambulant individuals. The addition of a “brain-first” approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. 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This chapter reviews the contribution of neuropsychology and cognitive aging research to the understanding of serial multitasking and discusses the cognitive processes that act cooperatively with one another to allow us to achieve our complex cognitive goals.Inferior parietal lobule involved in representation of “what” in a delayed-action Libet task2021, Consciousness and CognitionShow abstractIntentional motor action is typically characterized by the decision about the timing, and the selection of the action variant, known as the “what” component. We compared free action selection with instructed action, where the movement type was externally cued, in order to investigate the action selection and action representation in a Libet’s task. Temporal and spatial locus of these processes was examined using the combination of high-density electroencephalography, topographic analysis of variance, and source reconstruction. Instructed action, engaging representation of the response movement, was associated with distinct negativity at the parietal and centro-parietal channels starting around 750 ms before the movement, which has a source particularly in the bilateral inferior parietal lobule. This suggests that in delayed-action tasks, the process of action representation in the inferior parietal lobule may play an important part in the larger parieto-frontal activity responsible for movement selection.The neurological and neuropsychological effects of child maltreatment2020, Aggression and Violent BehaviorShow abstractDue to the increase in annual cases, child maltreatment has become a significant focus in research. There are four types of childhood abuse that have been identified and discussed throughout this review: sexual abuse, physical abuse, emotional abuse, and neglect. In order for psychologists and other related professionals to recognize the contributions maltreatment has on psychiatric disorders, it is necessary to identify and understand the complex sequela of childhood abuse and neglect that may appear throughout the victim's lifespan. This paper reviewed current research on child maltreatment and its relationship to neurological impairments and neuropsychological effects. Additionally, a comparison between typical and impaired neurological morphology emphasized alterations in volumetric structures and stress response functions occurring in the HPA axis, amygdala, hippocampus, and corpus callosum. Recent studies have discovered that changes to the preceding neurological structures can significantly impact different neuropsychological domains including working memory, processing speed, language, visual-spatial abilities, and motor skills.View all citing articles on Scopus1Delegates at the 2nd International Conference on Prospective Memory, Zurich, July 2005, voted that the use of the abbreviation “PM” for prospective memory was preferable to other extant forms.2An event-related analysis was also conducted as verification, which produced activation in similar brain regions as the block analysis with reduced effect sizes, consistent with the lower statistical power associated with event-related designs (Henson, 2003).View AbstractCopyright © 2006 Elsevier Ltd. 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Elsevier

Neuropsychologia

Volume 44, Issue 8, 2006, Pages 1388-1397
Neuropsychologia

Differential components of prospective memory?: Evidence from fMRI

https://doi.org/10.1016/j.neuropsychologia.2006.01.005Get rights and content

Abstract

Two of the principal components of prospective memory (i.e., remembering to carry out delayed intentions) are recognizing the appropriate context to act (“cue identification”) and remembering the action to be performed (“intention retrieval”). In this experiment, the demands on these components were manipulated while measuring brain activity using fMRI to explore whether the two components share a common neural basis. The results showed significant behavioral differences between the cue identification and intention retrieval conditions. However, a consistent pattern of hemodynamic changes was found in both prospective memory conditions in anterior prefrontal cortex (BA 10), with lateral BA 10 activation accompanied by medial BA 10 deactivation. These effects were more pronounced when demands on intention retrieval were high. This is consistent with the hypothesis that anterior prefrontal cortex (area 10) supports the biasing of attention between external events (e.g., identifying the cue amid distracting stimuli) and internal thought processes (i.e., maintaining the intention and remembering the intended actions). Together, the results suggest that whilst cue identification and intention retrieval may be behaviorally separable, they share at least some common neural basis in anterior prefrontal cortex.

Keywords

Anterior prefrontal cortex
Rostral prefrontal cortex
Frontal lobes
Functional magnetic resonance imaging (fMRI)
Executive function

1. Introduction

Prospective memory (PM1), remembering to perform an intended action after a delay (Meacham & Singer, 1977), may involve a number of processing stages: forming an intention, maintaining the intention in memory over an interval while being engaged in another (or ongoing) task, executing the intended action at the appropriate moment, and evaluating the outcome (Freud, 1901, Ellis, 1996). Much research into PM has focused on the third of these stages, involving recognition of the appropriate moment to act and remembering what action was to be performed (see Table 1 in Burgess, Scott, & Frith, 2003, for a list of cardinal properties of PM). The most often studied example is where an action needs to be performed when an external event occurs, such as remembering to stop and buy a loaf of bread when you drive past the grocery store (“event-based PM”; Einstein, Holland, McDaniel, & Guynn, 1992). McDaniel and Einstein (1992) proposed a division of event-based PM into two components: cue identification and intention retrieval. Cue identification involves the detection of the cue event (e.g., the grocery store) signaling that the intended action should be performed; intention retrieval involves the subsequent recovery of that intention (e.g., buying the bread) from memory. There are a considerable number of behavioral studies that have investigated these components (e.g., Brandimonte & Passolunghi, 1994; Cohen, West, & Craik, 2001; Marsh, Hicks, Cook, Hansen, & Pallos, 2003; Einstein et al., 1992; Einstein, McDaniel, Manzi, Cochran, & Baker, 2000; Ellis & Milne, 1996; West, Herndon, & Crewdson, 2001; West & Ross-Munroe, 2002; West, Wymbs, Jakubek, & Herndon, 2003).

Much of this research has focused on one or other of the two components, such as on the effect of cue characteristics in triggering a response. It has been shown that cues that are particularly salient tend to be noticed more frequently (Einstein et al., 2000), that unfamiliar cues benefit prospective remembering (Brandimonte & Passolunghi, 1994), and that when an intention has been formed to respond to a particular category of cues, highly typical category members evoke the intention more often than less typical exemplars (Ellis & Milne, 1996). Studies of intention retrieval have concentrated primarily on the association between the cue and the stored intention. When this association is strong, retrieval may be relatively automatic, as opposed to more effortful processing when the association is weak (McDaniel & Einstein, 2000; McDaniel, Einstein, Guynn, & Breneiser, 2004).

Despite the extensive research on PM processes, however, relatively few studies have investigated whether cue identification and intention retrieval might rely on separable cognitive processes. Cohen et al. (2001) evaluated in separate experiments the hypothesis that cue identification and intention retrieval are primarily supported by stimulus-driven and conceptually driven processes, respectively. Cue identification was manipulated by a change in format of the cue from study session to test session and, in a second experiment, intention retrieval was manipulated by a change in semantic relatedness between the cue and the intention from study session to test session. The authors found that a change in cue format reduced the number of PM cues detected, while semantically unrelated intentions were less often correctly recalled upon detection of the cue, consistent with their hypothesis. However, only accuracy data were reported, although many PM studies have reported an effect of prospective memory retrieval on reaction times (e.g., Burgess, Scott, & Frith, 2003; Marsh, Hicks, & Watson, 2002; Marsh et al., 2003; West et al., 2001). Marsh et al. (2003) examined reaction times while investigating the extent to which maintenance of a PM intention might affect cognitive processing of the ongoing task at the time a PM cue is encountered (see also Smith, 2003). Marsh et al. manipulated cue identification and intention retrieval demands separately and found differential effects on reaction times in the ongoing task. However, despite showing a differential effect of maintaining a PM intention on performance of the ongoing task, these authors did not study the effect of manipulating cue identification and intention retrieval on the PM task itself.

The few available results thus suggest that cue identification and intention retrieval might be separable behaviorally, in that manipulating the demand on the two components may differentially affect error rates and/or reaction times. However, even if this is the case, it may not necessarily follow that the two components are supported by exclusively different brain regions. Previous neuroimaging studies of PM have used a variety of paradigms which, although not designed to manipulate cue identification and intention retrieval as experimental variables, have nevertheless involved cue identification and intention retrieval processes to differing extents. In all of these studies, a consistent pattern of activation has been observed, involving particularly anterior prefrontal cortex (approximating Brodmann area 10) (Burgess, Quayle, & Frith, 2001; Burgess et al., 2003; den Ouden, Frith, Frith, & Blakemore, 2005; Okuda et al., 1998). It is not clear, therefore, whether the anterior prefrontal cortex network is involved in PM function to a similar degree irrespective of the demands on cue identification and intention retrieval, or whether varying cue identification and intention retrieval as experimental variables will reveal that the key neural correlate of PM reflects processing relating to one of the hypothesized components more than the other.

The experiment presented here examined these issues by scanning participants using fMRI while they were undertaking a task in which PM trials were embedded in an ongoing task in such a way as to prevent participants from actively rehearsing the intentions. Two PM conditions were used, one with high cue identification demand and low intention retrieval demand (the ‘cue identification PM condition’), and one with low cue identification demand and high intention retrieval demand (the ‘intention retrieval PM condition’). Cue identification was manipulated by altering the perceptual salience of the PM cues (Brandimonte & Passolunghi, 1994; Einstein et al., 2000). In the low cue identification demand condition, the cues were perceptually distinct from the ongoing trials, while in the high demand condition, the cues were perceptually similar but conceptually distinct. Intention retrieval demand was manipulated by varying the number of actions participants needed to perform in order to determine the appropriate response. If, as predicted by previous neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), an anterior prefrontal cortex network supports PM function regardless of the demands on cue identification and intention retrieval, then substantial overlap can be expected between the patterns of activation associated with each PM condition.

The use of word and shape versions of the task enabled analysis involving conjunction contrasts across tasks to identify brain regions that are commonly activated across stimulus types, and might be considered to reflect “central”, task-independent PM processes, as opposed to those that might be specific to a particular stimulus type or task. In addition, to examine the effect of maintaining a PM intention on performance of the ongoing task (Marsh et al., 2003, Smith, 2003), a session consisting solely of ongoing trials was included at the beginning of the experiment, before participants had received any instructions concerning PM trials. Previous studies have shown that once instructed about a PM condition, the expectation that a PM trial will occur continues even if participants are subsequently instructed that there will be no PM trials in the upcoming block (Burgess et al., 2003; Einstein et al., 2005; Holbrook, Bost, & Cave, 2003). Ongoing trials presented before exposure to a PM condition should not be contaminated by the expectation of a PM trial, so were termed ‘uncontaminated’ ongoing trials, with ongoing trials occurring after presentation of PM instructions termed ‘contaminated’ ongoing trials. Burgess et al. (2001) have shown that not only the execution, but also the expectation, of a PM trial can be associated with lateral anterior prefrontal cortex activation. If this region is involved in maintenance of the PM intention, it should show greater activation in the present experiment during contaminated versus uncontaminated ongoing trials and, indeed, between contaminated ongoing trials in the intention retrieval versus cue identification PM conditions.

2. Methods

2.1. Participants

Sixteen right-handed native speakers of English (eight males, eight females, mean age 23.4 years, range 18–30 years) volunteered to take part in the experiment. They were screened using a comprehensive medical questionnaire and written informed consent was obtained before participating.

2.2. Design and materials

Two PM tasks, a word and a shape task, were administered to each participant. Each task consisted of ongoing trials, prospective memory trials with high cue identification and low intention retrieval demands (termed cue identification PM trials), and prospective memory trials with low cue identification and high intention retrieval demands (termed intention retrieval PM trials). The two PM conditions occurred in separate sessions.

Each trial in the word task consisted of two nouns presented in the middle of the screen, one of which was written in upper case and the other in lower case letters (see Fig. 1). Words were drawn from the MRC Psycholinguistic Database (Wilson, 1988), and were matched for written frequency, familiarity, and concreteness. For ongoing trials, participants were instructed to indicate using a keypad whether the left or the right of the two words contained more letters. In the cue identification PM condition, in which trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the words belonged to the same semantic category, for example cow and horse. Conversely, in the intention retrieval PM condition, in which trials were perceptually distinct from the ongoing condition, words were written in the same case and participants were required to retrieve a greater number of actions than in the cue identification PM condition: count up the syllables of both words and press one key if the total was four or less, or another key if the total was higher than four. To avoid interference between the instructions, words of the same semantic category were never written in the same case.

  1. Download : Download full-size image

Fig. 1. The words (top) and shapes (bottom) experimental tasks. An ongoing trial, a cue identification PM trial, and an intention retrieval PM trial are shown for each task.

The shape task consisted of a 4 × 4 grid, in which a colored triangle and a random other shape, such as a pentagon, were presented (see Fig. 1). Each shape was drawn in a different color, selected from six possible hues. Irregular shapes were used to avoid recognition at first glance. For ongoing trials, participants were instructed to indicate whether the shape other than the triangle was presented to the left or the right of the triangle (see Fig. 1). In the cue identification PM condition in which, as before, trials were perceptually similar but conceptually distinct from the ongoing condition, a different key was to be pressed if the two shapes were a chess knight's move away from each other. In the intention retrieval condition, in which trials were perceptually distinct from the ongoing condition, the two shapes were of the same color and participants were required to retrieve a greater number of actions than in the cue identification PM condition: determine the number of sides of the shape other than the triangle, and press one key if this number was five or less, and another key if this number was higher than five. Again, to avoid conflicting instructions, shapes were never both a knight's move away from each other and drawn in the same color.

Word and shape tasks were administered in short blocks of approximately 35 s, interrupted by approximately 10 s of an unrelated task which was used as a baseline condition, common to all scanning sessions, against which hemodynamic activity relating to the different experimental conditions could be contrasted across sessions. In the unrelated task, participants were asked to press two keys alternately to make a row of Xs flip as quickly as possible between a horizontal and a vertical configuration. The inter-trial interval in the unrelated task was varied randomly between 300 and 700 ms to induce subjects to pay attention to the stimuli.

2.3. Procedure

Each trial consisted of 500 ms of a fixation cross, followed by presentation of the stimulus (the two words or shapes) for a maximum of 3000 ms. The tasks were subject-paced to prevent rehearsal of the given instructions (cf. Burgess et al., 2003) and ensure variable onset times of the trials, which improves fMRI design estimation efficiency (Henson, 2003). There was an inter-trial interval of 250 ms.

The tasks were administered in six sessions. The first two sessions (one words, one shapes, with task order counterbalanced between subjects) consisted of “uncontaminated” ongoing trials only without any expectation of a PM trial (no mention of PM conditions was made in the instructions for these first sessions). Two PM sessions then followed for each task, one session containing “contaminated” ongoing and cue identification PM trials, and one session containing “contaminated” ongoing and intention retrieval PM trials. The order of the PM conditions was also counterbalanced between subjects. Each session consisted of blocks of approximately 35 s of task (range 34–36 s, randomized) alternating with around 10 s of the unrelated task (range 9–11 s, randomized), with a 1 s pause between blocks. All sessions were preceded by instructions and a practice round. Each of the four PM sessions (two PM conditions for each of the two tasks) consisted of a number of ongoing trials interspersed with one group of four PM trials per 35 s block. This group of PM trials was always presented after approximately 20 s of ongoing task, to ensure that the participant would be fully engaged in the ongoing task and to control for the time between the last PM trial of a group and the first PM trial of the next group. To minimize the possibility that the appearance of the PM trials could be anticipated, two of the 35 s blocks in each condition did not contain any PM trials at all, and in two other 35 s blocks the PM trials were presented close to the beginning of the block. Anticipation of the PM task on a trial to trial basis was reduced further by placing an extra ongoing trial randomly somewhere in between the four PM trials and by varying the position of the group of PM trials within the block. In total, 32 PM trials were presented per session. The four PM sessions each lasted 517 s.

2.4. Image acquisition and data analysis

T2-weighted echo-planar functional images were acquired using a 3T Siemens Allegra system. For each subject, two time series of 21 followed by four time series of 227 whole-brain images were obtained (TR = 2.34 s, TE = 30 ms, 36 sequential axial slices aligned at approximately 10° to the AC–PC transverse plane, 2 mm thickness, 1 mm inter-slice skip). The first six images of each session were discarded to allow for T1 equilibration. Prior to the actual experiment, a magnetic fieldmap was acquired for each subject, which was used in the pre-processing of the functional images to reduce the distorting effects of the sinus area on the prefrontal cortex.

FMRI data were pre-processed and analyzed using the statistical parametric mapping procedure as implemented in SPM2 (Wellcome Department of Imaging Neuroscience, London). All images were realigned to the first image to correct for motion (using 4th-degree B-spline interpolation), after which the magnetic fieldmap was used to create a mean undistorted image. After realignment, all images were resampled in time to match the middle slice, to correct for differences in slice acquisition timing. The images were then normalized to an EPI template in MNI stereotactic space (Cocosco, Kollokian, Kwan, & Evans, 1997). Normalized images were resampled into 3 mm cubic voxels and smoothed using a Gaussian filter (8 mm FWHM kernel). A high-pass filter of 1/128 Hz was used to remove low-frequency noise, and an AR(1) + white noise model corrected for temporal autocorrelation. Finally, the time series was scaled to a grand mean of 100 across voxels and scans within each session.

Random effects statistical analysis was undertaken twice, once using a block design to estimate the main effects of interest, and once using an event-related design to allow investigation of effects that might be correlated with reaction time. Each analysis was conducted in two stages of a mixed effects model. In the first analysis, 16 conditions were defined: baseline and ongoing conditions for all six sessions, and a PM condition in the last four sessions. Blocks of PM condition lasted from the moment that the first PM trial which the subject responded correctly to appeared on the screen until the subject responded to the last PM trial in a group. Blocks of all conditions were modeled by convolving a boxcar that had a specific onset time and duration with a canonical haemodynamic response function. In the second analysis, the aforementioned conditions were re-defined as event-types for separate trials; a separate regressor coded for missed responses in each session. In both analyses, parameter values for each covariate were then estimated using a subject-specific fixed-effects model. Movement parameters in the three directions of motion and 3° of rotation were included as confounds, as well as a single covariate representing the mean session effect. In the event-related analysis, RTs were included as a parametric modulator of each event-type regressor.

Subject-specific estimates for the contrasts of interest were obtained using linear contrasts across sessions. To control for between-session signal differences, the effects of interest within each session were contrasted against the baseline condition from that same session. These estimates were entered into the second stage of analysis treating subjects as a random effect, using a one-sample t-test across subjects. Since activations that were independent of the type of stimuli involved (words or shapes) were of principal theoretical interest, a cognitive conjunction analysis was applied to the data which identifies as significant only those brain regions that are commonly activated in both tasks. Statistical parametric maps of the independent word and shape contrasts of interest were constructed and a one-way ANOVA on the 16 subjects was used to reveal brain regions significantly activated across both tasks, at an uncorrected threshold of p < 0.001. The anatomical locations and approximate Brodmann areas of significant cluster maxima of at least five contiguous voxels were localized using the Talairach and Tournoux atlas (Talairach & Tournoux, 1988) after adjusting coordinates to allow for differences between the MNI and Talairach templates (Brett, Christoff, Cusack, & Lancaster, 2001). The activation in prefrontal cortex associated with both PM conditions was examined in more detail by extracting mean percentage signal change magnitude relative to the baseline conditions from the subject-specific parameter estimates of cluster maxima, and subjecting them to a repeated-measures analysis that included region (left lateral BA 10, right lateral BA 10, and medial BA 10) and PM condition as repeated factors.

3. Results

3.1. Behavioral results

The accuracy and reaction time data as a function of task and condition are displayed in Table 1. There was no main effect of task in terms of accuracy, F(1,15) = 0.67, n.s., but a significant main effect of condition, F(3,45) = 102.74, p < 0.0001, and a trend towards an interaction between the two factors, F(3,45) = 2.73, p = 0.055. Accuracy scores were lower on PM trials compared to both types of ongoing trials, F(1,15) = 53.57, p < 0.0001. The accuracy difference between the cue identification and intention retrieval PM conditions was also significant, participants being less accurate in the intention retrieval PM condition compared to the cue identification PM condition, F(1,15) = 15.44, p < 0.001.

Table 1. Accuracy (%) and reaction time (ms) data per task (standard deviations in parentheses)

Empty CellWords taskShapes task
Uncontaminated ongoing
 Accuracy98.9 (1.7)97.4 (3.0)
 RT566 (151)720 (129)

Contaminated ongoing
 Accuracy97.0 (1.7)96.8 (2.2)
 RT991 (260)839 (102)

Cue identification PM
 Accuracy80.6 (12.0)83.3 (13.6)
 RT1133 (169)836 (113)

Intention retrieval PM
 Accuracy75.9 (10.2)68.3 (7.9)
 RT1596 (190)1394 (207)

Uncontaminated ongoing: ongoing trials where no PM trial was expected. Contaminated ongoing: ongoing trials where a PM trial was expected. Cue identification PM: prospective memory trials with high demand on cue identification and low demand on intention retrieval. Intention retrieval PM: prospective memory trials with high demand on intention retrieval and low demand on cue identification.

Reaction times were significantly increased in the word compared to the shape task, F(1,15) = 23.34, p < 0.0001, and showed a main effect of condition, F(3,45) = 145.20, p < 0.0001, and an interaction, F(3,45) = 23.07, p < 0.0001. Both tasks showed an increase in reaction times between uncontaminated and contaminated ongoing trials, F(1,15) = 38.08, p < 0.0001, and between cue identification PM and intention retrieval PM trials, F(1,15) = 191.08, p < 0.0001. A comparison between the contaminated ongoing trials in the two PM conditions revealed that ongoing trials in the cue identification PM condition were associated with significantly longer RTs than in the intention retrieval PM condition, t(15) = 4.55, p < 0.001, though when separated per task this difference was only significant in the words task, t(15) = 4.93, p < 0.001.

3.2. Neuroimaging results

The results from the block analysis are reported first.2 To identify the brain areas involved in prospective memory, each of the PM conditions in the word and shape tasks was contrasted with the uncontaminated ongoing condition, with between-session differences controlled for using the common baseline condition as described in the Methods section. Because we are interested in the brain regions associated with prospective memory that are not dependent on the kind of task used, a conjunction contrast was used to identify activations that were common to both word and shape tasks. The conjunction contrast revealed a remarkably consistent pattern of significant activation in both the cue identification and intention retrieval PM conditions in anterior prefrontal cortex (BA 10), with activation bilaterally in lateral BA 10 and deactivation in medial BA 10 (see Fig. 2A and B). Examination of the signal confirmed that there was no significant effect of PM condition on activation in the anterior prefrontal cortex clusters identified, F(1,15) = 3.22, n.s. Significant activation was also found in a number of other areas including ventrolateral prefrontal and lateral parietal cortex in both PM conditions, and dorsolateral prefrontal cortex and orbitofrontal cortex in the intention retrieval PM condition (see Table 2, Table 3).

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Fig. 2. Group functional activation maps of percentage signal change, in a conjunction across the word and the shape task. Activations are shown in yellow/red, deactivations are shown in blue, with activations of particular interest circled. Z coordinates are shown in top left corner. (A) In the cue identification PM > uncontaminated ongoing contrast, bilateral BA 10 activation and medial BA 10 deactivation was observed. A highly similar pattern was observed in (B) the intention retrieval PM > uncontaminated ongoing contrast. Differences between conditions emerge in (C), the direct intention retrieval PM > cue identification PM contrast, with significantly greater activation in anterior prefrontal cortex bilaterally in the intention retrieval PM condition, and evidence of deactivation in medial anterior BA 10. Left lateral BA 10 activation was found in (D) the contaminated ongoing > uncontaminated ongoing condition (left: cue identification PM ongoing condition; right: intention retrieval PM ongoing condition) (for interpretation of the references to color in this figure legend, the reader is referred to the web version of the article).

Table 2. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification PM > uncontaminated ongoing
 Left middle frontal gyrus (BA 10)−395733.96
 Right middle frontal gyrus (BA 10)425794.423
 Right inferior frontal gyrus (BA 47)5421−63.921
 Left inferior frontal gyrus (BA 47)−4218−63.99
 Right insula (BA 13)4815123.78
 Left insula (BA 13)−390274.415
 Right inferior parietal lobule (BA 40)45−42514.137
 Right superior parietal lobule (BA 7)33−57514.425
 Right angular gyrus (BA 39)42−57483.78

Uncontaminated ongoing > cue identification PM
 Medial anterior prefrontal cortex (BA 10)048−63.923
 Right superior frontal gyrus (BA 8)3039513.45
 Right cingulate gyrus (BA 24)612363.97
 Left precentral gyrus (BA 4/3)−42−15424.211
 Left medial frontal gyrus (BA 6/31)−3−21573.717
 Right medial frontal gyrus (BA 6)6−24693.919
 Right precentral gyrus (BA 4)24−24723.611
 Right precuneus (BA 7)3−33543.713
 Left precuneus (BA 7)−3−48603.514
 Right lingual gyrus (BA 18/19)12−5163.617
 Left lingual gyrus/cuneus (BA 18/30)−9−63124.024

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Table 3. Regions of increased and decreased activation in the contrast between the intention retrieval PM condition and the uncontaminated ongoing condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Intention retrieval PM > uncontaminated ongoing
 Left middle frontal gyrus (BA 10)−395733.68
 Right middle frontal gyrus (BA 10)3954155.1203
 Right middle/superior frontal gyrus (BA 11)2754−123.716
 Right inferior frontal gyrus (BA 47)4521−154.122
 Right medial frontal gyrus (BA 8)921483.615
 Left insula (BA 13)−3918−63.920
 Right precentral gyrus (BA 9)4512393.724
 Right precentral gyrus/insula (BA 44)511294.036
 Left inferior/middle central gyrus (BA 9)−519394.531
 Left precentral gyrus (BA 6)−423275.154
 Right inferior parietal lobule (BA 40)45−42515.2199
 Left inferior parietal lobule (BA 40)−48−45485.0155
 Right precuneus (BA 19)12−66394.312
 Right inferior occipital gyrus (BA 18)36−90−124.046

Uncontaminated ongoing > intention retrieval PM
 Medial anterior prefrontal cortex (BA 10)048−65.3243
 Right cingulate gyrus (BA 24)612363.75
 Right lateral parietal cortex (BA 3)54−12574.430
 Left precentral gyrus (BA 4/3)−42−15453.613
 Left medial frontal gyrus (BA 6/31)−3−18574.019
 Right medial frontal gyrus (BA 6/4)6−30723.713
 Right precuneus (BA 7)3−33573.68
 Right lingual gyrus (BA 18)12−5163.68
 Left lingual gyrus/cuneus (BA 30)−6−57154.748
 Right cuneus (BA 18)6−87243.76

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Separate examination of the word and shape task contrasts revealed additional, potentially task-dependent, activation associated with cue identification in left premotor cortex, precuneus, and occipital cortex for words, and parahippocampal cortex for shapes. Similarly, the intention retrieval PM condition was associated with additional activation in left cingulate gyrus, premotor cortex, and precuneus for words, and a more anterior part of cingulate gyrus and postcentral gyrus for shapes.

Returning to the task-independent conjunction contrasts, despite the similarities in activation patterns associated with the two PM conditions versus the uncontaminated ongoing condition, a number of brain regions were identified in the direct task-independent contrast between cue identification PM and intention retrieval PM (see Fig. 2C). These results provide evidence of additional differential BA 10 involvement in the PM conditions. Significantly greater activation was found in the intention retrieval PM condition bilaterally in regions of BA 10 that peaked slightly less laterally than those observed in the contrasts above. Medial BA 10 appeared to be more active in the cue identification PM condition (see blue activation in Fig. 2C), although this effect did not exceed the whole-brain threshold of p < 0.001. At a threshold of p < 0.05 corrected for the voxels in an 8-mm sphere around the peak that was identified in both the cue identification PM versus ongoing and intention retrieval PM versus ongoing contrasts (0, 48, −6; see Table 2, Table 3), however, significant medial BA 10 activation did emerge in the cue identification versus intention retrieval PM contrast (0, 45, −9; Z = 3.5; voxels = 19). Other brain regions which were more active at the whole-brain threshold in the cue identification PM condition included the anterior cingulate, whereas in the intention retrieval PM condition more extensive areas of the cingulate gyri were among regions showing greater activation (see Table 4).

Table 4. Regions of increased and decreased activation in the contrast between the cue identification PM condition and the intention retrieval PM condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification PM > intention retrieval PM
 Right anterior cingulate (BA 32/11)336−123.753
 Left anterior cingulate (BA 25)−315−64.531
 Left putamen−240−95.051
 Right insula (BA 40)57−18213.624
 Left postcentral gyrus/insula (BA 40)−54−21214.5113
 Left middle temporal gyrus (BA 39/19)−54−72243.47
 Left lingual gyrus (BA 18)−21−78−63.817

Intention retrieval PM > cue identification PM
 Left middle frontal gyrus (BA 10)−3351183.46
 Right superior frontal gyrus (BA 10)2751−33.812
 Left medial frontal gyrus (BA 9)−1842213.45
 Left anterior cingulate (BA 32/24)−1227273.911
 Right insula (BA 13/47)3624−34.213
 Left cingulate gyrus (BA 32)−321456.3364
 Right lateral frontal cortex (BA 6)2412634.5118
 Right cingulate gyrus (BA 24)39304.05
 Left lateral frontal cortex (BA 6)−276665.146
 Left middle frontal gyrus (BA 6)−420573.45
 Left inferior parietal lobule (BA 40)−45−54573.45
 Left cuneus (BA 18)−12−75335.8246

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

Since the behavioral data showed longer RTs in the intention retrieval PM than in the cue identification PM condition, it is possible that the greater bilateral BA 10 activation in the intention retrieval PM condition might be attributable to differences in time on task. To test this possibility, a second analysis was conducted including RT as a parametric modulator to identify brain regions where activation was positively or negatively correlated with RTs. No significant correlation was found between RT and activation in the regions of BA 10 identified in the previous contrasts, even when the threshold was dropped to p < 0.1 uncorrected. It thus seems safe to attribute the greater bilateral BA 10 activation during intention retrieval PM trials primarily to the demands on recovering the delayed intention.

To examine the effect of maintaining a PM intention on performance of the ongoing task, the contaminated ongoing trials in both PM conditions were contrasted with the uncontaminated ongoing trials. In both PM conditions, contaminated ongoing trials were associated with greater activation in left lateral BA 10/47 (−48, 39, 0 for cue identification; −48, 42, −3 for intention retrieval; see Fig. 2D). Again, no correlation was found between RTs and activation in these regions in the analysis that included RT as a parametric modulator. Contrasting directly the contaminated ongoing trials in both PM conditions, significantly greater activation in left lateral BA 10 was observed during the intention retrieval PM condition than the cue identification PM condition (see Table 5). This is consistent with the idea that activation in this region reflects maintenance of the intended PM actions (Burgess et al., 2001).

Table 5. Regions of increased and decreased activation in the contrast between the ongoing trials in the cue identification PM and intention retrieval PM condition, combining across the word and shape tasks

Brain regionCoordinatesZVoxels
Empty CellxyzEmpty CellEmpty Cell
Cue identification ongoing > intention retrieval ongoing
 Right inferior frontal gyrus (BA 47)3630−64.131
 Left insula (BA 13)−4221154.39
 Left precentral gyrus (BA 6)−519333.511
 Left putamen−276−63.930
 Left fusiform gyrus (BA 37)−42−48−93.89
 Left middle occipital gyrus (BA 19)−30−93214.39
 Left occipital lingual gyrus (BA 18)−15−99−34.979
 Right cuneus (BA 18)15−102124.458

Intention retrieval ongoing > cue identification ongoing
 Left medial frontal gyrus (BA 10)−215464.215
 Right middle frontal gyrus (BA 6)1818663.78
 Left paracentral lobule (BA 6)−12−18603.814
 Right paracentral lobule (BA 6)12−30663.920
 Left postcentral gyrus (BA 4)−15−30664.010
 Left cuneus (BA 18)3−75363.96

Coordinates are in MNI atlas space (Cocosco et al., 1997), with brain regions and Brodmann areas (BA) estimated from the Talairach and Tournoux (1988) atlas.

4. Discussion

The main finding of the present experiment was that, despite behavioral differences between the cue identification and intention retrieval PM conditions, a strikingly similar pattern of hemodynamic changes, involving anterior prefrontal cortex (BA 10), was observed across both conditions. Finding such consistent results even when the demands on cue identification and intention retrieval were manipulated sufficiently to produce significant behavioral effects, confirms the view from previous neuroimaging studies that this region is likely to be of central importance to prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005).

For example, Okuda et al. (1998) reported activation in the left frontal pole (BA 10), as well as right dorsolateral and ventrolateral prefrontal cortex (BA 8/9/47) and anterior cingulate (BA 24), when participants remembered and acted upon a list of target words relative to performing an ongoing routine activity (word repetition). Activation in the frontal pole (BA 10, bilaterally) was also found by Burgess et al. (2001) across several cognitive tasks. In that study, activation during an ongoing task was compared to activation in two PM conditions: one in which PM trials were expected but never actually occurred, and one in which PM trials were expected and acted upon. In both PM conditions, activation in bilateral frontal pole, right lateral prefrontal and parietal cortex, plus precuneus, was found. Burgess et al. (2003) extended these results by showing that the bilateral activation of lateral BA 10 associated with retrieving a delayed intention was accompanied by deactivation in medial BA 10. Recently, den Ouden et al. (2005) reported activation in lateral BA 10, lateral parietal cortex, and precuneus, associated with keeping an intention in mind while performing an ongoing task responding to questions about intentions and actions.

In the present experiment, the reliability of the consistently observed lateral BA 10 activation was assessed by manipulating the demands placed on cue identification and intention retrieval processes as experimental variables. Of course, both PM conditions involved cue identification and intention retrieval to some degree; the experimental manipulation was not all-or-none. However, the demands on the two PM components were manipulated sufficiently for significant behavioral differences to emerge between them: accuracy was lower, and reaction times longer, in the intention retrieval condition than in the cue identification condition, although correlational analysis indicated that these behavioral differences could not explain the consistent hemodynamic changes observed across conditions. Significant lateral BA 10 activation was seen bilaterally in both PM conditions, confirming the characteristic nature of this activation, as reported by a number of neuroimaging studies (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005). Moreover, both PM conditions were additionally associated with medial BA 10 deactivation when contrasted with ongoing trials, consistent with the findings of Burgess et al. (2003), who also observed reduced medial BA 10 activation during PM trials versus ongoing trials. Of course, the limitations of fMRI do not allow us to speculate as to whether precisely the same neurons were recruited by both PM conditions, but it does seem clear that, at the macro level of Brodmann areas at least, similar patterns of activation in BA 10 were observed during both cue identification and intention retrieval PM conditions.

Despite finding that similar regions of BA 10 were activated (and deactivated) in both PM conditions, there was evidence that the level of activation in lateral BA 10 was greater in the intention retrieval PM condition than in the cue identification PM condition. The correlational RT analysis showed this effect was not attributable to differences in reaction time as the activations observed did not correlate with RT durations, consistent with results observed by Burgess et al. (2003). This suggests that the results cannot be explained by a simple task difficulty account, since greater difficulty (in terms at least of the amount of effort required to perform the task) could be expected to be reflected in increased RTs. Left lateral BA 10 was also significantly activated in the contrast between contaminated and uncontaminated PM trials, suggesting that activation in the region is affected by prior exposure to a PM intention. This is consistent with the finding from Burgess et al. (2001) that BA 10 activation is seen when PM trials are expected, regardless of whether they actually occur, and perhaps indicates that the region contributes to the maintenance of the delayed intention, a conclusion supported by the finding in the present experiment that BA 10 activation during contaminated ongoing trials was greater in the intention retrieval than the cue identification PM conditions. Moreover, there are echoes in these results of data from a number of electrophysiology studies which found that intention retrieval was associated with a sustained frontal slow-wave modulation that appeared to be particularly evident when retrieval of the PM intention was made more demanding (West et al., 2001; West & Ross-Munroe, 2002). Increasing the number of intentions to be remembered resulted in a larger amplitude of this frontal slow-wave (West et al., 2003). Although it is problematic to compare electrophysiological and neuroimaging results directly, there are similarities between the ERP increase over frontal sites and the present finding of greater lateral anterior prefrontal cortex activation when intention retrieval is more demanding.

The greater lateral BA 10 activation associated with intention retrieval can be conceived of in terms of differential attention towards external events and internally generated thought processes (Burgess, Simons, Dumontheil, & Gilbert, 2005; Gilbert, Frith, & Burgess, 2005; Gilbert, Simons, Frith, & Burgess, in press; Simons, Owen, Fletcher, & Burgess, 2005; Simons, Gilbert, Owen, Fletcher, & Burgess, 2005; see also Christoff & Gabrieli, 2000; Gusnard, Akbudak, Shulman, & Raichle, 2001). By this account, when one's primary aim is to detect a cue, attention must be turned towards the external world; once the cue has been detected, however, one must disengage from the external stimuli and attend to internal representations so that the relevant intention can be retrieved from memory. Thus, placing higher demands on cue identification implies biasing attention more towards external stimuli, whereas a higher load on intention retrieval implies an increase of attentional focus upon internally generated thought.

A number of previous studies have shown that lateral BA 10 plays an important role in the recollection of details about the context in which previous events occurred (Ranganath, Johnson, & D’Esposito, 2000; Rugg, Fletcher, Chua, & Dolan, 1999; Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005), an ability that requires retrieval of internally represented mnemonic information (Simons, Gilbert, et al., 2005; Simons, Owen, et al., 2005). In contrast, medial BA 10 has been associated with performance of tasks that emphasize the processing of externally presented stimuli (Gilbert et al., 2005, Janata et al., 2002, Small et al., 2003). Thus, Burgess et al. (2005) have suggested that lateral areas of BA 10 may play a role in maintaining attention towards internal cognition and more medial areas in maintaining attention towards external stimuli. The results from the PM versus ongoing contrasts in the present experiment can be interpreted along these lines, with medial BA 10 deactivation reflecting disengagement from the external ongoing task stimuli and the lateral BA 10 activation being associated with the directing of attention towards the internally represented PM intention (see Burgess et al., 2003, for a similar suggestion). Consistent with this view, lateral BA 10 activation was greater when the demands on intention retrieval were higher, and there was evidence of greater medial BA 10 activation associated with the cue identification PM condition, in which external cue processing demands were greater. Taken as a whole, these results are in agreement with the hypothesis that BA 10 acts as a “gateway”, biasing attention between externally derived perceptual information used to detect the occurrence of a PM cue and internal thought processes relating to the stored PM intention (Burgess et al., 2005; Gilbert et al., 2005; Simons, Gilbert, et al., 2005).

PM-related activation was also seen in a number of areas outside the anterior PFC region of interest. When contrasted against uncontaminated ongoing trials, the cue identification and intention retrieval PM conditions provoked activation in regions of lateral PFC and parietal cortex, among other areas. A number of regions showed differential activation in the two PM conditions. These included anterior cingulate cortex, which was activated more in the cue identification PM condition, and posterior cingulate and precuneus, which showed greater activation in the intention retrieval PM condition. Activation in all these regions has been observed in previous neuroimaging studies of prospective memory (Okuda et al., 1998, Burgess et al., 2001, Burgess et al., 2003, den Ouden et al., 2005), and has been linked with a number of processes relevant to PM retrieval. For example, lateral PFC, anterior cingulate and lateral parietal cortex have been proposed to constitute a cognitive control network involved in sustained attention and vigilance to particularly visual stimuli (Burgess et al., 2001; Coull, Frith, Frackowiak, & Grasby, 1996; Cabeza et al., 2003). Posterior cingulate, precuneus, and lateral parietal cortex have been linked in many studies with functions related to retrieval of stored mnemonic information such as recollection, retrieval confidence, and imagery (see Wagner, Shannon, Kahn, & Buckner, 2005, for a recent review).

In conclusion, the present experiment demonstrated that it is possible to tease apart behaviorally cue identification and intention retrieval components of PM, but that they may reflect the operation of similar underlying processes supported by anterior prefrontal cortex (BA 10). Evidence suggests that lateral BA 10 may reflect the maintenance and/or retrieval of the stored PM intention, showing greater activation not only during intention retrieval PM trials, but also when maintaining an intention in memory during the ongoing task.

Acknowledgements

We are grateful to Daniel Glaser and Hanneke den Ouden for discussion and advice, and the staff of the Functional Imaging Laboratory for scanning assistance. This work was supported by Wellcome Trust Grant 061171.

References

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1

Delegates at the 2nd International Conference on Prospective Memory, Zurich, July 2005, voted that the use of the abbreviation “PM” for prospective memory was preferable to other extant forms.

2

An event-related analysis was also conducted as verification, which produced activation in similar brain regions as the block analysis with reduced effect sizes, consistent with the lower statistical power associated with event-related designs (Henson, 2003).

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\ No newline at end of file diff --git a/tests/data/sample_inputs/6nTazJPV7TRM/identifiers.json b/tests/data/sample_inputs/6nTazJPV7TRM/identifiers.json new file mode 100644 index 0000000..82aaf2c --- /dev/null +++ b/tests/data/sample_inputs/6nTazJPV7TRM/identifiers.json @@ -0,0 +1 @@ +{"pmid": "28648549", "doi": "10.1016/j.dcn.2017.06.001", "pmcid": null} diff --git a/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/coordinates.csv b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/coordinates.csv new file mode 100644 index 0000000..bed8b36 --- /dev/null +++ b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/coordinates.csv @@ -0,0 +1,11 @@ +table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,-24.0,-96.0,10.0,,L. Middle Occipital Gyrus,24640,5.37, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,14.0,-92.0,2.0,,R. Middle Occipital Gyrus/Calcarine,-,4.77, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,-34.0,-8.0,55.0,,L. Frontal Eye Field,5824,5.34, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,54.0,-52.0,13.0,,R. Middle Temporal Gyrus,9534,4.68, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,28.0,-4.0,48.0,,R. Frontal Eye Field,5166,4.65, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,18.0,-66.0,66.0,,R. Posterior Superior Parietal Lobule,5250,4.61, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,-22.0,-64.0,66.0,,L. Posterior Superior Parietal Lobule,3178,4.58, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,56.0,10.0,20.0,,L. dorsal Inferior Frontal Gyrus,1316,4.52, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,-50.0,-54.0,13.0,,L. Middle Temporal Gyrus,3626,4.33, +9081,Table 1,Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.,1,24.0,-16.0,-18.0,,R. Hippocampus,630,3.48, diff --git a/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/metadata.json b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/metadata.json new file mode 100644 index 0000000..5591223 --- /dev/null +++ b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/metadata.json @@ -0,0 +1,11 @@ +{ + "title": "Hippocampal spatial mechanisms relate to the development of arithmetic symbol processing in children.", + "authors": "Mathieu, Romain;Epinat-Duclos, Justine;L\u00e9one, Jessica;Fayol, Michel;Thevenot, Catherine;Prado, J\u00e9r\u00f4me", + "journal": "Developmental cognitive neuroscience", + "keywords": null, + "abstract": "Understanding the meaning of abstract mathematical symbols is a cornerstone of arithmetic learning in children. Studies have long focused on the role of spatial intuitions in the processing of numerals. However, it has been argued that such intuitions may also underlie symbols that convey fundamental arithmetic concepts, such as arithmetic operators. In the present cross-sectional study, we used fMRI to investigate how and when associations between arithmetic operators and brain regions processing spatial information emerge in children from 3 to 10 grade. We found that the mere perception of a '+' sign elicited grade-related increases of spatial activity in the right hippocampus. That is, merely perceiving '+' signs - without any operands - elicited enhanced hippocampal activity after around 7 grade (12-13 years old). In these children, hippocampal activity in response to a '+' sign was further correlated with the degree to which calculation performance was facilitated by the preview of that sign before an addition problem, an effect termed operator-priming. Grade-related increases of hippocampal spatial activity were operation-specific because they were not observed with '\u00d7' signs, which might evoke rote retrieval rather than numerical manipulation. Our study raises the possibility that hippocampal spatial mechanisms help build associations between some arithmetic operators and space throughout age and/or education.", + "publication_year": 2017, + "coordinate_space": "MNI", + "license": null, + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/text.txt b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/text.txt new file mode 100644 index 0000000..d388062 --- /dev/null +++ b/tests/data/sample_inputs/6nTazJPV7TRM/processed/ace/text.txt @@ -0,0 +1,724 @@ + + + + + + + + + + + + + + + + + + +Hippocampal spatial mechanisms relate to the development of arithmetic symbol processing in children - PMC + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Back to Top + +Skip to main content + + + + + + +An official website of the United States government + +Here's how you know + + + + + + + + +The .gov means it’s official. + + Federal government websites often end in .gov or .mil. 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Published online 2017 Jun 13. doi: 10.1016/j.dcn.2017.06.001PMCID: PMC6969119PMID: 28648549Hippocampal spatial mechanisms relate to the development of arithmetic symbol processing in childrenRomain Mathieu,a,b,⁎ Justine Epinat-Duclos,a Jessica Léone,a Michel Fayol,c Catherine Thevenot,d and Jérôme Pradoa,⁎Romain MathieuaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, Bron, FrancebFaculté de Psychologie et des Sciences de l’Education, Université de Genève, 1205 Genève, SwitzerlandFind articles by Romain MathieuJustine Epinat-DuclosaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, Bron, FranceFind articles by Justine Epinat-DuclosJessica LéoneaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, Bron, FranceFind articles by Jessica LéoneMichel FayolcUniversité de Clermont Auvergne & CNRS, 63037 Clermont-Ferrand, FranceFind articles by Michel FayolCatherine ThevenotdInstitut de Psychologie, Université de Lausanne, 1015 Lausanne, SwitzerlandFind articles by Catherine ThevenotJérôme PradoaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, Bron, FranceFind articles by Jérôme PradoAuthor information Article notes Copyright and License information PMC DisclaimeraInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, Bron, FrancebFaculté de Psychologie et des Sciences de l’Education, Université de Genève, 1205 Genève, SwitzerlandcUniversité de Clermont Auvergne & CNRS, 63037 Clermont-Ferrand, FrancedInstitut de Psychologie, Université de Lausanne, 1015 Lausanne, SwitzerlandRomain Mathieu: rf.srnc.csi@ueihtamr; Jérôme Prado: rf.srnc.csi@odarpj ⁎Corresponding authors at: Institut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Université de Lyon, 67 Boulevard Pinel, 69675 Bron cedex, France. rf.srnc.csi@ueihtamr, rf.srnc.csi@odarpjReceived 2016 Aug 13; Revised 2017 May 4; Accepted 2017 Jun 3.Copyright © 2017 The AuthorsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Associated DataSupplementary Materialsmmc1.docx (414K)GUID: 753C89A9-7F53-4790-896E-8D8C1A531EE4AbstractUnderstanding the meaning of abstract mathematical symbols is a cornerstone of arithmetic learning in children. Studies have long focused on the role of spatial intuitions in the processing of numerals. However, it has been argued that such intuitions may also underlie symbols that convey fundamental arithmetic concepts, such as arithmetic operators. In the present cross-sectional study, we used fMRI to investigate how and when associations between arithmetic operators and brain regions processing spatial information emerge in children from 3rd to 10th grade. We found that the mere perception of a ‘+’ sign elicited grade-related increases of spatial activity in the right hippocampus. That is, merely perceiving ‘+’ signs – without any operands – elicited enhanced hippocampal activity after around 7th grade (12–13 years old). In these children, hippocampal activity in response to a ‘+’ sign was further correlated with the degree to which calculation performance was facilitated by the preview of that sign before an addition problem, an effect termed operator-priming. Grade-related increases of hippocampal spatial activity were operation-specific because they were not observed with ‘×’ signs, which might evoke rote retrieval rather than numerical manipulation. Our study raises the possibility that hippocampal spatial mechanisms help build associations between some arithmetic operators and space throughout age and/or education.Keywords: Arithmetic, Development, Attention, Space, fMRI, Hippocampus1. IntroductionHumans are unique in their ability to represent abstract mathematical concepts by culturally invented symbols, such as Arabic numerals and arithmetic signs. Because these symbols are arbitrary, learning the relationship between their identity and the concept they represent is a challenge during early math education in children. Most prior studies have focused on the mechanisms supporting the acquisition of symbols representing numerical quantities (Piazza et al., 2007, Ansari, 2008, Holloway and Ansari, 2009, Lyons and Ansari, 2009, Mundy and Gilmore, 2009). However, efficient processing of symbols that convey fundamental arithmetic concepts (i.e., operators) may be an important and largely neglected aspect of arithmetic skills. This is suggested by the operator-priming effect (Roussel et al., 2002, Fayol and Thevenot, 2012, Mathieu et al., 2017), whereby the anticipated presentation of a ‘+’ or ‘-’ sign 150 ms before a single-digit addition or subtraction problem facilitates problem-solving in adults.What aspect of the processing of an operator may cause the operator-priming effect in adults? A first possibility is that an arithmetic sign may automatically evoke a network of facts. For example, the perception of a ‘+’ or ‘-’ sign might pre-activate a network of additive or subtractive facts that would have been built in declarative memory after years of practice (Campbell and Xue, 2001, Ashcraft, 1992). Pre-activating such a network would facilitate the retrieval of the answer from memory when operands are presented. A second possibility is that an arithmetic sign may prime a specific procedure that would have been “automatized” after its repeated practice during arithmetic learning. For instance, Fayol and Thevenot argued that perceiving a ‘+’ or ‘-’ sign might trigger an automatized procedure that could be “linked to the convocation of the mental number line and could correspond to a preparation for a quick left-to-right or right-to-left browsing of this mental line” (Fayol and Thevenot, 2012). This proposal echoes the idea that adding or subtracting numbers involves rightward and leftward shifts of attention from a source to a target number along a mental map of numbers oriented from left to right, i.e., the mental number line (MNL) (Hubbard et al., 2005, Masson and Pesenti, 2014, Mathieu et al., 2016, Pinheiro-Chagas et al., 2017). Pre-activating such a procedure would result in a facilitation of subsequent calculation when operands are presented, thereby explaining the operator-priming effect.Interestingly, two lines of evidence favor the procedural over the declarative interpretation of the operator-priming effect. First, the effect is not observed with the ‘×’ sign and multiplication problems (Roussel et al., 2002, Mathieu et al., 2017). Multiplication problems, however, are explicitly learned by rote in school and multiplication is unanimously viewed as the operation having the strongest association with a network of facts in memory (Campbell and Xue, 2001, Galfano et al., 2003, Thibodeau et al., 1996). Therefore, the lack of operator-priming effect for multiplication problems is difficult to reconcile with the idea that the effect is due to associations between operators and networks of stored facts. Second, in line with Fayol and Thevenot’s proposal that ‘+’ and ‘−’ signs may prime a spatial scanning of the MNL, a recent study suggests that ‘+’ and ‘−’ signs do evoke spatial intuitions. Specifically, Pinhas et al. (2014) found that, when instructed to categorize ‘+’ and ‘−’ signs with left-hand or right-hand responses, adults tend to respond faster to ‘+’ signs with the right hand than with the left hand, whereas they tend to respond faster to ‘-’ signs with the left hand than with the right hand (Pinhas et al., 2014). Thus, ‘+’ and ‘−’ signs appear to have some automatic associations with the right and left sides of space, respectively.Using fMRI, we recently found that such spatial associations may stem from the fact that some arithmetic operators are automatically processed in brain regions involved in spatial attention in adults. We showed that the mere perception of a ‘+’ sign elicits greater activity than the mere perception of a ‘×’ sign in brain regions underlying overt spatial attention. These included the frontal eye fields (FEF) and the posterior superior parietal lobule (PSPL) (Mathieu et al., 2017). Thus, perceiving a ‘+’ sign (but not a ‘×’ sign) may be associated with a deployment of spatial attention in educated adults. Therefore, the rightward shifts of attention that have been posited to underlie addition problem-solving (Hubbard et al., 2005, Masson and Pesenti, 2014, Mathieu et al., 2016) might be primed by the mere preview of the addition sign (but not by the preview of a multiplication sign because multiplication is typically learned by rote and unlikely to be associated with movements along the MNL). Overall, there is mounting evidence that at least some arithmetic operators (e.g., ‘+’ but not ‘×’ signs) evoke spatial intuitions in adults, and that these intuitions may relate to the operator-priming effect.However, associations between operators and space are arguably not innate. Therefore, a fundamental outstanding question is how and when such associations emerge in the developing brain. To answer that question, we studied 34 children from 3rd to 10th grade while they performed 3 tasks. First, fMRI activity was measured while children were instructed to make eye saccades towards visually presented targets. This allowed us to precisely localize several regions of interest (ROIs) involved in spatial attention across children. Second, fMRI activity was measured in these spatial attention ROIs while children were presented with trials in which a ‘+’ sign was displayed without any operands (hereafter addition sign-only trials). As in our previous study in adults (Mathieu et al., accepted), activity during the perception of addition sign-only trials was compared to activity associated with trials in which a ‘×’ sign was displayed without any operands (hereafter multiplication sign-only trials) because these do not appear to evoke any specific intuitions in adults (Fayol and Thevenot 2012). This allowed us to identify the spatial attention ROIs in which activity in response to a ‘+’ sign (as compared to a ‘×’ sign) increases with age and/or education, as well as the developmental time course of these effects.1 Third, outside of the scanner, we asked subjects to perform an operator-priming task and measured the correlation between inter-individual differences in the size of the operator-priming effect and inter-individual differences in sign-related activity in spatial attention ROIs as a function of grade. This allowed us to evaluate when sign-related activity in spatial attention ROIs leads to an operator-priming effect in children.2. Material and methods2.1. ParticipantsForty-two right-handed children from 3rd to 10th grade participated in the study. All were native French speakers. Participants did not have prior history of neurological disease, psychiatric disorders, learning disabilities or attention deficits. All children and parents provided written informed consent to participate in the study, which was approved by the local ethics committee (CPP Sud-Est-II). Families received 80€ for their participation. Data from 8 subjects were excluded because of excessive head-movement in the scanner (see criteria in the Section 2.7., n = 3), poor whole-brain coverage (i.e. susceptibility artefacts from dental braces, n = 3) and unacceptably low performance during the task (i.e., lower than 50% accuracy on the sign-plus-operand trials, n = 2). Therefore, the final sample consisted of 34 children (20 males) from 3rd to 10th grade (age range: 8–15, mean age = 11.37, SD = 1.84). For each child, a continuous measure of grade was calculated by taking into account the specific date within the grade year when that child was scanned. The whole sample (n = 34) was evenly split into three groups as a function of grade: 11 children were from the ‘lower grades’ group (grade 3.2–5.4; mean = 4.4), 11 children were from the ‘intermediate grades’ group (grade 5.6–6.9; mean = 6.2), and 12 children were from the ‘higher grades’ group (grade 7.6–10.2; mean = 8.5).2.2. Standardized measuresChildren were administered standardized tests of intellectual and arithmetic abilities to ensure that there were no age differences with respect to those measures. Full-scale IQ was measured using the NEMI-2 (Cognet, 2006). Basic arithmetic knowledge was evaluated with the Math-Fluency subtest of the Woodcock-Johnson-III Tests of Achievement (WJ-III) (Woodcock et al., 2001). Across all participants, standardized (i.e., age-normalized) scores on IQ (mean = 112; SD = 10) and Math Fluency (mean = 106; SD = 16) tests were within the normal range. One-way ANOVAs with the between-subject factor group (lower, intermediate, higher grades) revealed no main effect of group on IQ (F(2,31) = 0.591, p = 0.560, BF10 = 0.29), indicating that age-normalized intellectual abilities were similar across groups. However, there was a main effect of group on Math Fluency (F(2,31) = 5.867, p = 0.007, BF10 = 7.24): Children from intermediate grades had a higher age-normalized score (mean = 118; SD = 18) than children from lower (mean = 100; SD = 11) and higher grades (mean = 100; SD = 13). Therefore, we included standardized Math-Fluency scores as nuisance covariate in all of our analyses.2.3. Behavioral sessionAfter standardized testing, children participated in a behavioral session during which they performed an operator-priming task adapted from Fayol and Thevenot (2012) and Roussel et al. (2002). Children were asked to evaluate 56 single-digit addition and 56 multiplication problems composed of operands between 2 and 9. Problems were presented in both commutative orders. Tie problems were excluded. Problems with a sum smaller than or equal to 11 and a product smaller or equal to 24 were considered small. Other problems were considered large.In each trial, a problem was presented with an answer (Fig. 1a). The arithmetic sign was presented either 150 ms before (Negative SOA condition) or at the same time (Null SOA condition) as the operands (Fig. 1a). All problems were presented once in both SOA condition with a valid answer. Twenty-eight addition and 28 multiplication problems were also presented in both SOA condition with an invalid answer (obtained by adding or subtracting 1 to or from the valid answer). Trials were pseudorandomly ordered so that no more than three problems of the same type appeared consecutively. Problems with an invalid answer were randomly chosen across subjects and the order of blocks was counter-balanced between subjects. The experiment started with 8 practice trials.Open in a separate windowFig. 1Experimental design. (a) During the behavioral session, children (n = 34) were asked to evaluate the result of single-digit addition and multiplication problems. For both operations, the arithmetic sign was presented either 150 ms before (negative SOA trials), or at the same time as the operands (null SOA trials). (b) In the scanner, children (n = 34) performed an arithmetic task during which they were presented with sign-only (left) and sign-plus-operands (right) addition, multiplication and baseline trials. In each trial, a sign (‘+’, ‘×’ or ‘◊’) was presented at the center of the screen for 150 ms. In sign-only trials, the trial ended with the presentation of the sign and was simply followed by the inter-trial period of fixation. In sign-plus-operands trials (filler trials), the ‘+’ or ‘×’ sign was immediately followed by a single-digit addition or multiplication problem (respectively) presented along an answer and the ‘◊’ sign was followed by 3 letters. In those cases, children had 5000 ms to evaluate whether the answer of the problem was true or false or to indicate whether one of the 3 letters was a B.The experiment was controlled by Presentation software (Neurobehavioral Systems, Albany, CA). Problems were displayed in white Arial 60-point font on a black background. All trials started with the presentation of a white central fixation dot for 1500 ms, immediately followed by a red central fixation dot for 1000 ms signaling that the problem was about to be presented, either in the negative SOA condition or in the null SOA condition (Fig. 1a). Subjects had a maximum of 5000 ms to evaluate whether the response was valid or invalid as quickly as possible by pressing one of two keys on the computer keyboard.2.4. fMRI sessionDuring fMRI scanning, children performed a spatial attention localizer task and an arithmetic task. The spatial attention localizer task consisted in alternating blocks of fixation and saccades. During saccade blocks (n = 9), participants were asked to make saccades towards several successive target dots. Each saccade block contained 16 target dots (0.2° visual angle) that appeared at random positions with an eccentricity of 3°, 3.5°, 4°, 4.5°, 5° or 5.5° in the left or right visual field for an average of 800 ms (with a jitter of ±200 ms). During fixation blocks (n = 9), participants were asked to maintain fixation on a central dot for 12,800 ms. Block order was counterbalanced across children.During the arithmetic task, children were presented with sign-only and sign-plus-operands versions of addition and multiplication trials (Fig. 1b). Each trial started with the presentation of either a ‘+’ or a ‘×’ sign at the center of the screen for 150 ms. In sign-only trials (n = 30), the trial ended with the presentation of the sign and was simply followed by the inter-trial period of fixation (see below). These sign-only trials were our trials of interest and allowed us to isolate neural activity due to the presentation of a sign alone. We also included in the experiment sign-plus-operands trials (n = 50). In those filler trials, the ‘+’ or ‘×’ sign was immediately followed by a single-digit addition or multiplication problem (respectively) presented with an answer. Participants were asked to indicate whether the answer was true or false. The goal of these filler trials (for which associated activity would be difficult to interpret because any effects could be attributable to the anticipatory presentation of the operator, the appearance of the operands, or a combination of both of these factors) was only to keep children engaged and attentive in the scanner. They also induced an arithmetic context, thereby ensuring that the ‘+’ and ‘×’ signs presented in sign-only trials were perceived as arithmetic signs. Problems in sign-plus-operand trials were constructed following the same criteria as in the behavioral session. Finally, the baseline consisted in trials in which the arithmetic sign was replaced by an abstract non-arithmetic sign (i.e., ‘◊’). We included 30 baseline sign-only trials (in which the ‘◊’ sign was presented in isolation) and 50 baseline sign-plus-operand trials (in which the ‘◊’ sign was followed by 3 letters and participants had to indicate whether one of these letters was a B). All trials were followed by a variable period of visual fixation ranging from 3000 ms to 3800 ms. That period consisted in a central white fixation dot that turned red 1000 ms before the onset of the next trial. The arithmetic task was decomposed in 4 functional runs. All trials were intermixed and the timing and order of trial presentation within each run was optimized for estimation efficiency using optseq2 (http://surfer.nmr.mgh.harvard.edu/optseq/). Behavioral responses were recorded using an MR-compatible response device.Stimuli were generated using Presentation software (Neurobehavioral Systems, Albany, CA). Prior scanning, children were familiarized with the fMRI environment during a practice session that took place after the standardized testing and the behavioral session. During this practice session, children learned to minimize head movement in a mock fMRI scanner. The actual scanning session took place no more than 3 weeks after the practice session.2.5. Behavioral analysesRT data associated with the operator-priming task were normalized using a logarithmic transformation prior all analyses to improve the conformity of the data to the standard assumptions of parametric testing. Following Fayol and Thevenot (2012), mean RT was analyzed using planned comparisons that followed from a within-subject ANOVA with the factors Operation (Addition/Multiplication) and SOA (Negative/Null), conducted separately for each group. We report for all effects the corresponding Bayes factors (BF10), indicating the strength of evidence for the alternative hypothesis (H1) relative to the null hypothesis (H0). Substantial evidence in favor of the alternative hypothesis is typically suggested by a BF10 greater than 3 (Jeffreys, 1961, Dienes, 2011).2.6. fMRI data acquisitionImages were collected with a Siemens Prisma 3T MRI scanner (Siemens Healthcare, Erlangen, Germany) at the CERMEP Imagerie du vivant in Lyon, France. The BOLD signal was measured with a susceptibility weighted single-shot EPI sequence. Imaging parameters were as follows: TR = 2000 ms, TE = 24 ms, flip angle = 80°, matrix size = 128 × 120, field of view = 220 × 206 mm, slice thickness = 3 mm (0.48 mm gap), number of slices = 32. A high-resolution T1-weighted whole-brain anatomical volume was also collected for each participant. Parameters were as follows: TR = 3500 ms, TE = 2.24 ms, flip angle = 8°, matrix size = 256 × 256, field of view = 224 × 224 mm, slice thickness = 0.9 mm, number of slices = 192.2.7. fMRI preprocessingData analysis was performed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Functional images were corrected for slice acquisition delays and spatially realigned to the first image of the first run. Images were then spatially smoothed with a Gaussian filter equal to twice the voxel size. ArtRepair was used to help remove motion from the functional images prior to normalization (Mazaika et al., 2009). Volumes with rapid scan-to-scan movements of greater than 1.5 mm were repaired by interpolation of the two nearest non-repaired scans. Each run with more than 5% of the total number of volumes replaced was removed from the analyses. A subject was excluded from further analysis if more than one run was removed. The number of volumes replaced did not differ between grade groups (F(2,31) = 2.20; p = 0.13). Finally, functional images were normalized into the standard MNI space (normalized voxel size, 2 × 2 × 3.5 mm3).2.8. fMRI processingEvent-related statistical analysis was performed according to the general linear model (GLM). For the localizer task, brain activity associated with saccades and fixation blocks was modeled as epochs with onsets and offsets time-locked to the beginning and the end of each block. Each epoch was convolved with a canonical hemodynamic response function (HRF) and the time series data from each run were high-pass filtered (1/128 Hz). Finally, serial correlations were corrected using an autoregressive AR(1) model. Following our previous study using the same task in adults (Mathieu et al., 2017), brain activity associated with sign-only trials during the arithmetic task was estimated using a finite impulse response (FIR) model. We modeled 8 time points with an interval of 2 s (corresponding to one TR) ranging from the onset of the sign to 16 s after the sign. The magnitude of the fMRI response for each type of sign-only trial was calculated by subtracting activity at the onset of the sign (i.e., 1st bin, or 0 s after the onset) from the peak activity (i.e., 4th bin, or ∼8 s after the onset). The time series data from each run were high-pass filtered (1/128 Hz), and serial correlations were corrected using an autoregressive AR(1) model.2.9. Region of interest (ROI) definition and analysesThe present study used a Region-of-Interest (ROI) approach to analyze brain activity associated with sign-only trials in brain regions involved in the orienting of spatial attention in children. All ROIs were independently defined using the contrast of saccades versus fixation blocks in the spatial attention localizer task. All subject-specific contrasts were entered into a random effect (RFX) one-sample t-test across subjects. The RFX contrast map was then thresholded across the whole-brain using an uncorrected voxel-level threshold of p < 0.001 and a false-discovery-rate (FDR) corrected cluster-level threshold of p < 0.05 (Chumbley and Friston 2009). Using the SPM toolbox Marsbar (http://marsbar.sourceforge.net/), ROIs were defined as 6-mm radius spheres around the peak coordinate of each region.Within each ROI and for each participant, we calculated the average response (parameter estimates) for ‘+’ signs using the contrast of addition sign-only trials versus baseline sign-only trials. Similarly, we calculated the average response for ‘×’ signs using the contrast of multiplication sign-only trials versus baseline sign-only trials. Two analyses were performed in each ROI. First, we identified the ROI(s) in which a difference in fMRI activity between ‘+’ and ‘×’ signs emerged throughout age and/or education, using a 3 × 2 ANOVA with the between-subject factor group (lower/intermediate/higher grades) and the within-subject factor sign (‘+’/‘×’). Second, we tested in these ROIs whether inter-individual differences in the fMRI response to an arithmetic sign were correlated with inter-individual differences in the size of the operator-priming effect (measured in the behavioral session) for each group. In all analyses, we report uncorrected P values as well as P values corrected for multiple comparisons across all identified ROIs using the Bonferroni procedure. Bayes factor are also reported.3. Results3.1. The spatial localizer task activates a brain network encompassing frontal, parietal, occipital and hippocampal regionsContrasting saccades to fixation blocks in the spatial attention localizer task, we first identified 10 clusters supporting the orienting of spatial attention across subjects: the bilateral Frontal Eye Field (FEF), bilateral Posterior Superior Parietal Lobule (PSPL), bilateral Middle Temporal Gyri (MTG), bilateral Middle Occipital Gyri (MOG), right dorsal Inferior Frontal Gyrus (dIFG), and right Hippocampus (see Table 1 and Fig. 2). Therefore, a large brain network was involved in the orienting of spatial attention across subjects. Each of these regions served as an ROI in subsequent analyses.Table 1Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.Anatomical location∼BACluster size (mm3)MNI coordinatesZ-scoreXYZL. Middle Occipital Gyrus1724640−24−96105.37R. Middle Occipital Gyrus/Calcarine18–14−9224.77L. Frontal Eye Field65824−34−8555.34R. Middle Temporal Gyrus21953454−52134.68R. Frontal Eye Field6516628−4484.65R. Posterior Superior Parietal Lobule7525018−66664.61L. Posterior Superior Parietal Lobule73178−22−64664.58L. dorsal Inferior Frontal Gyrus6/4413165610204.52L. Middle Temporal Gyrus213626−50−54134.33R. Hippocampus–63024−16−183.48Open in a separate windowL. = left; R. = right; ∼BA = approximate Brodmannʼs area; MNI = Montreal Neurological Institute.Open in a separate windowFig. 2Brain regions activated in the spatial attention localizer task. Brain regions that were more activated during saccades than fixation blocks. Activations are overlaid on slices of the MNI-normalized anatomical brain. PSPL, Posterior Superior Parietal Lobule; FEF, Frontal Eye Field; MTG, Middle Temporal Gyrus; dIFG, dorsal Inferior Frontal Gyrus; MOG, Middle Occipital Gyrus.; HIPP, Hippocampus.3.2. The right hippocampus region identified by the spatial localizer task is increasingly activated in response to a ‘+’ sign but not to a ‘×’ sign throughout age and/or educationA 3 × 2 ANOVA with the between-subject factor group (lower/intermediate/higher grades) and the within-subject factor sign (‘+’/‘×’) was then conducted in each of the 10 ROIs identified by the spatial attention localizer. We found an interaction between group and sign in the right hippocampus (F(2.30) = 6.75, p = 0.0038, pcorr = 0.038; Fig. 3a and b), but not in any other ROIs (all Fs < 3.44, all ps > 0.046, all pscorr > 0.46). Bayes Factor analysis indicated substantial evidence for this interaction in the right hippocampus (BF10 = 11.53), while no or anecdotal evidence for such an interaction was found in the other ROIs (BF10 < 1.63). Follow-up t-tests in the hippocampus ROI revealed that children from higher grades (average grade = 8.5) exhibited greater activity for addition than multiplication sign-only trials (t11 = 3.02, p = 0.012), whereas there was no difference between signs in children from intermediate grades (average grade = 6.2) (t10 = 0.87, p = 0.41) and even a trend for less activity for addition than multiplication sign-only trials in children from lower grades (average grade = 4.4) (t10 = 2.22, p = 0.051). Bayes Factor analysis indicated substantial evidence for a difference of activity between addition and multiplication sign-only trials in children from higher grades (BF10 = 5.21), but evidence for this difference was absent in intermediate grades (BF10 = 0.41) and anecdotal in lower grades (BF10 = 1.69). Finally, across all groups, addition sign-only trials were associated with greater activity than baseline sign-only trials in children from higher grades (t11 = 2.63, p = 0.023), but not in any other groups (all ts < 1.32, all ps > 0.21). Multiplication sign-only trials were associated with greater activity than baseline sign-only trials in none of the groups (all ts < 1.38, all ps > 0.20). Bayes Factor analysis indicated substantial evidence for a difference between addition sign-only trials and baseline sign-only trials in children from higher grades (BF10 = 3.00), but no or anecdotal evidence in the other groups (all BF10s < 0.60) and for multiplication sign-only trials (all BF10s < 0.63). Overall, then, a difference in fMRI response to a ‘+’ and a ‘×’ sign emerged throughout age and/or education in the right hippocampus.Open in a separate windowFig. 3Grade-related changes of activity in the right hippocampus.(a) Location of the right hippocampus ROI overlaid on a coronal slice of the MNI-normalized anatomical brain. (b) Activity in the right Hippocampus for addition versus baseline sign-only trials (red) and multiplication versus baseline sign-only trials (blue) in children from lower (n = 11; grade 3.2–5.4; mean grade = 4.4; mean age = 9.4), intermediate (n = 11; grade 5.6–6.9; mean grade = 6.2; mean age = 11.1) and higher grades (n = 12; grade 7.6–10.2; mean grade = 8.5; mean age = 13.4). (c) Difference in activity between addition and multiplication sign-only trials over grade in the right Hippocampus. *p < 0.05; r represents the Pearson correlation coefficient.The findings above were confirmed by an additional correlation analysis in which grade was treated as a continuous predictor across all subjects. The difference in activity between addition and multiplication sign-only trials was positively correlated with grade in the right hippocampus (r = 0.53, p = 0.001, pcorr = 0.01; Fig. 3c). No other significant grade-related changes were found in any other regions (all rs < 0.38, all ps > 0.03, all pscorr > 0.30). Bayes Factor analysis indicated substantial evidence for this correlation in the right hippocampus (BF10 = 20.97), but no or anecdotal evidence in any other ROIs (all BF10s < 2.03). In the right hippocampus, the correlation between grade and the contrast of addition sign-only trials versus baseline sign-only trials, was also near significance (r = 0.33, p = 0.056; BF10 = 1.22).2 The correlation between grade and the contrast of multiplication sign-only trials versus baseline sign-only trials, however, was not significant (r = −0.18, p = 0.32; BF10 = 0.35).3.3. Spatial hippocampal activity in response to a ‘+’ sign relates to an addition-priming effect in children from higher gradesWe then tested whether the hippocampal response to a ‘+’ sign observed in children from higher grades was related to the operator-priming effect. To this aim, each child performed a version of the operator-priming task outside of the scanner (see Fig. 1a).First, we tested whether the results obtained by Fayol and Thevenot (2012) in adults (i.e., an operator-priming effect for addition but not for multiplication across subjects) could be extended to our children participants. Because children from lower grades had a performance close to chance on large problems (58%), we exclusively focused our analyses on small problems for which accuracy was significantly above chance in all groups (lower grades: 80%, intermediate grades: 92%, higher grades: 96%). Planned comparisons revealed an operator-priming effect for addition in children from higher grades (1491 ms versus 1577 ms; F(1,11) = 8.11, p = 0.016), but not in children from lower grades (2289 ms versus 2417 ms; F(1,10) = 2.66, p = 0.134) and intermediate grades (1530 versus 1509 ms; F(1,10) = 0.01, p = 0.941). No operator-priming effect for multiplication was observed in any groups (lower grades: F(1,10) = 3.50, p = 0.091; intermediate grades: F(1,10) = 1.52, p = 0.246; higher grades: F(1,11) = 0.14, p = 0.715). Bayes Factor analysis indicated substantial evidence for an operator-priming effect with addition problems in children from higher grades (BF10 = 4.08), but no evidence in children from intermediate (BF10 = 0.30) and lower (BF10 = 0.83) grades. There was also no or anecdotal evidence for an operator-priming effect with multiplication problems in any group (higher grades: BF10 = 0.31; intermediate grades: BF10 = 0.55; lower grades: BF10 = 1.09).Second, we tested whether the size of the operator-priming effect in children (measured on small problems) was correlated to the magnitude of the response for addition sign-only trials versus baseline sign-only trials in the right hippocampus. Such a correlation was found to be highly significant in children from higher grades (r = 0.82, p = 0.0012, Fig. 4), surviving Bonferroni correction for multiple comparisons between the two conditions and across the three groups (pcorr = 0.007). That is, children from higher grades who show greater responses to ‘+’ signs in the right hippocampus are those who show larger operator-priming effect with addition problems. No significant correlation was found in children from lower (r = 0.15, p = 0.66 Fig. 4) and intermediate (r = 0.24, p = 0.48, Fig. 4) grades. Bayes Factor analysis indicated substantial evidence for the correlation in children from higher grades (BF10 = 38.15), but no evidence in children from lower (BF10 = 0.40) and intermediate (BF10 = 0.46) grades. There was also no significant (and anecdotal evidence for a) correlation between the operator-priming effect for addition problems and the fMRI response to multiplication sign-only trials (compared to baseline sign-only trials) in the right hippocampus, in any of the groups (lower grades: r = 0.06, p = 0.87, BF10 = 0.37; intermediate grades: r = 0.32, p = 0.34, BF10 = 0.56; higher grades: r = 0.51, p = 0.09, BF10 = 1.28). Therefore, not only did we observe an operator-priming effect for addition in the only group in which we also observed a greater hippocampal response to ‘+’ than ‘×’ signs (i.e., children from higher grades), but inter-individual differences in the size of the operator-priming effect in that group was also related to hippocampal activity.Open in a separate windowFig. 4Hippocampus brain-behavior correlation over grade.Activity in the right hippocampus in response to addition sign-only trials versus baseline sign-only trials as a function of the operator-priming effect calculated in the behavioral session for addition problems in children from lower (n = 11; grade 3.2–5.4; mean grade = 4.4; mean age = 9.4), intermediate (n = 11; grade 5.6–6.9; mean grade = 6.2; mean age = 11.1) and higher grades (n = 12; grade 7.6–10.2; mean grade = 8.5; mean age = 13.4). r represents the Pearson correlation coefficient.Third, we tested whether the correlation between the operator-priming effect and the contrast of addition sign-only trials versus baseline sign-only trials increased over grade. This was done by transforming the correlation coefficient in each group to a Fischer’s z score before comparing the groups using the cocor package (Diedenhofen and Musch, 2015). Although the correlation was not greater in children from intermediate than lower grades (z = 0.19, p = 0.43, one-tailed), it was significantly greater in children from higher than lower grades (z = 2.07, p = 0.019, one-tailed) and in children from higher than intermediate grades (z = 1.88, p = 0.030, one-tailed). Therefore, this brain-behavior correlation increased over grade.4. DiscussionIn the present study, we used fMRI and a cross-sectional design to investigate (i) how and when spatial processing related to the perception of an addition sign emerges in the developing brain, and (ii) to what extent it contributes to the emergence of an operator-priming effect.4.1. The mere perception of a ‘+’ sign is associated with increased hippocampal spatial activity throughout age and/or educationIt has been shown that the processing of a ‘+’ sign is associated with the right side of space (Pinhas et al., 2014) and activates brain regions involved in overt spatial attention in adults (Mathieu et al., 2017). Therefore, we expected arithmetic learning to be associated with increased recruitment of brain regions involved in spatial attention in response to the perception of a ‘+’ sign throughout age and/or education in children. This was the case in a region of the right hippocampus that we identified in our spatial attention localizer task. Therefore, it is possible that hippocampal spatial mechanisms may scaffold the progressive association between an arithmetic operator (i.e., a ‘+’ sign) and spatial intuitions throughout age and/or education. There is increasing evidence that the hippocampal formation, and particularly the right hippocampus, may house a ‘sense of space’ (Buffalo, 2015). Specifically, the right hippocampus has been extensively reported to support spatial representation and navigation in humans (Maguire et al., 1998, Burgess et al., 2002) as well as in non-human primates and rodents (O'keefe and Nadel, 1978, Bird and Burgess, 2008). For example, the hippocampus is typically activated when human participants learn to navigate through a mental representation of space (i.e., mental scanning) (Mellet et al., 2002, Spiers and Maguire, 2006). Interestingly, a recent study in monkeys demonstrated that neurons in the hippocampal formation may encode the direction of overt (Killian et al., 2015) as well as covert (Wilming et al., 2015) shifts of attention. Therefore, the hippocampal formation is likely a critical region for both representing a mental map of space and navigating along that map (Killian et al., 2012, Meister and Buffalo, 2016).Why would such a hippocampal spatial navigation mechanism be increasingly recruited by the mere perception of a’+’ sign throughout age and/or education? One possibility is that this mechanism might enable children to construct a detailed representation of numbers in mental space, as well as to navigate along that mental representation. Indeed, there is overwhelming evidence that numbers of increasing size are organized along a left-to-right mental map (i.e., the MNL) in adults (Fischer and Shaki 2014). This spatial representation may enable individuals to add or subtract numbers by navigating from a source to a target number to the left or right of that MNL. This is supported by behavioral studies showing that addition and subtraction problem-solving is associated with rightward and leftward shifts of attention (Masson and Pesenti, 2014, Mathieu et al., 2016), as well as by a neuroimaging study indicating an overlap between the brain regions involved in overt shifts of attention and those involved in arithmetic calculation in adults (Knops et al., 2009). Such strategies may be acquired early by children, sometimes even explicitly in the classroom where addition and subtraction is often demonstrated on visual number lines. Yet, it is only with practice that they might become progressively attached to and evoked by an arithmetic operator such as a ‘+’, which might explain the grade-related increases of activity in this region in response to the ‘+’ sign (and the fact that it is only by 7th grade that children exhibit significant activity in response to that sign).4.2. Hippocampal spatial activity in response to a ‘+’ sign relates to the operator-priming effect in children from higher gradesA critical question is to what extent this automatic processing of a ‘+’ sign in hippocampal spatial mechanisms is associated with children’s behavior. To answer this question, we asked all children to perform a version of the operator-priming task developed by Fayol and Thevenot (2012) and Roussel et al. (2002). First, we replicated the operator-priming effect observed in adults with addition problems (i.e., a facilitation of problem-solving when the operator is presented 150 ms before the operands), but only in children from higher grades (after around 7th grade). Like in adults, this effect was specific to addition problems and not observed with multiplication problems. Thus, the perception of a ‘+’ sign (but not that of a ‘×’ sign) appears to pre-activate a process that is likely used to solve the subsequent problem in children from higher grades. More central to our current interest, we found that the size of the operator-priming effect in these children was highly correlated with the degree of activation of hippocampal spatial mechanisms in response to a ‘+’ sign. This indicates that hippocampal spatial activity may be at the source of the operator priming-effect in older children, perhaps because these children might prepare for an attentional movement along the MNL as soon as a ‘+’ sign is presented. Because no brain-behavior correlation was observed in younger children, extensive practice might be needed before such mechanisms are triggered by the mere perception of the sign.4.3. Hippocampal spatial activity in response to a ‘+’ sign is transient in developmentStrikingly, the spatial brain mechanisms that respond to the mere perception of a ‘+’ sign appear to be different in children and adults. That is, albeit we found increased hippocampal spatial activity throughout age and/or education in the present study, we did not identify these mechanisms in our previous study in adult participants using the exact same task (Mathieu et al., 2017). Rather, we found increased activity in response to a ‘+’ sign in neocortical regions of the FEF and PSPL in adults. Therefore, the contribution of the hippocampus to the automatic processing of a ‘+’ sign is likely transient. Such a transient involvement of the hippocampus is consistent with a wealth of studies that have demonstrated that the spatial representations initially supported by the hippocampus during learning become independent from this brain structure over experience and transferred to neocortical regions (Rosenbaum et al., 2004, Hirshhorn et al., 2012b). For example, longitudinal studies demonstrate that right hippocampal activity associated with learning to mentally navigate through a new environment disappears and is replaced by neocortical activity when individuals become familiar with that environment (Spiers and Maguire, 2007, Hirshhorn et al., 2012a). It is possible that the same phenomenon is at play here: The hippocampus may be involved in the early representation of (and navigation along) the MNL before that representation is transferred to neocortical regions of the fronto-parietal cortex. Future investigations with a wider age sample than in the present study are needed to test this hypothesis.4.4. Can right hippocampal involvement in the present study reflect mnemonic operations involved in learning arithmetic?Although there is no doubt that the hippocampus supports spatial processing (Burgess et al., 2002, Spiers and Maguire, 2007), this brain structure is also well known to support the encoding and consolidation of verbal declarative knowledge into long-term memory (Eichenbaum, 2004). In fact, previous developmental studies have largely explained the involvement of the hippocampus during arithmetic learning by referring to its role in declarative memory rather than spatial processing (Rivera et al., 2005, De Smedt et al., 2011, Cho et al., 2011, Cho et al., 2012, Qin et al., 2014). This interpretation relies on the claim that results of well-practiced arithmetic facts (e.g., 2 + 3 or 4 × 2) might become progressively retrieved from memory (rather than calculated) over the course of learning and development (Campbell and Xue, 2001). The hippocampus might thus support the encoding and consolidation of networks of arithmetic facts in children.Can the role of the hippocampus in declarative memory explain the operator-specific activity over grade (and correlation with the operator-priming effect) observed in the region of the right hippocampus identified by our spatial localizer task? We acknowledge that we did not have a task identifying processes involved in declarative memory. Thus, even if the right hippocampus is usually more associated with spatial than mnemonic processes (Burgess et al., 2002), it is possible that the hippocampal cluster that we identified as being involved in spatial processing may also be involved in some aspects of declarative memory. One might thus argue that grade-related increases of activity in relation to ‘+’ signs reflect the progressive association between a ‘+’ and a network of additive facts. This explanation, however, can be ruled out by an examination of activity related to ‘×’ signs. Because single-digit multiplication problems are almost exclusively learned by rote in school, multiplication is the operation that is perhaps the most associated with a network of stored facts in the literature (Campbell and Xue, 2001). Thus, if increased hippocampal activity in relation to ‘+’ signs were due to the progressive building of a network of additive facts, increased activity in that same region should have been observed during the perception of ‘×’ signs (perhaps even more so for the perception of ‘+’ signs). Yet, this is not the case. Not only did we not find any grade-related increase of activity for ‘×’ signs in the hippocampal cluster identified by our spatial localizer task, but activity was significantly greater for ‘+’ than ‘×’ signs in higher graders (who are as proficient in single-digit multiplication as addition). Similarly, no operator-priming effect was observed for multiplication problems in higher graders, indicating that the operator-priming effect observed for addition is likely to have little to do with the pre-activation of a network of stored facts (because this should be also observed for multiplication). Therefore, the specificity of our results to ‘+’ signs (as compared to ‘×’ signs) in the right hippocampus ROI makes it very unlikely that our results are related to mnemonic operations. In our view, emerging associations between ‘+’ signs and spatial intuitions related to the MNL are the best explanation of the effects reported here.Of course, the fact that the role of the hippocampus in declarative memory is unlikely to explain our operator-specific findings in the right hippocampus ROI does not mean that hippocampal mechanisms supporting mnemonic operations do not contribute to arithmetic learning. Instead, they indicate that the hippocampus might contribute to arithmetic learning through its role in both declarative memory and spatial processing. Interestingly, the operator-specific activity observed in our (spatially localized) right hippocampal cluster is not observed in a mirror (left lateralized) cluster that is not activated in the localizer contrast (see Supplementary information). In that mirror region, no difference was observed between activity related to ‘+’ and ‘×’ signs in any group of children (and left hippocampal activity was not related to the operator-priming effect). Thus, the developmental effect reported here appears to be restricted to the right hippocampus. This specificity suggests that the observed developmental changes in the right hippocampus may not simply reflect general brain maturation but rather mechanisms that are specific to arithmetic learning.4.5. LimitationsIt is worth acknowledging here 2 potential limitations of the present work. First, as is the case for any cross-sectional fMRI studies, our study is correlational in nature. Thus, although our findings are consistent with the idea that the right hippocampus might scaffold the progressive association between (at least some) arithmetic operators and space throughout age and/or education, future studies might specifically investigate the causal role of these hippocampal mechanisms. Second, our finding of a correlation between grade and the processing of an addition sign in the right hippocampus (see Fig. 3C) relies on a relatively large sample size of 34 children. However, other findings involve subgroups of participants and therefore rely on smaller sample sizes. In particular, null findings in relation to these subgroups might be difficult to interpret because of potential lack of power. For example, whereas we found an operator-priming effect in children from higher grades and no effect in children from intermediate grades, there was no significant difference between these groups in terms of response times in negative SOA trials (1491 ms versus 1530 ms; t21 = 0.21; p = 0.84; BF10 = 0.39). Behavioral studies focusing on the operator-priming effect in children might test whether this difference emerges with larger sample sizes. More generally, future studies are needed to improve our understanding of the present results.5. ConclusionIn sum, our findings suggest that the right hippocampus might contribute to the progressive association between (at least some) arithmetic operators and space throughout age and/or education. Therefore, our study raises the possibility that increased hippocampal activity during arithmetic learning in children may be explained by the role of this structure in spatial representations as well as in declarative memory.Conflict of interestThe authors declare no competing financial interests.AcknowledgmentsThis research was supported by a grant from the European Union (Marie Curie Career Integration Grant n° PCIG12-GA-2012-333602) to J.P. and a grant from the French Ministry of Higher Education and Research to R.M. We thank the Hospices Civils de Lyon for sponsoring the research, as well as Flora Schwartz and the MRI engineers (Franck Lamberton and Danielle Ibarrola) at the CERMEP-Lyon platform for their assistance in collecting the fMRI data. Finally, we are grateful to Pr. Christian Scheiber for his help with the pre-MRI medical exams.Footnotes1Note that to induce an arithmetic context and disguise the goal of the experiment, we also included trials in which a ‘ + ’ or a ‘ × ’ sign was followed 150 ms later by operands and participants were asked to solve the problem. The low temporal resolution of fMRI, however, makes it impossible to dissociate activity associated with the sign from activity associated with operands in these problems. Therefore, they were simply designed to be filler trials.2There was a tendency for a correlation between grade and activity associated with addition sign-only trials (versus fixation) in the right hippocampus (r = 0.29, p = 0.09; BF10 = 0.82), but no correlation for baseline sign-only trials (versus fixation) (r = −0.10, p = 0.58; BF10 = 0.25). 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Elsevier

Developmental Cognitive Neuroscience

Volume 30, April 2018, Pages 324-332
open access
Developmental Cognitive Neuroscience

Hippocampal spatial mechanisms relate to the development of arithmetic symbol processing in children

Under a Creative Commons license

Abstract

Understanding the meaning of abstract mathematical symbols is a cornerstone of arithmetic learning in children. Studies have long focused on the role of spatial intuitions in the processing of numerals. However, it has been argued that such intuitions may also underlie symbols that convey fundamental arithmetic concepts, such as arithmetic operators. In the present cross-sectional study, we used fMRI to investigate how and when associations between arithmetic operators and brain regions processing spatial information emerge in children from 3rd to 10th grade. We found that the mere perception of a ‘+’ sign elicited grade-related increases of spatial activity in the right hippocampus. That is, merely perceiving ‘+’ signs – without any operands – elicited enhanced hippocampal activity after around 7th grade (12–13 years old). In these children, hippocampal activity in response to a ‘+’ sign was further correlated with the degree to which calculation performance was facilitated by the preview of that sign before an addition problem, an effect termed operator-priming. Grade-related increases of hippocampal spatial activity were operation-specific because they were not observed with ‘×’ signs, which might evoke rote retrieval rather than numerical manipulation. Our study raises the possibility that hippocampal spatial mechanisms help build associations between some arithmetic operators and space throughout age and/or education.

Keywords

Arithmetic
Development
Attention
Space
fMRI
Hippocampus

1. Introduction

Humans are unique in their ability to represent abstract mathematical concepts by culturally invented symbols, such as Arabic numerals and arithmetic signs. Because these symbols are arbitrary, learning the relationship between their identity and the concept they represent is a challenge during early math education in children. Most prior studies have focused on the mechanisms supporting the acquisition of symbols representing numerical quantities (Piazza et al., 2007, Ansari, 2008, Holloway and Ansari, 2009, Lyons and Ansari, 2009, Mundy and Gilmore, 2009). However, efficient processing of symbols that convey fundamental arithmetic concepts (i.e., operators) may be an important and largely neglected aspect of arithmetic skills. This is suggested by the operator-priming effect (Roussel et al., 2002, Fayol and Thevenot, 2012, Mathieu et al., 2017), whereby the anticipated presentation of a ‘+’ or ‘-’ sign 150 ms before a single-digit addition or subtraction problem facilitates problem-solving in adults.

What aspect of the processing of an operator may cause the operator-priming effect in adults? A first possibility is that an arithmetic sign may automatically evoke a network of facts. For example, the perception of a ‘+’ or ‘-’ sign might pre-activate a network of additive or subtractive facts that would have been built in declarative memory after years of practice (Campbell and Xue, 2001, Ashcraft, 1992). Pre-activating such a network would facilitate the retrieval of the answer from memory when operands are presented. A second possibility is that an arithmetic sign may prime a specific procedure that would have been “automatized” after its repeated practice during arithmetic learning. For instance, Fayol and Thevenot argued that perceiving a ‘+’ or ‘-’ sign might trigger an automatized procedure that could be “linked to the convocation of the mental number line and could correspond to a preparation for a quick left-to-right or right-to-left browsing of this mental line” (Fayol and Thevenot, 2012). This proposal echoes the idea that adding or subtracting numbers involves rightward and leftward shifts of attention from a source to a target number along a mental map of numbers oriented from left to right, i.e., the mental number line (MNL) (Hubbard et al., 2005, Masson and Pesenti, 2014, Mathieu et al., 2016, Pinheiro-Chagas et al., 2017). Pre-activating such a procedure would result in a facilitation of subsequent calculation when operands are presented, thereby explaining the operator-priming effect.

Interestingly, two lines of evidence favor the procedural over the declarative interpretation of the operator-priming effect. First, the effect is not observed with the ‘×’ sign and multiplication problems (Roussel et al., 2002, Mathieu et al., 2017). Multiplication problems, however, are explicitly learned by rote in school and multiplication is unanimously viewed as the operation having the strongest association with a network of facts in memory (Campbell and Xue, 2001, Galfano et al., 2003, Thibodeau et al., 1996). Therefore, the lack of operator-priming effect for multiplication problems is difficult to reconcile with the idea that the effect is due to associations between operators and networks of stored facts. Second, in line with Fayol and Thevenot’s proposal that ‘+’ and ‘−’ signs may prime a spatial scanning of the MNL, a recent study suggests that ‘+’ and ‘−’ signs do evoke spatial intuitions. Specifically, Pinhas et al. (2014) found that, when instructed to categorize ‘+’ and ‘−’ signs with left-hand or right-hand responses, adults tend to respond faster to ‘+’ signs with the right hand than with the left hand, whereas they tend to respond faster to ‘-’ signs with the left hand than with the right hand (Pinhas et al., 2014). Thus, ‘+’ and ‘−’ signs appear to have some automatic associations with the right and left sides of space, respectively.

Using fMRI, we recently found that such spatial associations may stem from the fact that some arithmetic operators are automatically processed in brain regions involved in spatial attention in adults. We showed that the mere perception of a ‘+’ sign elicits greater activity than the mere perception of a ‘×’ sign in brain regions underlying overt spatial attention. These included the frontal eye fields (FEF) and the posterior superior parietal lobule (PSPL) (Mathieu et al., 2017). Thus, perceiving a ‘+’ sign (but not a ‘×’ sign) may be associated with a deployment of spatial attention in educated adults. Therefore, the rightward shifts of attention that have been posited to underlie addition problem-solving (Hubbard et al., 2005, Masson and Pesenti, 2014, Mathieu et al., 2016) might be primed by the mere preview of the addition sign (but not by the preview of a multiplication sign because multiplication is typically learned by rote and unlikely to be associated with movements along the MNL). Overall, there is mounting evidence that at least some arithmetic operators (e.g., ‘+’ but not ‘×’ signs) evoke spatial intuitions in adults, and that these intuitions may relate to the operator-priming effect.

However, associations between operators and space are arguably not innate. Therefore, a fundamental outstanding question is how and when such associations emerge in the developing brain. To answer that question, we studied 34 children from 3rd to 10th grade while they performed 3 tasks. First, fMRI activity was measured while children were instructed to make eye saccades towards visually presented targets. This allowed us to precisely localize several regions of interest (ROIs) involved in spatial attention across children. Second, fMRI activity was measured in these spatial attention ROIs while children were presented with trials in which a ‘+’ sign was displayed without any operands (hereafter addition sign-only trials). As in our previous study in adults (Mathieu et al., accepted), activity during the perception of addition sign-only trials was compared to activity associated with trials in which a ‘×’ sign was displayed without any operands (hereafter multiplication sign-only trials) because these do not appear to evoke any specific intuitions in adults (Fayol and Thevenot 2012). This allowed us to identify the spatial attention ROIs in which activity in response to a ‘+’ sign (as compared to a ‘×’ sign) increases with age and/or education, as well as the developmental time course of these effects.1 Third, outside of the scanner, we asked subjects to perform an operator-priming task and measured the correlation between inter-individual differences in the size of the operator-priming effect and inter-individual differences in sign-related activity in spatial attention ROIs as a function of grade. This allowed us to evaluate when sign-related activity in spatial attention ROIs leads to an operator-priming effect in children.

2. Material and methods

2.1. Participants

Forty-two right-handed children from 3rd to 10th grade participated in the study. All were native French speakers. Participants did not have prior history of neurological disease, psychiatric disorders, learning disabilities or attention deficits. All children and parents provided written informed consent to participate in the study, which was approved by the local ethics committee (CPP Sud-Est-II). Families received 80€ for their participation. Data from 8 subjects were excluded because of excessive head-movement in the scanner (see criteria in the Section 2.7., n = 3), poor whole-brain coverage (i.e. susceptibility artefacts from dental braces, n = 3) and unacceptably low performance during the task (i.e., lower than 50% accuracy on the sign-plus-operand trials, n = 2). Therefore, the final sample consisted of 34 children (20 males) from 3rd to 10th grade (age range: 8–15, mean age = 11.37, SD = 1.84). For each child, a continuous measure of grade was calculated by taking into account the specific date within the grade year when that child was scanned. The whole sample (n = 34) was evenly split into three groups as a function of grade: 11 children were from the ‘lower grades’ group (grade 3.2–5.4; mean = 4.4), 11 children were from the ‘intermediate grades’ group (grade 5.6–6.9; mean = 6.2), and 12 children were from the ‘higher grades’ group (grade 7.6–10.2; mean = 8.5).

2.2. Standardized measures

Children were administered standardized tests of intellectual and arithmetic abilities to ensure that there were no age differences with respect to those measures. Full-scale IQ was measured using the NEMI-2 (Cognet, 2006). Basic arithmetic knowledge was evaluated with the Math-Fluency subtest of the Woodcock-Johnson-III Tests of Achievement (WJ-III) (Woodcock et al., 2001). Across all participants, standardized (i.e., age-normalized) scores on IQ (mean = 112; SD = 10) and Math Fluency (mean = 106; SD = 16) tests were within the normal range. One-way ANOVAs with the between-subject factor group (lower, intermediate, higher grades) revealed no main effect of group on IQ (F(2,31) = 0.591, p = 0.560, BF10 = 0.29), indicating that age-normalized intellectual abilities were similar across groups. However, there was a main effect of group on Math Fluency (F(2,31) = 5.867, p = 0.007, BF10 = 7.24): Children from intermediate grades had a higher age-normalized score (mean = 118; SD = 18) than children from lower (mean = 100; SD = 11) and higher grades (mean = 100; SD = 13). Therefore, we included standardized Math-Fluency scores as nuisance covariate in all of our analyses.

2.3. Behavioral session

After standardized testing, children participated in a behavioral session during which they performed an operator-priming task adapted from Fayol and Thevenot (2012) and Roussel et al. (2002). Children were asked to evaluate 56 single-digit addition and 56 multiplication problems composed of operands between 2 and 9. Problems were presented in both commutative orders. Tie problems were excluded. Problems with a sum smaller than or equal to 11 and a product smaller or equal to 24 were considered small. Other problems were considered large.

In each trial, a problem was presented with an answer (Fig. 1a). The arithmetic sign was presented either 150 ms before (Negative SOA condition) or at the same time (Null SOA condition) as the operands (Fig. 1a). All problems were presented once in both SOA condition with a valid answer. Twenty-eight addition and 28 multiplication problems were also presented in both SOA condition with an invalid answer (obtained by adding or subtracting 1 to or from the valid answer). Trials were pseudorandomly ordered so that no more than three problems of the same type appeared consecutively. Problems with an invalid answer were randomly chosen across subjects and the order of blocks was counter-balanced between subjects. The experiment started with 8 practice trials.

Fig. 1

Fig. 1. Experimental design. (a) During the behavioral session, children (n = 34) were asked to evaluate the result of single-digit addition and multiplication problems. For both operations, the arithmetic sign was presented either 150 ms before (negative SOA trials), or at the same time as the operands (null SOA trials). (b) In the scanner, children (n = 34) performed an arithmetic task during which they were presented with sign-only (left) and sign-plus-operands (right) addition, multiplication and baseline trials. In each trial, a sign (‘+’, ‘×’ or ‘◊’) was presented at the center of the screen for 150 ms. In sign-only trials, the trial ended with the presentation of the sign and was simply followed by the inter-trial period of fixation. In sign-plus-operands trials (filler trials), the ‘+’ or ‘×’ sign was immediately followed by a single-digit addition or multiplication problem (respectively) presented along an answer and the ‘◊’ sign was followed by 3 letters. In those cases, children had 5000 ms to evaluate whether the answer of the problem was true or false or to indicate whether one of the 3 letters was a B.

The experiment was controlled by Presentation software (Neurobehavioral Systems, Albany, CA). Problems were displayed in white Arial 60-point font on a black background. All trials started with the presentation of a white central fixation dot for 1500 ms, immediately followed by a red central fixation dot for 1000 ms signaling that the problem was about to be presented, either in the negative SOA condition or in the null SOA condition (Fig. 1a). Subjects had a maximum of 5000 ms to evaluate whether the response was valid or invalid as quickly as possible by pressing one of two keys on the computer keyboard.

2.4. fMRI session

During fMRI scanning, children performed a spatial attention localizer task and an arithmetic task. The spatial attention localizer task consisted in alternating blocks of fixation and saccades. During saccade blocks (n = 9), participants were asked to make saccades towards several successive target dots. Each saccade block contained 16 target dots (0.2° visual angle) that appeared at random positions with an eccentricity of 3°, 3.5°, 4°, 4.5°, 5° or 5.5° in the left or right visual field for an average of 800 ms (with a jitter of ±200 ms). During fixation blocks (n = 9), participants were asked to maintain fixation on a central dot for 12,800 ms. Block order was counterbalanced across children.

During the arithmetic task, children were presented with sign-only and sign-plus-operands versions of addition and multiplication trials (Fig. 1b). Each trial started with the presentation of either a ‘+’ or a ‘×’ sign at the center of the screen for 150 ms. In sign-only trials (n = 30), the trial ended with the presentation of the sign and was simply followed by the inter-trial period of fixation (see below). These sign-only trials were our trials of interest and allowed us to isolate neural activity due to the presentation of a sign alone. We also included in the experiment sign-plus-operands trials (n = 50). In those filler trials, the ‘+’ or ‘×’ sign was immediately followed by a single-digit addition or multiplication problem (respectively) presented with an answer. Participants were asked to indicate whether the answer was true or false. The goal of these filler trials (for which associated activity would be difficult to interpret because any effects could be attributable to the anticipatory presentation of the operator, the appearance of the operands, or a combination of both of these factors) was only to keep children engaged and attentive in the scanner. They also induced an arithmetic context, thereby ensuring that the ‘+’ and ‘×’ signs presented in sign-only trials were perceived as arithmetic signs. Problems in sign-plus-operand trials were constructed following the same criteria as in the behavioral session. Finally, the baseline consisted in trials in which the arithmetic sign was replaced by an abstract non-arithmetic sign (i.e., ‘◊’). We included 30 baseline sign-only trials (in which the ‘◊’ sign was presented in isolation) and 50 baseline sign-plus-operand trials (in which the ‘◊’ sign was followed by 3 letters and participants had to indicate whether one of these letters was a B). All trials were followed by a variable period of visual fixation ranging from 3000 ms to 3800 ms. That period consisted in a central white fixation dot that turned red 1000 ms before the onset of the next trial. The arithmetic task was decomposed in 4 functional runs. All trials were intermixed and the timing and order of trial presentation within each run was optimized for estimation efficiency using optseq2 (http://surfer.nmr.mgh.harvard.edu/optseq/). Behavioral responses were recorded using an MR-compatible response device.

Stimuli were generated using Presentation software (Neurobehavioral Systems, Albany, CA). Prior scanning, children were familiarized with the fMRI environment during a practice session that took place after the standardized testing and the behavioral session. During this practice session, children learned to minimize head movement in a mock fMRI scanner. The actual scanning session took place no more than 3 weeks after the practice session.

2.5. Behavioral analyses

RT data associated with the operator-priming task were normalized using a logarithmic transformation prior all analyses to improve the conformity of the data to the standard assumptions of parametric testing. Following Fayol and Thevenot (2012), mean RT was analyzed using planned comparisons that followed from a within-subject ANOVA with the factors Operation (Addition/Multiplication) and SOA (Negative/Null), conducted separately for each group. We report for all effects the corresponding Bayes factors (BF10), indicating the strength of evidence for the alternative hypothesis (H1) relative to the null hypothesis (H0). Substantial evidence in favor of the alternative hypothesis is typically suggested by a BF10 greater than 3 (Jeffreys, 1961, Dienes, 2011).

2.6. fMRI data acquisition

Images were collected with a Siemens Prisma 3T MRI scanner (Siemens Healthcare, Erlangen, Germany) at the CERMEP Imagerie du vivant in Lyon, France. The BOLD signal was measured with a susceptibility weighted single-shot EPI sequence. Imaging parameters were as follows: TR = 2000 ms, TE = 24 ms, flip angle = 80°, matrix size = 128 × 120, field of view = 220 × 206 mm, slice thickness = 3 mm (0.48 mm gap), number of slices = 32. A high-resolution T1-weighted whole-brain anatomical volume was also collected for each participant. Parameters were as follows: TR = 3500 ms, TE = 2.24 ms, flip angle = 8°, matrix size = 256 × 256, field of view = 224 × 224 mm, slice thickness = 0.9 mm, number of slices = 192.

2.7. fMRI preprocessing

Data analysis was performed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Functional images were corrected for slice acquisition delays and spatially realigned to the first image of the first run. Images were then spatially smoothed with a Gaussian filter equal to twice the voxel size. ArtRepair was used to help remove motion from the functional images prior to normalization (Mazaika et al., 2009). Volumes with rapid scan-to-scan movements of greater than 1.5 mm were repaired by interpolation of the two nearest non-repaired scans. Each run with more than 5% of the total number of volumes replaced was removed from the analyses. A subject was excluded from further analysis if more than one run was removed. The number of volumes replaced did not differ between grade groups (F(2,31) = 2.20; p = 0.13). Finally, functional images were normalized into the standard MNI space (normalized voxel size, 2 × 2 × 3.5 mm3).

2.8. fMRI processing

Event-related statistical analysis was performed according to the general linear model (GLM). For the localizer task, brain activity associated with saccades and fixation blocks was modeled as epochs with onsets and offsets time-locked to the beginning and the end of each block. Each epoch was convolved with a canonical hemodynamic response function (HRF) and the time series data from each run were high-pass filtered (1/128 Hz). Finally, serial correlations were corrected using an autoregressive AR(1) model. Following our previous study using the same task in adults (Mathieu et al., 2017), brain activity associated with sign-only trials during the arithmetic task was estimated using a finite impulse response (FIR) model. We modeled 8 time points with an interval of 2 s (corresponding to one TR) ranging from the onset of the sign to 16 s after the sign. The magnitude of the fMRI response for each type of sign-only trial was calculated by subtracting activity at the onset of the sign (i.e., 1st bin, or 0 s after the onset) from the peak activity (i.e., 4th bin, or ∼8 s after the onset). The time series data from each run were high-pass filtered (1/128 Hz), and serial correlations were corrected using an autoregressive AR(1) model.

2.9. Region of interest (ROI) definition and analyses

The present study used a Region-of-Interest (ROI) approach to analyze brain activity associated with sign-only trials in brain regions involved in the orienting of spatial attention in children. All ROIs were independently defined using the contrast of saccades versus fixation blocks in the spatial attention localizer task. All subject-specific contrasts were entered into a random effect (RFX) one-sample t-test across subjects. The RFX contrast map was then thresholded across the whole-brain using an uncorrected voxel-level threshold of p < 0.001 and a false-discovery-rate (FDR) corrected cluster-level threshold of p < 0.05 (Chumbley and Friston 2009). Using the SPM toolbox Marsbar (http://marsbar.sourceforge.net/), ROIs were defined as 6-mm radius spheres around the peak coordinate of each region.

Within each ROI and for each participant, we calculated the average response (parameter estimates) for ‘+’ signs using the contrast of addition sign-only trials versus baseline sign-only trials. Similarly, we calculated the average response for ‘×’ signs using the contrast of multiplication sign-only trials versus baseline sign-only trials. Two analyses were performed in each ROI. First, we identified the ROI(s) in which a difference in fMRI activity between ‘+’ and ‘×’ signs emerged throughout age and/or education, using a 3 × 2 ANOVA with the between-subject factor group (lower/intermediate/higher grades) and the within-subject factor sign (‘+’/‘×’). Second, we tested in these ROIs whether inter-individual differences in the fMRI response to an arithmetic sign were correlated with inter-individual differences in the size of the operator-priming effect (measured in the behavioral session) for each group. In all analyses, we report uncorrected P values as well as P values corrected for multiple comparisons across all identified ROIs using the Bonferroni procedure. Bayes factor are also reported.

3. Results

3.1. The spatial localizer task activates a brain network encompassing frontal, parietal, occipital and hippocampal regions

Contrasting saccades to fixation blocks in the spatial attention localizer task, we first identified 10 clusters supporting the orienting of spatial attention across subjects: the bilateral Frontal Eye Field (FEF), bilateral Posterior Superior Parietal Lobule (PSPL), bilateral Middle Temporal Gyri (MTG), bilateral Middle Occipital Gyri (MOG), right dorsal Inferior Frontal Gyrus (dIFG), and right Hippocampus (see Table 1 and Fig. 2). Therefore, a large brain network was involved in the orienting of spatial attention across subjects. Each of these regions served as an ROI in subsequent analyses.

Table 1. Brain regions that were activated during the spatial attention localizer task. Each of these regions constituted an ROI.

Anatomical location∼BACluster size (mm3)MNI coordinatesZ-score
XYZ
L. Middle Occipital Gyrus1724640−24−96105.37
R. Middle Occipital Gyrus/Calcarine1814−9224.77
L. Frontal Eye Field65824−34−8555.34
R. Middle Temporal Gyrus21953454−52134.68
R. Frontal Eye Field6516628−4484.65
R. Posterior Superior Parietal Lobule7525018−66664.61
L. Posterior Superior Parietal Lobule73178−22−64664.58
L. dorsal Inferior Frontal Gyrus6/4413165610204.52
L. Middle Temporal Gyrus213626−50−54134.33
R. Hippocampus63024−16−183.48

L. = left; R. = right; ∼BA = approximate Brodmannʼs area; MNI = Montreal Neurological Institute.

Fig. 2

Fig. 2. Brain regions activated in the spatial attention localizer task. Brain regions that were more activated during saccades than fixation blocks. Activations are overlaid on slices of the MNI-normalized anatomical brain. PSPL, Posterior Superior Parietal Lobule; FEF, Frontal Eye Field; MTG, Middle Temporal Gyrus; dIFG, dorsal Inferior Frontal Gyrus; MOG, Middle Occipital Gyrus.; HIPP, Hippocampus.

3.2. The right hippocampus region identified by the spatial localizer task is increasingly activated in response to a ‘+’ sign but not to a ‘×’ sign throughout age and/or education

A 3 × 2 ANOVA with the between-subject factor group (lower/intermediate/higher grades) and the within-subject factor sign (‘+’/‘×’) was then conducted in each of the 10 ROIs identified by the spatial attention localizer. We found an interaction between group and sign in the right hippocampus (F(2.30) = 6.75, p = 0.0038, pcorr = 0.038; Fig. 3a and b), but not in any other ROIs (all Fs < 3.44, all ps > 0.046, all pscorr > 0.46). Bayes Factor analysis indicated substantial evidence for this interaction in the right hippocampus (BF10 = 11.53), while no or anecdotal evidence for such an interaction was found in the other ROIs (BF10 < 1.63). Follow-up t-tests in the hippocampus ROI revealed that children from higher grades (average grade = 8.5) exhibited greater activity for addition than multiplication sign-only trials (t11 = 3.02, p = 0.012), whereas there was no difference between signs in children from intermediate grades (average grade = 6.2) (t10 = 0.87, p = 0.41) and even a trend for less activity for addition than multiplication sign-only trials in children from lower grades (average grade = 4.4) (t10 = 2.22, p = 0.051). Bayes Factor analysis indicated substantial evidence for a difference of activity between addition and multiplication sign-only trials in children from higher grades (BF10 = 5.21), but evidence for this difference was absent in intermediate grades (BF10 = 0.41) and anecdotal in lower grades (BF10 = 1.69). Finally, across all groups, addition sign-only trials were associated with greater activity than baseline sign-only trials in children from higher grades (t11 = 2.63, p = 0.023), but not in any other groups (all ts < 1.32, all ps > 0.21). Multiplication sign-only trials were associated with greater activity than baseline sign-only trials in none of the groups (all ts < 1.38, all ps > 0.20). Bayes Factor analysis indicated substantial evidence for a difference between addition sign-only trials and baseline sign-only trials in children from higher grades (BF10 = 3.00), but no or anecdotal evidence in the other groups (all BF10s < 0.60) and for multiplication sign-only trials (all BF10s < 0.63). Overall, then, a difference in fMRI response to a ‘+’ and a ‘×’ sign emerged throughout age and/or education in the right hippocampus.

Fig. 3

Fig. 3. Grade-related changes of activity in the right hippocampus.

(a) Location of the right hippocampus ROI overlaid on a coronal slice of the MNI-normalized anatomical brain. (b) Activity in the right Hippocampus for addition versus baseline sign-only trials (red) and multiplication versus baseline sign-only trials (blue) in children from lower (n = 11; grade 3.2–5.4; mean grade = 4.4; mean age = 9.4), intermediate (n = 11; grade 5.6–6.9; mean grade = 6.2; mean age = 11.1) and higher grades (n = 12; grade 7.6–10.2; mean grade = 8.5; mean age = 13.4). (c) Difference in activity between addition and multiplication sign-only trials over grade in the right Hippocampus. *< 0.05; r represents the Pearson correlation coefficient.

The findings above were confirmed by an additional correlation analysis in which grade was treated as a continuous predictor across all subjects. The difference in activity between addition and multiplication sign-only trials was positively correlated with grade in the right hippocampus (r = 0.53, p = 0.001, pcorr = 0.01; Fig. 3c). No other significant grade-related changes were found in any other regions (all rs < 0.38, all ps > 0.03, all pscorr > 0.30). Bayes Factor analysis indicated substantial evidence for this correlation in the right hippocampus (BF10 = 20.97), but no or anecdotal evidence in any other ROIs (all BF10s < 2.03). In the right hippocampus, the correlation between grade and the contrast of addition sign-only trials versus baseline sign-only trials, was also near significance (r = 0.33, p = 0.056; BF10 = 1.22).2 The correlation between grade and the contrast of multiplication sign-only trials versus baseline sign-only trials, however, was not significant (r = −0.18, p = 0.32; BF10 = 0.35).

3.3. Spatial hippocampal activity in response to a ‘+’ sign relates to an addition-priming effect in children from higher grades

We then tested whether the hippocampal response to a ‘+’ sign observed in children from higher grades was related to the operator-priming effect. To this aim, each child performed a version of the operator-priming task outside of the scanner (see Fig. 1a).

First, we tested whether the results obtained by Fayol and Thevenot (2012) in adults (i.e., an operator-priming effect for addition but not for multiplication across subjects) could be extended to our children participants. Because children from lower grades had a performance close to chance on large problems (58%), we exclusively focused our analyses on small problems for which accuracy was significantly above chance in all groups (lower grades: 80%, intermediate grades: 92%, higher grades: 96%). Planned comparisons revealed an operator-priming effect for addition in children from higher grades (1491 ms versus 1577 ms; F(1,11) = 8.11, p = 0.016), but not in children from lower grades (2289 ms versus 2417 ms; F(1,10) = 2.66, p = 0.134) and intermediate grades (1530 versus 1509 ms; F(1,10) = 0.01, p = 0.941). No operator-priming effect for multiplication was observed in any groups (lower grades: F(1,10) = 3.50, p = 0.091; intermediate grades: F(1,10) = 1.52, p = 0.246; higher grades: F(1,11) = 0.14, p = 0.715). Bayes Factor analysis indicated substantial evidence for an operator-priming effect with addition problems in children from higher grades (BF10 = 4.08), but no evidence in children from intermediate (BF10 = 0.30) and lower (BF10 = 0.83) grades. There was also no or anecdotal evidence for an operator-priming effect with multiplication problems in any group (higher grades: BF10 = 0.31; intermediate grades: BF10 = 0.55; lower grades: BF10 = 1.09).

Second, we tested whether the size of the operator-priming effect in children (measured on small problems) was correlated to the magnitude of the response for addition sign-only trials versus baseline sign-only trials in the right hippocampus. Such a correlation was found to be highly significant in children from higher grades (r = 0.82, p = 0.0012, Fig. 4), surviving Bonferroni correction for multiple comparisons between the two conditions and across the three groups (pcorr = 0.007). That is, children from higher grades who show greater responses to ‘+’ signs in the right hippocampus are those who show larger operator-priming effect with addition problems. No significant correlation was found in children from lower (r = 0.15, p = 0.66 Fig. 4) and intermediate (r = 0.24, p = 0.48, Fig. 4) grades. Bayes Factor analysis indicated substantial evidence for the correlation in children from higher grades (BF10 = 38.15), but no evidence in children from lower (BF10 = 0.40) and intermediate (BF10 = 0.46) grades. There was also no significant (and anecdotal evidence for a) correlation between the operator-priming effect for addition problems and the fMRI response to multiplication sign-only trials (compared to baseline sign-only trials) in the right hippocampus, in any of the groups (lower grades: r = 0.06, p = 0.87, BF10 = 0.37; intermediate grades: r = 0.32, p = 0.34, BF10 = 0.56; higher grades: r = 0.51, p = 0.09, BF10 = 1.28). Therefore, not only did we observe an operator-priming effect for addition in the only group in which we also observed a greater hippocampal response to ‘+’ than ‘×’ signs (i.e., children from higher grades), but inter-individual differences in the size of the operator-priming effect in that group was also related to hippocampal activity.

Fig. 4

Fig. 4. Hippocampus brain-behavior correlation over grade.

Activity in the right hippocampus in response to addition sign-only trials versus baseline sign-only trials as a function of the operator-priming effect calculated in the behavioral session for addition problems in children from lower (n = 11; grade 3.2–5.4; mean grade = 4.4; mean age = 9.4), intermediate (n = 11; grade 5.6–6.9; mean grade = 6.2; mean age = 11.1) and higher grades (n = 12; grade 7.6–10.2; mean grade = 8.5; mean age = 13.4). r represents the Pearson correlation coefficient.

Third, we tested whether the correlation between the operator-priming effect and the contrast of addition sign-only trials versus baseline sign-only trials increased over grade. This was done by transforming the correlation coefficient in each group to a Fischer’s z score before comparing the groups using the cocor package (Diedenhofen and Musch, 2015). Although the correlation was not greater in children from intermediate than lower grades (z = 0.19, p = 0.43, one-tailed), it was significantly greater in children from higher than lower grades (z = 2.07, p = 0.019, one-tailed) and in children from higher than intermediate grades (z = 1.88, p = 0.030, one-tailed). Therefore, this brain-behavior correlation increased over grade.

4. Discussion

In the present study, we used fMRI and a cross-sectional design to investigate (i) how and when spatial processing related to the perception of an addition sign emerges in the developing brain, and (ii) to what extent it contributes to the emergence of an operator-priming effect.

4.1. The mere perception of a ‘+’ sign is associated with increased hippocampal spatial activity throughout age and/or education

It has been shown that the processing of a ‘+’ sign is associated with the right side of space (Pinhas et al., 2014) and activates brain regions involved in overt spatial attention in adults (Mathieu et al., 2017). Therefore, we expected arithmetic learning to be associated with increased recruitment of brain regions involved in spatial attention in response to the perception of a ‘+’ sign throughout age and/or education in children. This was the case in a region of the right hippocampus that we identified in our spatial attention localizer task. Therefore, it is possible that hippocampal spatial mechanisms may scaffold the progressive association between an arithmetic operator (i.e., a ‘+’ sign) and spatial intuitions throughout age and/or education. There is increasing evidence that the hippocampal formation, and particularly the right hippocampus, may house a ‘sense of space’ (Buffalo, 2015). Specifically, the right hippocampus has been extensively reported to support spatial representation and navigation in humans (Maguire et al., 1998, Burgess et al., 2002) as well as in non-human primates and rodents (O'keefe and Nadel, 1978, Bird and Burgess, 2008). For example, the hippocampus is typically activated when human participants learn to navigate through a mental representation of space (i.e., mental scanning) (Mellet et al., 2002, Spiers and Maguire, 2006). Interestingly, a recent study in monkeys demonstrated that neurons in the hippocampal formation may encode the direction of overt (Killian et al., 2015) as well as covert (Wilming et al., 2015) shifts of attention. Therefore, the hippocampal formation is likely a critical region for both representing a mental map of space and navigating along that map (Killian et al., 2012, Meister and Buffalo, 2016).

Why would such a hippocampal spatial navigation mechanism be increasingly recruited by the mere perception of a’+’ sign throughout age and/or education? One possibility is that this mechanism might enable children to construct a detailed representation of numbers in mental space, as well as to navigate along that mental representation. Indeed, there is overwhelming evidence that numbers of increasing size are organized along a left-to-right mental map (i.e., the MNL) in adults (Fischer and Shaki 2014). This spatial representation may enable individuals to add or subtract numbers by navigating from a source to a target number to the left or right of that MNL. This is supported by behavioral studies showing that addition and subtraction problem-solving is associated with rightward and leftward shifts of attention (Masson and Pesenti, 2014, Mathieu et al., 2016), as well as by a neuroimaging study indicating an overlap between the brain regions involved in overt shifts of attention and those involved in arithmetic calculation in adults (Knops et al., 2009). Such strategies may be acquired early by children, sometimes even explicitly in the classroom where addition and subtraction is often demonstrated on visual number lines. Yet, it is only with practice that they might become progressively attached to and evoked by an arithmetic operator such as a ‘+’, which might explain the grade-related increases of activity in this region in response to the ‘+’ sign (and the fact that it is only by 7th grade that children exhibit significant activity in response to that sign).

4.2. Hippocampal spatial activity in response to a ‘+’ sign relates to the operator-priming effect in children from higher grades

A critical question is to what extent this automatic processing of a ‘+’ sign in hippocampal spatial mechanisms is associated with children’s behavior. To answer this question, we asked all children to perform a version of the operator-priming task developed by Fayol and Thevenot (2012) and Roussel et al. (2002). First, we replicated the operator-priming effect observed in adults with addition problems (i.e., a facilitation of problem-solving when the operator is presented 150 ms before the operands), but only in children from higher grades (after around 7th grade). Like in adults, this effect was specific to addition problems and not observed with multiplication problems. Thus, the perception of a ‘+’ sign (but not that of a ‘×’ sign) appears to pre-activate a process that is likely used to solve the subsequent problem in children from higher grades. More central to our current interest, we found that the size of the operator-priming effect in these children was highly correlated with the degree of activation of hippocampal spatial mechanisms in response to a ‘+’ sign. This indicates that hippocampal spatial activity may be at the source of the operator priming-effect in older children, perhaps because these children might prepare for an attentional movement along the MNL as soon as a ‘+’ sign is presented. Because no brain-behavior correlation was observed in younger children, extensive practice might be needed before such mechanisms are triggered by the mere perception of the sign.

4.3. Hippocampal spatial activity in response to a ‘+’ sign is transient in development

Strikingly, the spatial brain mechanisms that respond to the mere perception of a ‘+’ sign appear to be different in children and adults. That is, albeit we found increased hippocampal spatial activity throughout age and/or education in the present study, we did not identify these mechanisms in our previous study in adult participants using the exact same task (Mathieu et al., 2017). Rather, we found increased activity in response to a ‘+’ sign in neocortical regions of the FEF and PSPL in adults. Therefore, the contribution of the hippocampus to the automatic processing of a ‘+’ sign is likely transient. Such a transient involvement of the hippocampus is consistent with a wealth of studies that have demonstrated that the spatial representations initially supported by the hippocampus during learning become independent from this brain structure over experience and transferred to neocortical regions (Rosenbaum et al., 2004, Hirshhorn et al., 2012b). For example, longitudinal studies demonstrate that right hippocampal activity associated with learning to mentally navigate through a new environment disappears and is replaced by neocortical activity when individuals become familiar with that environment (Spiers and Maguire, 2007, Hirshhorn et al., 2012a). It is possible that the same phenomenon is at play here: The hippocampus may be involved in the early representation of (and navigation along) the MNL before that representation is transferred to neocortical regions of the fronto-parietal cortex. Future investigations with a wider age sample than in the present study are needed to test this hypothesis.

4.4. Can right hippocampal involvement in the present study reflect mnemonic operations involved in learning arithmetic?

Although there is no doubt that the hippocampus supports spatial processing (Burgess et al., 2002, Spiers and Maguire, 2007), this brain structure is also well known to support the encoding and consolidation of verbal declarative knowledge into long-term memory (Eichenbaum, 2004). In fact, previous developmental studies have largely explained the involvement of the hippocampus during arithmetic learning by referring to its role in declarative memory rather than spatial processing (Rivera et al., 2005, De Smedt et al., 2011, Cho et al., 2011, Cho et al., 2012, Qin et al., 2014). This interpretation relies on the claim that results of well-practiced arithmetic facts (e.g., 2 + 3 or 4 × 2) might become progressively retrieved from memory (rather than calculated) over the course of learning and development (Campbell and Xue, 2001). The hippocampus might thus support the encoding and consolidation of networks of arithmetic facts in children.

Can the role of the hippocampus in declarative memory explain the operator-specific activity over grade (and correlation with the operator-priming effect) observed in the region of the right hippocampus identified by our spatial localizer task? We acknowledge that we did not have a task identifying processes involved in declarative memory. Thus, even if the right hippocampus is usually more associated with spatial than mnemonic processes (Burgess et al., 2002), it is possible that the hippocampal cluster that we identified as being involved in spatial processing may also be involved in some aspects of declarative memory. One might thus argue that grade-related increases of activity in relation to ‘+’ signs reflect the progressive association between a ‘+’ and a network of additive facts. This explanation, however, can be ruled out by an examination of activity related to ‘×’ signs. Because single-digit multiplication problems are almost exclusively learned by rote in school, multiplication is the operation that is perhaps the most associated with a network of stored facts in the literature (Campbell and Xue, 2001). Thus, if increased hippocampal activity in relation to ‘+’ signs were due to the progressive building of a network of additive facts, increased activity in that same region should have been observed during the perception of ‘×’ signs (perhaps even more so for the perception of ‘+’ signs). Yet, this is not the case. Not only did we not find any grade-related increase of activity for ‘×’ signs in the hippocampal cluster identified by our spatial localizer task, but activity was significantly greater for ‘+’ than ‘×’ signs in higher graders (who are as proficient in single-digit multiplication as addition). Similarly, no operator-priming effect was observed for multiplication problems in higher graders, indicating that the operator-priming effect observed for addition is likely to have little to do with the pre-activation of a network of stored facts (because this should be also observed for multiplication). Therefore, the specificity of our results to ‘+’ signs (as compared to ‘×’ signs) in the right hippocampus ROI makes it very unlikely that our results are related to mnemonic operations. In our view, emerging associations between ‘+’ signs and spatial intuitions related to the MNL are the best explanation of the effects reported here.

Of course, the fact that the role of the hippocampus in declarative memory is unlikely to explain our operator-specific findings in the right hippocampus ROI does not mean that hippocampal mechanisms supporting mnemonic operations do not contribute to arithmetic learning. Instead, they indicate that the hippocampus might contribute to arithmetic learning through its role in both declarative memory and spatial processing. Interestingly, the operator-specific activity observed in our (spatially localized) right hippocampal cluster is not observed in a mirror (left lateralized) cluster that is not activated in the localizer contrast (see Supplementary information). In that mirror region, no difference was observed between activity related to ‘+’ and ‘×’ signs in any group of children (and left hippocampal activity was not related to the operator-priming effect). Thus, the developmental effect reported here appears to be restricted to the right hippocampus. This specificity suggests that the observed developmental changes in the right hippocampus may not simply reflect general brain maturation but rather mechanisms that are specific to arithmetic learning.

4.5. Limitations

It is worth acknowledging here 2 potential limitations of the present work. First, as is the case for any cross-sectional fMRI studies, our study is correlational in nature. Thus, although our findings are consistent with the idea that the right hippocampus might scaffold the progressive association between (at least some) arithmetic operators and space throughout age and/or education, future studies might specifically investigate the causal role of these hippocampal mechanisms. Second, our finding of a correlation between grade and the processing of an addition sign in the right hippocampus (see Fig. 3C) relies on a relatively large sample size of 34 children. However, other findings involve subgroups of participants and therefore rely on smaller sample sizes. In particular, null findings in relation to these subgroups might be difficult to interpret because of potential lack of power. For example, whereas we found an operator-priming effect in children from higher grades and no effect in children from intermediate grades, there was no significant difference between these groups in terms of response times in negative SOA trials (1491 ms versus 1530 ms; t21 = 0.21; p = 0.84; BF10 = 0.39). Behavioral studies focusing on the operator-priming effect in children might test whether this difference emerges with larger sample sizes. More generally, future studies are needed to improve our understanding of the present results.

5. Conclusion

In sum, our findings suggest that the right hippocampus might contribute to the progressive association between (at least some) arithmetic operators and space throughout age and/or education. Therefore, our study raises the possibility that increased hippocampal activity during arithmetic learning in children may be explained by the role of this structure in spatial representations as well as in declarative memory.

Conflict of interest

The authors declare no competing financial interests.

Acknowledgments

This research was supported by a grant from the European Union (Marie Curie Career Integration Grant n° PCIG12-GA-2012-333602) to J.P. and a grant from the French Ministry of Higher Education and Research to R.M. We thank the Hospices Civils de Lyon for sponsoring the research, as well as Flora Schwartz and the MRI engineers (Franck Lamberton and Danielle Ibarrola) at the CERMEP-Lyon platform for their assistance in collecting the fMRI data. Finally, we are grateful to Pr. Christian Scheiber for his help with the pre-MRI medical exams.

Appendix A. Supplementary data

The following is Supplementary data to this article:

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1

Note that to induce an arithmetic context and disguise the goal of the experiment, we also included trials in which a ‘ + ’ or a ‘ × ’ sign was followed 150 ms later by operands and participants were asked to solve the problem. The low temporal resolution of fMRI, however, makes it impossible to dissociate activity associated with the sign from activity associated with operands in these problems. Therefore, they were simply designed to be filler trials.

2

There was a tendency for a correlation between grade and activity associated with addition sign-only trials (versus fixation) in the right hippocampus (r = 0.29, p = 0.09; BF10 = 0.82), but no correlation for baseline sign-only trials (versus fixation) (r = −0.10, p = 0.58; BF10 = 0.25). Thus, the correlation between grade and the contrast of addition sign-only trials versus baseline sign-only trials was more likely driven by changes of activity in addition sign-only trials than in baseline sign-only trials.

+ + + + + + + + + + + + + + +
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Yet, during social challenges, they often show uncontrolled emotional behavior. Healthy individuals can control their social emotional behavior through anterior prefrontal cortex (aPFC) downregulation of neural activity in the amygdala, with testosterone modulating aPFC\u2013amygdala coupling. This study tests whether individual differences in this neuroendocrine system relate to the paradoxical lack of emotional control observed in human psychopathic offenders. Emotional control was operationalized with an fMRI-adapted approach\u2013avoidance task requiring rule-driven control over rapid emotional responses. Fifteen psychopathic offenders and 19 matched healthy control subjects made approaching and avoiding movements in response to emotional faces. Control of social emotional behavior was required during affect-incongruent trials, when participants had to override affect-congruent, automatic action tendencies and select the opposite response. Psychopathic offenders showed less control-related aPFC activity and aPFC\u2013amygdala coupling during trials requiring control of emotional actions, when compared with healthy control subjects. This pattern was particularly pronounced in psychopathic individuals with high endogenous testosterone levels. These findings suggest that reduced prefrontal coordination underlies reduced behavioral control in psychopathic offenders during emotionally provoking situations. Even though the modest sample size warrants replication, the modulatory role of endogenous testosterone on the aPFC\u2013amygdala circuit suggests a neurobiological substrate of individual differences that is relevant for the advancement of treatment and the reduction of recidivism.\n \n ", + "publication_year": 2016, + "coordinate_space": "MNI", + "license": "http://creativecommons.org/licenses/by/4.0/", + "text": true +} \ No newline at end of file diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/processed/pubget/text.txt b/tests/data/sample_inputs/8EVW7TUtC9cx/processed/pubget/text.txt new file mode 100644 index 0000000..b96f0ed --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/processed/pubget/text.txt @@ -0,0 +1,154 @@ + +## Significance Statement + +Psychopathic criminals are commonly seen as instrumentally abusive and emotionally callous, yet social challenges often trigger uncontrolled emotional behavior in those individuals. This study shows how this paradoxical aspect of psychopathy relates to altered neuroendocrine interactions between testosterone and the cerebral circuit coordinating emotional action tendencies. The anterior prefrontal cortex, a region necessary for controlling emotional behavior, showed blunted responses and reduced connectivity with the amygdala in psychopathic criminals engaged in controlling their emotional action tendencies. This cerebral pattern was strongest in psychopathic individuals with high endogenous testosterone levels. This neuroendocrine signature of altered emotional control highlights the relevance of considering the testosterone level of individual psychopathic patients during treatment of their impulsive behavior. + + +## Introduction + +Psychopathy is a disorder often associated with blunted emotional responding and increased goal-directed behavior ( ; ). On the other hand, offenders with psychopathy also show a paradoxical increase in impulsive behavior and uncontrolled aggression after emotional provocations ( ; ; ; ; ; ), which may be related to heightened testosterone levels ( ; ). These two aspects of psychopathy are also distinguished within the most commonly used psychopathy checklist, the Psychopathy Check List-Revised (PCL-R), potentially reflecting differing traits among psychopathic individuals ( ; ). Importantly, enhanced difficulty in controlling emotional impulses, a crucial component of criminal psychopathy associated with PCL-R factor 2, has been largely neglected by cognitive neuroscience. Yet, the clinical relevance of this cognitive trait is large: reduced behavioral control and increased impulsivity predict recidivism in psychopathic offenders ( ), and behavioral control in psychopathic offenders appears particularly fragile when dealing with emotionally relevant behavior ( ; , chapter 7; ). Accordingly, understanding the neurobiological systems underlying the altered control of social emotional behavior in psychopathic individuals is relevant for improving currently available interventions, which are plagued by low treatment response and high recidivism ( ). Here we study those neuroendocrine systems in a group of psychopathic offenders engaged in an experimental paradigm that requires rule-driven control of emotional behavior. + +Previous investigations of psychopathy showed altered reactivity to emotional material in several brain regions that include the anterior part of the PFC (aPFC) and the amygdala ( ; ; ). Furthermore, individuals with psychopathy showed decreased functional and anatomical connectivity between the PFC and amygdala at rest ( ; ), an indication that these brain regions might have a reduced ability to interact effectively. Studies in healthy participants have shown that this cerebral circuit is necessary for implementing the control of emotionally relevant actions ( ). Namely, aPFC downregulates neural processing in the amygdala during emotional control ( ), while high levels of endogenous testosterone reduce such control-related connectivity between aPFC and amygdala ( ). Those findings raise the possibility that aPFC–amygdala connectivity is altered when psychopathic offenders need to control emotionally relevant actions, with high levels of endogenous testosterone exacerbating that altered connectivity. + +This study tests these hypotheses by measuring brain activity with functional magnetic resonance imaging (fMRI) in 15 psychopathic criminals and 19 matched healthy control subjects dealing with a challenge to control their emotional behavior. The psychopathy sample was obtained by focused and comprehensive screening excluding confounds that are frequently associated with random criminal sampling (e.g., medication use, comorbidity). The social approach–avoidance (AA) task was used to provide reliable indexes of control over social emotional behavior ( ; ; , ). Behaviorally, psychopathic participants previously showed altered AA behavior to explicitly approaching and avoiding emotional faces ( ). Similar findings occurred after testosterone administration in healthy participants ( ). Interestingly, a more subtle version of the AA task has been shown to be sensitive to testosterone-related alterations and genetic variations in the aPFC–amygdala pathway, while keeping behavior constant across experimental groups ( ), opening the way for isolating neural vulnerability factors ( ) in psychopathy. During this task, participants respond to affective faces (happy, angry) presented for a short time with approach and avoidance movements. Automatic emotional tendencies (approach–happy and avoid–angry faces; affect-congruent response conditions) need to be controlled during affect-incongruent response conditions in order to apply the counterintuitive action of approaching angry and avoiding happy faces ( ; ). Healthy participants respond more slowly and rely more strongly on the aPFC when emotional control is required, operationalized by the differences evoked between affect-incongruent and affect-congruent trials ( ; ). Accordingly, this study tests whether exerting control over emotionally relevant actions is reflected by reduced functionality of the aPFC–amygdala circuit in psychopathic individuals, suggesting less prefrontal regulation of emotional actions. In addition, it sets out to test whether this alteration is intensified by high levels of endogenous testosterone. + +The emotional control AA task. The AA task involved the presentation of happy and angry faces, and the performance of approach and avoidance responses. During the AA task, the participants had to select their response according to the perceived emotion of the face. At the beginning of each block of 12 trials, the participants received instructions on whether to pull the joystick toward themselves (approach) or push it away (avoid) when seeing a face with a particular emotion. When viewing happy or angry faces, automatic stimulus–response tendencies trigger corresponding approach or avoidance actions. These tendencies could be followed during the affect-congruent condition (approach–happy, avoid–angry). In contrast, when task instructions required participants to avoid happy faces or to approach angry faces, automatic tendencies needed to be controlled and overridden with the instructed response (affect-incongruent condition). Participants saw the faces and moved the joystick while lying in a MR scanner (top left corner of the table). Figure adapted from ). + + +## Materials and Methods + +### Participants + +The psychopathic group was recruited from in-patient populations of the Pompestichting and Oldenkotte, forensic psychiatric institutes (TBS-clinics) in the Netherlands. TBS-clinics are facilities for criminal offenders with a mental disorder treated on behalf of the state. + +Seventeen male psychopathic violent offenders (age range, 23-56 years) participated; all had received a diagnosis with a PCL-R score of ≥26, according to European standards ( ; ; ). PCL-R consensus scores were obtained by trained clinicians based on a structured PCL-R interview, clinical status, and history. After the independent scoring, the two raters compared their scores and came to the consensus score. When no consensus could be found, a third independent rater was included in the process. Dutch versions of the National Adult Reading Test and Edinburgh Handedness Inventory were used to assess IQ levels and right-handedness ( ; ). Twenty-one healthy male control subjects (HCs) matched for age, right-handedness, and IQ, without criminal records or history of psychiatric disorders, were recruited from staff of the clinics. All participants received oral and written information about the experiment and gave written informed consent according to guidelines of the local ethics committee (Commissie Mensengebonden Onderzoek region Arnhem-Nijmegen). Psychiatric exclusion criteria consisted of neurological, axis-I, and axis-II disorders, besides antisocial personality disorder for the psychopathic group. They were screened for these exclusion criteria by trained psychologists using Dutch versions of the Structured Clinical Interview (SCID; ) and Mini-International Neuropsychiatric Interview (MINI; ) for Diagnostic and Statistical Manual of Mental Disorders , 4th edition, disorders. All participants were asked about drug use and medical/neurological history to exclude the following: alcohol use of >3 units/day, cannabis, or other illicit drug use 1 week before, psychotropic medication other than oxazepam 5 d before, 1 unit of alcohol or oxazepam use within 24 h before the experiment; history of trauma capitis; visual and auditive disorder; and neurological disorder. Furthermore, general exclusion criteria for MRI experiments were applied. Two psychopathic patients (PPs) and two HCs were excluded from the analyses, due to incomplete scanning procedures (1 PP, 1 HC) or too many errors on the task (>16%, representing the outlier with a z -score >3). The final groups did not differ in age, IQ, and handedness (see ). + +Demographical data + + +### Procedure + +Two test sessions took place. During the first session, right-handedness, IQ, MINI, and SCID were assessed. During the second session, participants completed several questionnaires upon arrival in the laboratory, including the State-Trait Anxiety Inventory (STAI) to measure anxiety levels ( ). Next, they provided saliva for the testosterone measurement. Afterward, participants were positioned in the 1.5 T MR scanner and familiarized with the task setup. Immediately after this, the fMRI session started with the AA task (duration, 30 min) followed by another task (not included in this report). After a short break outside the scanner, the anatomical scan (duration, 5 min) and an unrelated task were acquired in the side-by-side 3 T MR scanner. + + +### Experimental task + +The AA task consisted of 24 blocks (with 12 trials per block and a baseline period of 21-24 s) during which participants had to respond to visually presented faces either by pulling a joystick toward themselves (approach) or by pushing it away from themselves (avoid; ). The participants had to categorize faces as happy, angry, and neutral (filler items), based on their affective expressions. During each block, two of the three affective expressions were presented as stimuli, because only two responses could be given to categorize the stimulus. This resulted in six different block types each used four times, representing the affect (happy–angry, happy–neutral, angry–neutral) × movement (approach–avoid) combinations. At the start of each block, participants received written instructions regarding the required response mapping. The affect × movement combinations were pseudorandomly and evenly distributed (with no affect combination repetition), and the combination of the first block was counterbalanced across participants. Within each block, affective expressions and gender types were pseudorandomly presented, avoiding three or more sequential presentations of the same expression/gender, and two presentations of the same facial model. Each face was presented for 100 ms, preceded by a 300 ms blank screen, and followed by the participant’s response, a blank screen, and by a pseudorandom intertrial interval (ITI; 1-3 s). A baseline period of 21-24 s preceded each block. The faces were from 36 models (18 male) obtained from several databases ( ; ; ; ), each showing all expressions. The pictures were in grayscale, matched for brightness and contrast values, and displayed against a black background. To exclude influence from hair and nonfacial contours, the faces were trimmed. Joystick displacements of >80% along the sagittal plane within 2 s from stimulus presentation were marked as valid responses. Invalid responses were signaled for 1 s with written feedback stating “you did not move your joystick far enough.” After moving the joystick, participants had to return to the starting position (defined as the central area extending 20% along the sagittal plane) before the end of the ITI. Otherwise, visual feedback indicated “return the joystick to the starting position,” and the ITI was repeated after participants returned the joystick. The training at the beginning consisted of six blocks; one block of eight trials for each of the six affect × movement combinations. Different visual stimuli were used during the training and scanning blocks. + + +### Materials and apparatus + +The fMR images were acquired on a 1.5 T MRI scanner (Avanto, Siemens Medical Systems) with an eight-channel head coil using a multiecho generalized autocalibrating partially parallel acquisitions (GRAPPA) sequence [ ; repetition time (TR), 2.14 ms; five echo times (TEs), 9.4/21/33/44/56 ms; 34 transversal slices; ascending acquisition; distance factor, 17%; effective voxel size, 3.3 × 3.3 × 3.5 mm; field of view (FOV), 212 mm]. High-resolution anatomical images were acquired on a 3 T MRI scanner with a 32-channel head coil using a magnetization prepared rapid gradient echo sequence (TR, 2300 ms; TE, 3.03 ms; 192 sagittal slices; voxel size, 1.0 × 1.0 × 1.0 mm; FOV, 256 mm). + +An MR-compatible joystick (Fiber Optic Joystick, Current Designs; sampling rate, 550 Hz) was placed on participants’ abdomens to ensure comfortable push-and-pull movements ( ). Participants wore MR-compatible headphones to reduce scanner noise (Commander XG MRI Audio System, Resonance Technologies). Stimuli were projected at the center of a screen, viewed via a mirror above the participant’s head, with a visual angle of 4° × 6° (width × height). Stimuli presentation and acquisition of joystick positions were controlled by a PC running Presentation version 13 ( ). + + +### Salivary measurements + +Participants filled two Salicaps (IBL) with saliva for testosterone measurement, which were stored at −25°C. Testosterone concentration was measured using competitive chemiluminescence immunoassay with a sensitivity of 0.0025 ng/ml (IBL International, Tecan). Intra-assay and interassay coefficients are between 10% and 12%. To control variables influencing testosterone levels, participants were instructed to refrain from any food, cigarettes, and drinks (except water) for 1 h before the experiment. + + +### Behavioral analysis + +Behavioral data was analyzed using MATLAB version 7.9 (MathWorks) and PASW Statistics 18 (SPSS Inc.). First, to obtain a precise measure of movement onset [reaction time (RT)], the joystick movement for each trial was reconstructed using the joystick displacement measurements. Excluded trials showed a joystick movement in the wrong direction, an extreme RT (<150 or >1500 ms), peak velocity (<0.1 cm/s), or movement time (>400 ms); or an error rate of above chance level in a block (in that case, the whole block was excluded). RTs and testosterone levels were log transformed to obtain a normal distribution. Second, following previous studies ( ; ), we conducted three-way repeated-measures ANOVA (ANCOVArm) on the mean RT and error rates, with factors group (PP, HC), movement (approach, avoid), and valence (happy, angry), including standardized testosterone and STAI state as covariate. A measure of anxiety (STAI) was included to account for the effects of psychopathy type (e.g., primary vs secondary); and the possible effects on emotional behavior, hormonal levels, amygdala, and prefrontal cortex functioning ( ; ; ; ). The α-level was set at p < 0.05. + + +### Functional MRI data + +#### Single-subject analyses + +Imaging data were preprocessed and analyzed using SPM8 (Statistical Parametric Mapping; ). The first four volumes of each participant’s dataset were discarded to allow for T equilibration. Given the multiecho GRAPPA MR sequence (Poser et al., 2006), head motion parameters were estimated on MR images with the shortest TE (9.4 ms), since these are least affected by possible artifacts. These motion correction parameters, estimated using a least-squares approach with six rigid body transformation parameters (translations, rotations), were applied to the five echo images collected for each excitation. After spatial realignment, the five echo images were combined into a single MR volume using an optimized echo weighting method (Poser et al., 2006). The time series for each voxel was temporally realigned to the first slice in time. The T -weighted image was spatially coregistered to the mean of the functional images. The fMRI time series were transformed and resampled at an isotropic voxel size of 2 mm into standard Montreal Neurological Institute (MNI) space by unified segmentation and normalization using the coregistered T -weighted image ( ). The normalized functional images were spatially smoothed using an isotropic 8 mm full-width at half-maximum Gaussian kernel. + +The fMRI time series of each subject were further analyzed using an event-related approach in the context of general linear model, including the following effects: approach–happy, approach–neutral, approach–angry, avoid–happy, avoid–neutral, and avoid–angry. Trials excluded from behavioral analyses and periods of instructions or feedback were modeled as regressors. Vectors describing the time of picture presentation (onset) and RT of each event (duration) were convolved with the canonical hemodynamic response function. Potential confounding effects of residual head movement were modeled using original, squared, cubic, first-order, and second-order derivatives of the movement correction parameters ( ). Three further regressors, describing the time course of signal intensities of white matter, CSF, and the portion of the MR image outside the skull were also added. This procedure accounts for image intensity shifts due to hand movements within or near the magnetic field of the scanner ( ). Finally, fMRI time series were high-pass filtered (cutoff 120 s). Temporal autocorrelation was modeled as a first-order autoregressive process. + + +#### Group analyses + +Consistent effects across participants and between groups were tested using a random-effects multiple regression analysis that included six contrast images (approach–happy, approach–neutral, approach–angry, avoid–happy, avoid–neutral, avoid–angry) per participant. Together, these images represented the estimated cerebral effects from 12 conditions of the experimental design [group (PP, HC) × valence (happy, neutral, angry) × response (approach, avoid)]. Standardized log-transformed testosterone and standardized STAI state levels were included in the multiple regression analysis as condition-specific [group (PP, HC) × valence (happy, neutral, angry) × response (approach, avoid)] regressors, generating another 12 regressors per variable. + +All analyses assessed the congruency effect, reflecting task-related differences of affect-incongruent (approach–angry, avoid–happy) versus affect-congruent trials (approach–happy, avoid–angry; ; ). We considered two effects. First, to test for general effects of congruency, we performed an analysis on the congruency effect over both groups and for each group separately. When assessing the effects of one group explicitly, we also tested whether those effects were specific to that group and were significantly weaker in the other group (at p < 0.05 uncorrected) by masking the statistical map describing the congruency effect in the first group (using multiple comparisons correction, see below) with the statistical map describing the group × congruency contrast. Second, to test whether testosterone differentially modulated the control of emotionally relevant actions in the groups, we performed a group × congruency contrast on the regressor parametrizing interindividual differences in testosterone on task-related conditions. If such an interaction is present, the testosterone modulation on the congruency effect of each group separately is considered. In addition to whole-brain analyses, we used a volume of interest (VOI) on coordinates previously found to be modulated by testosterone during the congruency effect in healthy students (two 8-mm-radius spheres centered on the following MNI coordinates: x , −30; y , 58; and z, 2; and x , 32; y , 54; and z , 8; ). + +The reported activations are corrected for multiple comparisons using familywise error (FWE) correction. For whole-brain analyses, we made inferences at cluster level (FWE: p < 0.05, corresponding to a cluster size of >140 on the basis of intensity threshold, p < 0.001). For VOI analyses, we made inferences at voxel-level (FWE corrected, p < 0.05; ; ). Anatomical inference is drawn by superimposing SPM showing significant signal changes on structural images of participants. For anatomical accuracy, we report only activation peaks in gray matter. Anatomical landmarks were identified using the atlas of . Brodmann areas (BAs) were assigned by superimposing significant SPM on the SPM anatomy toolbox ( ) and MRIcron template ( /). + + + +### Connectivity analyses + +The aim of the following analysis was to test whether inter-regional coupling of the aPFC (see Results) with the amygdala and other brain regions during the congruency effect was different between the groups and modulated by testosterone. To test for these effects, we used the psychophysiological interactions (PPIs) method ( ). More specifically, we tested for significant differences between the regression coefficients of each voxel over the right aPFC during the affect-incongruent versus the affect-congruent conditions. To select voxels to be included in the VOI, we used the following anatomical constraints ( ): for each participant, selected voxels fell within a sphere with a radius of 4 mm around the peak voxel corresponding to the activated cluster of the congruency effect over both groups (coordinates: x , 30; y , 58; z , 14; see Results). Participant specific contrast images were generated describing the PPI between the time courses of the right aPFC VOI and affect-incongruent versus affect-congruent conditions. Group differences and testosterone modulations on task-related coupling between the aPFC and other regions were then assessed using a multiple regression design on participant-specific contrast images with their corresponding testosterone (log-transformed, standardized) and STAI state (standardized) levels as subject- and group-specific regressors. In addition to whole-brain analyses, we assessed significant voxel-level effects (FWE corrected for multiple comparisons, p < 0.05) within the amygdala, defined on the Automated Anatomical Labeling atlas ( ) using the WFU PickAtlas tool ( ). + + + +## Results + +### Behavioral results + +Fifteen psychopathic criminals (PPs; PCL-R score of ≥26, according to European standards ( ; ; ) and 19 HCs (for demographics, see ) were included in the analyses. Participants performed the task accurately and consistently (error rates: PPs, 7.9%; HCs, 7.3%; omissions: PPs, 1.6%; HCs, 1.5%; undefined responses: PPs, 0.9%; HCs, 0.3%; ). + +RTs and error rates for each group and factor of the AA task + +A significant movement × valence interaction for the RTs indicated that, over groups, participants responded more slowly during affect-incongruent (approach–angry, avoid–happy) than during affect-congruent trials (approach–happy, avoid–angry; F = 10.4, p = 0.003; ). This congruency effect replicates the behavioral results from previous fMRI studies ( ; ). Furthermore, there were main effects of movement ( F = 26.3, p < 0.001) and valence ( F = 28.7, p < 0.001), reflecting the slowing of avoidance movements and responses to angry faces in general ( ). There were no significant effects involving group, including no main effect ( p > 0.3). The congruency effect correlated positively (without corrections for multiple comparisons) with the PCL-R total score ( p = 0.048, R = 0.517, respectively). Excluding anxiety from the analyses did not affect the outcomes. Moreover, when including the neutral conditions in the analyses, the movement × valence (happy, neutral, angry) interaction for RTs remained significant ( F = 5.5, p = 0.010), showing that neutral approach–avoidance effects are intermediary compared with happy and angry ( ). + +Behavioral results. Mean RTs (±SEM) for the affect-congruent and affect-incongruent conditions of the AA task for the healthy control subjects and psychopathic offenders. The groups were significantly slower to provide affect-incongruent responses (approach–angry; avoid–happy) than affect-congruent responses (approach–happy; avoid–angry), with no significant group differences. + +For the error rates, the three-way ANCOVArm showed main effects of movement ( F = 27.5, p < 0.001), valence ( F = 25.9, p < 0.001), and testosterone ( F = 4.6, p = 0.040), and a valence × testosterone interaction ( F = 4.3, p = 0.047). There were no other significant effects for the error rates ( p > 0.15). + +Endogenous testosterone levels [median (SD): PPs, 101 pg/ml (70 pg/ml); HCs, 90 pg/ml (46 pg/ml)] and state anxiety levels [STAI mean (SD): PPs, 32 (8); HCs, 32 (5)] did not differ between groups ( p > 0.4), and showed no correlations with psychopathy (PCL-R) scores or with each other ( p > 0.1). + + +### fMRI results + +#### Multiple regression analyses + +To assess the two main questions of this study, we isolated cerebral structures showing stronger responses during affect-incongruent than affect-congruent trials (congruency effect), and cerebral structures in which the congruency effect was modulated by testosterone levels. + +The results showed a significant congruency effect across groups in the aPFC [ROI analysis: MNI coordinates ( x , y , z ): (30, 58, 14) and (−30 58 10); p = 0.001 and 0.036; t = 4.46 and 3.43; for further details, see ]. As expected, this effect was driven by the healthy control group, and it was significantly weaker in the psychopathic offenders [ p = 0.001 and 0.040; t = 4.58 and 3.40, on the congruency effect in healthy control subjects masked implicitly by group (HC > PP) × congruency interaction]. The implicit masking demonstrates that the group × congruency interaction is also significant at p < 0.05 within the significant voxels corrected for multiple comparisons on the HC congruency effect. The psychopathy group showed no significant congruency effect in this region ( p > 0.3). There was also a significant congruency effect across groups in the right superior parietal lobule (whole-brain analysis); this effect was driven mainly by the psychopathy group ( ). + +Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect) + +Critically, testosterone modulated the congruency effect in the aPFC differently in psychopathic offenders and healthy control subjects (whole-brain analysis on testosterone × group × congruency: MINI coordinates ( x , y , z ): (30, 58, 12); p < 0.001; t = 5.10; for all details, see ). Post hoc analyses revealed that, in the psychopathy group, congruency effects decreased as testosterone levels increased [MNI coordinates ( x , y , z ): (32, 56, 10) and (−30, 58, 8); p = 0.002 and 0.015; t = 4.34 and 3.74]. The modulatory effect of testosterone on congruency was absent in the healthy control subjects ( p ≥ 0.05; ). The whole-brain analysis also showed an effect in the right caudate nucleus and right inferior supramarginal gyrus, driven by reduced congruency effects as a function of testosterone in the psychopathy group ( ; ). + +Testosterone modulations of the cerebral congruency effect in psychopathic offenders and healthy control subjects. A , D , Brain image showing testosterone-modulated congruency effects (affect-incongruent−affect-congruent) in the psychopathic offenders in the bilateral aPFC ( A ) and right supramarginal gyrus ( D ). B , E , Bar graphs showing the mean activation (±SEM) of the active voxels within the yellow circles per group. * p < 0.05. ns, Not significant. C , F , Scatterplots showing the correlation of the mean activation of active voxels within the yellow circles with testosterone (log-transformed and standardized) for the healthy control group and the psychopathy group. The ROI activations are presented at p < 0.05, uncorrected for visualization purposes. There are no outliers [Mahalanobis distances D < 4.2 (cutoff at p < 0.05; D = 7.74); ; ]. Healthy control subjects show an increased aPFC activity for the congruency effect and no modulation by testosterone, while in psychopathic offenders endogenous testosterone levels modulate the activity of the aPFC and right supramarginal gyrus. + + +#### Effective connectivity analyses + +Given the relevance of aPFC–amygdala connectivity for implementing emotional control as evoked by the AA task ( ), we assessed whether psychopathy also resulted in altered connectivity along that neural pathway. Connectivity analyses using the right aPFC [4-mm-radius sphere; central voxel from main analysis (MNI coordinates: x , 30; y , 58; z , 14)] as the seed region on the congruency effect indicated a significant group difference (PP > HC) with the right amygdala ( ; ROI analysis; extent, 3 voxels; t = 3.82; p = 0.027; MNI coordinates of local maxima: x , 32; y , 0; z , −16). When testing effects for both groups separately, healthy control subjects showed a significant negative coupling between the right aPFC and amygdala (ROI analysis; extent: 3 voxels, t = 3.70; p = 0.036; MNI coordinates of local maxima: x , 32; y , 0; z , −16), while psychopathic offenders showed no differential connectivity effect. Post hoc testing on right amygdala voxels showing the group interaction (threshold, p < 0.05 FWE) indicated a significant positive correlation with testosterone over both groups (ROI analysis; extent, 1 voxel; t = 2.29; p = 0.029; MNI coordinates of local maxima: x , 32; y , 2; z , −16). There was no correlation between aPFC–amygdala connectivity and the PCL-R scores ( p > 0.2). + +Group difference on congruency-related aPFC–amygdala connectivity. A , Brain images illustrating the congruency-related modulation of connectivity between the right aPFC (yellow circle, axial slice) and the right amygdala (coronal slice) for the congruency contrast. The activations are presented at p < 0.05, uncorrected for visualization purposes. B , Bar graph visualizing the strength of the congruency-specific change (±SEM) in aPFC–amygdala connectivity for the healthy control subjects and psychopathic offenders. There is a significant negative aPFC–amygdala coupling in the healthy control subjects, which is not present in the psychopathic offenders. + + + + +## Discussion + +This study indicates that psychopathic offenders show reduced aPFC activity as well as less aPFC–amygdala connectivity during the control of emotional behavior. Emotional control was measured by comparing affect-incongruent and affect-congruent approach–avoidance responses to emotional faces (congruency effect on the AA task; ). When healthy control subjects exerted emotional control, reaction times, aPFC activity, and aPFC–amygdala anticorrelations increased, confirming previous observations ( ). In contrast, psychopathic offenders did not show this typical control-related pattern of aPFC activity and connectivity. In addition, these effects were significantly modulated by endogenous testosterone. Namely, psychopathic individuals with relatively lower testosterone levels showed a neural activity and connectivity pattern that resembled the findings in healthy control subjects, while this pattern was absent in those with higher testosterone levels. This indicates that especially psychopathic individuals with high testosterone levels have less prefrontal regulation of amygdala-driven emotional actions when the control of emotional behavior is required. + +### Emotional control in psychopathy + +Imaging studies have illustrated an association between psychopathy and altered processing of fear, including altered amygdala responses ( ; ; ), attentional deficits for peripheral stimuli ( ), and moral/empathic insensitivity ( ; ). However, psychopathic offenders also show clear impulsivity problems ( ), for example, when control is required during emotionally provoking situations. To address this relatively unexplored but crucial component of criminal psychopathy, we used a paradigm requiring rule-driven control of emotional actions. With this paradigm, it was possible to move beyond simple motor inhibition and to target the flexible control of emotionally driven action tendencies. + +First, the aPFC (also called BA 10) was less active in psychopathic offenders as a function of testosterone. The aPFC is a region crucial for the control of social emotional behavior. When aPFC functioning is temporarily disrupted, participants have increased difficulty in overriding emotional tendencies with rule-driven behavior ( ). Moreover, the aPFC seems especially important for integrating and coordinating multiple cognitive processes to facilitate response selection ( ; ). For example, transcranial magnetic stimulation-induced reduction of aPFC functioning during the control of emotional behavior decreased activity in brain areas associated with rule selection (posterior parietal cortex), while both amygdala activity and automatic action tendencies increased ( ). The current study indicates that psychopathic individuals with especially high testosterone levels recruited the aPFC less when the control of emotional responses was needed. This finding suggests that they have reduced coordination of rule-based behavior with emotional information. + +Second, connectivity between the aPFC and amygdala also differed significantly between groups. Healthy control subjects showed a negative aPFC–amygdala coupling during the control of social emotional behavior, whereas psychopathic individuals showed no significant coupling between these regions. Evidence of anatomical connectivity alterations between these regions in psychopathic individuals and the relation of that tract to social emotional behavior modifications support these findings ( ). Although these results cannot resolve the direction of these connectivity effects, a previous study ( ) using this paradigm showed an effective connectivity modulation of emotional control on the connection from aPFC to amygdala. Also, animal studies ( ) suggest strong prefrontal inhibitory connections that control automatic amygdala responses. The absence of this aPFC–amygdala coupling in psychopathic offenders suggests that in this group the aPFC has a reduced ability to inhibit amygdala-driven responses. This study used subtle emotional provocations, but stronger emotional events result in stronger amygdala responses, increasing the bias for automatic emotional behavior ( ). A lack of prefrontal control likely reduces the ability to inhibit these biases and lead to an increased expression of automatic emotional actions even when they are not beneficial ( ; ). + +Testosterone administration studies also illustrated a decoupling between the prefrontal cortex and the amygdala, suggesting that testosterone reduces the communication between the PFC and amygdala ( ; ; ) and, within the AA task, reduces top-down control. The association between testosterone levels and enhanced social aggression and dominance seeking, and reduced impulse control in the general population ( ; ; ) supports the relevance of testosterone in this process. Even amygdala responses to angry faces have recently been found to be enhanced after testosterone administration and in psychopathic individuals ( ; ; ). There is a clear association between testosterone and aggression after provocation, which has been related to reduced activity in the orbital frontal cortex, a region just ventral of the aPFC ( ). Interestingly, psychopathic offenders with lower testosterone levels displayed a pattern similar to that in healthy control subjects, while the psychopathic individuals with high testosterone levels showed less aPFC activity and aPFC–amygdala coupling. This could provide a potential vulnerability factor explaining the difference between the goal-directed “successful” psychopath and the “unsuccessful” psychopath with reduced impulse control ( ; ). We hypothesize that especially psychopathic individuals with high testosterone levels fail to inhibit amygdala-driven action tendencies using the aPFC during the control of emotional behavior. + + +Endogenous testosterone levels also modulated control-related activity in the supramarginal gyrus and caudate nucleus of the psychopathy group. The supramarginal gyrus was previously found to be involved during emotional control on the AA task in a healthy student sample ( ). Previous work indicated that it plays an important role in action organization ( ), and that psychopathic individuals show reduced supramarginal gyrus activity compared with control subjects when reasoning about other people’s emotional state ( ). The current findings, emphasizing the role of supramarginal gyrus during emotional control in psychopathic offenders with low testosterone levels, could indicate the facilitation of action preparation in trials with affect-incongruent stimulus–response mapping. The caudate nucleus is important for incorporating predicted action outcome, when selecting the most beneficial behavioral goal ( ), and has previously found to be larger in psychopathy ( ). In light of these findings, our results suggest that psychopathic offenders with low endogenous testosterone levels, as opposed to those with high testosterone levels, have more interference of automatic action tendencies and outcomes associated with the facial emotions (e.g., approach–happy) that are opposite to the required actions during affect-incongruent trials ( ). + + +### Interpretational issues + +Individuals with psychopathy have been suggested to have difficulty recognizing emotional expressions. However, this impairment seems quite specific to fear, rather than the emotional expressions used here (anger and happiness; ; ). Furthermore, the groups assessed in this study made comparable numbers of errors, suggesting that psychopathic offenders had no special difficulty in recognizing the clear emotional expressions used in this study. + +This study used a relatively subtle manipulation to target the emotional control system. The rationale of this choice was to detect neural vulnerability markers without affecting behavioral performance. Psychopathic offenders performing a more salient behavioral version of the AA task showed reduced avoidance of angry faces ( ). In this study, angry faces evoked numerically similar behavioral effects ( ) and, additionally, aPFC effects ( post hoc inspection of extracted parameters). Although these observations could be interpreted as a sign that psychopathic offenders have a tendency to approach angry faces, those observations were not statistically significant between groups [behavioral and aPFC group effects on angry faces: p > 0.2; p = 0.271; z = 2.54, on the angry–congruency effect in healthy control subjects masked implicitly by group (HC > PP) × angry–congruency interaction]. Future investigation is needed to directly test whether more provocative paradigms induce specific effects for angry faces. A previous study ( ) using this fMRI task in participants with genetic susceptibility for developing aggressive disorders, also found no group-specific behavioral effects. That study suggested that alterations of the aPFC–amygdala pathway might reflect a vulnerability factor for psychopathologies. + +Previously, endogenous testosterone modulated the aPFC and aPFC–amygdala coupling in a sample of healthy students ( ). In that study, a different demographic group of healthy control subjects similarly showed a testosterone modulation of aPFC–amygdala coupling, but no testosterone modulation of aPFC activity. This difference in the strength of testosterone-modulatory effects might be related to between-group differences in age (mean healthy control subjects, 41; mean students, 22; ), educational level (staff of forensic psychiatric institute vs university students), or general anxiety [STAI trait, lower in healthy control subjects of the current study; mean (SD): 29 (4.4) and 34 (6.9), respectively; t = −2.605; p = 0.014]. A limitation of this study is the modest sample size. Our focus to exclude moderating factors of comorbid disorders (except antisocial personality disorder) and recent drug use has the advantage that the sample is relatively homogeneous, but future studies using larger samples are needed for replication and to define subsamples. + + +### Conclusion + +Psychopathic offenders showed reduced aPFC activity and aPFC–amygdala connectivity during control of emotional actions, suggesting a decreased coordination of emotional information during rule-driven behavior. Moreover, endogenous testosterone modulated the involvement of these neural mechanisms. Psychopathic offenders with high testosterone levels showed less involvement of the aPFC, aPFC–amygdala connectivity, supramarginal gyrus, and caudate nucleus, whereas psychopathic individuals with low testosterone levels recruited the aPFC in a fashion similar to that of healthy control subjects. These findings suggest that a lack of prefrontal control during emotional actions may explain enhanced impulsivity in psychopathic offenders during emotionally provoking situations. They outline a neuroendocrine model underlying impulsive emotional behavior in psychopathy and support the relevance of assessing a potential imbalance in testosterone function to guide treatment. It remains to be seen whether these neuroendocrine alterations of emotional control are also present in highly impulsive or antisocial individuals. + + + \ No newline at end of file diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/26878057.xml b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/26878057.xml new file mode 100644 index 0000000..10d0863 --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/26878057.xml @@ -0,0 +1,1650 @@ + +
+ + + + eNeuro + eNeuro + eneuro + eneuro + eNeuro + + eNeuro + + 2373-2822 + + Society for Neuroscience + + + + 26878057 + 4745181 + eN-NWR-0107-15 + 10.1523/ENEURO.0107-15.2016 + + + 1 + + + New Research + + Cognition and Behavior + + + + + Testosterone Modulates Altered Prefrontal Control of Emotional Actions in Psychopathic Offenders123 + Prefrontal–Amygdala Connectivity in Psychopathy + + + + http://orcid.org/0000-0003-3467-1841 + + Volman + Inge + + + 1 + + + 2 + + + 3 + + + + + von Borries + Anna Katinka Louise + + + 2 + + + 3 + + + 4 + + + 5 + + + + + Bulten + Berend Hendrik + + + 5 + + + + + Verkes + Robbert Jan + + + 3 + + + 4 + + + 5 + + + + http://orcid.org/0000-0003-0936-3601 + + Toni + Ivan + + + 3 + + + + http://orcid.org/0000-0002-8863-8978 + + Roelofs + Karin + + + 2 + + + 3 + + + Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London WC1N 3BG, United Kingdom + Behavioural Science Institute, Radboud University Nijmegen, 6525 HR, Nijmegen, The Netherlands + Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 EN, Nijmegen, The Netherlands + Department of Psychiatry, UMC Sint Radboud, 6525 GA, Nijmegen, The Netherlands + Pompestichting, 6532 CN, Nijmegen, The Netherlands + + + + +

The authors declare no competing financial interests.

+
+ + +

Author contributions: I.V., A.K.L.v.B., B.H.B., R.J.V., I.T., and K.R. designed research; I.V. and A.K.L.v.B. performed research; I.V., A.K.L.v.B., I.T., and K.R. analyzed data; and I.V., A.K.L.v.B., B.H.B., R.J.V., I.T., and K.R. wrote the paper.

+
+ + +

This work was supported by VIDI Grant 452-07-008 from the Netherlands Organization for Scientific Research (NWO) awarded to K.R. supporting I.V., Marie Curie Individual Fellowship MSCA-IF-2014-EF_660397 within the European Union's Horizon 2020 Framework Programme awarded to I.V., VICI Grant 453-08-002 from the NWO awarded to I.T., and Starting Grant ERC_StG2012_313749 from the European Research Council and VICI Grant 453-12-001 from the NWO awarded to K.R.

+
+ Correspondence should be addressed to Inge Volman, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Box 146, 33 Queen Square, London WC1N 3BG, UK; Email: i.volman@ucl.ac.uk. +
+ + 15 + 1 + 2016 + + + 8 + 2 + 2016 + + + Jan-Feb + 2016 + + 3 + 1 + ENEURO.0107-15.2016 + + + 15 + 9 + 2015 + + + 15 + 12 + 2015 + + + 3 + 1 + 2016 + + + + Copyright © 2016 Volman et al. + 2016 + Volman et al. + + This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. + + + + + + Abstract +

Psychopathic individuals are notorious for their controlled goal-directed aggressive behavior. Yet, during social challenges, they often show uncontrolled emotional behavior. Healthy individuals can control their social emotional behavior through anterior prefrontal cortex (aPFC) downregulation of neural activity in the amygdala, with testosterone modulating aPFC–amygdala coupling. This study tests whether individual differences in this neuroendocrine system relate to the paradoxical lack of emotional control observed in human psychopathic offenders. Emotional control was operationalized with an fMRI-adapted approach–avoidance task requiring rule-driven control over rapid emotional responses. Fifteen psychopathic offenders and 19 matched healthy control subjects made approaching and avoiding movements in response to emotional faces. Control of social emotional behavior was required during affect-incongruent trials, when participants had to override affect-congruent, automatic action tendencies and select the opposite response. Psychopathic offenders showed less control-related aPFC activity and aPFC–amygdala coupling during trials requiring control of emotional actions, when compared with healthy control subjects. This pattern was particularly pronounced in psychopathic individuals with high endogenous testosterone levels. These findings suggest that reduced prefrontal coordination underlies reduced behavioral control in psychopathic offenders during emotionally provoking situations. Even though the modest sample size warrants replication, the modulatory role of endogenous testosterone on the aPFC–amygdala circuit suggests a neurobiological substrate of individual differences that is relevant for the advancement of treatment and the reduction of recidivism. +

+
+ + amygdala + connectivity + emotion + fMRI + prefrontal + psychopathy + + + + Netherlands organisation for scientific research + 452-07-008 + + + Netherlands organisation for scientific research + 453-08-002 + + + European research council + ERC_StG2012_313749 + + + + + + + + + + + + + cover-date + January/February 2016 + + +
+
+ + + Significance Statement +

Psychopathic criminals are commonly seen as instrumentally abusive and emotionally callous, yet social challenges often trigger uncontrolled emotional behavior in those individuals. This study shows how this paradoxical aspect of psychopathy relates to altered neuroendocrine interactions between testosterone and the cerebral circuit coordinating emotional action tendencies. The anterior prefrontal cortex, a region necessary for controlling emotional behavior, showed blunted responses and reduced connectivity with the amygdala in psychopathic criminals engaged in controlling their emotional action tendencies. This cerebral pattern was strongest in psychopathic individuals with high endogenous testosterone levels. This neuroendocrine signature of altered emotional control highlights the relevance of considering the testosterone level of individual psychopathic patients during treatment of their impulsive behavior.

+
+ + Introduction +

Psychopathy is a disorder often associated with blunted emotional responding and increased goal-directed behavior (Blair, 2010; Anderson and Kiehl, 2012). On the other hand, offenders with psychopathy also show a paradoxical increase in impulsive behavior and uncontrolled aggression after emotional provocations (Cornell et al., 1996; Hare, 2003; Patrick et al., 2005; Malterer et al., 2008; Blair, 2010; Anderson and Kiehl, 2012), which may be related to heightened testosterone levels (Stålenheim et al., 1998; Dolan et al., 2001). These two aspects of psychopathy are also distinguished within the most commonly used psychopathy checklist, the Psychopathy Check List-Revised (PCL-R), potentially reflecting differing traits among psychopathic individuals (Hare, 2003; Anderson and Kiehl, 2012). Importantly, enhanced difficulty in controlling emotional impulses, a crucial component of criminal psychopathy associated with PCL-R factor 2, has been largely neglected by cognitive neuroscience. Yet, the clinical relevance of this cognitive trait is large: reduced behavioral control and increased impulsivity predict recidivism in psychopathic offenders (Walters, 2003), and behavioral control in psychopathic offenders appears particularly fragile when dealing with emotionally relevant behavior (Hare, 2003; Blair et al., 2005, chapter 7; Malterer et al. 2008). Accordingly, understanding the neurobiological systems underlying the altered control of social emotional behavior in psychopathic individuals is relevant for improving currently available interventions, which are plagued by low treatment response and high recidivism (Hare, 2003). Here we study those neuroendocrine systems in a group of psychopathic offenders engaged in an experimental paradigm that requires rule-driven control of emotional behavior.

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Previous investigations of psychopathy showed altered reactivity to emotional material in several brain regions that include the anterior part of the PFC (aPFC) and the amygdala (Anderson and Kiehl, 2012; Blair, 2013; Decety et al., 2015). Furthermore, individuals with psychopathy showed decreased functional and anatomical connectivity between the PFC and amygdala at rest (Craig et al., 2009; Motzkin et al., 2011), an indication that these brain regions might have a reduced ability to interact effectively. Studies in healthy participants have shown that this cerebral circuit is necessary for implementing the control of emotionally relevant actions (Volman et al., 2011a). Namely, aPFC downregulates neural processing in the amygdala during emotional control (Volman et al., 2011a, 2013), while high levels of endogenous testosterone reduce such control-related connectivity between aPFC and amygdala (Volman et al., 2011b). Those findings raise the possibility that aPFC–amygdala connectivity is altered when psychopathic offenders need to control emotionally relevant actions, with high levels of endogenous testosterone exacerbating that altered connectivity.

+

This study tests these hypotheses by measuring brain activity with functional magnetic resonance imaging (fMRI) in 15 psychopathic criminals and 19 matched healthy control subjects dealing with a challenge to control their emotional behavior. The psychopathy sample was obtained by focused and comprehensive screening excluding confounds that are frequently associated with random criminal sampling (e.g., medication use, comorbidity). The social approach–avoidance (AA) task was used to provide reliable indexes of control over social emotional behavior (Fig. 1; Roelofs et al., 2009; Volman et al., 2011a,b). Behaviorally, psychopathic participants previously showed altered AA behavior to explicitly approaching and avoiding emotional faces (Von Borries et al., 2012). Similar findings occurred after testosterone administration in healthy participants (Enter et al., 2014). Interestingly, a more subtle version of the AA task has been shown to be sensitive to testosterone-related alterations and genetic variations in the aPFC–amygdala pathway, while keeping behavior constant across experimental groups (Volman et al., 2011b, 2013), opening the way for isolating neural vulnerability factors (Price and Friston, 1999) in psychopathy. During this task, participants respond to affective faces (happy, angry) presented for a short time with approach and avoidance movements. Automatic emotional tendencies (approach–happy and avoid–angry faces; affect-congruent response conditions) need to be controlled during affect-incongruent response conditions in order to apply the counterintuitive action of approaching angry and avoiding happy faces (Chen and Bargh, 1999; Roelofs et al., 2009). Healthy participants respond more slowly and rely more strongly on the aPFC when emotional control is required, operationalized by the differences evoked between affect-incongruent and affect-congruent trials (Roelofs et al., 2009; Volman et al., 2011b). Accordingly, this study tests whether exerting control over emotionally relevant actions is reflected by reduced functionality of the aPFC–amygdala circuit in psychopathic individuals, suggesting less prefrontal regulation of emotional actions. In addition, it sets out to test whether this alteration is intensified by high levels of endogenous testosterone.

+ + + +

The emotional control AA task. The AA task involved the presentation of happy and angry faces, and the performance of approach and avoidance responses. During the AA task, the participants had to select their response according to the perceived emotion of the face. At the beginning of each block of 12 trials, the participants received instructions on whether to pull the joystick toward themselves (approach) or push it away (avoid) when seeing a face with a particular emotion. When viewing happy or angry faces, automatic stimulus–response tendencies trigger corresponding approach or avoidance actions. These tendencies could be followed during the affect-congruent condition (approach–happy, avoid–angry). In contrast, when task instructions required participants to avoid happy faces or to approach angry faces, automatic tendencies needed to be controlled and overridden with the instructed response (affect-incongruent condition). Participants saw the faces and moved the joystick while lying in a MR scanner (top left corner of the table). Figure adapted from Volman et al. (2011a, 2013).

+ + +
+
+ + Materials and Methods + + Participants +

The psychopathic group was recruited from in-patient populations of the Pompestichting and Oldenkotte, forensic psychiatric institutes (TBS-clinics) in the Netherlands. TBS-clinics are facilities for criminal offenders with a mental disorder treated on behalf of the state.

+

Seventeen male psychopathic violent offenders (age range, 23-56 years) participated; all had received a diagnosis with a PCL-R score of ≥26, according to European standards (Hare et al., 1991; Rasmussen et al., 1999; Hildebrand et al., 2004). PCL-R consensus scores were obtained by trained clinicians based on a structured PCL-R interview, clinical status, and history. After the independent scoring, the two raters compared their scores and came to the consensus score. When no consensus could be found, a third independent rater was included in the process. Dutch versions of the National Adult Reading Test and Edinburgh Handedness Inventory were used to assess IQ levels and right-handedness (Oldfield, 1971; Schmand et al., 1991). Twenty-one healthy male control subjects (HCs) matched for age, right-handedness, and IQ, without criminal records or history of psychiatric disorders, were recruited from staff of the clinics. All participants received oral and written information about the experiment and gave written informed consent according to guidelines of the local ethics committee (Commissie Mensengebonden Onderzoek region Arnhem-Nijmegen). Psychiatric exclusion criteria consisted of neurological, axis-I, and axis-II disorders, besides antisocial personality disorder for the psychopathic group. They were screened for these exclusion criteria by trained psychologists using Dutch versions of the Structured Clinical Interview (SCID; Groenestijn et al., 1999) and Mini-International Neuropsychiatric Interview (MINI; Van Vliet et al., 2000) for Diagnostic and Statistical Manual of Mental Disorders, 4th edition, disorders. All participants were asked about drug use and medical/neurological history to exclude the following: alcohol use of >3 units/day, cannabis, or other illicit drug use 1 week before, psychotropic medication other than oxazepam 5 d before, 1 unit of alcohol or oxazepam use within 24 h before the experiment; history of trauma capitis; visual and auditive disorder; and neurological disorder. Furthermore, general exclusion criteria for MRI experiments were applied. Two psychopathic patients (PPs) and two HCs were excluded from the analyses, due to incomplete scanning procedures (1 PP, 1 HC) or too many errors on the task (>16%, representing the outlier with a z-score >3). The final groups did not differ in age, IQ, and handedness (see Table 1).

+ + + +

Demographical data

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Psychopathic offenders(n = 15)HCs(n = 19)p value
Age37.8 (7.9)40.7 (10.3)0.368
IQ101 (10)102 (9)0.761
Handedness50.7 (81)59.2 (62)0.729
PCL-R total30.4 (3.5) + +
PCL-R F112.1 (2.6) + +
PCL-R F214.1 (2.3) + +
+ + +

Values are presented as the mean (SD), unless otherwise indicated. F1, factor 1; F2, factor 2.

+
+
+
+
+ + Procedure +

Two test sessions took place. During the first session, right-handedness, IQ, MINI, and SCID were assessed. During the second session, participants completed several questionnaires upon arrival in the laboratory, including the State-Trait Anxiety Inventory (STAI) to measure anxiety levels (Spielberger, 1983). Next, they provided saliva for the testosterone measurement. Afterward, participants were positioned in the 1.5 T MR scanner and familiarized with the task setup. Immediately after this, the fMRI session started with the AA task (duration, 30 min) followed by another task (not included in this report). After a short break outside the scanner, the anatomical scan (duration, 5 min) and an unrelated task were acquired in the side-by-side 3 T MR scanner.

+
+ + Experimental task +

The AA task consisted of 24 blocks (with 12 trials per block and a baseline period of 21-24 s) during which participants had to respond to visually presented faces either by pulling a joystick toward themselves (approach) or by pushing it away from themselves (avoid; Fig. 1). The participants had to categorize faces as happy, angry, and neutral (filler items), based on their affective expressions. During each block, two of the three affective expressions were presented as stimuli, because only two responses could be given to categorize the stimulus. This resulted in six different block types each used four times, representing the affect (happy–angry, happy–neutral, angry–neutral) × movement (approach–avoid) combinations. At the start of each block, participants received written instructions regarding the required response mapping. The affect × movement combinations were pseudorandomly and evenly distributed (with no affect combination repetition), and the combination of the first block was counterbalanced across participants. Within each block, affective expressions and gender types were pseudorandomly presented, avoiding three or more sequential presentations of the same expression/gender, and two presentations of the same facial model. Each face was presented for 100 ms, preceded by a 300 ms blank screen, and followed by the participant’s response, a blank screen, and by a pseudorandom intertrial interval (ITI; 1-3 s). A baseline period of 21-24 s preceded each block. The faces were from 36 models (18 male) obtained from several databases (Ekman and Friesen, 1976; Matsumoto and Ekman, 1988; Lundqvist et al., 1998; Martinez and Benavente, 1998), each showing all expressions. The pictures were in grayscale, matched for brightness and contrast values, and displayed against a black background. To exclude influence from hair and nonfacial contours, the faces were trimmed. Joystick displacements of >80% along the sagittal plane within 2 s from stimulus presentation were marked as valid responses. Invalid responses were signaled for 1 s with written feedback stating “you did not move your joystick far enough.” After moving the joystick, participants had to return to the starting position (defined as the central area extending 20% along the sagittal plane) before the end of the ITI. Otherwise, visual feedback indicated “return the joystick to the starting position,” and the ITI was repeated after participants returned the joystick. The training at the beginning consisted of six blocks; one block of eight trials for each of the six affect × movement combinations. Different visual stimuli were used during the training and scanning blocks.

+
+ + Materials and apparatus +

The fMR images were acquired on a 1.5 T MRI scanner (Avanto, Siemens Medical Systems) with an eight-channel head coil using a multiecho generalized autocalibrating partially parallel acquisitions (GRAPPA) sequence [Poser et al., 2006; repetition time (TR), 2.14 ms; five echo times (TEs), 9.4/21/33/44/56 ms; 34 transversal slices; ascending acquisition; distance factor, 17%; effective voxel size, 3.3 × 3.3 × 3.5 mm; field of view (FOV), 212 mm]. High-resolution anatomical images were acquired on a 3 T MRI scanner with a 32-channel head coil using a magnetization prepared rapid gradient echo sequence (TR, 2300 ms; TE, 3.03 ms; 192 sagittal slices; voxel size, 1.0 × 1.0 × 1.0 mm; FOV, 256 mm).

+

An MR-compatible joystick (Fiber Optic Joystick, Current Designs; sampling rate, 550 Hz) was placed on participants’ abdomens to ensure comfortable push-and-pull movements (Fig. 1). Participants wore MR-compatible headphones to reduce scanner noise (Commander XG MRI Audio System, Resonance Technologies). Stimuli were projected at the center of a screen, viewed via a mirror above the participant’s head, with a visual angle of 4° × 6° (width × height). Stimuli presentation and acquisition of joystick positions were controlled by a PC running Presentation version 13 (http://www.neurobs.com).

+
+ + Salivary measurements +

Participants filled two Salicaps (IBL) with saliva for testosterone measurement, which were stored at −25°C. Testosterone concentration was measured using competitive chemiluminescence immunoassay with a sensitivity of 0.0025 ng/ml (IBL International, Tecan). Intra-assay and interassay coefficients are between 10% and 12%. To control variables influencing testosterone levels, participants were instructed to refrain from any food, cigarettes, and drinks (except water) for 1 h before the experiment.

+
+ + Behavioral analysis +

Behavioral data was analyzed using MATLAB version 7.9 (MathWorks) and PASW Statistics 18 (SPSS Inc.). First, to obtain a precise measure of movement onset [reaction time (RT)], the joystick movement for each trial was reconstructed using the joystick displacement measurements. Excluded trials showed a joystick movement in the wrong direction, an extreme RT (<150 or >1500 ms), peak velocity (<0.1 cm/s), or movement time (>400 ms); or an error rate of above chance level in a block (in that case, the whole block was excluded). RTs and testosterone levels were log transformed to obtain a normal distribution. Second, following previous studies (Roelofs et al., 2009; Volman et al., 2011b), we conducted three-way repeated-measures ANOVA (ANCOVArm) on the mean RT and error rates, with factors group (PP, HC), movement (approach, avoid), and valence (happy, angry), including standardized testosterone and STAI state as covariate. A measure of anxiety (STAI) was included to account for the effects of psychopathy type (e.g., primary vs secondary); and the possible effects on emotional behavior, hormonal levels, amygdala, and prefrontal cortex functioning (Freitas-Ferrari et al., 2010; Koenigs et al., 2011; Giltay et al., 2012; Fouche et al., 2013). The α-level was set at p < 0.05.

+
+ + Functional MRI data + + Single-subject analyses +

Imaging data were preprocessed and analyzed using SPM8 (Statistical Parametric Mapping; http://www.fil.ion.ucl.ac.uk/spm). The first four volumes of each participant’s dataset were discarded to allow for T1 equilibration. Given the multiecho GRAPPA MR sequence (Poser et al., 2006), head motion parameters were estimated on MR images with the shortest TE (9.4 ms), since these are least affected by possible artifacts. These motion correction parameters, estimated using a least-squares approach with six rigid body transformation parameters (translations, rotations), were applied to the five echo images collected for each excitation. After spatial realignment, the five echo images were combined into a single MR volume using an optimized echo weighting method (Poser et al., 2006). The time series for each voxel was temporally realigned to the first slice in time. The T1-weighted image was spatially coregistered to the mean of the functional images. The fMRI time series were transformed and resampled at an isotropic voxel size of 2 mm into standard Montreal Neurological Institute (MNI) space by unified segmentation and normalization using the coregistered T1-weighted image (Ashburner and Friston, 2005). The normalized functional images were spatially smoothed using an isotropic 8 mm full-width at half-maximum Gaussian kernel.

+

The fMRI time series of each subject were further analyzed using an event-related approach in the context of general linear model, including the following effects: approach–happy, approach–neutral, approach–angry, avoid–happy, avoid–neutral, and avoid–angry. Trials excluded from behavioral analyses and periods of instructions or feedback were modeled as regressors. Vectors describing the time of picture presentation (onset) and RT of each event (duration) were convolved with the canonical hemodynamic response function. Potential confounding effects of residual head movement were modeled using original, squared, cubic, first-order, and second-order derivatives of the movement correction parameters (Lund et al., 2005). Three further regressors, describing the time course of signal intensities of white matter, CSF, and the portion of the MR image outside the skull were also added. This procedure accounts for image intensity shifts due to hand movements within or near the magnetic field of the scanner (Verhagen et al., 2006). Finally, fMRI time series were high-pass filtered (cutoff 120 s). Temporal autocorrelation was modeled as a first-order autoregressive process.

+
+ + Group analyses +

Consistent effects across participants and between groups were tested using a random-effects multiple regression analysis that included six contrast images (approach–happy, approach–neutral, approach–angry, avoid–happy, avoid–neutral, avoid–angry) per participant. Together, these images represented the estimated cerebral effects from 12 conditions of the experimental design [group (PP, HC) × valence (happy, neutral, angry) × response (approach, avoid)]. Standardized log-transformed testosterone and standardized STAI state levels were included in the multiple regression analysis as condition-specific [group (PP, HC) × valence (happy, neutral, angry) × response (approach, avoid)] regressors, generating another 12 regressors per variable.

+

All analyses assessed the congruency effect, reflecting task-related differences of affect-incongruent (approach–angry, avoid–happy) versus affect-congruent trials (approach–happy, avoid–angry; Roelofs et al., 2009; Volman et al., 2011b). We considered two effects. First, to test for general effects of congruency, we performed an analysis on the congruency effect over both groups and for each group separately. When assessing the effects of one group explicitly, we also tested whether those effects were specific to that group and were significantly weaker in the other group (at p < 0.05 uncorrected) by masking the statistical map describing the congruency effect in the first group (using multiple comparisons correction, see below) with the statistical map describing the group × congruency contrast. Second, to test whether testosterone differentially modulated the control of emotionally relevant actions in the groups, we performed a group × congruency contrast on the regressor parametrizing interindividual differences in testosterone on task-related conditions. If such an interaction is present, the testosterone modulation on the congruency effect of each group separately is considered. In addition to whole-brain analyses, we used a volume of interest (VOI) on coordinates previously found to be modulated by testosterone during the congruency effect in healthy students (two 8-mm-radius spheres centered on the following MNI coordinates: x, −30; y, 58; and z, 2; and x, 32; y, 54; and z, 8; Volman et al., 2011b).

+

The reported activations are corrected for multiple comparisons using familywise error (FWE) correction. For whole-brain analyses, we made inferences at cluster level (FWE: p < 0.05, corresponding to a cluster size of >140 on the basis of intensity threshold, p < 0.001). For VOI analyses, we made inferences at voxel-level (FWE corrected, p < 0.05; Worsley et al., 1996; Friston, 1997). Anatomical inference is drawn by superimposing SPM showing significant signal changes on structural images of participants. For anatomical accuracy, we report only activation peaks in gray matter. Anatomical landmarks were identified using the atlas of Duvernoy et al. (1991). Brodmann areas (BAs) were assigned by superimposing significant SPM on the SPM anatomy toolbox (Eickhoff et al., 2005) and MRIcron template (http://www.mccauslandcenter.sc.edu/mricro/mricron/).

+
+
+ + Connectivity analyses +

The aim of the following analysis was to test whether inter-regional coupling of the aPFC (see Results) with the amygdala and other brain regions during the congruency effect was different between the groups and modulated by testosterone. To test for these effects, we used the psychophysiological interactions (PPIs) method (Friston et al., 1997). More specifically, we tested for significant differences between the regression coefficients of each voxel over the right aPFC during the affect-incongruent versus the affect-congruent conditions. To select voxels to be included in the VOI, we used the following anatomical constraints (Stephan et al., 2010): for each participant, selected voxels fell within a sphere with a radius of 4 mm around the peak voxel corresponding to the activated cluster of the congruency effect over both groups (coordinates: x, 30; y, 58; z, 14; see Results). Participant specific contrast images were generated describing the PPI between the time courses of the right aPFC VOI and affect-incongruent versus affect-congruent conditions. Group differences and testosterone modulations on task-related coupling between the aPFC and other regions were then assessed using a multiple regression design on participant-specific contrast images with their corresponding testosterone (log-transformed, standardized) and STAI state (standardized) levels as subject- and group-specific regressors. In addition to whole-brain analyses, we assessed significant voxel-level effects (FWE corrected for multiple comparisons, p < 0.05) within the amygdala, defined on the Automated Anatomical Labeling atlas (Tzourio-Mazoyer et al., 2002) using the WFU PickAtlas tool (Maldjian et al., 2003).

+
+
+ + Results + + Behavioral results +

Fifteen psychopathic criminals (PPs; PCL-R score of ≥26, according to European standards (Rasmussen et al., 1999; Hare, 2003; Hildebrand et al., 2004) and 19 HCs (for demographics, see Table 1) were included in the analyses. Participants performed the task accurately and consistently (error rates: PPs, 7.9%; HCs, 7.3%; omissions: PPs, 1.6%; HCs, 1.5%; undefined responses: PPs, 0.9%; HCs, 0.3%; Table 2).

+ + + +

RTs and error rates for each group and factor of the AA task

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Psychopathic offendersHCs
+ ApproachAvoidApproachAvoid
Errors (%) + + + +
Happy3.2 (0.9)8.9 (1.8)2.4 (0.8)7.7 (1.1)
Neutral6.1 (1.3)5.8 (1.1)7.1 (1.4)5.2 (1.0)
Angry10.1 (2.2)13.1 (2.1)9.6 (1.8)11.6 (1.8)
RT (ms) + + + +
Happy554 (25)625 (35)553 (23)603 (25)
Neutral666 (28)687 (31)639 (21)668 (24)
Angry630 (25)665 (33)620 (24)630 (23)
+ + +

Values are presented as the mean (SE).

+
+
+
+

A significant movement × valence interaction for the RTs indicated that, over groups, participants responded more slowly during affect-incongruent (approach–angry, avoid–happy) than during affect-congruent trials (approach–happy, avoid–angry; F(1,29) = 10.4, p = 0.003; Fig. 2). This congruency effect replicates the behavioral results from previous fMRI studies (Roelofs et al., 2009; Volman et al., 2011b, 2013). Furthermore, there were main effects of movement (F(1,29) = 26.3, p < 0.001) and valence (F(1,29) = 28.7, p < 0.001), reflecting the slowing of avoidance movements and responses to angry faces in general (Table 2). There were no significant effects involving group, including no main effect (p > 0.3). The congruency effect correlated positively (without corrections for multiple comparisons) with the PCL-R total score (p = 0.048, R = 0.517, respectively). Excluding anxiety from the analyses did not affect the outcomes. Moreover, when including the neutral conditions in the analyses, the movement × valence (happy, neutral, angry) interaction for RTs remained significant (F(1,28) = 5.5, p = 0.010), showing that neutral approach–avoidance effects are intermediary compared with happy and angry (Table 2).

+ + + +

Behavioral results. Mean RTs (±SEM) for the affect-congruent and affect-incongruent conditions of the AA task for the healthy control subjects and psychopathic offenders. The groups were significantly slower to provide affect-incongruent responses (approach–angry; avoid–happy) than affect-congruent responses (approach–happy; avoid–angry), with no significant group differences.

+ + +
+

For the error rates, the three-way ANCOVArm showed main effects of movement (F(1,29) = 27.5, p < 0.001), valence (F(1,29) = 25.9, p < 0.001), and testosterone (F(1,29) = 4.6, p = 0.040), and a valence × testosterone interaction (F(1,29) = 4.3, p = 0.047). There were no other significant effects for the error rates (p > 0.15).

+

Endogenous testosterone levels [median (SD): PPs, 101 pg/ml (70 pg/ml); HCs, 90 pg/ml (46 pg/ml)] and state anxiety levels [STAI mean (SD): PPs, 32 (8); HCs, 32 (5)] did not differ between groups (p > 0.4), and showed no correlations with psychopathy (PCL-R) scores or with each other (p > 0.1).

+
+ + fMRI results + + Multiple regression analyses +

To assess the two main questions of this study, we isolated cerebral structures showing stronger responses during affect-incongruent than affect-congruent trials (congruency effect), and cerebral structures in which the congruency effect was modulated by testosterone levels.

+

The results showed a significant congruency effect across groups in the aPFC [ROI analysis: MNI coordinates (x, y, z): (30, 58, 14) and (−30 58 10); pFWE = 0.001 and 0.036; t = 4.46 and 3.43; for further details, see Table 3]. As expected, this effect was driven by the healthy control group, and it was significantly weaker in the psychopathic offenders [pFWE = 0.001 and 0.040; t = 4.58 and 3.40, on the congruency effect in healthy control subjects masked implicitly by group (HC > PP) × congruency interaction]. The implicit masking demonstrates that the group × congruency interaction is also significant at puncorrected < 0.05 within the significant voxels corrected for multiple comparisons on the HC congruency effect. The psychopathy group showed no significant congruency effect in this region (pFWE > 0.3). There was also a significant congruency effect across groups in the right superior parietal lobule (whole-brain analysis); this effect was driven mainly by the psychopathy group (Table 3).

+ + + +

Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect)

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Anatomical regionPutative BASidex +y +z +Voxels (n)p valuet value
+ Whole-brain effects + + + + + + + + +
+ Congruency effect over groups + + + + + + + + +
Cuneus18R/L−16−9624634<0.0014.85
SPL/Superior occipital gyrus7/19L−30−76342540.0044.37
IFG45/47L−5222−102230.0074.37
Cuneus18R18−98161900.0164.52
+ Congruency effect for psychopathy group + + + + + + + + +
SPL/superior occipital gyrus7/19R/L8−8238925<0.0014.98
Angular gyrus39/19L−30−72343370.0014.35
Superior temporal gyrus42L−32−3262140.0094.51
SPL7R28−74461580.0344.58
Cerebellum + L−24−66−381460.0464.63
+ Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction + + + + + + + + +
aPFC10R305812391<0.0015.10
Supramarginal gyrus40R54−42543250.0014.66
Caudate nucleus + R101022730.0024.69
Putamen/Insula + L−346−101760.0225.22
Cerebellum + R18−76−381880.0165.09
+ Negative testosterone modulation of Congruency effect in psychopathy + + + + + + + + +
Supramarginal gyrus40R52−4054657<0.0015.32
Precentral/superior frontal gyrus6R/L62266471<0.0015.65
Caudate nucleus + R6642280.0064.28
+ VOI on bilateral aPFC + + + + + + + + +
+ Congruency effect over groups + 10R305814310.0014.46
+ 10L−30581050.0363.43
+ Congruency effect in healthy control subjects + 10R325814120.0014.58
+ 10L−3452420.0403.40
+ Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction + 10R305812145<0.0015.10
10L−24566150.0103.87
+ Negative testosterone modulation of congruency effect in psychopathy + 10R325610770.0024.34
10L−30588170.0153.74
+ + +

Coordinates are defined in MNI (x, y, z) space. The p values represent the FWE cluster-level corrected values for the whole-brain analyses and FWE voxel-level corrected values for the VOI analyses. IFG, Inferior frontal gyrus; L, left; R, right; SPL, superior parietal lobule.

+
+
+
+

Critically, testosterone modulated the congruency effect in the aPFC differently in psychopathic offenders and healthy control subjects (whole-brain analysis on testosterone × group × congruency: MINI coordinates (x, y, z): (30, 58, 12); pFWE < 0.001; t = 5.10; for all details, see Table 3). Post hoc analyses revealed that, in the psychopathy group, congruency effects decreased as testosterone levels increased [MNI coordinates (x, y, z): (32, 56, 10) and (−30, 58, 8); pFWE = 0.002 and 0.015; t = 4.34 and 3.74]. The modulatory effect of testosterone on congruency was absent in the healthy control subjects (pFWE ≥ 0.05; Fig. 3A–C). The whole-brain analysis also showed an effect in the right caudate nucleus and right inferior supramarginal gyrus, driven by reduced congruency effects as a function of testosterone in the psychopathy group (Fig. 3D–F; Table 3).

+ + + +

Testosterone modulations of the cerebral congruency effect in psychopathic offenders and healthy control subjects. A, D, Brain image showing testosterone-modulated congruency effects (affect-incongruent−affect-congruent) in the psychopathic offenders in the bilateral aPFC (A) and right supramarginal gyrus (D). B, E, Bar graphs showing the mean activation (±SEM) of the active voxels within the yellow circles per group. *pFWE < 0.05. ns, Not significant. C, F, Scatterplots showing the correlation of the mean activation of active voxels within the yellow circles with testosterone (log-transformed and standardized) for the healthy control group and the psychopathy group. The ROI activations are presented at p < 0.05, uncorrected for visualization purposes. There are no outliers [Mahalanobis distances D +2i < 4.2 (cutoff at p < 0.05; D = 7.74); Barnett and Lewis, 1978; Stevens, 1996]. Healthy control subjects show an increased aPFC activity for the congruency effect and no modulation by testosterone, while in psychopathic offenders endogenous testosterone levels modulate the activity of the aPFC and right supramarginal gyrus.

+ + +
+
+ + Effective connectivity analyses +

Given the relevance of aPFC–amygdala connectivity for implementing emotional control as evoked by the AA task (Volman et al., 2011a, 2013), we assessed whether psychopathy also resulted in altered connectivity along that neural pathway. Connectivity analyses using the right aPFC [4-mm-radius sphere; central voxel from main analysis (MNI coordinates: x, 30; y, 58; z, 14)] as the seed region on the congruency effect indicated a significant group difference (PP > HC) with the right amygdala (Fig. 4A,B; ROI analysis; extent, 3 voxels; t = 3.82; pFWE = 0.027; MNI coordinates of local maxima: x, 32; y, 0; z, −16). When testing effects for both groups separately, healthy control subjects showed a significant negative coupling between the right aPFC and amygdala (ROI analysis; extent: 3 voxels, t = 3.70; pFWE = 0.036; MNI coordinates of local maxima: x, 32; y, 0; z, −16), while psychopathic offenders showed no differential connectivity effect. Post hoc testing on right amygdala voxels showing the group interaction (threshold, p < 0.05 FWE) indicated a significant positive correlation with testosterone over both groups (ROI analysis; extent, 1 voxel; t = 2.29; pFWE = 0.029; MNI coordinates of local maxima: x, 32; y, 2; z, −16). There was no correlation between aPFC–amygdala connectivity and the PCL-R scores (p > 0.2).

+ + + +

Group difference on congruency-related aPFC–amygdala connectivity. A, Brain images illustrating the congruency-related modulation of connectivity between the right aPFC (yellow circle, axial slice) and the right amygdala (coronal slice) for the congruency contrast. The activations are presented at p < 0.05, uncorrected for visualization purposes. B, Bar graph visualizing the strength of the congruency-specific change (±SEM) in aPFC–amygdala connectivity for the healthy control subjects and psychopathic offenders. There is a significant negative aPFC–amygdala coupling in the healthy control subjects, which is not present in the psychopathic offenders.

+ + +
+
+
+
+ + Discussion +

This study indicates that psychopathic offenders show reduced aPFC activity as well as less aPFC–amygdala connectivity during the control of emotional behavior. Emotional control was measured by comparing affect-incongruent and affect-congruent approach–avoidance responses to emotional faces (congruency effect on the AA task; Roelofs et al. 2009). When healthy control subjects exerted emotional control, reaction times, aPFC activity, and aPFC–amygdala anticorrelations increased, confirming previous observations (Volman et al., 2011b, 2013). In contrast, psychopathic offenders did not show this typical control-related pattern of aPFC activity and connectivity. In addition, these effects were significantly modulated by endogenous testosterone. Namely, psychopathic individuals with relatively lower testosterone levels showed a neural activity and connectivity pattern that resembled the findings in healthy control subjects, while this pattern was absent in those with higher testosterone levels. This indicates that especially psychopathic individuals with high testosterone levels have less prefrontal regulation of amygdala-driven emotional actions when the control of emotional behavior is required.

+ + Emotional control in psychopathy +

Imaging studies have illustrated an association between psychopathy and altered processing of fear, including altered amygdala responses (Blair, 2010; Moul et al., 2012; Decety et al., 2015), attentional deficits for peripheral stimuli (Baskin-Sommers et al., 2011), and moral/empathic insensitivity (Decety et al., 2013; Marsh and Cardinale, 2012). However, psychopathic offenders also show clear impulsivity problems (Hare, 2003), for example, when control is required during emotionally provoking situations. To address this relatively unexplored but crucial component of criminal psychopathy, we used a paradigm requiring rule-driven control of emotional actions. With this paradigm, it was possible to move beyond simple motor inhibition and to target the flexible control of emotionally driven action tendencies.

+

First, the aPFC (also called BA 10) was less active in psychopathic offenders as a function of testosterone. The aPFC is a region crucial for the control of social emotional behavior. When aPFC functioning is temporarily disrupted, participants have increased difficulty in overriding emotional tendencies with rule-driven behavior (Volman et al., 2011a). Moreover, the aPFC seems especially important for integrating and coordinating multiple cognitive processes to facilitate response selection (Ramnani and Owen, 2004; Haggard, 2008). For example, transcranial magnetic stimulation-induced reduction of aPFC functioning during the control of emotional behavior decreased activity in brain areas associated with rule selection (posterior parietal cortex), while both amygdala activity and automatic action tendencies increased (Volman et al., 2011a). The current study indicates that psychopathic individuals with especially high testosterone levels recruited the aPFC less when the control of emotional responses was needed. This finding suggests that they have reduced coordination of rule-based behavior with emotional information.

+

Second, connectivity between the aPFC and amygdala also differed significantly between groups. Healthy control subjects showed a negative aPFC–amygdala coupling during the control of social emotional behavior, whereas psychopathic individuals showed no significant coupling between these regions. Evidence of anatomical connectivity alterations between these regions in psychopathic individuals and the relation of that tract to social emotional behavior modifications support these findings (Von Der Heide et al., 2013). Although these results cannot resolve the direction of these connectivity effects, a previous study (Volman et al., 2013) using this paradigm showed an effective connectivity modulation of emotional control on the connection from aPFC to amygdala. Also, animal studies (Quirk and Gehlert, 2003) suggest strong prefrontal inhibitory connections that control automatic amygdala responses. The absence of this aPFC–amygdala coupling in psychopathic offenders suggests that in this group the aPFC has a reduced ability to inhibit amygdala-driven responses. This study used subtle emotional provocations, but stronger emotional events result in stronger amygdala responses, increasing the bias for automatic emotional behavior (Quirk and Gehlert, 2003). A lack of prefrontal control likely reduces the ability to inhibit these biases and lead to an increased expression of automatic emotional actions even when they are not beneficial (Quirk and Gehlert, 2003; Volman et al., 2011a).

+

Testosterone administration studies also illustrated a decoupling between the prefrontal cortex and the amygdala, suggesting that testosterone reduces the communication between the PFC and amygdala (Eisenegger et al., 2011; Van Wingen et al., 2011; Bos et al., 2012) and, within the AA task, reduces top-down control. The association between testosterone levels and enhanced social aggression and dominance seeking, and reduced impulse control in the general population (Van Wingen et al., 2011; Montoya et al., 2012; Carré et al., 2013) supports the relevance of testosterone in this process. Even amygdala responses to angry faces have recently been found to be enhanced after testosterone administration and in psychopathic individuals (Van Wingen et al., 2011; Carré et al., 2013; Radke et al., 2015). There is a clear association between testosterone and aggression after provocation, which has been related to reduced activity in the orbital frontal cortex, a region just ventral of the aPFC (Mehta and Beer, 2010). Interestingly, psychopathic offenders with lower testosterone levels displayed a pattern similar to that in healthy control subjects, while the psychopathic individuals with high testosterone levels showed less aPFC activity and aPFC–amygdala coupling. This could provide a potential vulnerability factor explaining the difference between the goal-directed “successful” psychopath and the “unsuccessful” psychopath with reduced impulse control (Gao and Raine, 2010; Anderson and Kiehl, 2012). We hypothesize that especially psychopathic individuals with high testosterone levels fail to inhibit amygdala-driven action tendencies using the aPFC during the control of emotional behavior. +

+

Endogenous testosterone levels also modulated control-related activity in the supramarginal gyrus and caudate nucleus of the psychopathy group. The supramarginal gyrus was previously found to be involved during emotional control on the AA task in a healthy student sample (Volman et al., 2011b). Previous work indicated that it plays an important role in action organization (Jubault et al., 2007), and that psychopathic individuals show reduced supramarginal gyrus activity compared with control subjects when reasoning about other people’s emotional state (Sommer et al., 2010). The current findings, emphasizing the role of supramarginal gyrus during emotional control in psychopathic offenders with low testosterone levels, could indicate the facilitation of action preparation in trials with affect-incongruent stimulus–response mapping. The caudate nucleus is important for incorporating predicted action outcome, when selecting the most beneficial behavioral goal (Grahn et al., 2008), and has previously found to be larger in psychopathy (Glenn et al., 2010). In light of these findings, our results suggest that psychopathic offenders with low endogenous testosterone levels, as opposed to those with high testosterone levels, have more interference of automatic action tendencies and outcomes associated with the facial emotions (e.g., approach–happy) that are opposite to the required actions during affect-incongruent trials (Grahn et al., 2008).

+
+ + Interpretational issues +

Individuals with psychopathy have been suggested to have difficulty recognizing emotional expressions. However, this impairment seems quite specific to fear, rather than the emotional expressions used here (anger and happiness; Marsh and Blair, 2008; Von Borries et al., 2012). Furthermore, the groups assessed in this study made comparable numbers of errors, suggesting that psychopathic offenders had no special difficulty in recognizing the clear emotional expressions used in this study.

+

This study used a relatively subtle manipulation to target the emotional control system. The rationale of this choice was to detect neural vulnerability markers without affecting behavioral performance. Psychopathic offenders performing a more salient behavioral version of the AA task showed reduced avoidance of angry faces (Von Borries et al., 2012). In this study, angry faces evoked numerically similar behavioral effects (Table 2) and, additionally, aPFC effects (post hoc inspection of extracted parameters). Although these observations could be interpreted as a sign that psychopathic offenders have a tendency to approach angry faces, those observations were not statistically significant between groups [behavioral and aPFC group effects on angry faces: p > 0.2; pFWE = 0.271; z = 2.54, on the angry–congruency effect in healthy control subjects masked implicitly by group (HC > PP) × angry–congruency interaction]. Future investigation is needed to directly test whether more provocative paradigms induce specific effects for angry faces. A previous study (Volman et al., 2013) using this fMRI task in participants with genetic susceptibility for developing aggressive disorders, also found no group-specific behavioral effects. That study suggested that alterations of the aPFC–amygdala pathway might reflect a vulnerability factor for psychopathologies.

+

Previously, endogenous testosterone modulated the aPFC and aPFC–amygdala coupling in a sample of healthy students (Volman et al., 2011b). In that study, a different demographic group of healthy control subjects similarly showed a testosterone modulation of aPFC–amygdala coupling, but no testosterone modulation of aPFC activity. This difference in the strength of testosterone-modulatory effects might be related to between-group differences in age (mean healthy control subjects, 41; mean students, 22; Peper et al., 2011), educational level (staff of forensic psychiatric institute vs university students), or general anxiety [STAI trait, lower in healthy control subjects of the current study; mean (SD): 29 (4.4) and 34 (6.9), respectively; t(37) = −2.605; p = 0.014]. A limitation of this study is the modest sample size. Our focus to exclude moderating factors of comorbid disorders (except antisocial personality disorder) and recent drug use has the advantage that the sample is relatively homogeneous, but future studies using larger samples are needed for replication and to define subsamples.

+
+ + Conclusion +

Psychopathic offenders showed reduced aPFC activity and aPFC–amygdala connectivity during control of emotional actions, suggesting a decreased coordination of emotional information during rule-driven behavior. Moreover, endogenous testosterone modulated the involvement of these neural mechanisms. Psychopathic offenders with high testosterone levels showed less involvement of the aPFC, aPFC–amygdala connectivity, supramarginal gyrus, and caudate nucleus, whereas psychopathic individuals with low testosterone levels recruited the aPFC in a fashion similar to that of healthy control subjects. These findings suggest that a lack of prefrontal control during emotional actions may explain enhanced impulsivity in psychopathic offenders during emotionally provoking situations. They outline a neuroendocrine model underlying impulsive emotional behavior in psychopathy and support the relevance of assessing a potential imbalance in testosterone function to guide treatment. It remains to be seen whether these neuroendocrine alterations of emotional control are also present in highly impulsive or antisocial individuals.

+
+
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diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000.csv new file mode 100644 index 0000000..b4a7369 --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000.csv @@ -0,0 +1,7 @@ +Unnamed: 0,Psychopathic offenders(n = 15),HCs(n = 19),p value +Age,37.8 (7.9),40.7 (10.3),0.368 +IQ,101 (10),102 (9),0.761 +Handedness,50.7 (81),59.2 (62),0.729 +PCL-R total,30.4 (3.5),, +PCL-R F1,12.1 (2.6),, +PCL-R F2,14.1 (2.3),, diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000_info.json new file mode 100644 index 0000000..4d4ae78 --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_000_info.json @@ -0,0 +1 @@ +{"table_id": "T1", "table_label": "Table 1:", "table_caption": "Demographical data", "table_foot": "Values are presented as the mean (SD), unless otherwise indicated. F1, factor 1; F2, factor 2.", "n_header_rows": 1, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001.csv b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001.csv new file mode 100644 index 0000000..e75ba72 --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001.csv @@ -0,0 +1,10 @@ +Unnamed: 0_level_0,Psychopathic offenders,Psychopathic offenders,HCs,HCs +Unnamed: 0_level_1,Approach,Avoid,Approach,Avoid +Errors (%),,,, +Happy,3.2 (0.9),8.9 (1.8),2.4 (0.8),7.7 (1.1) +Neutral,6.1 (1.3),5.8 (1.1),7.1 (1.4),5.2 (1.0) +Angry,10.1 (2.2),13.1 (2.1),9.6 (1.8),11.6 (1.8) +RT (ms),,,, +Happy,554 (25),625 (35),553 (23),603 (25) +Neutral,666 (28),687 (31),639 (21),668 (24) +Angry,630 (25),665 (33),620 (24),630 (23) diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001_info.json b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001_info.json new file mode 100644 index 0000000..cb4eedf --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_001_info.json @@ -0,0 +1 @@ +{"table_id": "T2", "table_label": "Table 2:", "table_caption": "RTs and error rates for each group and factor of the AA task", "table_foot": "Values are presented as the mean (SE).", "n_header_rows": 2, "table_data_file": "table_001.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002.csv b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002.csv new file mode 100644 index 0000000..20751bb --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002.csv @@ -0,0 +1,32 @@ +Anatomical region,Putative BA,Side,x,y,z,Voxels (n),p value,t value +Whole-brain effects,,,,,,,, +Congruency effect over groups,,,,,,,, +Cuneus,18,R/L,−16,−96,24,634.0,<0.001,4.85 +SPL/Superior occipital gyrus,7/19,L,−30,−76,34,254.0,0.004,4.37 +IFG,45/47,L,−52,22,−10,223.0,0.007,4.37 +Cuneus,18,R,18,−98,16,190.0,0.016,4.52 +Congruency effect for psychopathy group,,,,,,,, +SPL/superior occipital gyrus,7/19,R/L,8,−82,38,925.0,<0.001,4.98 +Angular gyrus,39/19,L,−30,−72,34,337.0,0.001,4.35 +Superior temporal gyrus,42,L,−32,−32,6,214.0,0.009,4.51 +SPL,7,R,28,−74,46,158.0,0.034,4.58 +Cerebellum,,L,−24,−66,−38,146.0,0.046,4.63 +Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction,,,,,,,, +aPFC,10,R,30,58,12,391.0,<0.001,5.1 +Supramarginal gyrus,40,R,54,−42,54,325.0,0.001,4.66 +Caudate nucleus,,R,10,10,2,273.0,0.002,4.69 +Putamen/Insula,,L,−34,6,−10,176.0,0.022,5.22 +Cerebellum,,R,18,−76,−38,188.0,0.016,5.09 +Negative testosterone modulation of Congruency effect in psychopathy,,,,,,,, +Supramarginal gyrus,40,R,52,−40,54,657.0,<0.001,5.32 +Precentral/superior frontal gyrus,6,R/L,6,22,66,471.0,<0.001,5.65 +Caudate nucleus,,R,6,6,4,228.0,0.006,4.28 +VOI on bilateral aPFC,,,,,,,, +Congruency effect over groups,10,R,30,58,14,31.0,0.001,4.46 +,10,L,−30,58,10,5.0,0.036,3.43 +Congruency effect in healthy control subjects,10,R,32,58,14,12.0,0.001,4.58 +,10,L,−34,52,4,2.0,0.040,3.4 +Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction,10,R,30,58,12,145.0,<0.001,5.1 +Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction,10,L,−24,56,6,15.0,0.010,3.87 +Negative testosterone modulation of congruency effect in psychopathy,10,R,32,56,10,77.0,0.002,4.34 +Negative testosterone modulation of congruency effect in psychopathy,10,L,−30,58,8,17.0,0.015,3.74 diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002_info.json b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002_info.json new file mode 100644 index 0000000..70d5cb8 --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/table_002_info.json @@ -0,0 +1 @@ +{"table_id": "T3", "table_label": "Table 3:", "table_caption": "Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect)", "table_foot": "Coordinates are defined in MNI (x, y, z) space. The p values represent the FWE cluster-level corrected values for the whole-brain analyses and FWE voxel-level corrected values for the VOI analyses. IFG, Inferior frontal gyrus; L, left; R, right; SPL, superior parietal lobule.", "n_header_rows": 1, "table_data_file": "table_002.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml new file mode 100644 index 0000000..7904b9d --- /dev/null +++ b/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml @@ -0,0 +1,2 @@ + +4745181268780574745181eN-NWR-0107-1510.1523/ENEURO.0107-15.2016T1Table 1:Demographical dataValues are presented as the mean (SD), unless otherwise indicated. F1, factor 1; F2, factor 2.

Demographical data

Psychopathic offenders(n = 15)HCs(n = 19)p value
Age37.8 (7.9)40.7 (10.3)0.368
IQ101 (10)102 (9)0.761
Handedness50.7 (81)59.2 (62)0.729
PCL-R total30.4 (3.5)
PCL-R F112.1 (2.6)
PCL-R F214.1 (2.3)

Values are presented as the mean (SD), unless otherwise indicated. F1, factor 1; F2, factor 2.

Table 1:
Demographical data
Psychopathic offenders(n = 15)HCs(n = 19)p value
Age37.8 (7.9)40.7 (10.3)0.368
IQ101 (10)102 (9)0.761
Handedness50.7 (81)59.2 (62)0.729
PCL-R total30.4 (3.5)
PCL-R F112.1 (2.6)
PCL-R F214.1 (2.3)
Values are presented as the mean (SD), unless otherwise indicated. F1, factor 1; F2, factor 2.
T2Table 2:RTs and error rates for each group and factor of the AA taskValues are presented as the mean (SE).

RTs and error rates for each group and factor of the AA task

Psychopathic offendersHCs
ApproachAvoidApproachAvoid
Errors (%)
Happy3.2 (0.9)8.9 (1.8)2.4 (0.8)7.7 (1.1)
Neutral6.1 (1.3)5.8 (1.1)7.1 (1.4)5.2 (1.0)
Angry10.1 (2.2)13.1 (2.1)9.6 (1.8)11.6 (1.8)
RT (ms)
Happy554 (25)625 (35)553 (23)603 (25)
Neutral666 (28)687 (31)639 (21)668 (24)
Angry630 (25)665 (33)620 (24)630 (23)

Values are presented as the mean (SE).

Table 2:
RTs and error rates for each group and factor of the AA task
Psychopathic offendersHCs
ApproachAvoidApproachAvoid
Errors (%)
Happy3.2 (0.9)8.9 (1.8)2.4 (0.8)7.7 (1.1)
Neutral6.1 (1.3)5.8 (1.1)7.1 (1.4)5.2 (1.0)
Angry10.1 (2.2)13.1 (2.1)9.6 (1.8)11.6 (1.8)
RT (ms)
Happy554 (25)625 (35)553 (23)603 (25)
Neutral666 (28)687 (31)639 (21)668 (24)
Angry630 (25)665 (33)620 (24)630 (23)
Values are presented as the mean (SE).
T3Table 3:Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect)Coordinates are defined in MNI (x, y, z) space. The p values represent the FWE cluster-level corrected values for the whole-brain analyses and FWE voxel-level corrected values for the VOI analyses. IFG, Inferior frontal gyrus; L, left; R, right; SPL, superior parietal lobule.

Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect)

Anatomical regionPutative BASidexyzVoxels (n)p valuet value
Whole-brain effects
Congruency effect over groups
Cuneus18R/L−16−9624634<0.0014.85
SPL/Superior occipital gyrus7/19L−30−76342540.0044.37
IFG45/47L−5222−102230.0074.37
Cuneus18R18−98161900.0164.52
Congruency effect for psychopathy group
SPL/superior occipital gyrus7/19R/L8−8238925<0.0014.98
Angular gyrus39/19L−30−72343370.0014.35
Superior temporal gyrus42L−32−3262140.0094.51
SPL7R28−74461580.0344.58
CerebellumL−24−66−381460.0464.63
Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction
aPFC10R305812391<0.0015.10
Supramarginal gyrus40R54−42543250.0014.66
Caudate nucleusR101022730.0024.69
Putamen/InsulaL−346−101760.0225.22
CerebellumR18−76−381880.0165.09
Negative testosterone modulation of Congruency effect in psychopathy
Supramarginal gyrus40R52−4054657<0.0015.32
Precentral/superior frontal gyrus6R/L62266471<0.0015.65
Caudate nucleusR6642280.0064.28
VOI on bilateral aPFC
Congruency effect over groups10R305814310.0014.46
10L−30581050.0363.43
Congruency effect in healthy control subjects10R325814120.0014.58
10L−3452420.0403.40
Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction10R305812145<0.0015.10
10L−24566150.0103.87
Negative testosterone modulation of congruency effect in psychopathy10R325610770.0024.34
10L−30588170.0153.74

Coordinates are defined in MNI (x, y, z) space. The p values represent the FWE cluster-level corrected values for the whole-brain analyses and FWE voxel-level corrected values for the VOI analyses. IFG, Inferior frontal gyrus; L, left; R, right; SPL, superior parietal lobule.

Table 3:
Clusters showing significantly larger activity for the affect-incongruent vs the affect-congruent conditions (emotion-control effect)
Anatomical regionPutative BASidexyzVoxels (n)p valuet value
Whole-brain effects
Congruency effect over groups
Cuneus18R/L−16−9624634<0.0014.85
SPL/Superior occipital gyrus7/19L−30−76342540.0044.37
IFG45/47L−5222−102230.0074.37
Cuneus18R18−98161900.0164.52
Congruency effect for psychopathy group
SPL/superior occipital gyrus7/19R/L8−8238925<0.0014.98
Angular gyrus39/19L−30−72343370.0014.35
Superior temporal gyrus42L−32−3262140.0094.51
SPL7R28−74461580.0344.58
CerebellumL−24−66−381460.0464.63
Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction
aPFC10R305812391<0.0015.10
Supramarginal gyrus40R54−42543250.0014.66
Caudate nucleusR101022730.0024.69
Putamen/InsulaL−346−101760.0225.22
CerebellumR18−76−381880.0165.09
Negative testosterone modulation of Congruency effect in psychopathy
Supramarginal gyrus40R52−4054657<0.0015.32
Precentral/superior frontal gyrus6R/L62266471<0.0015.65
Caudate nucleusR6642280.0064.28
VOI on bilateral aPFC
Congruency effect over groups10R305814310.0014.46
10L−30581050.0363.43
Congruency effect in healthy control subjects10R325814120.0014.58
10L−3452420.0403.40
Negative testosterone modulation of group (psychopathic offenders > healthy control subjects) × congruency interaction10R305812145<0.0015.10
10L−24566150.0103.87
Negative testosterone modulation of congruency effect in psychopathy10R325610770.0024.34
10L−30588170.0153.74
Coordinates are defined in MNI (x, y, z) space. The p values represent the FWE cluster-level corrected values for the whole-brain analyses and FWE voxel-level corrected values for the VOI analyses. IFG, Inferior frontal gyrus; L, left; R, right; SPL, superior parietal lobule.
\ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/identifiers.json b/tests/data/sample_inputs/Lb3HDCyzoerL/identifiers.json deleted file mode 100644 index edbf604..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/identifiers.json +++ /dev/null @@ -1 +0,0 @@ -{"pmid": "29113357", "doi": "10.18632/oncotarget.20895", "pmcid": "PMC5655252"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/coordinates.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/coordinates.csv deleted file mode 100644 index f662127..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/coordinates.csv +++ /dev/null @@ -1,21 +0,0 @@ -table_id,table_label,table_caption,table_number,x,y,z,p_value,region,size,statistic,groups -T2,Table 2,,,-48.0,18.0,0.0,,,,, -T2,Table 2,,,4.0,-14.0,6.0,,,,, -T2,Table 2,,,-4.0,12.0,44.0,,,,, -T2,Table 2,,,54.0,16.0,16.0,,,,, -T2,Table 2,,,-14.0,-44.0,-24.0,,,,, -T4,Table 4,,,46.0,14.0,2.0,,,,, -T4,Table 4,,,-40.0,0.0,-2.0,,,,, -T4,Table 4,,,2.0,50.0,8.0,,,,, -T4,Table 4,,,2.0,-18.0,-8.0,,,,, -T4,Table 4,,,-50.0,20.0,22.0,,,,, -T4,Table 4,,,-8.0,-34.0,-18.0,,,,, -T4,Table 4,,,12.0,-2.0,20.0,,,,, -T5,Table 5,,,2.0,-14.0,6.0,,,,, -T5,Table 5,,,-44.0,12.0,4.0,,,,, -T5,Table 5,,,-6.0,-4.0,14.0,,,,, -T5,Table 5,,,-32.0,12.0,10.0,,,,, -T5,Table 5,,,-32.0,22.0,0.0,,,,, -T5,Table 5,,,0.0,-8.0,12.0,,,,, -T5,Table 5,,,54.0,12.0,16.0,,,,, -T5,Table 5,,,-2.0,24.0,56.0,,,,, diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/metadata.json b/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/metadata.json deleted file mode 100644 index 21afca9..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/metadata.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "title": "Brain gray matter abnormalities in progressive supranuclear palsy revisited", - "authors": "Pan, PingLei; Liu, Yi; Zhang, Yang; Zhao, Hui; Ye, Xing; Xu, Yun", - "journal": "Oncotarget", - "keywords": "progressive supranuclear palsy\nvoxel-based morphometry\nmeta-analysis\ncortical-subcortical circuitries\nseed-based d mapping\n", - "abstract": " \nWhole-brain voxel-based morphometry (VBM) studies of progressive supranuclear palsy (PSP) have demonstrated heterogeneous findings regarding gray matter (GM) abnormalities. Here, we used Seed-based d Mapping, a coordinate-based meta-analytic approach to identify consistent regions of GM anomalies across studies of PSP. Totally, 18 original VBM studies, comprising 284 patients with PSP and 367 healthy controls were included. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the frontal motor cortices, medial (including anterior cingulate cortex) and lateral frontal cortices, insula, superior temporal gyrus, striatum (putamen and caudate nucleus), thalamus, midbrain, and anterior cerebellum. Our study further suggests that many confounding factors, such as age, male ratio, motor severity, cognitive impairment severity, and illness duration of PSP patients, and scanner field-strength, could contribute to the heterogeneity of GM alterations in PSP across studies. Our comprehensive meta-analysis demonstrates a specific neuroanatomical pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries that mediate vertical supranuclear gaze palsy, motor disabilities (postural instability with falls and parkinsonism), and cognitive-behavioral disturbances. Confounding factors merit attention in future studies. \n ", - "publication_year": 2017, - "coordinate_space": "MNI", - "license": "http://creativecommons.org/licenses/by/3.0/", - "text": true -} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt b/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt deleted file mode 100644 index 437be6a..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/processed/pubget/text.txt +++ /dev/null @@ -1,107 +0,0 @@ - -## INTRODUCTION - -Progressive supranuclear palsy (PSP) is a clinical syndrome characterized mainly by early postural instability with falls, vertical supranuclear gaze palsy, parkinsonism, and cognitive-behavioral disturbances that lead to significant disabilities with a mean survival of 6.38 years [ – ]. PSP is a rapidly progressive neurodegenerative disorder, pathologically confirmed by the accumulation of tau protein and neuropil threads in cortical and subcortical structures [ , ]. Several clinical subtypes of PSP have been identified, of which the classic Richardson's syndrome (PSP-RS) and the PSP-parkinsonism variant (PSP-P) are the most common [ ]. No effective treatments are available for PSP [ ]. Its differential diagnosis from other parkinsonian disorders is critical but presents challenges in clinical practice, especially in the early disease stages [ , ]. Despite impressive advances in understanding its pathophysiology, the reliably validated biomarkers for the ante-mortem diagnosis and the prognosis of PSP have not yet been established [ , , , ]. - -Major improvements in modern magnetic resonance imaging (MRI) techniques increase our ability to identify brain structural alterations in vivo that shed light on the neuroanatomical basis of PSP and hold promise for its diagnosis [ , – ]. Midbrain atrophy is a hallmark of PSP [ , ]. Recent evidence from whole-brain voxel-based morphometry (VBM) studies in PSP has additionally demonstrated gray matter (GM) atrophy in a number of brain regions, such as the thalamus, basal ganglia, insula, frontal cortices, temporal cortices, parietal cortices, and cerebellum [ – ]. Compared with conventional MRI investigations that draw regions of interest (ROIs) for morphometric comparisons, VBM is a hypothesis-free analytic tool to quantify regional structural differences between groups at a whole-brain level [ ]. VBM has been widely used in neurodegenerative disorders [ – ]. Despite the strengths, inconsistent results across different VBM studies were reported [ ]. Shi, et al. in 2012, Shao, et al. in 2013, and Yu, et al. in 2014 thus conducted three coordinate-based meta-analyses to test the consistency of GM changes in PSP, which included nine, nine, and 12 VBM studies, respectively [ – ]. However, these meta-analyses had several limitations. First, these meta-analyses did not examine the confounding variables, such as age, gender, disease duration, and symptom severity that potentially lead to heterogeneity of the structural alterations associated with PSP [ ]. Second, the quantitative voxel-based meta-analytic tools have been modified [ – ]. Several complementary analyses, such as jackknife sensitivity, heterogeneity, and publication bias analyses could be further performed to explore the robustness of the findings [ , ]. Third, the numbers of VBM studies included in these meta-analyses were limited. To achieve sufficient power for moderate effects, Eickhoff and co-workers recently recommended that 17 or more experiments were needed for a coordinate-based meta-analysis to detect moderately sized effects [ ]. In recent two years, we identified six more VBM studies of PSP eligibly for the meta-analysis. As such, results from previous meta-analyses cannot be considered conclusive and will need replication in a more exhaustive meta-analysis. - -Against this background, we aimed to conduct an updated meta-analysis based on 18 whole-brain VBM studies in PSP using a modified Seed-based d Mapping (SDM) approach to obtain more accurate results. In addition, analyses of jackknife sensitivity, heterogeneity, publication bias and meta-regression were comprehensively performed to examine the robustness and replicability of GM abnormalities associated with PSP. - - -## RESULTS - -### Characteristics of included studies - -Figure presents the flow diagram for inclusion/exclusion of studies in the meta-analysis. Totally, 18 original studies reporting GM differences between 284 patients with PSP and 367 healthy controls were included in this meta-analysis [ – ]. Across the 18 studies, one study included all pathologically confirmed patients with nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) and PSP (nfvPPA-PSP) [ ] and another two studies included some pathologically confirmed patients with PSP in the samples [ , ]. Patients with PSP in the remaining 15 studies were clinically diagnosed. Three of the 17 studies explicitly indicated that the samples included two subtypes of PSP, PSP-RS and PSP-P [ , , ]. Patients in the remaining 14 of 17 studies were clinically diagnosed based on the NINDS-SPSP clinical criteria [ , ], which has over 95% sensitivity and specificity in diagnosing PSP-RS [ ]. No significant differences between patients with PSP and healthy controls regarding mean age (standardized mean difference = 0.17; 95% confidence interval [CI] = -0.003 to 0.344, z = 0.61, p = 0.055) or gender distribution (relative risk = 1.12, 95% CI = 0.974 to 1.289, z = 0.159, p = 0.112) were observed. Mean Unified Parkinson's Disease Rating Scale-motor examination (UPDRS-III) score in 11/18 studies (rang from 20.4 to 52.9), Hoehn and Yahr disability scale (H&Y) in 7/18 studies (rang from 2.6 to 3.8), illness duration in 15/18 studies (rang from 2.5 years to 4.8 years), Mini-Mental State Examination (MMSE) score in 13/18 studies (rang from 21 to 28), and Frontal Assessment Battery (FAB) examination in 6/18 studies (rang from 7.81 to 12.9) were reported. Nine out of the 18 studies were conducted on 1.5T MRI systems and 8/18 studies were on 3.0T systems. One study used either a 1.5T or a 3.0T MRI system. 13 out of the 18 studies reported the corrected results and the remaining 5 studies used the uncorrected thresholds. All the studies included used Statistical Parametric Mapping (SPM) softwares for imaging analyses. - Flowchart to identify the eligible studies for the meta-analysis - Key: PSP, Progressive Supranuclear Palsy; GM, Gray Matter; VBM, Voxel-Based Morphometry; ROI, Region Of Interest. - -The quality score of each study included in this meta-analysis was not less than 8 (a maximum score of 10 for each study), which indicates the high rigour of these studies. The list, demographic, clinical and imaging characteristics, and scores of quality assessment of the included studies are presented in Table . - Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis -Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; , a maximum score of 10 for each study. - - -### Main voxel-wise meta-analysis - -As shown in Figure , the voxel-wise meta-analysis identified significant GM reductions in the left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and orbitofrontal cortex (OFC), in the bilateral thalamus extending to the midbrain and caudate nucleus, in the bilateral anterior cingulate cortex (ACC) extending to the supplementary motor areas (SMA) and pre-SMA, superior medial frontal cortex, and medial OFC, in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex), and in the left anterior cerebellum (lobule III/IV/V) in patients with PSP compared with healthy controls. In contrast, no significant GM increases were observed in patients with PSP relative to healthy controls. Details of the results are summarized in Table . - Meta-analytic results of gray matter reductions in patients with PSP compared to healthy controls -Key: (A) , Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/ orbitofrontal cortex; (B) , Right/Left thalamus/midbrain/caudate nucleus; (C) , Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; (D) , Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); (E) , Left anterior cerebellum (lobule III/IV/V); PSP, Progressive Supranuclear Palsy; HC, healthy controls; SDM, Seed-based d Mapping. The color bar indicates the maximum and the minimum SDM-Z values. - GM reductions in patients with PSP compared to healthy controls -Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area. - -The subgroup analysis of 14 VBM studies that patients were suggestive of PSP-RS showed that the results remained largely unchanged. - - -### Supplemental analyses - -The jackknife sensitivity analysis revealed that regions of GM reductions in left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and OFC, in the bilateral thalami extending to the midbrain and caudate nucleus, in the bilateral ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex) in patients with PSP relative to healthy controls were replicable in all 18 studies. The region of GM reductions in the left anterior cerebellum (lobule III/IV/V) in patients with PSP relative to healthy controls was replicable in 15 studies (Table ). - Jackknife sensitivity analysis -Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported. - -The heterogeneity analysis revealed that there is significant between-study variability of GM differences in patients with PSP relative to healthy controls in the right inferior frontal gyrus extending to the insula and superior temporal gyrus, in the left insula extending to the superior temporal gyrus, in the bilateral ACC extending to the medial OFC, in the bilateral thalamus, in the left inferior frontal gyrus, in the left cerebellum (lobule III), and in the right caudate nucleus (Table ). - Regions of GM heterogeneity from the SDM analysis -Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex. - -No publication biases were detected in the regions obtained from the main voxel-wise meta-analysis as revealed by the symmetrical funnel plots (Figure ) and statistically non-significant Egger's tests (Table ). - Funnel plots of the peak coordinates of gray matter abnormalities in progressive supranuclear palsy -Meta-regression analysis revealed that the PSP group with older mean age (available from 17 studies) exhibited more GM reductions in the bilateral thalamus extending to the midbrain (Figure ) and in the left insula extending to the inferior frontal gyrus (Figure ). Higher male ratio of patients in the PSP group (available from 17 studies) was associated with more GM reductions in the left caudate nucleus extending to the thalamus (Figure ). Higher average UPDRS-III score (Figure ) or lower mean MMSE score (Figure ) in the PSP group (available from 11 and 13 studies, respectively) correlated with more GM reductions in the left insula. Meta-regression analysis indicated that longer mean illness duration of the PSP group (available from 15 studies) was associated with more GM reductions in the Left caudate nucleus extending to bilateral thalami (Figure ). Higher scanner field-strength in VBM studies tended to detect more GM atrophy in the right inferior frontal gyrus (Figure ) and in the left SMA (Figure ). Findings of these meta-regression analyses are presented in Table and the regression lines are shown in Figure . - Results of the meta-regression analyses - Key: UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. Each study is represented as a dot, with a larger dot indicating a larger sample size. (A) and (B) , meta-regression with mean age; (C) , meta-regression with male ratio of patients; (D) , meta-regression with mean UPDRS-III score; (E) , meta-regression with mean MMSE score; (F) , meta-regression with illness duration; (G) and (H) , meta-regression with scanner field-strength. - Meta-regression analyses -Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. - - - -## DISCUSSION - -Using a modified SDM approach, the present quantitative meta-analysis is timely given with a sufficient number of VBM studies that have recently become available. This comprehensive study synthesized the findings from 18 VBM studies comprising 284 patients with PSP and 367 healthy controls. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen, precentral gyrus (premotor cortex) and OFC, the thalamus extending to the midbrain and caudate nucleus, the ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and the anterior cerebellum (lobule III/IV/V). These GM changes in PSP were highly robust as verified by jackknife sensitivity analyses. In addition, no publication biases in these regions were observed. However, the heterogeneity analysis revealed a significant between-study variability of GM atrophy differences in some of these regions. Further meta-regression analyses indicated that these variations in GM alterations across VBM studies were correlated with the mean age, male ratio, UPDRS-III score, MMSE score and illness duration of PSP patients, as well as scanner field-strength employed. - -The pattern of GM atrophy in PSP identified in our meta-analysis is consistent with the histopathological distribution of neuronal loss, gliosis, and accumulation of tau proteins in the midbrain, diencephalon, basal ganglia, cerebellum, frontal and temporal cortices [ ]. Midbrain atrophy, which is consistently validated by many imaging modalities, is the most characteristic alteration of PSP [ ]. The hallmark of the disease, vertical supranuclear gaze palsy, is considered to correlate with the neurodegeneration of the rostral interstitial nucleus of the medial longitudinal fasciculus (riMLF and the interstitial nucleus of Cajal located in the midbrain, and the central mesencephalic reticular formation [ – ]. In addition, a recent study by Amtage and colleagues employing 18F-Fluorodeoxyglucose (FDG) positron-emission tomography (PET) suggests that the ACC (cingulate eye field), which connections the supplementary eye field, frontal eye field and midbrain regions [ ], plays an important role in downward gaze palsy in PSP [ ]. The substantia nigra in the midbrain, coupled with the basal ganglia, thalamus, motor cortices, and anterior cerebellum are hubs of a motor control network [ – ]. GM atrophy in these brain regions identified in the current meta-analysis, probably indicative for damage of this network, contributes to the pathophysiology of parkinsonism, such as rigidity, bradykinesia, and postural instability in patients with PSP [ , , ]. Recent evidence suggests that gait disturbance with early falls, one of the characteristic clinical features of PSP, are closely associated with the thalamic dysfunction, which influences the mesencephalic brainstem-thalamus loop [ ]. - -Beyond the motor control, the subcortical structures such as the the substantia nigra, basal ganglia and thalamus are also implicated in mediating cognition and behavior via the frontal-subcortical circuits [ – ]. In addition to the motor symptoms, patients with PSP are frequently accompanied by cognitive-behavioral disturbances, such as executive dysfunction, apathy, and disinhibition, which are prevalent and may occur early in the disease course affecting their quality of daily life [ , ]. Early cognitive impairment in PSP is shown to be an independent predictor of shorter survival [ – ]. PSP is typically considered a “subcortical dementia” with the impairment of the frontal-subcortical circuits, prominently attributed to the subcortical pathology [ , , ]. Resting-state functional MRI studies have demonstrated a widespread disruption of cortical-subcortical connectivity involved in cognitive and motor dysfunction in PSP [ – ]. Previous studies using manual ROI approaches for the frontal lobe, demonstrated that the severity of behavioral and cognitive disturbances was associated with the degree of frontal atrophy in PSP patients [ – ]. In addition, a longitudinal ROI study further showed that the progression of executive dysfunction correlated with increased rates of frontal atrophy in patients with PSP [ ]. A VBM study demonstrated that the severity of behavioral disturbances in mid-stage PSP correlated with atrophy of the OFC surrounding the inferior frontal sulcus and the midbrain [ ]. In accordance with these data, our voxel-wise meta-analysis identified frontal GM atrophy noted in the lateral (inferior frontal cortex extending to OFC) and medial (ACC extending to superior medial frontal cortex and medial OFC) frontal cortices, apart from the frontal motor associated cortices including the (pre-) SMA and the premotor cortex. In addition, we identified extra-frontal GM atrophy in the insular cortex and superior temporal cortex. The insula has rich connections with the frontal and subcortical structures acting as a hub for integrating cognitive-affective, sensorimotor, and autonomic information [ , ]. Our meta-regression analyses showed that the severity of the motor disabilities and cognitive impairment as well as the illness duration of PSP had notable effects on GM atrophy in the insula. Atrophy of the superior temporal cortex along with the OFC, parts of the OFC-subcortical circuit may be associated with disinhibition, one of the frequently observed behavioral symptoms in PSP [ – , ]. The ACC is engaged in the medial frontal-subcortical circuit and in the dorsolateral prefrontal-subcortical circuit, dysfunction of which are responsible for apathy and executive dysfunction, respectively [ – , , ]. In contrast to previous meta-analyses [ – ], cortical atrophy in the current study was more prominent, which may be attributed to the methodological improvement and sufficient statistical power with enough studies as discussed in the introduction [ – ]. Taken together, GM matter atrophy in these cortical and subcortical regions identified in the meta-analysis may shed light on the pathophysiology of the cognitive-behavioral disturbances in PSP. - -Notably in the current meta-analysis, we observed heterogeneity of brain GM alterations in some of the regions across studies, which are attributed to the confounding factors, such as age, male ratio, motor severity, MMSE score, and illness duration of PSP patients, and scanner field-strength that were not analyzed by previous meta-analysis [ – ]. For example, meta-regression analysis revealed that older mean age in PSP patients was associated with more GM atrophy in the bilateral thalamus extending to the midbrain and the left insula extending to the inferior frontal gyrus. Age is an important risk factor for PSP [ ]. Severer motor disabilities and cognitive-behavioral disturbances in PSP are associated with more GM atrophy in the left insula. The human insula is strongly interconnected with the basal ganglia and cortical regions, which is implicated in cognitive/affective and sensorimotor processing [ , ]. These brain structure-behavior correlations provided additional insight into the neurobiology of PSP. The epidemiologic data shows that PSP affects men more frequently than women [ , ]. In the current meta-analysis, we noted that samples with PSP with a higher male ratio tended to have more GM atrophy in the left caudate nucleus extending to the thalamus, which may provide a neuroanatomical basis of such gender susceptibility. However, we could not find the factors that contribute to the heterogeneity of GM changes in the bilateral ACC extending to the medial OFC. As mentioned above, these regions are implicated in cognitive-behavioral disturbances. Due to the limited data of frontal assessment battery and frontal behavioral inventory available from the original studies, we could not further explore the source of GM heterogeneity in these regions. More studies are warranted to assess cognitive-behavioral disturbances and to conduct clinical-neuroanatomical correlations in PSP. - -### Limitations - -Some limitations of this meta-analysis warrant consideration. First, an intrinsic limitation for coordinate-based meta-analytic approaches is that they are based on coordinate data rather than raw imaging data, which may bias the results [ , ]. Second, due to that fact that most of the samples were not pathologically confirmed, the clinically heterogeneous nature of PSP might limit specificity of the findings, although the subgroup analysis indicates that this pattern of GM atrophy is specific for classic PSP-RS patients. Further studies with large homogenous samples both by clinically diagnosed and pathologically confirmed are warranted to validate these findings. - - - -## MATERIALS AND METHODS - -### Literature search and selection - -As the VBM method was introduced in the year of 2000 [ ], we systematically searched PubMed, Embase, and Web of Science databases between January 1, 2000 and September 17, 2016 using the Medical Subject Heading (MeSH) term “progressive supranuclear palsy” and its corresponding free terms, and the keywords “voxel-based morphometry” or “vbm” or “gray matter” or “grey matter” or “voxel*”. Furthermore, we checked the bibliographies of relevant review papers and retrieved articles by hand for additional studies. One study was considered for inclusion in the meta-analysis if it (1) was published in an English-language peer-reviewed journal as an original article; (2) reported regional GM changes using a whole-brain VBM analysis for direct comparison between patients with PSP and healthy controls; (3) reported three-dimensional coordinates of maxima (x, y, z) in a standardized stereotaxic space (i.e., Montreal Neurological Institute [MNI] or Talairach); (4) reported significant results of regional GM differences within one study using a constant threshold. Only the baseline dataset was included if the study was longitudinal. Studies were excluded if they limited their analyses to specific regions of interest (ROIs) or volume of interest (VOI). A study was excluded if its sample overlapped with another publication. The quality of each study included in this meta-analysis was evaluated using a 10point checklist that integrated both the clinical and demographic information and the imaging-specific methodology ( ), which was based on previous meta-analytic studies [ , ]. Recorded data were extracted from original studies, including the first author's name, year of publication, age, gender and number of patients and controls, clinical variables (e.g., illness duration, UPDRS-III score, H&Y stage, MMSE score, and FAB score), and the imaging characteristics (e.g., scanner field-strength, processing software, full width half maximum [FWHM] and statistical threshold). In addition, peak coordinates and effect sizes (e.g., t-values) of GM differences between patients with PSP and healthy controls from each VBM study were extracted for the following voxel-wise meta-analysis. Two investigators independently performed literature search and selection, assessment of study quality, and data extraction. Any discrepancies were discussed with another investigator until they were resolved. This study followed the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [ ]. - - -### Data analysis - - -### Main voxel-wise meta-analysis - -Voxel-wise meta-analysis of regional GM differences between patient with PSP and healthy controls was conducted using the modified SDM software package available at . The details of the approach have been described in other publications [ , , – ] and the tutorial available at An effect-size signed map and an effect-size variance map of the GM differences was first separately recreated for each study. The mean map was then created by voxel-wise calculation of the random-effects mean of the study maps, which was weighted by the sample size, intra- study variability, and additional between-study heterogeneity. Statistical significance was set at a default un-normalised Gaussian kernel kernel size and threshold (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels), which provided the optimal balance of false positives and negatives [ , ]. It must be noted that this un-normalised kernel is not designed to smooth any image but to assign indicators of proximity to reported coordinates [ , ]. - -In addition, we conduct a subgroup analysis of VBM studies that patients met the NINDS-SPSP (NINDS-SPSP, National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy) criteria suggestive of PSP-RS [ , ]. - - -### Supplemental analyses - -A leave-one-out and whole-brain voxel-based jackknife analysis was performed to assess the sensitivity of the results by iteratively repeating the same analysis, discarding one study each time [ , ]. - -A heterogeneity analysis was carried out using a random effects model with Q statistics in order to explore which brain regions are more heterogeneous between studies. Jackknife and heterogeneity analyses were thresholded with the same default settings (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels) [ , ]. - -In addition, the Stata/SE 12.0 software (Stata Corp LP, College Station, TX, USA) was used to examine possible publication bias. Funnel plots and Egger's test was performed by extracting the values from the meta-analytic peaks [ ]. An asymmetry of funnel plots and a p-value less than 0.05 of Egger's test were considered significant. - -Meta-regression analyses were further conducted to explore the effects of age, gender, UPDRS-III score, MMSE score, illness duration, and scanner field-strength that could potentially influence the meta-analytic results. Statistical significance was thresholded at a more conservative p-value less than 0.0005 and cluster extent more than 10 voxels [ , ]. Variables, such as H&Y stage, FAB, frontal behavioral inventory (FBI), and the Progressive Supranuclear Palsy Rating Scale (PSPRS), could not be explored by meta-regression analyses due to limited information that was available from less than 10 original studies. - - - -## CONCLUSIONS - -In summary, our comprehensive meta-analysis demonstrates a specific pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries in the pathophysiology of the supranuclear gaze palsy, motor disabilities, and cognitive-behavioral disturbances. These morphological findings may have implications for neuroanatomical diagnostic biomarkers of PSP. In addition, our study indicates that many confounding factors contribute to the heterogeneity of GM alterations in PSP across studies, which merits much attention in further studies. - - -## SUPPLEMENTARY MATERIALS TABLE - - \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml deleted file mode 100644 index 30cabf1..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/29113357.xml +++ /dev/null @@ -1,4829 +0,0 @@ - -
- - - - Oncotarget - Oncotarget - Oncotarget - ImpactJ - - Oncotarget - - 1949-2553 - - Impact Journals LLC - - - - 29113357 - 5655252 - 20895 - 10.18632/oncotarget.20895 - - - Research Paper - - - - Brain gray matter abnormalities in progressive supranuclear palsy revisited - - - - - Pan - PingLei - - - 1 - - - 2 - - - - - Liu - Yi - - - 1 - - - 3 - - - 4 - - - 5 - - - 6 - - - - - Zhang - Yang - - - 1 - - - 3 - - - 4 - - - 5 - - - 6 - - - - - Zhao - Hui - - - 1 - - - 3 - - - 4 - - - 5 - - - 6 - - - - - Ye - Xing - - - 1 - - - 3 - - - 4 - - - 5 - - - 6 - - - - - Xu - Yun - - - 1 - - - 3 - - - 4 - - - 5 - - - 6 - - - - 1 Department of Neurology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, PR China - 2 Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China - 3 The State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, PR China - 4 Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, PR China - 5 Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, PR China - 6 Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, PR China - - - - Correspondence to: - - Yun Xu, - xuyun20042001@aliyun.com - - - - 6 - 10 - 2017 - - - 15 - 9 - 2017 - - 8 - 46 - 80941 - 80955 - - - 21 - 2 - 2017 - - - 26 - 8 - 2017 - - - - Copyright: © 2017 Pan et al. - 2017 - - This article is distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. - - - -

Whole-brain voxel-based morphometry (VBM) studies of progressive supranuclear palsy (PSP) have demonstrated heterogeneous findings regarding gray matter (GM) abnormalities. Here, we used Seed-based d Mapping, a coordinate-based meta-analytic approach to identify consistent regions of GM anomalies across studies of PSP. Totally, 18 original VBM studies, comprising 284 patients with PSP and 367 healthy controls were included. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the frontal motor cortices, medial (including anterior cingulate cortex) and lateral frontal cortices, insula, superior temporal gyrus, striatum (putamen and caudate nucleus), thalamus, midbrain, and anterior cerebellum. Our study further suggests that many confounding factors, such as age, male ratio, motor severity, cognitive impairment severity, and illness duration of PSP patients, and scanner field-strength, could contribute to the heterogeneity of GM alterations in PSP across studies. Our comprehensive meta-analysis demonstrates a specific neuroanatomical pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries that mediate vertical supranuclear gaze palsy, motor disabilities (postural instability with falls and parkinsonism), and cognitive-behavioral disturbances. Confounding factors merit attention in future studies.

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- - progressive supranuclear palsy - voxel-based morphometry - meta-analysis - cortical-subcortical circuitries - seed-based d mapping - -
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- - - INTRODUCTION -

Progressive supranuclear palsy (PSP) is a clinical syndrome characterized mainly by early postural instability with falls, vertical supranuclear gaze palsy, parkinsonism, and cognitive-behavioral disturbances that lead to significant disabilities with a mean survival of 6.38 years [15]. PSP is a rapidly progressive neurodegenerative disorder, pathologically confirmed by the accumulation of tau protein and neuropil threads in cortical and subcortical structures [3, 6]. Several clinical subtypes of PSP have been identified, of which the classic Richardson's syndrome (PSP-RS) and the PSP-parkinsonism variant (PSP-P) are the most common [3]. No effective treatments are available for PSP [7]. Its differential diagnosis from other parkinsonian disorders is critical but presents challenges in clinical practice, especially in the early disease stages [3, 8]. Despite impressive advances in understanding its pathophysiology, the reliably validated biomarkers for the ante-mortem diagnosis and the prognosis of PSP have not yet been established [3, 4, 9, 10].

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Major improvements in modern magnetic resonance imaging (MRI) techniques increase our ability to identify brain structural alterations in vivo that shed light on the neuroanatomical basis of PSP and hold promise for its diagnosis [4, 1113]. Midbrain atrophy is a hallmark of PSP [12, 13]. Recent evidence from whole-brain voxel-based morphometry (VBM) studies in PSP has additionally demonstrated gray matter (GM) atrophy in a number of brain regions, such as the thalamus, basal ganglia, insula, frontal cortices, temporal cortices, parietal cortices, and cerebellum [1331]. Compared with conventional MRI investigations that draw regions of interest (ROIs) for morphometric comparisons, VBM is a hypothesis-free analytic tool to quantify regional structural differences between groups at a whole-brain level [32]. VBM has been widely used in neurodegenerative disorders [3335]. Despite the strengths, inconsistent results across different VBM studies were reported [13]. Shi, et al. in 2012, Shao, et al. in 2013, and Yu, et al. in 2014 thus conducted three coordinate-based meta-analyses to test the consistency of GM changes in PSP, which included nine, nine, and 12 VBM studies, respectively [3638]. However, these meta-analyses had several limitations. First, these meta-analyses did not examine the confounding variables, such as age, gender, disease duration, and symptom severity that potentially lead to heterogeneity of the structural alterations associated with PSP [13]. Second, the quantitative voxel-based meta-analytic tools have been modified [3941]. Several complementary analyses, such as jackknife sensitivity, heterogeneity, and publication bias analyses could be further performed to explore the robustness of the findings [42, 43]. Third, the numbers of VBM studies included in these meta-analyses were limited. To achieve sufficient power for moderate effects, Eickhoff and co-workers recently recommended that 17 or more experiments were needed for a coordinate-based meta-analysis to detect moderately sized effects [39]. In recent two years, we identified six more VBM studies of PSP eligibly for the meta-analysis. As such, results from previous meta-analyses cannot be considered conclusive and will need replication in a more exhaustive meta-analysis.

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Against this background, we aimed to conduct an updated meta-analysis based on 18 whole-brain VBM studies in PSP using a modified Seed-based d Mapping (SDM) approach to obtain more accurate results. In addition, analyses of jackknife sensitivity, heterogeneity, publication bias and meta-regression were comprehensively performed to examine the robustness and replicability of GM abnormalities associated with PSP.

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- - RESULTS - - Characteristics of included studies -

Figure 1 presents the flow diagram for inclusion/exclusion of studies in the meta-analysis. Totally, 18 original studies reporting GM differences between 284 patients with PSP and 367 healthy controls were included in this meta-analysis [1431]. Across the 18 studies, one study included all pathologically confirmed patients with nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) and PSP (nfvPPA-PSP) [30] and another two studies included some pathologically confirmed patients with PSP in the samples [21, 31]. Patients with PSP in the remaining 15 studies were clinically diagnosed. Three of the 17 studies explicitly indicated that the samples included two subtypes of PSP, PSP-RS and PSP-P [18, 27, 29]. Patients in the remaining 14 of 17 studies were clinically diagnosed based on the NINDS-SPSP clinical criteria [44, 45], which has over 95% sensitivity and specificity in diagnosing PSP-RS [45]. No significant differences between patients with PSP and healthy controls regarding mean age (standardized mean difference = 0.17; 95% confidence interval [CI] = -0.003 to 0.344, z = 0.61, p = 0.055) or gender distribution (relative risk = 1.12, 95% CI = 0.974 to 1.289, z = 0.159, p = 0.112) were observed. Mean Unified Parkinson's Disease Rating Scale-motor examination (UPDRS-III) score in 11/18 studies (rang from 20.4 to 52.9), Hoehn and Yahr disability scale (H&Y) in 7/18 studies (rang from 2.6 to 3.8), illness duration in 15/18 studies (rang from 2.5 years to 4.8 years), Mini-Mental State Examination (MMSE) score in 13/18 studies (rang from 21 to 28), and Frontal Assessment Battery (FAB) examination in 6/18 studies (rang from 7.81 to 12.9) were reported. Nine out of the 18 studies were conducted on 1.5T MRI systems and 8/18 studies were on 3.0T systems. One study used either a 1.5T or a 3.0T MRI system. 13 out of the 18 studies reported the corrected results and the remaining 5 studies used the uncorrected thresholds. All the studies included used Statistical Parametric Mapping (SPM) softwares for imaging analyses.

- - - - Flowchart to identify the eligible studies for the meta-analysis -

Key: PSP, Progressive Supranuclear Palsy; GM, Gray Matter; VBM, Voxel-Based Morphometry; ROI, Region Of Interest.

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The quality score of each study included in this meta-analysis was not less than 8 (a maximum score of 10 for each study), which indicates the high rigour of these studies. The list, demographic, clinical and imaging characteristics, and scores of quality assessment of the included studies are presented in Table 1.

- - - - Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
- Brenneis et al. (2004) - PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
- Price et al. (2004) - PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
- Cordato et al. (2005) - PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
- Boxer et al. (2006) - PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
- Padovani et al. (2006) - PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
- Agosta et al. (2010) - PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
- Lehericy et al. (2010) - PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
- Takahashi et al. (2011) - PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
- Ghosh et al. (2012) - PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
- Giordano et al. (2013) - PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
- Kamiya et al. (2013) - PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
- Lagarde et al. (2013) - PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
- Whitwell et al. (2013) - PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
- Sandhya et al. (2014) - PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
- Burciu et al. (2015) - PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
- Piattella et al. (2015) - PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
- Wang et al. (2015) - PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
- Santos-Santos et al. (2016) - PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0
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Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.

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- - Main voxel-wise meta-analysis -

As shown in Figure 2, the voxel-wise meta-analysis identified significant GM reductions in the left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and orbitofrontal cortex (OFC), in the bilateral thalamus extending to the midbrain and caudate nucleus, in the bilateral anterior cingulate cortex (ACC) extending to the supplementary motor areas (SMA) and pre-SMA, superior medial frontal cortex, and medial OFC, in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex), and in the left anterior cerebellum (lobule III/IV/V) in patients with PSP compared with healthy controls. In contrast, no significant GM increases were observed in patients with PSP relative to healthy controls. Details of the results are summarized in Table 2.

- - - - Meta-analytic results of gray matter reductions in patients with PSP compared to healthy controls -

Key: (A), Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/ orbitofrontal cortex; (B), Right/Left thalamus/midbrain/caudate nucleus; (C), Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; (D), Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); (E), Left anterior cerebellum (lobule III/IV/V); PSP, Progressive Supranuclear Palsy; HC, healthy controls; SDM, Seed-based d Mapping. The color bar indicates the maximum and the minimum SDM-Z values.

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- - - - GM reductions in patients with PSP compared to healthy controls - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
- A - Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
- B - Right/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
- C - Right/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
- D - Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
- E - Left anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83
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Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.

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The subgroup analysis of 14 VBM studies that patients were suggestive of PSP-RS showed that the results remained largely unchanged.

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- - Supplemental analyses -

The jackknife sensitivity analysis revealed that regions of GM reductions in left inferior frontal gyrus extending to the insula, superior temporal gyrus, precentral gyrus (premotor cortex), putamen, and OFC, in the bilateral thalami extending to the midbrain and caudate nucleus, in the bilateral ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and in the right inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen and precentral gyrus (premotor cortex) in patients with PSP relative to healthy controls were replicable in all 18 studies. The region of GM reductions in the left anterior cerebellum (lobule III/IV/V) in patients with PSP relative to healthy controls was replicable in 15 studies (Table 3).

- - - - Jackknife sensitivity analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
All studies but …ABCDE
- Brenneis et al. (2004) - YesYesYesYesYes
- Price et al. (2004) - YesYesYesYesYes
- Cordato et al. (2005) - YesYesYesYesYes
- Boxer et al. (2006) - YesYesYesYesYes
- Padovani et al. (2006) - YesYesYesYesYes
- Agosta et al. (2010) - YesYesYesYesYes
- Lehericy et al. (2010) - YesYesYesYesYes
- Takahashi et al. (2011) - YesYesYesYesYes
- Ghosh et al. (2012) - YesYesYesYesNo
- Giordano et al. (2013) - YesYesYesYesYes
- Kamiya et al. (2013) - YesYesYesYesNo
- Lagarde et al. (2013) - YesYesYesYesYes
- Whitwell et al. (2013) - YesYesYesYesYes
- Sandhya et al. (2014) - YesYesYesYesYes
- Burciu et al. (2015) - YesYesYesYesYes
- Piattella et al. (2015) - YesYesYesYesYes
- Wang et al. (2015) - YesYesYesYesNo
- Santos-Santos et al. (2016) - YesYesYesYesYes
- Total - - 18 out of 18 - - 18 out of 18 - - 18 out of 18 - - 18 out of 18 - - 15 out of 18 -
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Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.

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The heterogeneity analysis revealed that there is significant between-study variability of GM differences in patients with PSP relative to healthy controls in the right inferior frontal gyrus extending to the insula and superior temporal gyrus, in the left insula extending to the superior temporal gyrus, in the bilateral ACC extending to the medial OFC, in the bilateral thalamus, in the left inferior frontal gyrus, in the left cerebellum (lobule III), and in the right caudate nucleus (Table 4).

- - - - Regions of GM heterogeneity from the SDM analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
- Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48) - 46, 14, 215234.800.0000046
- Left insula/superior temporal gyrus (BAs 47, 13, and 48) - -40, 0, -29844.590.0000077
- Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24) - 2, 50, 89624.260.000034
- Right/Left thalamus - 2, -18, -83065.17∼0
- Left inferior frontal gyrus (BAs 45, and 44) - -50, 20, 221783.490.00044
- Left cerebellum (lobule III) - -8, -34, -18373.190.0011
- Right caudate nucleus - 12, -2, 20112.770.0030
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Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.

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No publication biases were detected in the regions obtained from the main voxel-wise meta-analysis as revealed by the symmetrical funnel plots (Figure 3) and statistically non-significant Egger's tests (Table 2).

- - - - Funnel plots of the peak coordinates of gray matter abnormalities in progressive supranuclear palsy - - - -

Meta-regression analysis revealed that the PSP group with older mean age (available from 17 studies) exhibited more GM reductions in the bilateral thalamus extending to the midbrain (Figure 4A) and in the left insula extending to the inferior frontal gyrus (Figure 4B). Higher male ratio of patients in the PSP group (available from 17 studies) was associated with more GM reductions in the left caudate nucleus extending to the thalamus (Figure 4C). Higher average UPDRS-III score (Figure 4D) or lower mean MMSE score (Figure 4E) in the PSP group (available from 11 and 13 studies, respectively) correlated with more GM reductions in the left insula. Meta-regression analysis indicated that longer mean illness duration of the PSP group (available from 15 studies) was associated with more GM reductions in the Left caudate nucleus extending to bilateral thalami (Figure 4F). Higher scanner field-strength in VBM studies tended to detect more GM atrophy in the right inferior frontal gyrus (Figure 4G) and in the left SMA (Figure 4H). Findings of these meta-regression analyses are presented in Table 5 and the regression lines are shown in Figure 4.

- - - - Results of the meta-regression analyses -

Key: UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination. Each study is represented as a dot, with a larger dot indicating a larger sample size. (A) and (B), meta-regression with mean age; (C), meta-regression with male ratio of patients; (D), meta-regression with mean UPDRS-III score; (E), meta-regression with mean MMSE score; (F), meta-regression with illness duration; (G) and (H), meta-regression with scanner field-strength.

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- - - - Meta-regression analyses - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
- Effect of age: GM changes in studies with older patients compared to younger patients -
- a. Right/Left thalamus/midbrain - 2, -14, 6655-4.98∼0
- b. Left insula/inferior frontal gyrus - -44, 12, 4641-4.150.0000026
- Effect of gender: GM changes in studies with a higher male ratio of patients -
- c. Left caudate nucleus/thalamus - -6, -4, 14155-3.710.000014
- Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score -
- d. Left insula - -32, 12, 1015-3.380.00015
- Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score -
- e. Left insula - -32, 22, 041-3.660.00015
- Effect of illness duration: GM changes in studies of patients with longer average illness duration -
- f. Left caudate nucleus/thalamus/Right thalamus - 0, -8, 121447-6.00∼0
- Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength -
- g. Right inferior frontal gyrus - 54, 12, 16127-4.020.000037
- h. Left supplementary motor area - -2, 24, 5611-3.490.00032
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Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.

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- - DISCUSSION -

Using a modified SDM approach, the present quantitative meta-analysis is timely given with a sufficient number of VBM studies that have recently become available. This comprehensive study synthesized the findings from 18 VBM studies comprising 284 patients with PSP and 367 healthy controls. As compared to healthy controls, patients with PSP demonstrated significant GM reductions in both cortical and subcortical regions, including the inferior frontal gyrus extending to the insula, superior temporal gyrus, putamen, precentral gyrus (premotor cortex) and OFC, the thalamus extending to the midbrain and caudate nucleus, the ACC extending to the (pre-) SMA, superior medial frontal cortex, and medial OFC, and the anterior cerebellum (lobule III/IV/V). These GM changes in PSP were highly robust as verified by jackknife sensitivity analyses. In addition, no publication biases in these regions were observed. However, the heterogeneity analysis revealed a significant between-study variability of GM atrophy differences in some of these regions. Further meta-regression analyses indicated that these variations in GM alterations across VBM studies were correlated with the mean age, male ratio, UPDRS-III score, MMSE score and illness duration of PSP patients, as well as scanner field-strength employed.

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The pattern of GM atrophy in PSP identified in our meta-analysis is consistent with the histopathological distribution of neuronal loss, gliosis, and accumulation of tau proteins in the midbrain, diencephalon, basal ganglia, cerebellum, frontal and temporal cortices [46]. Midbrain atrophy, which is consistently validated by many imaging modalities, is the most characteristic alteration of PSP [47]. The hallmark of the disease, vertical supranuclear gaze palsy, is considered to correlate with the neurodegeneration of the rostral interstitial nucleus of the medial longitudinal fasciculus (riMLF and the interstitial nucleus of Cajal located in the midbrain, and the central mesencephalic reticular formation [4850]. In addition, a recent study by Amtage and colleagues employing 18F-Fluorodeoxyglucose (FDG) positron-emission tomography (PET) suggests that the ACC (cingulate eye field), which connections the supplementary eye field, frontal eye field and midbrain regions [51], plays an important role in downward gaze palsy in PSP [52]. The substantia nigra in the midbrain, coupled with the basal ganglia, thalamus, motor cortices, and anterior cerebellum are hubs of a motor control network [5355]. GM atrophy in these brain regions identified in the current meta-analysis, probably indicative for damage of this network, contributes to the pathophysiology of parkinsonism, such as rigidity, bradykinesia, and postural instability in patients with PSP [3, 55, 56]. Recent evidence suggests that gait disturbance with early falls, one of the characteristic clinical features of PSP, are closely associated with the thalamic dysfunction, which influences the mesencephalic brainstem-thalamus loop [56].

-

Beyond the motor control, the subcortical structures such as the the substantia nigra, basal ganglia and thalamus are also implicated in mediating cognition and behavior via the frontal-subcortical circuits [5759]. In addition to the motor symptoms, patients with PSP are frequently accompanied by cognitive-behavioral disturbances, such as executive dysfunction, apathy, and disinhibition, which are prevalent and may occur early in the disease course affecting their quality of daily life [57, 60]. Early cognitive impairment in PSP is shown to be an independent predictor of shorter survival [6163]. PSP is typically considered a “subcortical dementia” with the impairment of the frontal-subcortical circuits, prominently attributed to the subcortical pathology [59, 63, 64]. Resting-state functional MRI studies have demonstrated a widespread disruption of cortical-subcortical connectivity involved in cognitive and motor dysfunction in PSP [6567]. Previous studies using manual ROI approaches for the frontal lobe, demonstrated that the severity of behavioral and cognitive disturbances was associated with the degree of frontal atrophy in PSP patients [6870]. In addition, a longitudinal ROI study further showed that the progression of executive dysfunction correlated with increased rates of frontal atrophy in patients with PSP [71]. A VBM study demonstrated that the severity of behavioral disturbances in mid-stage PSP correlated with atrophy of the OFC surrounding the inferior frontal sulcus and the midbrain [31]. In accordance with these data, our voxel-wise meta-analysis identified frontal GM atrophy noted in the lateral (inferior frontal cortex extending to OFC) and medial (ACC extending to superior medial frontal cortex and medial OFC) frontal cortices, apart from the frontal motor associated cortices including the (pre-) SMA and the premotor cortex. In addition, we identified extra-frontal GM atrophy in the insular cortex and superior temporal cortex. The insula has rich connections with the frontal and subcortical structures acting as a hub for integrating cognitive-affective, sensorimotor, and autonomic information [72, 73]. Our meta-regression analyses showed that the severity of the motor disabilities and cognitive impairment as well as the illness duration of PSP had notable effects on GM atrophy in the insula. Atrophy of the superior temporal cortex along with the OFC, parts of the OFC-subcortical circuit may be associated with disinhibition, one of the frequently observed behavioral symptoms in PSP [5860, 74]. The ACC is engaged in the medial frontal-subcortical circuit and in the dorsolateral prefrontal-subcortical circuit, dysfunction of which are responsible for apathy and executive dysfunction, respectively [5759, 74, 75]. In contrast to previous meta-analyses [3335], cortical atrophy in the current study was more prominent, which may be attributed to the methodological improvement and sufficient statistical power with enough studies as discussed in the introduction [3941]. Taken together, GM matter atrophy in these cortical and subcortical regions identified in the meta-analysis may shed light on the pathophysiology of the cognitive-behavioral disturbances in PSP.

-

Notably in the current meta-analysis, we observed heterogeneity of brain GM alterations in some of the regions across studies, which are attributed to the confounding factors, such as age, male ratio, motor severity, MMSE score, and illness duration of PSP patients, and scanner field-strength that were not analyzed by previous meta-analysis [3638]. For example, meta-regression analysis revealed that older mean age in PSP patients was associated with more GM atrophy in the bilateral thalamus extending to the midbrain and the left insula extending to the inferior frontal gyrus. Age is an important risk factor for PSP [76]. Severer motor disabilities and cognitive-behavioral disturbances in PSP are associated with more GM atrophy in the left insula. The human insula is strongly interconnected with the basal ganglia and cortical regions, which is implicated in cognitive/affective and sensorimotor processing [77, 78]. These brain structure-behavior correlations provided additional insight into the neurobiology of PSP. The epidemiologic data shows that PSP affects men more frequently than women [76, 79]. In the current meta-analysis, we noted that samples with PSP with a higher male ratio tended to have more GM atrophy in the left caudate nucleus extending to the thalamus, which may provide a neuroanatomical basis of such gender susceptibility. However, we could not find the factors that contribute to the heterogeneity of GM changes in the bilateral ACC extending to the medial OFC. As mentioned above, these regions are implicated in cognitive-behavioral disturbances. Due to the limited data of frontal assessment battery and frontal behavioral inventory available from the original studies, we could not further explore the source of GM heterogeneity in these regions. More studies are warranted to assess cognitive-behavioral disturbances and to conduct clinical-neuroanatomical correlations in PSP.

- - Limitations -

Some limitations of this meta-analysis warrant consideration. First, an intrinsic limitation for coordinate-based meta-analytic approaches is that they are based on coordinate data rather than raw imaging data, which may bias the results [40, 80]. Second, due to that fact that most of the samples were not pathologically confirmed, the clinically heterogeneous nature of PSP might limit specificity of the findings, although the subgroup analysis indicates that this pattern of GM atrophy is specific for classic PSP-RS patients. Further studies with large homogenous samples both by clinically diagnosed and pathologically confirmed are warranted to validate these findings.

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-
- - MATERIALS AND METHODS - - Literature search and selection -

As the VBM method was introduced in the year of 2000 [32], we systematically searched PubMed, Embase, and Web of Science databases between January 1, 2000 and September 17, 2016 using the Medical Subject Heading (MeSH) term “progressive supranuclear palsy” and its corresponding free terms, and the keywords “voxel-based morphometry” or “vbm” or “gray matter” or “grey matter” or “voxel*”. Furthermore, we checked the bibliographies of relevant review papers and retrieved articles by hand for additional studies. One study was considered for inclusion in the meta-analysis if it (1) was published in an English-language peer-reviewed journal as an original article; (2) reported regional GM changes using a whole-brain VBM analysis for direct comparison between patients with PSP and healthy controls; (3) reported three-dimensional coordinates of maxima (x, y, z) in a standardized stereotaxic space (i.e., Montreal Neurological Institute [MNI] or Talairach); (4) reported significant results of regional GM differences within one study using a constant threshold. Only the baseline dataset was included if the study was longitudinal. Studies were excluded if they limited their analyses to specific regions of interest (ROIs) or volume of interest (VOI). A study was excluded if its sample overlapped with another publication. The quality of each study included in this meta-analysis was evaluated using a 10point checklist that integrated both the clinical and demographic information and the imaging-specific methodology (Supplementary Table 1), which was based on previous meta-analytic studies [81, 82]. Recorded data were extracted from original studies, including the first author's name, year of publication, age, gender and number of patients and controls, clinical variables (e.g., illness duration, UPDRS-III score, H&Y stage, MMSE score, and FAB score), and the imaging characteristics (e.g., scanner field-strength, processing software, full width half maximum [FWHM] and statistical threshold). In addition, peak coordinates and effect sizes (e.g., t-values) of GM differences between patients with PSP and healthy controls from each VBM study were extracted for the following voxel-wise meta-analysis. Two investigators independently performed literature search and selection, assessment of study quality, and data extraction. Any discrepancies were discussed with another investigator until they were resolved. This study followed the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [83].

-
- - Data analysis - - - Main voxel-wise meta-analysis -

Voxel-wise meta-analysis of regional GM differences between patient with PSP and healthy controls was conducted using the modified SDM software package available at http://www.sdmproject.com. The details of the approach have been described in other publications [40, 80, 8486] and the tutorial available at http://www.sdmproject.com/software/tutorial.pdf An effect-size signed map and an effect-size variance map of the GM differences was first separately recreated for each study. The mean map was then created by voxel-wise calculation of the random-effects mean of the study maps, which was weighted by the sample size, intra- study variability, and additional between-study heterogeneity. Statistical significance was set at a default un-normalised Gaussian kernel kernel size and threshold (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels), which provided the optimal balance of false positives and negatives [40, 80]. It must be noted that this un-normalised kernel is not designed to smooth any image but to assign indicators of proximity to reported coordinates [80, 84].

-

In addition, we conduct a subgroup analysis of VBM studies that patients met the NINDS-SPSP (NINDS-SPSP, National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy) criteria suggestive of PSP-RS [44, 45].

-
- - Supplemental analyses -

A leave-one-out and whole-brain voxel-based jackknife analysis was performed to assess the sensitivity of the results by iteratively repeating the same analysis, discarding one study each time [80, 84].

-

A heterogeneity analysis was carried out using a random effects model with Q statistics in order to explore which brain regions are more heterogeneous between studies. Jackknife and heterogeneity analyses were thresholded with the same default settings (FWHM = 20 mm, p = 0.005, peak height Z = 1, cluster extent = 10 voxels) [40, 80].

-

In addition, the Stata/SE 12.0 software (Stata Corp LP, College Station, TX, USA) was used to examine possible publication bias. Funnel plots and Egger's test was performed by extracting the values from the meta-analytic peaks [42]. An asymmetry of funnel plots and a p-value less than 0.05 of Egger's test were considered significant.

-

Meta-regression analyses were further conducted to explore the effects of age, gender, UPDRS-III score, MMSE score, illness duration, and scanner field-strength that could potentially influence the meta-analytic results. Statistical significance was thresholded at a more conservative p-value less than 0.0005 and cluster extent more than 10 voxels [80, 85]. Variables, such as H&Y stage, FAB, frontal behavioral inventory (FBI), and the Progressive Supranuclear Palsy Rating Scale (PSPRS), could not be explored by meta-regression analyses due to limited information that was available from less than 10 original studies.

-
-
- - CONCLUSIONS -

In summary, our comprehensive meta-analysis demonstrates a specific pattern of GM atrophy in PSP with the involvement of the cortical-subcortical circuitries in the pathophysiology of the supranuclear gaze palsy, motor disabilities, and cognitive-behavioral disturbances. These morphological findings may have implications for neuroanatomical diagnostic biomarkers of PSP. In addition, our study indicates that many confounding factors contribute to the heterogeneity of GM alterations in PSP across studies, which merits much attention in further studies.

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- - SUPPLEMENTARY MATERIALS TABLE - - - - - - - -

We are greatly indebted to the authors of the included studies.

-
- - -

- Author contributions -

-

YX, PLP and YL designed the protocol. PLP and YL wrote the main manuscript. YZ and HZ obtained the data. PLP and XY analyzed the results. YX revised the manuscript. All authors reviewed the manuscript.

-
- -

- CONFLICTS OF INTEREST -

-

The authors declare no conflicts of interest.

-
- -

- FUNDING -

-

This research was supported by the National Natural Science Foundation of China (81230026, 81630028, 81171085, 81601161), the Natural Science Foundation (BE2016610) of Jiangsu Province of China, the Ministry of Science and Technology in China (2016YFC0901004).

-
-
- - Abbreviations - - - VBM - -

voxel-based morphometry

-
-
- - PSP - -

progressive supranuclear palsy

-
-
- - GM - -

gray matter

-
-
- - PSP-RS - -

PSP Richardson's syndrome

-
-
- - PSP-P - -

PSP-parkinsonism variant

-
-
- - MRI - -

magnetic resonance imaging

-
-
- - ROIs - -

regions of interest

-
-
- - SDM - -

Seed-based d Mapping

-
-
- - nfvPPA-PSP - -

nonfluent/agrammatic variant of primary progressive aphasia and PSP

-
-
- - MeSH - -

Medical Subject Heading

-
-
- - MNI - -

Montreal Neurological Institute [MNI]

-
-
- - VOI - -

volume of interest

-
-
- - UPDRS-III - -

Unified Parkinson's Disease Rating Scale-motor examination

-
-
- - H&Y - -

Hoehn and Yahr

-
-
- - MMSE - -

Mini-Mental State Examination

-
-
- - FAB - -

frontal assessment battery

-
-
- - FWHM - -

full width half maximum

-
-
- - MOOSE - -

Meta-analysis Of Observational Studies in Epidemiology

-
-
- - NINDS-SPSP - -

National Institute of Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy

-
-
- - PSPRS - -

Progressive Supranuclear Palsy Rating Scale

-
-
- - CI - -

confidence interval

-
-
- - OFC - -

orbitofrontal cortex

-
-
- - ACC - -

anterior cingulate cortex

-
-
- - SMA - -

supplementary motor areas

-
-
- - BA - -

Brodmann Area

-
-
-
-
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diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv deleted file mode 100644 index 3b19e5b..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000.csv +++ /dev/null @@ -1,19 +0,0 @@ -Study,Sample (male),Age (SD),UPDRS-III (SD),H&Y stage (SD),Duration (SD),MMSE (SD),FAB (SD),Scanner,Software,FWHM,Threshold,Quality# -Brenneis et al. (2004),PSP 12 (NA)HC 12 (NA),67.5 (6.6)60 (5.8),38.9 (10.9),,2.7 (0.9),,,1.5T,SPM99,10.0,p < 0.05corrected,8.5 -Price et al. (2004),PSP 12 (7)HC 12 (8),65.3 (5.8)67.4 (4.6),20.4 (8.7),,4.8 (1.7),27 (3.3),12.4 (3.1),1.5T,SPM99,8.0,p < 0.05corrected,9.5 -Cordato et al. (2005),PSP 21 (14)HC 23 (14),70.3 (6.4)71.5 (7.2),23.1 (10.1),3.8 (1.1),4.0 (2.8),25.4 (3.2),,1.5T,SPM99,12.0,p < 0.05corrected,9.5 -Boxer et al. (2006),PSP 15 (9)HC 80 (37),70.9 (6.9)67.9 (8.6),,3.3 (0.5),4.8 (1.7),24.0 (3.2),,1.5T,SPM2,12.0,p < 0.05corrected,8.5 -Padovani et al. (2006),PSP 14 (7)HC 14 (7),73 (5.6)65.6 (4.1),22.1 (8.9),,3.1 (1.0),25.8 (2.7),,1.5T,SPM2,10.0,p < 0.005corrected,9.0 -Agosta et al. (2010),PSP 20 (14)HC 24 (13),64.9 (NA)63.8 (NA),32.8 (NA),3.0 (NA),4.5 (NA),27.0 (NA),,1.5T,SPM5,8.0,p < 0.001uncorrected,9.0 -Lehericy et al. (2010),PSP 10 (6)HC 9 (5),66.9 (6.4)66.5 (4.8),30 (NA),,4.3 (1.0),27 (NA),11.5 (NA),1.5T,SPM5,8.0,p < 0.05corrected,8.5 -Takahashi et al. (2011),PSP 16 (11)HC 20 (16),64.6 (6.4)64.8 (6.4),,,,21.0 (4.4),,1.5T,SPM8,8.0,p < 0.001uncorrected,8.5 -Ghosh et al. (2012),PSP 23 (14)HC 22 (15),71.1 (8.6)71.4 (7.6),33.8 (15.7),,2.5 (NA),,,3.0T,SPM5,,p < 0.05corrected,9.0 -Giordano et al. (2013),PSP 15 (8)HC 15 (8),68.91 (1.2)65.5 (6.1),38.33 (4),3.80 (1.1),3.16 (1.3),21.23 (1.2),7.81 (0.9),3.0T,SPM8,8.0,p < 0.05corrected,9.5 -Kamiya et al. (2013),PSP 16 (10)HC 21 (12),71.4 (6.0)70.9 (8.0),,,,,,1.5T,SPM5,8.0,p < 0.001uncorrected,9.0 -Lagarde et al. (2013),PSP 19 (7)HC 18 (7),65.9 (6.5)67.8 (5.2),,,4.5 (1.8),25.5 (2.7),11.3 (2),3.0T,SPM8,8.0,p < 0.05corrected,9.0 -Whitwell et al. (2013),PSP 16 (8)HC 20 (4),72.1 (4.6)73.9 (6.3),52.9 (12.6),,4.0 (1.1),25.8 (2.7),12.9 (2.2),3.0T,SPM5,8.0,p < 0.05corrected,9.0 -Sandhya et al. (2014),PSP 10 (9)HC 8 (5),NANA,,,,,,3.0T,SPM8,,p < 0.001uncorrected,8.0 -Burciu et al. (2015),PSP 20 (10)HC 20 (10),67.8 (7.1)64.8 (8.8),39.0 (14.5),2.6 (0.9),2.6 (2.6),,,3.0T,SPM8,,p < 0.05corrected,9.0 -Piattella et al. (2015),PSP 16 (9)HC 16 (6),68.08 (5.9)69.4 (0.4),27.0 (17.4),2.9 (1.0),3.1 (NA),24.3 (3.9),11.1 (3.8),3.0T,SPM8,12.0,p < 0.05corrected,9.5 -Wang et al. (2015),PSP 24 (8)HC 23 (14),64.17 (6.72)60.52 (6.47),,3.1 (NA),3.87 (2.62),23.54 (4.28),,3.0T,SPM8,6.0,p < 0.001corrected,8.5 -Santos-Santos et al. (2016),PSP 5 (1) HC 10 (3),71.5 (NA)74 (NA),,,4 (NA),28 (NA),,,SPM12,,p<0.001uncorrected,8.0 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json deleted file mode 100644 index 533c435..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_000_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "T1", "table_label": "Table 1", "table_caption": "Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis", "table_foot": "Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.", "n_header_rows": 1, "table_data_file": "table_000.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv deleted file mode 100644 index 77bb4ba..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001.csv +++ /dev/null @@ -1,6 +0,0 @@ -Cluster,Anatomical label,"Peak MNI coordinate (x, y, z)",No. of voxels,SDM-Z value,SDM-p value,Egger's test (p value) -A,"Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)","-48, 18, 0",5063,-4.8,∼0,0.56 -B,Right/Left thalamus/midbrain/caudate nucleus,"4, -14, 6",3916,-4.7,∼0,0.29 -C,"Right/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)","-4, 12, 44",3457,-3.32,0.000067,0.27 -D,"Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)","54, 16, 16",3186,-4.27,0.027743R254,0.78 -E,Left anterior cerebellum (lobule III/IV/V),"-14, -44, -24",108,-2.74,0.0014,0.83 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json deleted file mode 100644 index 488dc0d..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_001_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "T2", "table_label": "Table 2", "table_caption": "GM reductions in patients with PSP compared to healthy controls", "table_foot": "Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.", "n_header_rows": 1, "table_data_file": "table_001.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv deleted file mode 100644 index 2ec4d07..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002.csv +++ /dev/null @@ -1,20 +0,0 @@ -All studies but …,A,B,C,D,E -Brenneis et al. (2004),Yes,Yes,Yes,Yes,Yes -Price et al. (2004),Yes,Yes,Yes,Yes,Yes -Cordato et al. (2005),Yes,Yes,Yes,Yes,Yes -Boxer et al. (2006),Yes,Yes,Yes,Yes,Yes -Padovani et al. (2006),Yes,Yes,Yes,Yes,Yes -Agosta et al. (2010),Yes,Yes,Yes,Yes,Yes -Lehericy et al. (2010),Yes,Yes,Yes,Yes,Yes -Takahashi et al. (2011),Yes,Yes,Yes,Yes,Yes -Ghosh et al. (2012),Yes,Yes,Yes,Yes,No -Giordano et al. (2013),Yes,Yes,Yes,Yes,Yes -Kamiya et al. (2013),Yes,Yes,Yes,Yes,No -Lagarde et al. (2013),Yes,Yes,Yes,Yes,Yes -Whitwell et al. (2013),Yes,Yes,Yes,Yes,Yes -Sandhya et al. (2014),Yes,Yes,Yes,Yes,Yes -Burciu et al. (2015),Yes,Yes,Yes,Yes,Yes -Piattella et al. (2015),Yes,Yes,Yes,Yes,Yes -Wang et al. (2015),Yes,Yes,Yes,Yes,No -Santos-Santos et al. (2016),Yes,Yes,Yes,Yes,Yes -Total,18 out of 18,18 out of 18,18 out of 18,18 out of 18,15 out of 18 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json deleted file mode 100644 index b4ea01a..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_002_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "T3", "table_label": "Table 3", "table_caption": "Jackknife sensitivity analysis", "table_foot": "Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.", "n_header_rows": 1, "table_data_file": "table_002.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv deleted file mode 100644 index b4f3c21..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003.csv +++ /dev/null @@ -1,8 +0,0 @@ -Anatomical regions,Maximum MNI coordinate,No. of Voxels,SDM-Z,p -"Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)","46, 14, 2",1523,4.8,0.0000046 -"Left insula/superior temporal gyrus (BAs 47, 13, and 48)","-40, 0, -2",984,4.59,0.0000077 -"Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)","2, 50, 8",962,4.26,0.000034 -Right/Left thalamus,"2, -18, -8",306,5.17,∼0 -"Left inferior frontal gyrus (BAs 45, and 44)","-50, 20, 22",178,3.49,0.00044 -Left cerebellum (lobule III),"-8, -34, -18",37,3.19,0.0011 -Right caudate nucleus,"12, -2, 20",11,2.77,0.0030 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json deleted file mode 100644 index 0e34441..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_003_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "T4", "table_label": "Table 4", "table_caption": "Regions of GM heterogeneity from the SDM analysis", "table_foot": "Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.", "n_header_rows": 1, "table_data_file": "table_003.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv deleted file mode 100644 index e98f66b..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004.csv +++ /dev/null @@ -1,15 +0,0 @@ -Anatomical label,"Peak MNI coordinate (x, y, z)",No. of voxels,SDM-Z value,p value -Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients,Effect of age: GM changes in studies with older patients compared to younger patients -a. Right/Left thalamus/midbrain,"2, -14, 6",655,-4.98,∼0 -b. Left insula/inferior frontal gyrus,"-44, 12, 4",641,-4.15,0.0000026 -Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients,Effect of gender: GM changes in studies with a higher male ratio of patients -c. Left caudate nucleus/thalamus,"-6, -4, 14",155,-3.71,0.000014 -Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score,Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score -d. Left insula,"-32, 12, 10",15,-3.38,0.00015 -Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score,Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score -e. Left insula,"-32, 22, 0",41,-3.66,0.00015 -Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration,Effect of illness duration: GM changes in studies of patients with longer average illness duration -f. Left caudate nucleus/thalamus/Right thalamus,"0, -8, 12",1447,-6.00,∼0 -Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength,Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength -g. Right inferior frontal gyrus,"54, 12, 16",127,-4.02,0.000037 -h. Left supplementary motor area,"-2, 24, 56",11,-3.49,0.00032 diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json deleted file mode 100644 index 3aa7558..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/table_004_info.json +++ /dev/null @@ -1 +0,0 @@ -{"table_id": "T5", "table_label": "Table 5", "table_caption": "Meta-regression analyses", "table_foot": "Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.", "n_header_rows": 1, "table_data_file": "table_004.csv"} \ No newline at end of file diff --git a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml b/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml deleted file mode 100644 index 3fc39d9..0000000 --- a/tests/data/sample_inputs/Lb3HDCyzoerL/source/pubget/tables/tables.xml +++ /dev/null @@ -1,2 +0,0 @@ - -56552522911335756552522089510.18632/oncotarget.20895T1Table 1Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysisKey: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
Brenneis et al. (2004)PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
Price et al. (2004)PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
Cordato et al. (2005)PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
Boxer et al. (2006)PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
Padovani et al. (2006)PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
Agosta et al. (2010)PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
Lehericy et al. (2010)PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
Takahashi et al. (2011)PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
Ghosh et al. (2012)PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
Giordano et al. (2013)PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
Kamiya et al. (2013)PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
Lagarde et al. (2013)PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
Whitwell et al. (2013)PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
Sandhya et al. (2014)PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
Burciu et al. (2015)PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
Piattella et al. (2015)PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
Wang et al. (2015)PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
Santos-Santos et al. (2016)PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0

Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.

Table 1
Demographic, clinical and imaging characteristics of VBM studies included in the meta-analysis
StudySample (male)Age (SD)UPDRS-III (SD)H&Y stage (SD)Duration (SD)MMSE (SD)FAB (SD)ScannerSoftwareFWHMThresholdQuality#
Brenneis et al. (2004)PSP 12 (NA)HC 12 (NA)67.5 (6.6)60 (5.8)38.9 (10.9)NA2.7 (0.9)NANA1.5TSPM9910p < 0.05corrected8.5
Price et al. (2004)PSP 12 (7)HC 12 (8)65.3 (5.8)67.4 (4.6)20.4 (8.7)NA4.8 (1.7)27 (3.3)12.4 (3.1)1.5TSPM998p < 0.05corrected9.5
Cordato et al. (2005)PSP 21 (14)HC 23 (14)70.3 (6.4)71.5 (7.2)23.1 (10.1)3.8 (1.1)4.0 (2.8)25.4 (3.2)NA1.5TSPM9912p < 0.05corrected9.5
Boxer et al. (2006)PSP 15 (9)HC 80 (37)70.9 (6.9)67.9 (8.6)NA3.3 (0.5)4.8 (1.7)24.0 (3.2)NA1.5TSPM212p < 0.05corrected8.5
Padovani et al. (2006)PSP 14 (7)HC 14 (7)73 (5.6)65.6 (4.1)22.1 (8.9)NA3.1 (1.0)25.8 (2.7)NA1.5TSPM210p < 0.005corrected9.0
Agosta et al. (2010)PSP 20 (14)HC 24 (13)64.9 (NA)63.8 (NA)32.8 (NA)3.0 (NA)4.5 (NA)27.0 (NA)NA1.5TSPM58p < 0.001uncorrected9.0
Lehericy et al. (2010)PSP 10 (6)HC 9 (5)66.9 (6.4)66.5 (4.8)30 (NA)NA4.3 (1.0)27 (NA)11.5 (NA)1.5TSPM58p < 0.05corrected8.5
Takahashi et al. (2011)PSP 16 (11)HC 20 (16)64.6 (6.4)64.8 (6.4)NANANA21.0 (4.4)NA1.5TSPM88p < 0.001uncorrected8.5
Ghosh et al. (2012)PSP 23 (14)HC 22 (15)71.1 (8.6)71.4 (7.6)33.8 (15.7)NA2.5 (NA)NANA3.0TSPM5NAp < 0.05corrected9.0
Giordano et al. (2013)PSP 15 (8)HC 15 (8)68.91 (1.2)65.5 (6.1)38.33 (4)3.80 (1.1)3.16 (1.3)21.23 (1.2)7.81 (0.9)3.0TSPM88p < 0.05corrected9.5
Kamiya et al. (2013)PSP 16 (10)HC 21 (12)71.4 (6.0)70.9 (8.0)NANANANANA1.5TSPM58p < 0.001uncorrected9.0
Lagarde et al. (2013)PSP 19 (7)HC 18 (7)65.9 (6.5)67.8 (5.2)NANA4.5 (1.8)25.5 (2.7)11.3 (2)3.0TSPM88p < 0.05corrected9.0
Whitwell et al. (2013)PSP 16 (8)HC 20 (4)72.1 (4.6)73.9 (6.3)52.9 (12.6)NA4.0 (1.1)25.8 (2.7)12.9 (2.2)3.0TSPM58p < 0.05corrected9.0
Sandhya et al. (2014)PSP 10 (9)HC 8 (5)NANANANANANANA3.0TSPM8NAp < 0.001uncorrected8.0
Burciu et al. (2015)PSP 20 (10)HC 20 (10)67.8 (7.1)64.8 (8.8)39.0 (14.5)2.6 (0.9)2.6 (2.6)NANA3.0TSPM8NAp < 0.05corrected9.0
Piattella et al. (2015)PSP 16 (9)HC 16 (6)68.08 (5.9)69.4 (0.4)27.0 (17.4)2.9 (1.0)3.1 (NA)24.3 (3.9)11.1 (3.8)3.0TSPM812p < 0.05corrected9.5
Wang et al. (2015)PSP 24 (8)HC 23 (14)64.17 (6.72)60.52 (6.47)NA3.1 (NA)3.87 (2.62)23.54 (4.28)NA3.0TSPM86p < 0.001corrected8.5
Santos-Santos et al. (2016)PSP 5 (1) HC 10 (3)71.5 (NA)74 (NA)NANA4 (NA)28 (NA)NANASPM12NAp<0.001uncorrected8.0
Key: VBM, Voxel-Based Morphometry; PSP, Progressive Supranuclear Palsy; HC, Healthy Controls; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; H&Y, Hoehn and Yahr disability scale; MMSE, Mini-Mental State Examination; FAB, Frontal Assessment Battery; NA, Not Available; SPM, Statistical Parametric Mapping; FWHM, Full Width Half Maximum, SD, Standard Deviation; #, a maximum score of 10 for each study.
T2Table 2GM reductions in patients with PSP compared to healthy controlsKey: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.GM reductions in patients with PSP compared to healthy controls
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
ALeft inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
BRight/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
CRight/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
DRight inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
ELeft anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83

Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.

Table 2
GM reductions in patients with PSP compared to healthy controls
ClusterAnatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valueSDM-p valueEgger's test (p value)
ALeft inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/OFC (BAs 47, 13, 44, 22, 6, 45, and 9)-48, 18, 05063-4.80∼00.56
BRight/Left thalamus/midbrain/caudate nucleus4, -14, 63916-4.70∼00.29
CRight/Left ACC/(pre-) SMA/superior medial frontal cortex/medial OFC (BAs 32, 24, 8, 9, 6,11, and 10)-4, 12, 443457-3.320.0000670.27
DRight inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex) (BAs 44, 13, 47, 22, 6, 45, and 9)54, 16, 163186-4.270.027743R2540.78
ELeft anterior cerebellum (lobule III/IV/V)-14, -44, -24108-2.740.00140.83
Key: GM, Gray Matter; PSP, Progressive Supranuclear Palsy; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; OFC, Orbitofrontal Cortex; ACC, Anterior Cingulate Cortex; SMA, Supplementary Motor Area.
T3Table 3Jackknife sensitivity analysisKey: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.Jackknife sensitivity analysis
All studies but …ABCDE
Brenneis et al. (2004)YesYesYesYesYes
Price et al. (2004)YesYesYesYesYes
Cordato et al. (2005)YesYesYesYesYes
Boxer et al. (2006)YesYesYesYesYes
Padovani et al. (2006)YesYesYesYesYes
Agosta et al. (2010)YesYesYesYesYes
Lehericy et al. (2010)YesYesYesYesYes
Takahashi et al. (2011)YesYesYesYesYes
Ghosh et al. (2012)YesYesYesYesNo
Giordano et al. (2013)YesYesYesYesYes
Kamiya et al. (2013)YesYesYesYesNo
Lagarde et al. (2013)YesYesYesYesYes
Whitwell et al. (2013)YesYesYesYesYes
Sandhya et al. (2014)YesYesYesYesYes
Burciu et al. (2015)YesYesYesYesYes
Piattella et al. (2015)YesYesYesYesYes
Wang et al. (2015)YesYesYesYesNo
Santos-Santos et al. (2016)YesYesYesYesYes
Total18 out of 1818 out of 1818 out of 1818 out of 1815 out of 18

Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.

Table 3
Jackknife sensitivity analysis
All studies but …ABCDE
Brenneis et al. (2004)YesYesYesYesYes
Price et al. (2004)YesYesYesYesYes
Cordato et al. (2005)YesYesYesYesYes
Boxer et al. (2006)YesYesYesYesYes
Padovani et al. (2006)YesYesYesYesYes
Agosta et al. (2010)YesYesYesYesYes
Lehericy et al. (2010)YesYesYesYesYes
Takahashi et al. (2011)YesYesYesYesYes
Ghosh et al. (2012)YesYesYesYesNo
Giordano et al. (2013)YesYesYesYesYes
Kamiya et al. (2013)YesYesYesYesNo
Lagarde et al. (2013)YesYesYesYesYes
Whitwell et al. (2013)YesYesYesYesYes
Sandhya et al. (2014)YesYesYesYesYes
Burciu et al. (2015)YesYesYesYesYes
Piattella et al. (2015)YesYesYesYesYes
Wang et al. (2015)YesYesYesYesNo
Santos-Santos et al. (2016)YesYesYesYesYes
Total18 out of 1818 out of 1818 out of 1818 out of 1815 out of 18
Key: A, Left inferior frontal gyrus/insula/superior temporal gyrus/precentral gyrus (premotor cortex)/putamen/orbitofrontal cortex; B, Right/Left thalamus/midbrain/caudate nucleus; C, Right/Left anterior cingulate cortex/(pre-) supplementary motor area/superior medial frontal cortex/medial orbitofrontal cortex; D, Right inferior frontal gyrus/insula/superior temporal gyrus/putamen/precentral gyrus (premotor cortex); E, Left anterior cerebellum (lobule III/IV/V); Yes, the cluster reported; No, the cluster not reported.
T4Table 4Regions of GM heterogeneity from the SDM analysisKey: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.Regions of GM heterogeneity from the SDM analysis
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)46, 14, 215234.800.0000046
Left insula/superior temporal gyrus (BAs 47, 13, and 48)-40, 0, -29844.590.0000077
Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)2, 50, 89624.260.000034
Right/Left thalamus2, -18, -83065.17∼0
Left inferior frontal gyrus (BAs 45, and 44)-50, 20, 221783.490.00044
Left cerebellum (lobule III)-8, -34, -18373.190.0011
Right caudate nucleus12, -2, 20112.770.0030

Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.

Table 4
Regions of GM heterogeneity from the SDM analysis
Anatomical regionsMaximum MNI coordinateNo. of VoxelsSDM-Zp
Right inferior frontal gyrus/insula/superior temporal gyrus (BAs 47, 13, 38, 45, and 48)46, 14, 215234.800.0000046
Left insula/superior temporal gyrus (BAs 47, 13, and 48)-40, 0, -29844.590.0000077
Right/Left ACC/medial OFC (BAs 32, 10, 11, and 24)2, 50, 89624.260.000034
Right/Left thalamus2, -18, -83065.17∼0
Left inferior frontal gyrus (BAs 45, and 44)-50, 20, 221783.490.00044
Left cerebellum (lobule III)-8, -34, -18373.190.0011
Right caudate nucleus12, -2, 20112.770.0030
Key: GM, Gray Matter; SDM, Seed-based d Mapping; MNI, Montreal Neurological Institute; No., Number; BA, Brodmann Area; ACC, Anterior Cingulate Cortex; OFC, Orbitofrontal Cortex.
T5Table 5Meta-regression analysesKey: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.Meta-regression analyses
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
Effect of age: GM changes in studies with older patients compared to younger patients
a. Right/Left thalamus/midbrain2, -14, 6655-4.98∼0
b. Left insula/inferior frontal gyrus-44, 12, 4641-4.150.0000026
Effect of gender: GM changes in studies with a higher male ratio of patients
c. Left caudate nucleus/thalamus-6, -4, 14155-3.710.000014
Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score
d. Left insula-32, 12, 1015-3.380.00015
Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score
e. Left insula-32, 22, 041-3.660.00015
Effect of illness duration: GM changes in studies of patients with longer average illness duration
f. Left caudate nucleus/thalamus/Right thalamus0, -8, 121447-6.00∼0
Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength
g. Right inferior frontal gyrus54, 12, 16127-4.020.000037
h. Left supplementary motor area-2, 24, 5611-3.490.00032

Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.

Table 5
Meta-regression analyses
Anatomical labelPeak MNI coordinate (x, y, z)No. of voxelsSDM-Z valuep value
Effect of age: GM changes in studies with older patients compared to younger patients
a. Right/Left thalamus/midbrain2, -14, 6655-4.98∼0
b. Left insula/inferior frontal gyrus-44, 12, 4641-4.150.0000026
Effect of gender: GM changes in studies with a higher male ratio of patients
c. Left caudate nucleus/thalamus-6, -4, 14155-3.710.000014
Effect of motor severity: GM changes in studies of patients with higher average UPDRS-III score
d. Left insula-32, 12, 1015-3.380.00015
Effect of cognitive impairment: GM changes in studies of patients with lower average MMSE score
e. Left insula-32, 22, 041-3.660.00015
Effect of illness duration: GM changes in studies of patients with longer average illness duration
f. Left caudate nucleus/thalamus/Right thalamus0, -8, 121447-6.00∼0
Effect of scanner field-strength: GM changes in studies of patients with higher scanner field-strength
g. Right inferior frontal gyrus54, 12, 16127-4.020.000037
h. Left supplementary motor area-2, 24, 5611-3.490.00032
Key: GM, Gray Matter; MNI, Montreal Neurological Institute; No., Number; SDM, Seed-based d Mapping; BA, Brodmann Area; UPDRS-III, Unified Parkinson's Disease Rating Scale-motor examination; MMSE, Mini-Mental State Examination.
\ No newline at end of file From d1e2a31cc0cc82c21ab435c219c03751009ddbcd Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 09:56:21 -0600 Subject: [PATCH 18/42] remove old functions --- .../participant_demographics/__init__.py | 3 -- ns_pipelines/participant_demographics/run.py | 48 +------------------ 2 files changed, 1 insertion(+), 50 deletions(-) diff --git a/ns_pipelines/participant_demographics/__init__.py b/ns_pipelines/participant_demographics/__init__.py index 8c71735..e69de29 100644 --- a/ns_pipelines/participant_demographics/__init__.py +++ b/ns_pipelines/participant_demographics/__init__.py @@ -1,3 +0,0 @@ -from .run import __main__ as run - -__all__ = ['run'] \ No newline at end of file diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index dcc3f41..8dc6c60 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -3,9 +3,6 @@ from publang.extract import extract_from_text from openai import OpenAI -from pathlib import Path -import json -import pandas as pd import logging from . import prompts @@ -43,52 +40,10 @@ def _load_client(model_name): return client + def _load_prompt_config(prompt_set): return getattr(prompts, prompt_set) -def _save_predictions(predictions, clean_preds, extraction_model, prompt_set, output_dir): - short_model_name = extraction_model.split('/')[-1] - outname = f"{prompt_set}_{short_model_name}" - predictions_path = output_dir / f'{outname}.json' - clean_predictions_path = output_dir / f'{outname}_clean.csv' - - json.dump(predictions, predictions_path.open('w')) - - clean_preds.to_csv( - clean_predictions_path, index=False - ) - -def __main__(extraction_model, docs_path, prompt_set, output_dir=None, **kwargs): - """ Run the participant demographics extraction pipeline. - - Args: - extraction_model (str): The model to use for extraction. - docs_path (str): The path to the csv file containing the documents. - prompt_set (str): The prompt set to use for the extraction. - output_dir (str): The directory to save the output files. - **kwargs: Additional keyword arguments to pass to the extraction function. - """ - - docs = pd.read_csv(docs_path) - - extraction_client = _load_client(extraction_model) - - prompt_config = _load_prompt_config(prompt_set) - if kwargs is not None: - prompt_config.update(kwargs) - - output_dir = Path(output_dir) - - predictions, clean_preds = extract( - extraction_model, extraction_client, docs, - **prompt_config - ) - - if output_dir is not None: - _save_predictions(predictions, clean_preds, extraction_model, prompt_set, output_dir) - - return predictions, clean_preds - class ParticipantDemographicsExtraction(IndependentPipeline): """Participant demographics extraction pipeline.""" @@ -130,5 +85,4 @@ def _run(self, study_inputs, n_cpus=1): ) # Save predictions - return {"predictions": predictions, "clean_predictions": clean_preds} From cd5bb836f7b09f4e51928a68d91ee8b68551303d Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 09:57:12 -0600 Subject: [PATCH 19/42] commit the cassette --- ...est_ParticipantDemographicsExtraction.yaml | 2110 +++++++++++++++++ 1 file changed, 2110 insertions(+) create mode 100644 tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml diff --git a/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml b/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml new file mode 100644 index 0000000..734c5a2 --- /dev/null +++ b/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml @@ -0,0 +1,2110 @@ +interactions: +- request: + body: '{"messages": [{"role": "user", "content": "\nYou will be provided with + a text sample from a scientific journal.\nThe sample is delimited with triple + backticks.\n\nYour task is to identify groups of participants that participated + in the study, and underwent MRI.\nIf there is no mention of any participant + groups, return a null array.\n\nFor each group identify:\n - the number of + participants in each group, and the diagnosis.\n - the number of male participants, + and their mean age, median age, minimum and maximum age\n - the number of + female participants, and their mean age, median age, minimum and maximum age.\n - + if this group of participants underwent MRI, fMRI or neuroimaging procedure.\n\nBe + as accurate as possible, and report information (especially diagnosis) using + the technical terms (and abbreviations) used in the article.\nIf any of the + information is missing, return `null` for that field.\n\nText sample: \n## + Introduction \n \nWith more than 25% of high school seniors reporting recent + use and 6.5% of 12th graders being daily users ( ), marijuana (MJ) is the most + frequently used illicit substance among adolescents. Across all age groups over + 70% of new drug initiates start with using MJ at an average age of 18 years + ( ). Indeed, the scope of MJ use prevalence is of great public interest, as + MJ use in early adolescence is associated with increased risk of greater substance + use, legal problems, disrupting education, injuries/medical problems, developing + psychopathology, cognitive changes and chronic psychosocial struggles ( , , , ). + Taken together, rates of MJ use are suggestive of an epidemic based in adolescence, + which is concerning not just due to societal cost, but also due to the potential + to offset sensitive brain development during this period. \n\nDespite its prevalence, + the impact of MJ use on adolescent brain development is not fully known. Important + neuromaturational processes during adolescence through young adulthood are believed + to bring about improved higher-order cognition by refining neural systems locally + and globally through white and gray matter development ( , , ). In general, + gray matter reductions and cortical thinning coincide with increased white matter + volume and organization through adolescence and young adulthood, suggestive + of synaptic pruning and axonal myelination ( , , , , ). The endogenous cannabinoid + (CB) system is also immature during adolescence ( , ). In an animal model ( + ) imaged CB1 receptor binding using PET and found relatively lower activation + of CB1 receptors in adolescent rats compared to adult rats in brain areas including + those in the frontal cortex, temporal lobe (hippocampus and amygdala) and sub-cortical + regions including striatal regions, thalamus, hypothalamus, superior colliculus. + Thus, adolescence represents a developmental period with vulnerability to structural + and functional changes due to exogenous MJ exposure. \n\nAdolescent MJ use has + the potential to cause structural and functional changes in the brain by altering + cannabinoid signaling. One possible mechanism would be blunt neurotoxic influence. + For example, delta9-tetrahydrocannabinol (THC), the primary psychoactive component + in MJ that binds CB1 receptors, is reported to cause cell shrinkage and damage + DNA strands in THC-treated neuron cultures ( ). This may be the mechanism by + which smaller volumes have been observed in individuals exposed to cannabis + during adolescence ( ). However, it is more likely that MJ exerts its influence + on brain development indirectly. The cannabinoid system plays a role in modulating + other neurotransmitters, including gamma-aminobutyric acid (GABA), glutamate + and monoamines ( ). Specifically, activation of CB1 receptors is associated + with down-regulating inhibitory GABAergic transmission in cortical interneurons + during adolescence ( , ). In addition, CB signaling inhibits microglia function + ( ). These two points are important because cortical pruning processes involve + glial-mediated synaptic elimination and altering the excitatory/inhibitory balance + is liable to disrupt the selective tagging and preserving synapses ( ). The + impact of this indirect influence on the developing brain may be in the observations + of abnormal connectivity in those who began MJ use in adolescence ( ). Evidence + from human neuroimaging studies lends greater support to MJ-related disruptions + to brain development. \n\nStructural neuroimaging studies have indicated that + volumes of several brain areas are smaller in heavy adult MJ users especially + in areas enriched with cannabinoid 1 (CB1) receptors, such as medial temporal + lobe, and prefrontal cortex ( ). Studies of adult chronic MJ users note brain + volume reductions in temporal lobe, insula, and prefrontal cortex, amygdala + and hippocampus ( , , , , ). Among different characteristics of MJ involvement + (e.g., dependence symptoms, use frequency, consumption), the age of initial + MJ use is a robust factor that has been associated with smaller brain volumes + in users. For example, observed left parahippocampal gyrus and right temporal + pole structural differences in 25 regular MJ users compared to 22 occasional + users, however, even the occasional users who began smoking MJ during adolescence + (before age 18) demonstrated similar brain changes as the regular users. Our + group has also found links with early MJ use onset ( ) and structural connectivity + with orbitofrontal cortex in a cohort of daily MJ users, suggesting complex + neuroadaptive processes related to MJ use in the context of adolescent brain + development ( ). These findings underscore the potential for significant heterogeneity + in brain changes among adult MJ users, especially those who began using MJ during + neurodevelopment. \n\nStudies comparing early adolescent MJ use to users initiating + MJ use in later adolescence provide further evidence for the potential of MJ + to cause enduring change. The few studies that have directly investigated the + timing of the effects of MJ during adolescence have noted divergent neurodevelopment + effects. For example, in an fMRI study by Gruber and colleagues, functional + and behavioral differences during an interference task were reported between + early (before age 16) and late (after age 16) MJ users ( ) ( ). The same group + also reported decreased white matter integrity in early onset vs. late onset + MJ users (mean age 14.46 vs. 17.93) ( ). Similar differential effects have also + been noted in parietal lobe activation between early and late adolescent binge + drinkers during a spatial working memory task ( ). These studies highlight the + importance of clarifying the differential neural effects of early- and late-adolescent + onset use. \n\nTo that end, in the current study, we compared daily MJ users + who were early onset users (<16 years old) versus late onset users (16 years + old) on measures of cortical morphology that are sensitive to developmental + changes. We aimed to characterize both the effect of early onset status on cortical + morphology as well as assess for morphological patterns linked to the continued + use of MJ after early and late adolescent MJ initiation. We expected early onset + users to show a morphological pattern consistent with disruption of early adolescent + brain development (e.g., increased cortical thickness, greater gray/white definition + of the cortical ribbon via disruptions to adolescent pruning processes) that + may be more consistent with indirect impact of MJ of brain development. While + gray matter decline has been shown to be associated with marijuana use, particularly + in areas rich in CB1 receptors, increased cortical thickness and greater gray/white + definition in the cortical ribbon point to potential disruption in neurodevelopment + (i.e. synaptic pruning) that may result from MJ use at key developmental stages + (i.e. earlier as opposed to later in adolescent neuronal development). Such + disruptions may extend to gyrification as well. While this process begins in + utero, there is evidence that gyrification is ongoing into adolescence ( , , ) + and may also display aberrant developmental patterns in the presence of MJ use. + \n\n\n## Methods \n \nThis study was approved by the University of Texas at + Dallas (UTD) and University of Texas Southwestern Medical Center (UTSW) Institutional + Review Boards. All participants were recruited from the Dallas-Ft.Worth metro + area via flyers and advertisements. Following informed consent, MJ users completed + two sessions a baseline appointment for collecting demographic, psychosocial + and behavioral measures and a thorough substance use history. Three days later + the participants returned for a neuroimaging appointment. Prior to their scanning + session, participants were asked to be abstinent from MJ use for 72h, from alcohol + for 24h, and from caffeine and cigarettes for the preceding 2h. These were confirmed + by self-report (MJ, alcohol, caffeine and cigarettes), quantitative THC urinalysis + (MJ), and by breath alcohol level of .000 (alcohol) at the start of their session. + \n\n### Participants \n \nWe scanned 45 regular heavy MJ users as part of the + parent project. Inclusion criteria were: right-handedness, English as the primary + language and no histories of psychosis, traumatic brain injury, and MRI contraindications + (e.g., pregnancy, non-removal metallic implants, claustrophobia). One subject + reported a history of anxiety and depression and one other reported a history + of ADHD as a child. Additional exclusions for the current study included: Axis + I diagnosis (via SCID) other than cannabis use disorder, unusable sMRI due to + motion artifact or poor signal-to-noise ratio that precluded accurate tissue + segmentation ( n =1) and incomplete drug use histories ( n =2). Of the 42 + remaining cases, 22 were early onset users (onset of first use before age 16). + Group categorization using onset of regular use as opposed to onset of first + use maintained the same grouping (mean early onset of regular use=16.5, mean + late onset of regular use=19.0). Regular use was defined as at least one time + per week. To determine how age of onset of regular MJ use influenced our reported + effects, we performed these analyses while covarying for age of onset of regular + use (see ). summarizes demographic and substance use information according + to onset status. summarizes the correlation between age and identified marijuana + use variables. Only MJ years of use and current age showed a statistically significant + correlation. Participants were recruited based on self-reported daily MJ use + and a positive urinalysis for THC metabolites at their baseline visit. All of + the participants were screened via urinalysis for other drugs of abuse and were + excluded if drugs (other than MJ) were detected. Participants were required + to have used MJ for a minimum of 5000 lifetime occasions and self-report daily + use (without >24h abstinence) for the last 60 days. \nSample characteristics. + MJ, marijuana. \n \nThe correlations between current age and all MJ + use variables. \n \n\n\n### MRI acquisition and analysis \n \n#### Image + acquisition \n \nScanning sessions took place at the Advanced Imaging Research + Center at the University of Texas, Southwestern Medical Center three days following + their initial visit. Another verification of THC metabolites via urinalysis + was also performed before the scan. MRI images were collected using a 3T Philips + whole-body scanner equipped with Quasar gradient subsystem (40mT/m amplitude, + a slew rate of 220mT/m/ms). High-resolution T1-weighted anatomical scans were + collected using a MPRAGE sequence: TR/TE/TI=2100/3.70/1100ms; flip angle=12; + field of view=256mm256mm; slab thickness=160mm (along left-right direction); + voxel size=1mm1mm1mm, Total scan time=3m 57s. \n\n\n#### Image processing \n \nMPRAGE + anatomical scans were pre-processed for surface-based analyses using FreeSurfer + v5.3 semi-automated pipeline ( ). This semi-automated pipeline included spatial + (Talairach) and signal intensity normalization of images, volumetric segmentation + and subcortical labeling ( , ). Outer gray matter and white matter boundaries + were then identified and reconstructed into a mesh of over 150,000 tessellated + vertices to allow point-to-point surface measures ( ). Next, gyral anatomy is + aligned to a standard spherical template using surface convexity and curvature + measures. Resulting surfaces were inspected, blind to MJ onset status, to identify + and correct any errors made during cortical reconstruction. Modifications to + the volumes were made as necessary to correct for tissue misclassifications + according to FreeSurfer''s wiki manual ( ). In preparation for analysis, each + morphological measure for each case was co-registered to a standard template + (fsaverage). Anatomical labels in FreeSurfer ( ) were used for interpretation + of results. \n\n\n\n### Morphological measures \n \n#### Cortical thickness + \n \nThe width of the cortical ribbon was measured as the distance between + corresponding vertices of the white matter and gray matter surfaces at each + vertex in the cortical mantel ( ). \n\n\n#### Graywhite matter ratio (GWR) \n \nTo + assess the quality of cortical ribbon definition, a tissue contrast between + gray and white matter signal intensities was computed as a percent ratio (WG)/(.5*(W+G)) + (from pctsurfcon v1.11.2.1, inbuilt component of FreeSurfer pipeline v5.3, 2011). + White matter signal intensities were measured at an absolute length of 1mm below + the graywhite border surface and gray matter signal was measured 30% into the + cortical ribbon ( ). \n\n\n#### Local gyrification index \n \nThe cortical + surface from FreeSurfer''s main pipeline is further processed to create an outer + surface that encapsulates the gyral and sulcal curvature for each hemisphere, + which serves as a basis for calculating a local gyrification index ( ). LGI + is measured as the amount of cortex within the sulcal folds beneath the outer + surface compared to the amount of visible cortex that touches the outer surface. + Cortical maps are generated from repeated iterations of delineating a 25mm radius + sphere on the outer surface and its corresponding point on the cortical surface + using a matching algorithm. \n\n\n\n### Background and premorbid characteristics + \n \n#### Sample characteristics \n \nAge, gender, education level, ethnicity, + along with other background information, was obtained using a standard demographics + questionnaire. The two-subtest administration of the Wechsler Abbreviated Scale + of Intelligence (Vocabulary and Matrix Reasoning) provided estimates of intellect + ( ). \n\n\n#### Substance use \n \nThe Substance Use Disorder modules of the + Structured Clinical Interview for DSM-IV (SCID) ( ) were administered by a trained + research assistant to assess for lifetime and current symptoms of abuse and + dependence for alcohol, nicotine, MJ and other substances. The SCID interview + also provided the onset of use information. A Time Line Follow-Back (TLFB) approach + was used to quantify alcohol, nicotine, and MJ use patterns for 90 days prior + to study participation ( ). Marijuana use in grams was obtained via self-report + in response to probes aimed at quantifying their regular use. \n\n\n\n### Statistical + analyses \n \nStatistical analyses were conducted in SPSS 18.0 for behavioral + and psychosocial measures whereas general linear model group comparisons on + surfaced-based morphology measures were carried out FreeSurfer''s built-in application + QDEC (v1.5). Independent samples t -tests, MannWhitney U -tests or chi-square + tests, compared groups on background and demographic variables (see ). Before + statistical analysis was conducted, the dependent measures of cortical thickness, + GWR and LGI were smoothed using a FWHM Gaussian filter with a width of either + 10 or 15mm. Separate univariate general linear model (GLM) was then used to + model cortical thickness, GWR and LGI with onset status of MJ use as a between + groups factor. The dependent variables were thickness, graywhite ratio or local + gyrification index and the independent variables were either recent monthly + MJ use in grams (MJ grams) or duration of MJ use (MJ years). Age and total drinks + in the past 2 months were treated as nuisance covariates in the model. Using + MJ years of use and MJ grams as independent predictors of interest allowed us + to characterize and differentiate the latent developmental effects from cumulative + and current effects of MJ use. The variable marijuana years of use was based + on the participants response to the question For how many years have you been + using marijuana regularly? Of note, an outlier in the early onset group was + removed before the statistical comparisons were performed. \n\n\n\n## Results + \n \n### Cortical thickness \n \nThere were no regions of group differences + in cortical thickness by early onset status alone, controlled for age and alcohol + use. However, MJ use characteristics were correlated with anterior dorsolateral + prefrontal cortex thickness based on onset status. Early onset users showed + increased thickness with increased MJ grams while late onset users showed thinner + cortex with increased MJ grams ( p <0.05 uncorrected) ( ). The same pattern + emerged with more years of MJ use being associated with thicker region of the + right medial temporal lobe in the early onset users and the reverse for the + late onset users ( p <0.05 uncorrected) ( ). \nClusters of significant age + of onsetmarijuana use interactions. GWR, gray/white matter border ratio; LGI, + local gyrification index. \n \nEarly vs. late onset marijuana users show + divergent morphological patterns based on current marijuana use (measured in + grams; MJ grams) in overlapping areas of anterior prefrontal cortex. GWR, gray/white + matter border ratio; LGI, local gyrification index. \n \n\n\n### Graywhite + matter contrast \n \nThere were no regions of group differences in graywhite + matter contrast by early onset status alone, controlled for age and alcohol + use. However, current MJ consumption (grams) and onset status were differentially + correlated with graywhite matter contrast in a left anterior dorsal frontal + region ( p <0.05, FWE corrected). Increased graywhite contrast with heavier + MJ use was seen in the early onset users and the opposite was seen in later + onset users (heavier current use linked to decreasing GWR). The same pattern + was seen between duration of MJ use in two prefrontal cortex clusters of the + right dorsal frontal and medial orbitofrontal area p <0.05, FWE corrected more + years of MJ use were linked to greater GWR among early users ( ). \n\n\n### + Gyrification \n \nMJ use onset status alone showed no significant main effects + above age and alcohol covariates. However, onset status was correlated with + divergent patterns between local gyrification and MJ use, whereby early onset + users showed decreasing LGI with increasing MJ consumption and longer duration + of use in prefrontal cortex regions p <0.05, FWE corrected. The left hemisphere + clusters encompassed the majority of the length of the middle lateral surface + of the left cortex, including motor cortices, parietal lobe and multimodal integration + areas ( ). \n\n\n\n## Discussion \n \nThe present study was designed to characterize + the cortical architecture in adolescent onset MJ users by comparing early adolescent + onset users to late adolescent onset in MJ use on measures of cortical thickness, + gray/white matter contrast and gyrification. The primary finding was that early + versus late onset MJ users showed a divergent pattern in cortical thickness, + definition of the cortical ribbon and local gyrification with continued use + through and beyond adolescent years. Specifically, early onset users showed + cortical thickening, enhanced gray/white matter contrast, and decreased gyrification + in association with more years of MJ use and current consumption of MJ in grams + in frontal and temporal regions areas that underlie higher order cognition + including executive functioning, learning and memory. Findings were above and + beyond effects of alcohol and current age, therefore, results are less likely + to reflect morphological trends due to aging. \n\nOur findings did not find + the expected age of onset differences previously reported in marijuana users + ( , ). This inconsistency suggests that the age of onset effects may be more + robust in brain white matter connectivity ( ) and function ( ) than brain surface + morphometry. To date, the few studies that have described altered cortical morphology + in MJ users have led to mixed findings. identified brain regions with decreased + sulcal depth suggestive of lower gyrification in a study of adult MJ users. recently + reported increased cortical thickness in the entorhinal cortex among 24 adolescent + MJ users (mean age=17.7, mean MJ onset age=15.4) relative to peer controls. + However, the authors also reported a negative relationship between cortical + thickness and total MJ use in the right paracentral gyrus, and they observed + consistent positive relationships in various brain regions between age of MJ + onset and thickness. In the only other known adolescent study of cortical thickness + and MJ, Lopez-Larson and colleagues studied 18 adolescent heavy MJ users (similar + in age and MJ onset as ) and reported mixed findings of increased thickness + in prefrontal/insula regions and decreased thickness in posterior/temporal lobe + areas in the MJ users compared to controls. In contrast to , found areas + of the frontal lobe and insula that were thinner with increased urine THC metabolites + and thicker with earlier age of onset. Select findings from the current study + align with aspects of both of these studies, with a consensus supporting findings + of a negative dose-dependent relationship between MJ use and cortical thickness. + Given the low availability of studies to compare, this consensus is very limited. + Although Jacobus et al. and Lopez-Larson et al. found the opposite effect of + age of onset on thickness, the pattern of divergence among early vs. late onset + users in the current study is more consistent with the latter study, whereby + we saw early onset users exhibit thicker cortex with continued MJ use. Taken + together, findings of increased thickness related to early MJ onset accompanied + by negative dose-dependent relationships with MJ exposure may reflect two distinct + processes. One process may be specific to the interactions with cortical development + during early adolescence, likely leading to a disruption in pruning, and, the + other, specific to the pharmacological effect with heavy chronic MJ use. \n\nIn + the only known study to examine the curvature-morphology of the cortex in adult + MJ users, identified decreased sulcal concavity and thinner sulci in 23MJ + users compared to controls ( n =44), also in prefrontal areas. However, they + did not observe significant relationships with age, MJ onset age, or cumulative + MJ use. It is interesting that the authors detected group level differences + (MJ vs. controls) but no correlations with MJ use characteristics such as dose + or age of onset, whereas our primary findings are the consistent effects of + continued MJ use differing after early or late adolescent onset. There are substantial + methodological explanations for this disparity. For example, the current study + did not compare morphology in MJ users to a normative control sample, therefore, + it is feasible that group-level differences may emerge with such a comparison. + Likewise, we deliberately covaried for current age in order to control for brain + changes with aging and thus optimize our interrogation of developmental effects + of early onset age and of aspects of continued use. \n\nThe heterogeneity of + MJ effects clearly suggests a multifactorial system of neurobiological processes + involved. The primary results uphold that age of onset is a robust variable + that differentiates heavy MJ users based on early versus late MJ onset. However, + this group distinction relied on current use characteristics. Therefore, in + the absence of group-level differences, the interactions between onset age and + current use indicates that continued cannabis exposure and early adolescent + developmental factors both contribute to a dynamic and sustained departure from + what is expected based on developmental studies. \n\nTypical synaptic refinement + processes during early adolescence are in the context of long-term depression + and potentiation of cortical neurons in order to facilitate neuronal remodeling. + Thus, the normal course of early adolescent development is uniquely vulnerable + to disruption by MJ due to the electrochemical conditions and maturity of brain + processes that would not present together again. Cass and colleagues tested + the sensitivity of early adolescence cannabinoid exposure in an animal model + ( ). They found that acute administration of cannabinoid agonists in early, + middle and late adolescent rats led to a state of frequency-dependent disinhibition + of neurons in the frontal cortex in the early-to-middle adolescent rats, but + not in the late adolescent rats. Moreover, the authors also noted that adult + rats previously exposed to cannabinoid agonists in adolescence displayed comparable + neuronal disinhibition. Thus, by changing the inhibitory/excitatory landscape + during adolescence, MJ can influence lasting changes to typical cortical remodeling + during sensitive early adolescent years. \n\nThe sequence of pruning and myelination + likely plays a formative role in lasting changes from early adolescent onset + MJ use. With decreased synaptic elimination, our findings of greater GW border + contrast may reflect greater proliferation of myelin at the boundary of the + cortical ribbon where non-pruned synapses remained with linked axons. Findings + of altered white matter tissue qualities are mixed in adolescent and adult MJ + user samples. Some report both increases and decreases in fractional anisotropy + (FA) and average water diffusion ( ) whereas others report consistent decreases + in FA among adolescent MJ users ( , ) or null findings ( ). Two studies of + diffusion tensor imaging in adult MJ users reported reduced FA in users compared + to controls ( , ). In addition to equivocal findings, research is needed to + address the microstructural changes that could result in altered definition + of the cortical ribbon. For example, rather than whole brain techniques that + assess diffusion measures along major white matter tracts, indices assessing + axonal organization along radial and interneuron association fibers along the + cortical ribbon are needed. This scenario played out could result in increased + gray matter (thicker cortex from disrupted pruning) and the myelination of connections + to these spared terminals would result in increased density of white matter + at the cortical boundary. Without any known studies of adolescent development + of the gray/white tissue contrast at the cortical border to serve as a point + of comparison, we speculate that early adolescent disruption of pruning and + subsequent myelination of connections at the cortical boundary would be reflected + by increased GWR as we saw in the current study. \n\n\n## Limitations and conclusions + \n \nThe cross-sectional nature of this study limits causal attributions in + terms of what we can infer to be directly related to the effects of MJ. Although + a longitudinal design is optimal for addressing brain changes directly due to + MJ, cross-sectional studies facilitate data-driven hypotheses that can be assessed + directly in prospective studies. \n\nIt is important to keep in mind that the + participants were not explicitly asked for possible years of abstinence during + their period of regular use, which may have created possible inflation in reported + duration of regular use. However, because the participants provided number of + years of regular marijuana use, this inherently suggests continued, uninterrupted + years of use. Concurrent nicotine use could have also influenced our reported + results. But in the absence of a larger sample size and the presence of huge + variance in nicotine use in the current sample, we were unable to verify the + effect of nicotine use in the reported results. \n\nInterpretation of these + findings is also limited by the lack of behavioral anchors for the observed + morphological effects and lack of information on other aspects of developmental + history that could further characterize the effects of marijuana during neurodevelopment. + This is further limited by the absence of expected patterns based on normative + data. Given the varied directions of effects and the small sample size, these + findings should be replicated and be viewed as preliminary. \n\nTo conclude, + early MJ use was linked to altered neurodevelopmental patterns in brain regions + sub-serving higher-order cognitive process. Clinical implications include need + for early, targeted intervention. Given that the most robust results were related + to interactions between onset age and continued use through emerging adulthood, + harm reduction approaches may be effective in moderating adolescent MJ use to + levels that are less likely to cause long-term developmental changes. \n\n\n## + Conflict of interest \n \nThe authors report no conflicts of interest. \n\n + \n\n Call the extractData function to save the output."}], "model": "gpt-4o-2024-08-06", + "response_format": null, "temperature": 0, "tools": [{"type": "function", "function": + {"name": "extractData", "description": "Extract data from scientific text", + "parameters": {"$defs": {"GroupImaging": {"properties": {"count": {"description": + "Number of participants in this group", "title": "Count", "type": "integer"}, + "diagnosis": {"description": "Diagnosis of the group, if any", "title": "Diagnosis", + "type": "string"}, "group_name": {"description": "Group name, healthy or patients", + "enum": ["healthy", "patients"], "title": "Group Name", "type": "string"}, "subgroup_name": + {"description": "Subgroup name", "title": "Subgroup Name", "type": "string"}, + "male_count": {"description": "Number of male participants in this group", "title": + "Male Count", "type": "integer"}, "female_count": {"description": "Number of + female participants in this group", "title": "Female Count", "type": "integer"}, + "age_mean": {"description": "Mean age of participants in this group", "title": + "Age Mean", "type": "number"}, "age_range": {"description": "Age range of participants + in this group, separated by a dash", "title": "Age Range", "type": "string"}, + "age_minimum": {"description": "Minimum age of participants in this group", + "title": "Age Minimum", "type": "integer"}, "age_maximum": {"description": "Maximum + age of participants in this group", "title": "Age Maximum", "type": "integer"}, + "age_median": {"description": "Median age of participants in this group", "title": + "Age Median", "type": "integer"}, "imaging_sample": {"description": "Did this + subgroup undergo fMRI, MRI or neuroimaging, yes or no", "enum": ["yes", "no"], + "title": "Imaging Sample", "type": "string"}}, "required": ["count", "diagnosis", + "group_name", "subgroup_name", "male_count", "female_count", "age_mean", "age_range", + "age_minimum", "age_maximum", "age_median", "imaging_sample"], "title": "GroupImaging", + "type": "object"}}, "properties": {"groups": {"items": {"$ref": "#/$defs/GroupImaging"}, + "title": "Groups", "type": "array"}}, "required": ["groups"], "title": "BaseDemographicsSchema", + "type": "object"}}}]}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '31493' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.37.1 + x-stainless-arch: + - x64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - Linux + x-stainless-package-version: + - 1.37.1 + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.8.10 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAA9RUTWvbQBC9+1csc7aL5NiOrVtpaUvS0rQNBBoFMV6N5HX2Q+yuWhvj/15W/pDs + OFDoqTqI5b2ZN7NPmtn0GAORQ8KAL9BzVcnB23tXf6LRD/rmpj/vbn+t7j7+norr+e+vSnyHfsgw + 8yVxf8h6w42qJHlh9I7mltBTUI2vr+LJdDy7um4IZXKSIa2s/GBkBsNoOBpE00E02ScujODkIGGP + PcYY2zTv0KLOaQUJi/oHRJFzWBIkxyDGwBoZEEDnhPOoPfRbkhvtSYeudS1lh/DGyIyjlG3h3bPp + nFufUMosfn5++PxudqOX2kWzh+Vsfu+K5e2HTr2d9LpqGipqzY/+dPgjnpwVYww0qiaXVt4i9+/R + 41k6Y4C2rBVpH1qHTQqlNXXlUkgeNylwU2ufQjIc9lPIBZbaOBHIFAitXDOjHXn25YbVjqxLob8X + yELtJq5CL4J8w7l6fka/KqNQUnao3/idQkGXUCwpU4T6FLGoSzoLElqoWp2BuLoAUi66gkJhKXSZ + OQz/adP4mlwK237XpOiFSRI9/btHr6n8HxY9beHkn9v2Lp2fOuNkqagdyv2c7fHtcXClKStr5u5s + DqEQWrhFZgldMw/dsewdqjV1oD6ZfKisUZXPvHkmHWTH08l+T0C7mVo6Ho/2rDceZUtMovjAnEhm + OXkUzXY4LiSOfEF5m9suJqxzYTpEr3P9l/1c0t5ZEL7GX8i3BOdUecqzylIu+Omd2zBLYXW/FnY0 + umkY3Np5UlkhdEm2sqLZnlBUWTye5dOrUcw59La9PwAAAP//AwD0g4FuRgYAAA== + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 8e306853cabf46e3-DFW + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Fri, 15 Nov 2024 15:52:21 GMT + Server: + - cloudflare + Set-Cookie: + - __cf_bm=g5eID2ld6R4b.XyWylyzBEfeIDJpKhYSOZvJcpNVZdE-1731685941-1.0.1.1-0iwvSpf0nRJRxl3OVhfVHJt.H7xxLT4E4a2hL_jIzrisbSKqg47tUNmupczlnhSX3GGNxInEkU1XRtklNMsHCA; 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Inclusion in an NLM + database does not imply endorsement of, or agreement with,\n the contents + by NLM or the National Institutes of Health.\n Learn more:\n PMC Disclaimer\n |\n \n PMC + Copyright Notice\n \n\n\n\n\n\nDev Cogn Neurosci. 2018 Apr; 30: 324-332. + Published online 2017 Jun 13. doi: 10.1016/j.dcn.2017.06.001PMCID: PMC6969119PMID: + 28648549Hippocampal spatial mechanisms relate to the development of arithmetic + symbol processing in childrenRomain Mathieu,a,b, Justine Epinat-Duclos,a Jessica + Lone,a Michel Fayol,c Catherine Thevenot,d and Jrme Pradoa,Romain MathieuaInstitut + des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National de la Recherche + Scientifique (CNRS) & Universit de Lyon, Bron, FrancebFacult de Psychologie + et des Sciences de lEducation, Universit de Genve, 1205 Genve, SwitzerlandFind + articles by Romain MathieuJustine Epinat-DuclosaInstitut des Sciences Cognitives + Marc Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) + & Universit de Lyon, Bron, FranceFind articles by Justine Epinat-DuclosJessica + LoneaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, Centre National + de la Recherche Scientifique (CNRS) & Universit de Lyon, Bron, FranceFind articles + by Jessica LoneMichel FayolcUniversit de Clermont Auvergne & CNRS, 63037 Clermont-Ferrand, + FranceFind articles by Michel FayolCatherine ThevenotdInstitut de Psychologie, + Universit de Lausanne, 1015 Lausanne, SwitzerlandFind articles by Catherine + ThevenotJrme PradoaInstitut des Sciences Cognitives Marc Jeannerod, UMR 5304, + Centre National de la Recherche Scientifique (CNRS) & Universit de Lyon, Bron, + FranceFind articles by Jrme PradoAuthor information Article notes Copyright + and License information PMC DisclaimeraInstitut des Sciences Cognitives Marc + Jeannerod, UMR 5304, Centre National de la Recherche Scientifique (CNRS) & Universit + de Lyon, Bron, FrancebFacult de Psychologie et des Sciences de lEducation, Universit + de Genve, 1205 Genve, SwitzerlandcUniversit de Clermont Auvergne & CNRS, 63037 + Clermont-Ferrand, FrancedInstitut de Psychologie, Universit de Lausanne, 1015 + Lausanne, SwitzerlandRomain Mathieu: rf.srnc.csi@ueihtamr; Jrme Prado: rf.srnc.csi@odarpj + Corresponding authors at: Institut des Sciences Cognitives Marc Jeannerod, UMR + 5304, Centre National de la Recherche Scientifique (CNRS) & Universit de Lyon, + 67 Boulevard Pinel, 69675 Bron cedex, France. rf.srnc.csi@ueihtamr, rf.srnc.csi@odarpjReceived + 2016 Aug 13; Revised 2017 May 4; Accepted 2017 Jun 3.Copyright 2017 The AuthorsThis + is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Associated + DataSupplementary Materialsmmc1.docx (414K)GUID: 753C89A9-7F53-4790-896E-8D8C1A531EE4AbstractUnderstanding + the meaning of abstract mathematical symbols is a cornerstone of arithmetic + learning in children. Studies have long focused on the role of spatial intuitions + in the processing of numerals. However, it has been argued that such intuitions + may also underlie symbols that convey fundamental arithmetic concepts, such + as arithmetic operators. In the present cross-sectional study, we used fMRI + to investigate how and when associations between arithmetic operators and brain + regions processing spatial information emerge in children from 3rd to 10th grade. + We found that the mere perception of a + sign elicited grade-related increases + of spatial activity in the right hippocampus. That is, merely perceiving + signs without + any operands elicited enhanced hippocampal activity after around 7th grade + (1213 years old). In these children, hippocampal activity in response to a + + sign was further correlated with the degree to which calculation performance + was facilitated by the preview of that sign before an addition problem, an effect + termed operator-priming. Grade-related increases of hippocampal spatial activity + were operation-specific because they were not observed with signs, which might + evoke rote retrieval rather than numerical manipulation. Our study raises the + possibility that hippocampal spatial mechanisms help build associations between + some arithmetic operators and space throughout age and/or education.Keywords: + Arithmetic, Development, Attention, Space, fMRI, Hippocampus1.IntroductionHumans + are unique in their ability to represent abstract mathematical concepts by culturally + invented symbols, such as Arabic numerals and arithmetic signs. Because these + symbols are arbitrary, learning the relationship between their identity and + the concept they represent is a challenge during early math education in children. + Most prior studies have focused on the mechanisms supporting the acquisition + of symbols representing numerical quantities (Piazza et al., 2007, Ansari, 2008, + Holloway and Ansari, 2009, Lyons and Ansari, 2009, Mundy and Gilmore, 2009). + However, efficient processing of symbols that convey fundamental arithmetic + concepts (i.e., operators) may be an important and largely neglected aspect + of arithmetic skills. This is suggested by the operator-priming effect (Roussel + et al., 2002, Fayol and Thevenot, 2012, Mathieu et al., 2017), whereby the anticipated + presentation of a + or - sign 150ms before a single-digit addition or subtraction + problem facilitates problem-solving in adults.What aspect of the processing + of an operator may cause the operator-priming effect in adults? A first possibility + is that an arithmetic sign may automatically evoke a network of facts. For example, + the perception of a + or - sign might pre-activate a network of additive or + subtractive facts that would have been built in declarative memory after years + of practice (Campbell and Xue, 2001, Ashcraft, 1992). Pre-activating such a + network would facilitate the retrieval of the answer from memory when operands + are presented. A second possibility is that an arithmetic sign may prime a specific + procedure that would have been automatized after its repeated practice during + arithmetic learning. For instance, Fayol and Thevenot argued that perceiving + a + or - sign might trigger an automatized procedure that could be linked to + the convocation of the mental number line and could correspond to a preparation + for a quick left-to-right or right-to-left browsing of this mental line (Fayol + and Thevenot, 2012). This proposal echoes the idea that adding or subtracting + numbers involves rightward and leftward shifts of attention from a source to + a target number along a mental map of numbers oriented from left to right, i.e., + the mental number line (MNL) (Hubbard et al., 2005, Masson and Pesenti, 2014, + Mathieu et al., 2016, Pinheiro-Chagas et al., 2017). Pre-activating such a procedure + would result in a facilitation of subsequent calculation when operands are presented, + thereby explaining the operator-priming effect.Interestingly, two lines of evidence + favor the procedural over the declarative interpretation of the operator-priming + effect. First, the effect is not observed with the sign and multiplication + problems (Roussel et al., 2002, Mathieu et al., 2017). Multiplication problems, + however, are explicitly learned by rote in school and multiplication is unanimously + viewed as the operation having the strongest association with a network of facts + in memory (Campbell and Xue, 2001, Galfano et al., 2003, Thibodeau et al., 1996). + Therefore, the lack of operator-priming effect for multiplication problems is + difficult to reconcile with the idea that the effect is due to associations + between operators and networks of stored facts. Second, in line with Fayol and + Thevenots proposal that + and signs may prime a spatial scanning of the MNL, + a recent study suggests that + and signs do evoke spatial intuitions. Specifically, + Pinhas et al. (2014) found that, when instructed to categorize + and signs + with left-hand or right-hand responses, adults tend to respond faster to + signs + with the right hand than with the left hand, whereas they tend to respond faster + to - signs with the left hand than with the right hand (Pinhas et al., 2014). + Thus, + and signs appear to have some automatic associations with the right + and left sides of space, respectively.Using fMRI, we recently found that such + spatial associations may stem from the fact that some arithmetic operators are + automatically processed in brain regions involved in spatial attention in adults. + We showed that the mere perception of a + sign elicits greater activity than + the mere perception of a sign in brain regions underlying overt spatial attention. + These included the frontal eye fields (FEF) and the posterior superior parietal + lobule (PSPL) (Mathieu et al., 2017). Thus, perceiving a + sign (but not a sign) + may be associated with a deployment of spatial attention in educated adults. + Therefore, the rightward shifts of attention that have been posited to underlie + addition problem-solving (Hubbard et al., 2005, Masson and Pesenti, 2014, Mathieu + et al., 2016) might be primed by the mere preview of the addition sign (but + not by the preview of a multiplication sign because multiplication is typically + learned by rote and unlikely to be associated with movements along the MNL). + Overall, there is mounting evidence that at least some arithmetic operators + (e.g., + but not signs) evoke spatial intuitions in adults, and that these + intuitions may relate to the operator-priming effect.However, associations between + operators and space are arguably not innate. Therefore, a fundamental outstanding + question is how and when such associations emerge in the developing brain. To + answer that question, we studied 34 children from 3rd to 10th grade while they + performed 3 tasks. First, fMRI activity was measured while children were instructed + to make eye saccades towards visually presented targets. This allowed us to + precisely localize several regions of interest (ROIs) involved in spatial attention + across children. Second, fMRI activity was measured in these spatial attention + ROIs while children were presented with trials in which a + sign was displayed + without any operands (hereafter addition sign-only trials). As in our previous + study in adults (Mathieu et al., accepted), activity during the perception of + addition sign-only trials was compared to activity associated with trials in + which a sign was displayed without any operands (hereafter multiplication sign-only + trials) because these do not appear to evoke any specific intuitions in adults + (Fayol and Thevenot 2012). This allowed us to identify the spatial attention + ROIs in which activity in response to a + sign (as compared to a sign) increases + with age and/or education, as well as the developmental time course of these + effects.1 Third, outside of the scanner, we asked subjects to perform an operator-priming + task and measured the correlation between inter-individual differences in the + size of the operator-priming effect and inter-individual differences in sign-related + activity in spatial attention ROIs as a function of grade. This allowed us to + evaluate when sign-related activity in spatial attention ROIs leads to an operator-priming + effect in children.2.Material and methods2.1. ParticipantsForty-two right-handed + children from 3rd to 10th grade participated in the study. All were native French + speakers. Participants did not have prior history of neurological disease, psychiatric + disorders, learning disabilities or attention deficits. All children and parents + provided written informed consent to participate in the study, which was approved + by the local ethics committee (CPP Sud-Est-II). Families received 80 for their + participation. Data from 8 subjects were excluded because of excessive head-movement + in the scanner (see criteria in the Section 2.7., n=3), poor whole-brain coverage + (i.e. susceptibility artefacts from dental braces, n=3) and unacceptably low + performance during the task (i.e., lower than 50% accuracy on the sign-plus-operand + trials, n=2). Therefore, the final sample consisted of 34 children (20 males) + from 3rd to 10th grade (age range: 815, mean age=11.37, SD=1.84). For each child, + a continuous measure of grade was calculated by taking into account the specific + date within the grade year when that child was scanned. The whole sample (n=34) + was evenly split into three groups as a function of grade: 11 children were + from the lower grades group (grade 3.25.4; mean=4.4), 11 children were from + the intermediate grades group (grade 5.66.9; mean=6.2), and 12 children were + from the higher grades group (grade 7.610.2; mean=8.5).2.2. Standardized measuresChildren + were administered standardized tests of intellectual and arithmetic abilities + to ensure that there were no age differences with respect to those measures. + Full-scale IQ was measured using the NEMI-2 (Cognet, 2006). Basic arithmetic + knowledge was evaluated with the Math-Fluency subtest of the Woodcock-Johnson-III + Tests of Achievement (WJ-III) (Woodcock et al., 2001). Across all participants, + standardized (i.e., age-normalized) scores on IQ (mean=112; SD=10) and Math + Fluency (mean=106; SD=16) tests were within the normal range. One-way ANOVAs + with the between-subject factor group (lower, intermediate, higher grades) revealed + no main effect of group on IQ (F(2,31)=0.591, p=0.560, BF10=0.29), indicating + that age-normalized intellectual abilities were similar across groups. However, + there was a main effect of group on Math Fluency (F(2,31)=5.867, p=0.007, BF10=7.24): + Children from intermediate grades had a higher age-normalized score (mean=118; + SD=18) than children from lower (mean=100; SD=11) and higher grades (mean=100; + SD=13). Therefore, we included standardized Math-Fluency scores as nuisance + covariate in all of our analyses.2.3. Behavioral sessionAfter standardized testing, + children participated in a behavioral session during which they performed an + operator-priming task adapted from Fayol and Thevenot (2012) and Roussel et + al. (2002). Children were asked to evaluate 56 single-digit addition and 56 + multiplication problems composed of operands between 2 and 9. Problems were + presented in both commutative orders. Tie problems were excluded. Problems with + a sum smaller than or equal to 11 and a product smaller or equal to 24 were + considered small. Other problems were considered large.In each trial, a problem + was presented with an answer (Fig. 1a). The arithmetic sign was presented either + 150ms before (Negative SOA condition) or at the same time (Null SOA condition) + as the operands (Fig. 1a). All problems were presented once in both SOA condition + with a valid answer. Twenty-eight addition and 28 multiplication problems were + also presented in both SOA condition with an invalid answer (obtained by adding + or subtracting 1 to or from the valid answer). Trials were pseudorandomly ordered + so that no more than three problems of the same type appeared consecutively. + Problems with an invalid answer were randomly chosen across subjects and the + order of blocks was counter-balanced between subjects. The experiment started + with 8 practice trials.Open in a separate windowFig. 1Experimental design. (a) + During the behavioral session, children (n=34) were asked to evaluate the result + of single-digit addition and multiplication problems. For both operations, the + arithmetic sign was presented either 150ms before (negative SOA trials), or + at the same time as the operands (null SOA trials). (b) In the scanner, children + (n=34) performed an arithmetic task during which they were presented with sign-only + (left) and sign-plus-operands (right) addition, multiplication and baseline + trials. In each trial, a sign (+, or ) was presented at the center of the screen + for 150ms. In sign-only trials, the trial ended with the presentation of the + sign and was simply followed by the inter-trial period of fixation. In sign-plus-operands + trials (filler trials), the + or sign was immediately followed by a single-digit + addition or multiplication problem (respectively) presented along an answer + and the sign was followed by 3 letters. In those cases, children had 5000ms + to evaluate whether the answer of the problem was true or false or to indicate + whether one of the 3 letters was a B.The experiment was controlled by Presentation + software (Neurobehavioral Systems, Albany, CA). Problems were displayed in white + Arial 60-point font on a black background. All trials started with the presentation + of a white central fixation dot for 1500ms, immediately followed by a red central + fixation dot for 1000ms signaling that the problem was about to be presented, + either in the negative SOA condition or in the null SOA condition (Fig. 1a). + Subjects had a maximum of 5000ms to evaluate whether the response was valid + or invalid as quickly as possible by pressing one of two keys on the computer + keyboard.2.4. fMRI sessionDuring fMRI scanning, children performed a spatial + attention localizer task and an arithmetic task. The spatial attention localizer + task consisted in alternating blocks of fixation and saccades. During saccade + blocks (n=9), participants were asked to make saccades towards several successive + target dots. Each saccade block contained 16 target dots (0.2 visual angle) + that appeared at random positions with an eccentricity of 3, 3.5, 4, 4.5, 5 + or 5.5 in the left or right visual field for an average of 800ms (with a jitter + of 200ms). During fixation blocks (n=9), participants were asked to maintain + fixation on a central dot for 12,800ms. Block order was counterbalanced across + children.During the arithmetic task, children were presented with sign-only + and sign-plus-operands versions of addition and multiplication trials (Fig. + 1b). Each trial started with the presentation of either a + or a sign at the + center of the screen for 150ms. In sign-only trials (n=30), the trial ended + with the presentation of the sign and was simply followed by the inter-trial + period of fixation (see below). These sign-only trials were our trials of interest + and allowed us to isolate neural activity due to the presentation of a sign + alone. We also included in the experiment sign-plus-operands trials (n=50). + In those filler trials, the + or sign was immediately followed by a single-digit + addition or multiplication problem (respectively) presented with an answer. + Participants were asked to indicate whether the answer was true or false. The + goal of these filler trials (for which associated activity would be difficult + to interpret because any effects could be attributable to the anticipatory presentation + of the operator, the appearance of the operands, or a combination of both of + these factors) was only to keep children engaged and attentive in the scanner. + They also induced an arithmetic context, thereby ensuring that the + and signs + presented in sign-only trials were perceived as arithmetic signs. Problems in + sign-plus-operand trials were constructed following the same criteria as in + the behavioral session. Finally, the baseline consisted in trials in which the + arithmetic sign was replaced by an abstract non-arithmetic sign (i.e., ). We + included 30 baseline sign-only trials (in which the sign was presented in isolation) + and 50 baseline sign-plus-operand trials (in which the sign was followed by + 3 letters and participants had to indicate whether one of these letters was + a B). All trials were followed by a variable period of visual fixation ranging + from 3000ms to 3800ms. That period consisted in a central white fixation dot + that turned red 1000ms before the onset of the next trial. The arithmetic task + was decomposed in 4 functional runs. All trials were intermixed and the timing + and order of trial presentation within each run was optimized for estimation + efficiency using optseq2 (http://surfer.nmr.mgh.harvard.edu/optseq/). Behavioral + responses were recorded using an MR-compatible response device.Stimuli were + generated using Presentation software (Neurobehavioral Systems, Albany, CA). + Prior scanning, children were familiarized with the fMRI environment during + a practice session that took place after the standardized testing and the behavioral + session. During this practice session, children learned to minimize head movement + in a mock fMRI scanner. The actual scanning session took place no more than + 3 weeks after the practice session.2.5. Behavioral analysesRT data associated + with the operator-priming task were normalized using a logarithmic transformation + prior all analyses to improve the conformity of the data to the standard assumptions + of parametric testing. Following Fayol and Thevenot (2012), mean RT was analyzed + using planned comparisons that followed from a within-subject ANOVA with the + factors Operation (Addition/Multiplication) and SOA (Negative/Null), conducted + separately for each group. We report for all effects the corresponding Bayes + factors (BF10), indicating the strength of evidence for the alternative hypothesis + (H1) relative to the null hypothesis (H0). Substantial evidence in favor of + the alternative hypothesis is typically suggested by a BF10 greater than 3 (Jeffreys, + 1961, Dienes, 2011).2.6. fMRI data acquisitionImages were collected with a Siemens + Prisma 3T MRI scanner (Siemens Healthcare, Erlangen, Germany) at the CERMEP + Imagerie du vivant in Lyon, France. The BOLD signal was measured with a susceptibility + weighted single-shot EPI sequence. Imaging parameters were as follows: TR=2000ms, + TE=24ms, flip angle=80, matrix size=128120, field of view=220206mm, slice thickness=3mm + (0.48mm gap), number of slices=32. A high-resolution T1-weighted whole-brain + anatomical volume was also collected for each participant. Parameters were as + follows: TR=3500ms, TE=2.24ms, flip angle=8, matrix size=256256, field of view=224224mm, + slice thickness=0.9mm, number of slices=192.2.7. fMRI preprocessingData analysis + was performed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Functional images + were corrected for slice acquisition delays and spatially realigned to the first + image of the first run. Images were then spatially smoothed with a Gaussian + filter equal to twice the voxel size. ArtRepair was used to help remove motion + from the functional images prior to normalization (Mazaika et al., 2009). Volumes + with rapid scan-to-scan movements of greater than 1.5mm were repaired by interpolation + of the two nearest non-repaired scans. Each run with more than 5% of the total + number of volumes replaced was removed from the analyses. A subject was excluded + from further analysis if more than one run was removed. The number of volumes + replaced did not differ between grade groups (F(2,31)=2.20; p=0.13). Finally, + functional images were normalized into the standard MNI space (normalized voxel + size, 223.5mm3).2.8. fMRI processingEvent-related statistical analysis was performed + according to the general linear model (GLM). For the localizer task, brain activity + associated with saccades and fixation blocks was modeled as epochs with onsets + and offsets time-locked to the beginning and the end of each block. Each epoch + was convolved with a canonical hemodynamic response function (HRF) and the time + series data from each run were high-pass filtered (1/128Hz). Finally, serial + correlations were corrected using an autoregressive AR(1) model. Following our + previous study using the same task in adults (Mathieu et al., 2017), brain activity + associated with sign-only trials during the arithmetic task was estimated using + a finite impulse response (FIR) model. We modeled 8 time points with an interval + of 2s (corresponding to one TR) ranging from the onset of the sign to 16s after + the sign. The magnitude of the fMRI response for each type of sign-only trial + was calculated by subtracting activity at the onset of the sign (i.e., 1st bin, + or 0s after the onset) from the peak activity (i.e., 4th bin, or 8s after the + onset). The time series data from each run were high-pass filtered (1/128Hz), + and serial correlations were corrected using an autoregressive AR(1) model.2.9. + Region of interest (ROI) definition and analysesThe present study used a Region-of-Interest + (ROI) approach to analyze brain activity associated with sign-only trials in + brain regions involved in the orienting of spatial attention in children. All + ROIs were independently defined using the contrast of saccades versus fixation + blocks in the spatial attention localizer task. All subject-specific contrasts + were entered into a random effect (RFX) one-sample t-test across subjects. The + RFX contrast map was then thresholded across the whole-brain using an uncorrected + voxel-level threshold of p<0.001 and a false-discovery-rate (FDR) corrected + cluster-level threshold of p<0.05 (Chumbley and Friston 2009). Using the SPM + toolbox Marsbar (http://marsbar.sourceforge.net/), ROIs were defined as 6-mm + radius spheres around the peak coordinate of each region.Within each ROI and + for each participant, we calculated the average response (parameter estimates) + for + signs using the contrast of addition sign-only trials versus baseline + sign-only trials. Similarly, we calculated the average response for signs using + the contrast of multiplication sign-only trials versus baseline sign-only trials. + Two analyses were performed in each ROI. First, we identified the ROI(s) in + which a difference in fMRI activity between + and signs emerged throughout + age and/or education, using a 32 ANOVA with the between-subject factor group + (lower/intermediate/higher grades) and the within-subject factor sign (+/). + Second, we tested in these ROIs whether inter-individual differences in the + fMRI response to an arithmetic sign were correlated with inter-individual differences + in the size of the operator-priming effect (measured in the behavioral session) + for each group. In all analyses, we report uncorrected P values as well as P + values corrected for multiple comparisons across all identified ROIs using the + Bonferroni procedure. Bayes factor are also reported.3.Results3.1. The spatial + localizer task activates a brain network encompassing frontal, parietal, occipital + and hippocampal regionsContrasting saccades to fixation blocks in the spatial + attention localizer task, we first identified 10 clusters supporting the orienting + of spatial attention across subjects: the bilateral Frontal Eye Field (FEF), + bilateral Posterior Superior Parietal Lobule (PSPL), bilateral Middle Temporal + Gyri (MTG), bilateral Middle Occipital Gyri (MOG), right dorsal Inferior Frontal + Gyrus (dIFG), and right Hippocampus (see Table 1 and Fig. 2). Therefore, a large + brain network was involved in the orienting of spatial attention across subjects. + Each of these regions served as an ROI in subsequent analyses.Table 1Brain regions + that were activated during the spatial attention localizer task. Each of these + regions constituted an ROI.Anatomical locationBACluster size (mm3)MNI coordinatesZ-scoreXYZL. + Middle Occipital Gyrus17246402496105.37R. Middle Occipital Gyrus/Calcarine18149224.77L. + Frontal Eye Field65824348555.34R. Middle Temporal Gyrus2195345452134.68R. Frontal + Eye Field65166284484.65R. Posterior Superior Parietal Lobule752501866664.61L. + Posterior Superior Parietal Lobule731782264664.58L. dorsal Inferior Frontal + Gyrus6/4413165610204.52L. Middle Temporal Gyrus2136265054134.33R. Hippocampus6302416183.48Open + in a separate windowL.=left; R.=right; BA=approximate Brodmanns area; MNI=Montreal + Neurological Institute.Open in a separate windowFig. 2Brain regions activated + in the spatial attention localizer task. Brain regions that were more activated + during saccades than fixation blocks. Activations are overlaid on slices of + the MNI-normalized anatomical brain. PSPL, Posterior Superior Parietal Lobule; + FEF, Frontal Eye Field; MTG, Middle Temporal Gyrus; dIFG, dorsal Inferior Frontal + Gyrus; MOG, Middle Occipital Gyrus.; HIPP, Hippocampus.3.2. The right hippocampus + region identified by the spatial localizer task is increasingly activated in + response to a + sign but not to a sign throughout age and/or educationA 32 + ANOVA with the between-subject factor group (lower/intermediate/higher grades) + and the within-subject factor sign (+/) was then conducted in each of the 10 + ROIs identified by the spatial attention localizer. We found an interaction + between group and sign in the right hippocampus (F(2.30)=6.75, p=0.0038, pcorr=0.038; + Fig. 3a and b), but not in any other ROIs (all Fs<3.44, all ps>0.046, all pscorr>0.46). + Bayes Factor analysis indicated substantial evidence for this interaction in + the right hippocampus (BF10=11.53), while no or anecdotal evidence for such + an interaction was found in the other ROIs (BF10<1.63). Follow-up t-tests in + the hippocampus ROI revealed that children from higher grades (average grade=8.5) + exhibited greater activity for addition than multiplication sign-only trials + (t11=3.02, p=0.012), whereas there was no difference between signs in children + from intermediate grades (average grade=6.2) (t10=0.87, p=0.41) and even a trend + for less activity for addition than multiplication sign-only trials in children + from lower grades (average grade=4.4) (t10=2.22, p=0.051). Bayes Factor analysis + indicated substantial evidence for a difference of activity between addition + and multiplication sign-only trials in children from higher grades (BF10=5.21), + but evidence for this difference was absent in intermediate grades (BF10=0.41) + and anecdotal in lower grades (BF10=1.69). Finally, across all groups, addition + sign-only trials were associated with greater activity than baseline sign-only + trials in children from higher grades (t11=2.63, p=0.023), but not in any other + groups (all ts<1.32, all ps>0.21). Multiplication sign-only trials were associated + with greater activity than baseline sign-only trials in none of the groups (all + ts<1.38, all ps>0.20). Bayes Factor analysis indicated substantial evidence + for a difference between addition sign-only trials and baseline sign-only trials + in children from higher grades (BF10=3.00), but no or anecdotal evidence in + the other groups (all BF10s<0.60) and for multiplication sign-only trials (all + BF10s<0.63). Overall, then, a difference in fMRI response to a + and a sign + emerged throughout age and/or education in the right hippocampus.Open in a separate + windowFig. 3Grade-related changes of activity in the right hippocampus.(a) Location + of the right hippocampus ROI overlaid on a coronal slice of the MNI-normalized + anatomical brain. (b) Activity in the right Hippocampus for addition versus + baseline sign-only trials (red) and multiplication versus baseline sign-only + trials (blue) in children from lower (n=11; grade 3.25.4; mean grade=4.4; mean + age=9.4), intermediate (n=11; grade 5.66.9; mean grade=6.2; mean age=11.1) and + higher grades (n=12; grade 7.610.2; mean grade=8.5; mean age=13.4). (c) Difference + in activity between addition and multiplication sign-only trials over grade + in the right Hippocampus. *p<0.05; r represents the Pearson correlation coefficient.The + findings above were confirmed by an additional correlation analysis in which + grade was treated as a continuous predictor across all subjects. The difference + in activity between addition and multiplication sign-only trials was positively + correlated with grade in the right hippocampus (r=0.53, p=0.001, pcorr=0.01; + Fig. 3c). No other significant grade-related changes were found in any other + regions (all rs<0.38, all ps>0.03, all pscorr>0.30). Bayes Factor analysis indicated + substantial evidence for this correlation in the right hippocampus (BF10=20.97), + but no or anecdotal evidence in any other ROIs (all BF10s<2.03). In the right + hippocampus, the correlation between grade and the contrast of addition sign-only + trials versus baseline sign-only trials, was also near significance (r=0.33, + p=0.056; BF10=1.22).2 The correlation between grade and the contrast of multiplication + sign-only trials versus baseline sign-only trials, however, was not significant + (r=0.18, p=0.32; BF10=0.35).3.3. Spatial hippocampal activity in response to + a + sign relates to an addition-priming effect in children from higher gradesWe + then tested whether the hippocampal response to a + sign observed in children + from higher grades was related to the operator-priming effect. To this aim, + each child performed a version of the operator-priming task outside of the scanner + (see Fig. 1a).First, we tested whether the results obtained by Fayol and Thevenot + (2012) in adults (i.e., an operator-priming effect for addition but not for + multiplication across subjects) could be extended to our children participants. + Because children from lower grades had a performance close to chance on large + problems (58%), we exclusively focused our analyses on small problems for which + accuracy was significantly above chance in all groups (lower grades: 80%, intermediate + grades: 92%, higher grades: 96%). Planned comparisons revealed an operator-priming + effect for addition in children from higher grades (1491ms versus 1577ms; F(1,11)=8.11, + p=0.016), but not in children from lower grades (2289ms versus 2417ms; F(1,10)=2.66, + p=0.134) and intermediate grades (1530 versus 1509ms; F(1,10)=0.01, p=0.941). + No operator-priming effect for multiplication was observed in any groups (lower + grades: F(1,10)=3.50, p=0.091; intermediate grades: F(1,10)=1.52, p=0.246; higher + grades: F(1,11)=0.14, p=0.715). Bayes Factor analysis indicated substantial + evidence for an operator-priming effect with addition problems in children from + higher grades (BF10=4.08), but no evidence in children from intermediate (BF10=0.30) + and lower (BF10=0.83) grades. There was also no or anecdotal evidence for an + operator-priming effect with multiplication problems in any group (higher grades: + BF10=0.31; intermediate grades: BF10=0.55; lower grades: BF10=1.09).Second, + we tested whether the size of the operator-priming effect in children (measured + on small problems) was correlated to the magnitude of the response for addition + sign-only trials versus baseline sign-only trials in the right hippocampus. + Such a correlation was found to be highly significant in children from higher + grades (r=0.82, p=0.0012, Fig. 4), surviving Bonferroni correction for multiple + comparisons between the two conditions and across the three groups (pcorr=0.007). + That is, children from higher grades who show greater responses to + signs in + the right hippocampus are those who show larger operator-priming effect with + addition problems. No significant correlation was found in children from lower + (r=0.15, p=0.66 Fig. 4) and intermediate (r=0.24, p=0.48, Fig. 4) grades. Bayes + Factor analysis indicated substantial evidence for the correlation in children + from higher grades (BF10=38.15), but no evidence in children from lower (BF10=0.40) + and intermediate (BF10=0.46) grades. There was also no significant (and anecdotal + evidence for a) correlation between the operator-priming effect for addition + problems and the fMRI response to multiplication sign-only trials (compared + to baseline sign-only trials) in the right hippocampus, in any of the groups + (lower grades: r=0.06, p=0.87, BF10=0.37; intermediate grades: r=0.32, p=0.34, + BF10=0.56; higher grades: r=0.51, p=0.09, BF10=1.28). Therefore, not only did + we observe an operator-priming effect for addition in the only group in which + we also observed a greater hippocampal response to + than signs (i.e., children + from higher grades), but inter-individual differences in the size of the operator-priming + effect in that group was also related to hippocampal activity.Open in a separate + windowFig. 4Hippocampus brain-behavior correlation over grade.Activity in the + right hippocampus in response to addition sign-only trials versus baseline sign-only + trials as a function of the operator-priming effect calculated in the behavioral + session for addition problems in children from lower (n=11; grade 3.25.4; mean + grade=4.4; mean age=9.4), intermediate (n=11; grade 5.66.9; mean grade=6.2; + mean age=11.1) and higher grades (n=12; grade 7.610.2; mean grade=8.5; mean + age=13.4). r represents the Pearson correlation coefficient.Third, we tested + whether the correlation between the operator-priming effect and the contrast + of addition sign-only trials versus baseline sign-only trials increased over + grade. This was done by transforming the correlation coefficient in each group + to a Fischers z score before comparing the groups using the cocor package (Diedenhofen + and Musch, 2015). Although the correlation was not greater in children from + intermediate than lower grades (z=0.19, p=0.43, one-tailed), it was significantly + greater in children from higher than lower grades (z=2.07, p=0.019, one-tailed) + and in children from higher than intermediate grades (z=1.88, p=0.030, one-tailed). + Therefore, this brain-behavior correlation increased over grade.4.DiscussionIn + the present study, we used fMRI and a cross-sectional design to investigate + (i) how and when spatial processing related to the perception of an addition + sign emerges in the developing brain, and (ii) to what extent it contributes + to the emergence of an operator-priming effect.4.1. The mere perception of a + + sign is associated with increased hippocampal spatial activity throughout + age and/or educationIt has been shown that the processing of a + sign is associated + with the right side of space (Pinhas et al., 2014) and activates brain regions + involved in overt spatial attention in adults (Mathieu et al., 2017). Therefore, + we expected arithmetic learning to be associated with increased recruitment + of brain regions involved in spatial attention in response to the perception + of a + sign throughout age and/or education in children. This was the case in + a region of the right hippocampus that we identified in our spatial attention + localizer task. Therefore, it is possible that hippocampal spatial mechanisms + may scaffold the progressive association between an arithmetic operator (i.e., + a + sign) and spatial intuitions throughout age and/or education. There is increasing + evidence that the hippocampal formation, and particularly the right hippocampus, + may house a sense of space (Buffalo, 2015). Specifically, the right hippocampus + has been extensively reported to support spatial representation and navigation + in humans (Maguire et al., 1998, Burgess et al., 2002) as well as in non-human + primates and rodents (O''keefe and Nadel, 1978, Bird and Burgess, 2008). For + example, the hippocampus is typically activated when human participants learn + to navigate through a mental representation of space (i.e., mental scanning) + (Mellet et al., 2002, Spiers and Maguire, 2006). Interestingly, a recent study + in monkeys demonstrated that neurons in the hippocampal formation may encode + the direction of overt (Killian et al., 2015) as well as covert (Wilming et + al., 2015) shifts of attention. Therefore, the hippocampal formation is likely + a critical region for both representing a mental map of space and navigating + along that map (Killian et al., 2012, Meister and Buffalo, 2016).Why would such + a hippocampal spatial navigation mechanism be increasingly recruited by the + mere perception of a+ sign throughout age and/or education? One possibility + is that this mechanism might enable children to construct a detailed representation + of numbers in mental space, as well as to navigate along that mental representation. + Indeed, there is overwhelming evidence that numbers of increasing size are organized + along a left-to-right mental map (i.e., the MNL) in adults (Fischer and Shaki + 2014). This spatial representation may enable individuals to add or subtract + numbers by navigating from a source to a target number to the left or right + of that MNL. This is supported by behavioral studies showing that addition and + subtraction problem-solving is associated with rightward and leftward shifts + of attention (Masson and Pesenti, 2014, Mathieu et al., 2016), as well as by + a neuroimaging study indicating an overlap between the brain regions involved + in overt shifts of attention and those involved in arithmetic calculation in + adults (Knops et al., 2009). Such strategies may be acquired early by children, + sometimes even explicitly in the classroom where addition and subtraction is + often demonstrated on visual number lines. Yet, it is only with practice that + they might become progressively attached to and evoked by an arithmetic operator + such as a +, which might explain the grade-related increases of activity in + this region in response to the + sign (and the fact that it is only by 7th grade + that children exhibit significant activity in response to that sign).4.2. Hippocampal + spatial activity in response to a + sign relates to the operator-priming effect + in children from higher gradesA critical question is to what extent this automatic + processing of a + sign in hippocampal spatial mechanisms is associated with + childrens behavior. To answer this question, we asked all children to perform + a version of the operator-priming task developed by Fayol and Thevenot (2012) + and Roussel et al. (2002). First, we replicated the operator-priming effect + observed in adults with addition problems (i.e., a facilitation of problem-solving + when the operator is presented 150ms before the operands), but only in children + from higher grades (after around 7th grade). Like in adults, this effect was + specific to addition problems and not observed with multiplication problems. + Thus, the perception of a + sign (but not that of a sign) appears to pre-activate + a process that is likely used to solve the subsequent problem in children from + higher grades. More central to our current interest, we found that the size + of the operator-priming effect in these children was highly correlated with + the degree of activation of hippocampal spatial mechanisms in response to a + + sign. This indicates that hippocampal spatial activity may be at the source + of the operator priming-effect in older children, perhaps because these children + might prepare for an attentional movement along the MNL as soon as a + sign + is presented. Because no brain-behavior correlation was observed in younger + children, extensive practice might be needed before such mechanisms are triggered + by the mere perception of the sign.4.3. Hippocampal spatial activity in response + to a + sign is transient in developmentStrikingly, the spatial brain mechanisms + that respond to the mere perception of a + sign appear to be different in children + and adults. That is, albeit we found increased hippocampal spatial activity + throughout age and/or education in the present study, we did not identify these + mechanisms in our previous study in adult participants using the exact same + task (Mathieu et al., 2017). Rather, we found increased activity in response + to a + sign in neocortical regions of the FEF and PSPL in adults. Therefore, + the contribution of the hippocampus to the automatic processing of a + sign + is likely transient. Such a transient involvement of the hippocampus is consistent + with a wealth of studies that have demonstrated that the spatial representations + initially supported by the hippocampus during learning become independent from + this brain structure over experience and transferred to neocortical regions + (Rosenbaum et al., 2004, Hirshhorn et al., 2012b). For example, longitudinal + studies demonstrate that right hippocampal activity associated with learning + to mentally navigate through a new environment disappears and is replaced by + neocortical activity when individuals become familiar with that environment + (Spiers and Maguire, 2007, Hirshhorn et al., 2012a). It is possible that the + same phenomenon is at play here: The hippocampus may be involved in the early + representation of (and navigation along) the MNL before that representation + is transferred to neocortical regions of the fronto-parietal cortex. Future + investigations with a wider age sample than in the present study are needed + to test this hypothesis.4.4. Can right hippocampal involvement in the present + study reflect mnemonic operations involved in learning arithmetic?Although there + is no doubt that the hippocampus supports spatial processing (Burgess et al., + 2002, Spiers and Maguire, 2007), this brain structure is also well known to + support the encoding and consolidation of verbal declarative knowledge into + long-term memory (Eichenbaum, 2004). In fact, previous developmental studies + have largely explained the involvement of the hippocampus during arithmetic + learning by referring to its role in declarative memory rather than spatial + processing (Rivera et al., 2005, De Smedt et al., 2011, Cho et al., 2011, Cho + et al., 2012, Qin et al., 2014). This interpretation relies on the claim that + results of well-practiced arithmetic facts (e.g., 2+3 or 42) might become progressively + retrieved from memory (rather than calculated) over the course of learning and + development (Campbell and Xue, 2001). The hippocampus might thus support the + encoding and consolidation of networks of arithmetic facts in children.Can the + role of the hippocampus in declarative memory explain the operator-specific + activity over grade (and correlation with the operator-priming effect) observed + in the region of the right hippocampus identified by our spatial localizer task? + We acknowledge that we did not have a task identifying processes involved in + declarative memory. Thus, even if the right hippocampus is usually more associated + with spatial than mnemonic processes (Burgess et al., 2002), it is possible + that the hippocampal cluster that we identified as being involved in spatial + processing may also be involved in some aspects of declarative memory. One might + thus argue that grade-related increases of activity in relation to + signs reflect + the progressive association between a + and a network of additive facts. This + explanation, however, can be ruled out by an examination of activity related + to signs. Because single-digit multiplication problems are almost exclusively + learned by rote in school, multiplication is the operation that is perhaps the + most associated with a network of stored facts in the literature (Campbell and + Xue, 2001). Thus, if increased hippocampal activity in relation to + signs were + due to the progressive building of a network of additive facts, increased activity + in that same region should have been observed during the perception of signs + (perhaps even more so for the perception of + signs). Yet, this is not the case. + Not only did we not find any grade-related increase of activity for signs in + the hippocampal cluster identified by our spatial localizer task, but activity + was significantly greater for + than signs in higher graders (who are as proficient + in single-digit multiplication as addition). Similarly, no operator-priming + effect was observed for multiplication problems in higher graders, indicating + that the operator-priming effect observed for addition is likely to have little + to do with the pre-activation of a network of stored facts (because this should + be also observed for multiplication). Therefore, the specificity of our results + to + signs (as compared to signs) in the right hippocampus ROI makes it very + unlikely that our results are related to mnemonic operations. In our view, emerging + associations between + signs and spatial intuitions related to the MNL are the + best explanation of the effects reported here.Of course, the fact that the role + of the hippocampus in declarative memory is unlikely to explain our operator-specific + findings in the right hippocampus ROI does not mean that hippocampal mechanisms + supporting mnemonic operations do not contribute to arithmetic learning. Instead, + they indicate that the hippocampus might contribute to arithmetic learning through + its role in both declarative memory and spatial processing. Interestingly, the + operator-specific activity observed in our (spatially localized) right hippocampal + cluster is not observed in a mirror (left lateralized) cluster that is not activated + in the localizer contrast (see Supplementary information). In that mirror region, + no difference was observed between activity related to + and signs in any group + of children (and left hippocampal activity was not related to the operator-priming + effect). Thus, the developmental effect reported here appears to be restricted + to the right hippocampus. This specificity suggests that the observed developmental + changes in the right hippocampus may not simply reflect general brain maturation + but rather mechanisms that are specific to arithmetic learning.4.5. LimitationsIt + is worth acknowledging here 2 potential limitations of the present work. First, + as is the case for any cross-sectional fMRI studies, our study is correlational + in nature. Thus, although our findings are consistent with the idea that the + right hippocampus might scaffold the progressive association between (at least + some) arithmetic operators and space throughout age and/or education, future + studies might specifically investigate the causal role of these hippocampal + mechanisms. Second, our finding of a correlation between grade and the processing + of an addition sign in the right hippocampus (see Fig. 3C) relies on a relatively + large sample size of 34 children. However, other findings involve subgroups + of participants and therefore rely on smaller sample sizes. In particular, null + findings in relation to these subgroups might be difficult to interpret because + of potential lack of power. For example, whereas we found an operator-priming + effect in children from higher grades and no effect in children from intermediate + grades, there was no significant difference between these groups in terms of + response times in negative SOA trials (1491ms versus 1530ms; t21=0.21; p=0.84; + BF10=0.39). Behavioral studies focusing on the operator-priming effect in children + might test whether this difference emerges with larger sample sizes. More generally, + future studies are needed to improve our understanding of the present results.5.ConclusionIn + sum, our findings suggest that the right hippocampus might contribute to the + progressive association between (at least some) arithmetic operators and space + throughout age and/or education. Therefore, our study raises the possibility + that increased hippocampal activity during arithmetic learning in children may + be explained by the role of this structure in spatial representations as well + as in declarative memory.Conflict of interestThe authors declare no competing + financial interests.AcknowledgmentsThis research was supported by a grant from + the European Union (Marie Curie Career Integration Grant n PCIG12-GA-2012-333602) + to J.P. and a grant from the French Ministry of Higher Education and Research + to R.M. We thank the Hospices Civils de Lyon for sponsoring the research, as + well as Flora Schwartz and the MRI engineers (Franck Lamberton and Danielle + Ibarrola) at the CERMEP-Lyon platform for their assistance in collecting the + fMRI data. Finally, we are grateful to Pr. Christian Scheiber for his help with + the pre-MRI medical exams.Footnotes1Note that to induce an arithmetic context + and disguise the goal of the experiment, we also included trials in which a + + or a sign was followed 150ms later by operands and participants were asked + to solve the problem. The low temporal resolution of fMRI, however, makes it + impossible to dissociate activity associated with the sign from activity associated + with operands in these problems. Therefore, they were simply designed to be + filler trials.2There was a tendency for a correlation between grade and activity + associated with addition sign-only trials (versus fixation) in the right hippocampus + (r=0.29, p=0.09; BF10=0.82), but no correlation for baseline sign-only trials + (versus fixation) (r=0.10, p=0.58; BF10=0.25). Thus, the correlation between + grade and the contrast of addition sign-only trials versus baseline sign-only + trials was more likely driven by changes of activity in addition sign-only trials + than in baseline sign-only trials.Appendix ASupplementary data associated with + this article can be found, in the online version, at http://dx.doi.org/10.1016/j.dcn.2017.06.001.Appendix + A.Supplementary dataThe following is Supplementary data to this article:Click + here to view.(414K, docx)ReferencesAnsari D. Effects of development and enculturation + on number representation in the brain. Nat. Rev. Neurosci. 2008;9:278-291. [PubMed] + [Google Scholar]Ashcraft M.H. Cognitive arithmetic: a review of data and theory. + Cognition. 1992;44:75-106. [PubMed] [Google Scholar]Bird C.M., Burgess N. The + hippocampus and memory: insights from spatial processing. Nat. Rev. Neurosci. + 2008;9:182-194. [PubMed] [Google Scholar]Buffalo E.A. 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'10000' + x-ratelimit-limit-tokens: + - '2000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '1983651' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 490ms + x-request-id: + - req_3618fc658a27141168865a306662499b + status: + code: 200 + message: OK +- request: + body: '{"messages": [{"role": "user", "content": "\nYou will be provided with + a text sample from a scientific journal.\nThe sample is delimited with triple + backticks.\n\nYour task is to identify groups of participants that participated + in the study, and underwent MRI.\nIf there is no mention of any participant + groups, return a null array.\n\nFor each group identify:\n - the number of + participants in each group, and the diagnosis.\n - the number of male participants, + and their mean age, median age, minimum and maximum age\n - the number of + female participants, and their mean age, median age, minimum and maximum age.\n - + if this group of participants underwent MRI, fMRI or neuroimaging procedure.\n\nBe + as accurate as possible, and report information (especially diagnosis) using + the technical terms (and abbreviations) used in the article.\nIf any of the + information is missing, return `null` for that field.\n\nText sample: \n## + Significance Statement \n \nPsychopathic criminals are commonly seen as instrumentally + abusive and emotionally callous, yet social challenges often trigger uncontrolled + emotional behavior in those individuals. This study shows how this paradoxical + aspect of psychopathy relates to altered neuroendocrine interactions between + testosterone and the cerebral circuit coordinating emotional action tendencies. + The anterior prefrontal cortex, a region necessary for controlling emotional + behavior, showed blunted responses and reduced connectivity with the amygdala + in psychopathic criminals engaged in controlling their emotional action tendencies. + This cerebral pattern was strongest in psychopathic individuals with high endogenous + testosterone levels. This neuroendocrine signature of altered emotional control + highlights the relevance of considering the testosterone level of individual + psychopathic patients during treatment of their impulsive behavior. \n\n\n## + Introduction \n \nPsychopathy is a disorder often associated with blunted emotional + responding and increased goal-directed behavior ( ; ). On the other hand, offenders + with psychopathy also show a paradoxical increase in impulsive behavior and + uncontrolled aggression after emotional provocations ( ; ; ; ; ; ), which + may be related to heightened testosterone levels ( ; ). These two aspects of + psychopathy are also distinguished within the most commonly used psychopathy + checklist, the Psychopathy Check List-Revised (PCL-R), potentially reflecting + differing traits among psychopathic individuals ( ; ). Importantly, enhanced + difficulty in controlling emotional impulses, a crucial component of criminal + psychopathy associated with PCL-R factor 2, has been largely neglected by cognitive + neuroscience. Yet, the clinical relevance of this cognitive trait is large: + reduced behavioral control and increased impulsivity predict recidivism in psychopathic + offenders ( ), and behavioral control in psychopathic offenders appears particularly + fragile when dealing with emotionally relevant behavior ( ; , chapter 7; ). + Accordingly, understanding the neurobiological systems underlying the altered + control of social emotional behavior in psychopathic individuals is relevant + for improving currently available interventions, which are plagued by low treatment + response and high recidivism ( ). Here we study those neuroendocrine systems + in a group of psychopathic offenders engaged in an experimental paradigm that + requires rule-driven control of emotional behavior. \n\nPrevious investigations + of psychopathy showed altered reactivity to emotional material in several brain + regions that include the anterior part of the PFC (aPFC) and the amygdala ( + ; ; ). Furthermore, individuals with psychopathy showed decreased functional + and anatomical connectivity between the PFC and amygdala at rest ( ; ), an + indication that these brain regions might have a reduced ability to interact + effectively. Studies in healthy participants have shown that this cerebral circuit + is necessary for implementing the control of emotionally relevant actions ( + ). Namely, aPFC downregulates neural processing in the amygdala during emotional + control ( ), while high levels of endogenous testosterone reduce such control-related + connectivity between aPFC and amygdala ( ). Those findings raise the possibility + that aPFCamygdala connectivity is altered when psychopathic offenders need to + control emotionally relevant actions, with high levels of endogenous testosterone + exacerbating that altered connectivity. \n\nThis study tests these hypotheses + by measuring brain activity with functional magnetic resonance imaging (fMRI) + in 15 psychopathic criminals and 19 matched healthy control subjects dealing + with a challenge to control their emotional behavior. The psychopathy sample + was obtained by focused and comprehensive screening excluding confounds that + are frequently associated with random criminal sampling (e.g., medication use, + comorbidity). The social approachavoidance (AA) task was used to provide reliable + indexes of control over social emotional behavior ( ; ; , ). Behaviorally, + psychopathic participants previously showed altered AA behavior to explicitly + approaching and avoiding emotional faces ( ). Similar findings occurred after + testosterone administration in healthy participants ( ). Interestingly, a more + subtle version of the AA task has been shown to be sensitive to testosterone-related + alterations and genetic variations in the aPFCamygdala pathway, while keeping + behavior constant across experimental groups ( ), opening the way for isolating + neural vulnerability factors ( ) in psychopathy. During this task, participants + respond to affective faces (happy, angry) presented for a short time with approach + and avoidance movements. Automatic emotional tendencies (approachhappy and avoidangry + faces; affect-congruent response conditions) need to be controlled during affect-incongruent + response conditions in order to apply the counterintuitive action of approaching + angry and avoiding happy faces ( ; ). Healthy participants respond more slowly + and rely more strongly on the aPFC when emotional control is required, operationalized + by the differences evoked between affect-incongruent and affect-congruent trials + ( ; ). Accordingly, this study tests whether exerting control over emotionally + relevant actions is reflected by reduced functionality of the aPFCamygdala circuit + in psychopathic individuals, suggesting less prefrontal regulation of emotional + actions. In addition, it sets out to test whether this alteration is intensified + by high levels of endogenous testosterone. \n \nThe emotional control AA task. + The AA task involved the presentation of happy and angry faces, and the performance + of approach and avoidance responses. During the AA task, the participants had + to select their response according to the perceived emotion of the face. At + the beginning of each block of 12 trials, the participants received instructions + on whether to pull the joystick toward themselves (approach) or push it away + (avoid) when seeing a face with a particular emotion. When viewing happy or + angry faces, automatic stimulusresponse tendencies trigger corresponding approach + or avoidance actions. These tendencies could be followed during the affect-congruent + condition (approachhappy, avoidangry). In contrast, when task instructions required + participants to avoid happy faces or to approach angry faces, automatic tendencies + needed to be controlled and overridden with the instructed response (affect-incongruent + condition). Participants saw the faces and moved the joystick while lying in + a MR scanner (top left corner of the table). Figure adapted from ). \n \n\n## + Materials and Methods \n \n### Participants \n \nThe psychopathic group was + recruited from in-patient populations of the Pompestichting and Oldenkotte, + forensic psychiatric institutes (TBS-clinics) in the Netherlands. TBS-clinics + are facilities for criminal offenders with a mental disorder treated on behalf + of the state. \n\nSeventeen male psychopathic violent offenders (age range, + 23-56 years) participated; all had received a diagnosis with a PCL-R score of + 26, according to European standards ( ; ; ). PCL-R consensus scores were obtained + by trained clinicians based on a structured PCL-R interview, clinical status, + and history. After the independent scoring, the two raters compared their scores + and came to the consensus score. When no consensus could be found, a third independent + rater was included in the process. Dutch versions of the National Adult Reading + Test and Edinburgh Handedness Inventory were used to assess IQ levels and right-handedness + ( ; ). Twenty-one healthy male control subjects (HCs) matched for age, right-handedness, + and IQ, without criminal records or history of psychiatric disorders, were recruited + from staff of the clinics. All participants received oral and written information + about the experiment and gave written informed consent according to guidelines + of the local ethics committee (Commissie Mensengebonden Onderzoek region Arnhem-Nijmegen). + Psychiatric exclusion criteria consisted of neurological, axis-I, and axis-II + disorders, besides antisocial personality disorder for the psychopathic group. + They were screened for these exclusion criteria by trained psychologists using + Dutch versions of the Structured Clinical Interview (SCID; ) and Mini-International + Neuropsychiatric Interview (MINI; ) for Diagnostic and Statistical Manual + of Mental Disorders , 4th edition, disorders. All participants were asked about + drug use and medical/neurological history to exclude the following: alcohol + use of >3 units/day, cannabis, or other illicit drug use 1 week before, psychotropic + medication other than oxazepam 5 d before, 1 unit of alcohol or oxazepam use + within 24 h before the experiment; history of trauma capitis; visual and auditive + disorder; and neurological disorder. Furthermore, general exclusion criteria + for MRI experiments were applied. Two psychopathic patients (PPs) and two HCs + were excluded from the analyses, due to incomplete scanning procedures (1 PP, + 1 HC) or too many errors on the task (>16%, representing the outlier with a z -score + >3). The final groups did not differ in age, IQ, and handedness (see ). \n \nDemographical + data \n \n\n### Procedure \n \nTwo test sessions took place. During the + first session, right-handedness, IQ, MINI, and SCID were assessed. During the + second session, participants completed several questionnaires upon arrival in + the laboratory, including the State-Trait Anxiety Inventory (STAI) to measure + anxiety levels ( ). Next, they provided saliva for the testosterone measurement. + Afterward, participants were positioned in the 1.5 T MR scanner and familiarized + with the task setup. Immediately after this, the fMRI session started with the + AA task (duration, 30 min) followed by another task (not included in this report). + After a short break outside the scanner, the anatomical scan (duration, 5 min) + and an unrelated task were acquired in the side-by-side 3 T MR scanner. \n\n\n### + Experimental task \n \nThe AA task consisted of 24 blocks (with 12 trials per + block and a baseline period of 21-24 s) during which participants had to respond + to visually presented faces either by pulling a joystick toward themselves (approach) + or by pushing it away from themselves (avoid; ). The participants had to categorize + faces as happy, angry, and neutral (filler items), based on their affective + expressions. During each block, two of the three affective expressions were + presented as stimuli, because only two responses could be given to categorize + the stimulus. This resulted in six different block types each used four times, + representing the affect (happyangry, happyneutral, angryneutral) movement (approachavoid) + combinations. At the start of each block, participants received written instructions + regarding the required response mapping. The affect movement combinations were + pseudorandomly and evenly distributed (with no affect combination repetition), + and the combination of the first block was counterbalanced across participants. + Within each block, affective expressions and gender types were pseudorandomly + presented, avoiding three or more sequential presentations of the same expression/gender, + and two presentations of the same facial model. Each face was presented for + 100 ms, preceded by a 300 ms blank screen, and followed by the participants + response, a blank screen, and by a pseudorandom intertrial interval (ITI; 1-3 + s). A baseline period of 21-24 s preceded each block. The faces were from 36 + models (18 male) obtained from several databases ( ; ; ; ), each showing all + expressions. The pictures were in grayscale, matched for brightness and contrast + values, and displayed against a black background. To exclude influence from + hair and nonfacial contours, the faces were trimmed. Joystick displacements + of >80% along the sagittal plane within 2 s from stimulus presentation were + marked as valid responses. Invalid responses were signaled for 1 s with written + feedback stating you did not move your joystick far enough. After moving the + joystick, participants had to return to the starting position (defined as the + central area extending 20% along the sagittal plane) before the end of the ITI. + Otherwise, visual feedback indicated return the joystick to the starting position, + and the ITI was repeated after participants returned the joystick. The training + at the beginning consisted of six blocks; one block of eight trials for each + of the six affect movement combinations. Different visual stimuli were used + during the training and scanning blocks. \n\n\n### Materials and apparatus \n \nThe + fMR images were acquired on a 1.5 T MRI scanner (Avanto, Siemens Medical Systems) + with an eight-channel head coil using a multiecho generalized autocalibrating + partially parallel acquisitions (GRAPPA) sequence [ ; repetition time (TR), + 2.14 ms; five echo times (TEs), 9.4/21/33/44/56 ms; 34 transversal slices; ascending + acquisition; distance factor, 17%; effective voxel size, 3.3 3.3 3.5 mm; field + of view (FOV), 212 mm]. High-resolution anatomical images were acquired on a + 3 T MRI scanner with a 32-channel head coil using a magnetization prepared rapid + gradient echo sequence (TR, 2300 ms; TE, 3.03 ms; 192 sagittal slices; voxel + size, 1.0 1.0 1.0 mm; FOV, 256 mm). \n\nAn MR-compatible joystick (Fiber Optic + Joystick, Current Designs; sampling rate, 550 Hz) was placed on participants + abdomens to ensure comfortable push-and-pull movements ( ). Participants wore + MR-compatible headphones to reduce scanner noise (Commander XG MRI Audio System, + Resonance Technologies). Stimuli were projected at the center of a screen, viewed + via a mirror above the participants head, with a visual angle of 4 6 (width height). + Stimuli presentation and acquisition of joystick positions were controlled by + a PC running Presentation version 13 ( ). \n\n\n### Salivary measurements \n \nParticipants + filled two Salicaps (IBL) with saliva for testosterone measurement, which were + stored at 25C. Testosterone concentration was measured using competitive chemiluminescence + immunoassay with a sensitivity of 0.0025 ng/ml (IBL International, Tecan). Intra-assay + and interassay coefficients are between 10% and 12%. To control variables influencing + testosterone levels, participants were instructed to refrain from any food, + cigarettes, and drinks (except water) for 1 h before the experiment. \n\n\n### + Behavioral analysis \n \nBehavioral data was analyzed using MATLAB version + 7.9 (MathWorks) and PASW Statistics 18 (SPSS Inc.). First, to obtain a precise + measure of movement onset [reaction time (RT)], the joystick movement for each + trial was reconstructed using the joystick displacement measurements. Excluded + trials showed a joystick movement in the wrong direction, an extreme RT (<150 + or >1500 ms), peak velocity (<0.1 cm/s), or movement time (>400 ms); or an error + rate of above chance level in a block (in that case, the whole block was excluded). + RTs and testosterone levels were log transformed to obtain a normal distribution. + Second, following previous studies ( ; ), we conducted three-way repeated-measures + ANOVA (ANCOVArm) on the mean RT and error rates, with factors group (PP, HC), + movement (approach, avoid), and valence (happy, angry), including standardized + testosterone and STAI state as covariate. A measure of anxiety (STAI) was included + to account for the effects of psychopathy type (e.g., primary vs secondary); + and the possible effects on emotional behavior, hormonal levels, amygdala, and + prefrontal cortex functioning ( ; ; ; ). The -level was set at p < 0.05. + \n\n\n### Functional MRI data \n \n#### Single-subject analyses \n \nImaging + data were preprocessed and analyzed using SPM8 (Statistical Parametric Mapping; ). + The first four volumes of each participants dataset were discarded to allow + for T equilibration. Given the multiecho GRAPPA MR sequence (Poser et al., + 2006), head motion parameters were estimated on MR images with the shortest + TE (9.4 ms), since these are least affected by possible artifacts. These motion + correction parameters, estimated using a least-squares approach with six rigid + body transformation parameters (translations, rotations), were applied to the + five echo images collected for each excitation. After spatial realignment, the + five echo images were combined into a single MR volume using an optimized echo + weighting method (Poser et al., 2006). The time series for each voxel was temporally + realigned to the first slice in time. The T -weighted image was spatially + coregistered to the mean of the functional images. The fMRI time series were + transformed and resampled at an isotropic voxel size of 2 mm into standard Montreal + Neurological Institute (MNI) space by unified segmentation and normalization + using the coregistered T -weighted image ( ). The normalized functional images + were spatially smoothed using an isotropic 8 mm full-width at half-maximum Gaussian + kernel. \n\nThe fMRI time series of each subject were further analyzed using + an event-related approach in the context of general linear model, including + the following effects: approachhappy, approachneutral, approachangry, avoidhappy, + avoidneutral, and avoidangry. Trials excluded from behavioral analyses and periods + of instructions or feedback were modeled as regressors. Vectors describing the + time of picture presentation (onset) and RT of each event (duration) were convolved + with the canonical hemodynamic response function. Potential confounding effects + of residual head movement were modeled using original, squared, cubic, first-order, + and second-order derivatives of the movement correction parameters ( ). Three + further regressors, describing the time course of signal intensities of white + matter, CSF, and the portion of the MR image outside the skull were also added. + This procedure accounts for image intensity shifts due to hand movements within + or near the magnetic field of the scanner ( ). Finally, fMRI time series were + high-pass filtered (cutoff 120 s). Temporal autocorrelation was modeled as a + first-order autoregressive process. \n\n\n#### Group analyses \n \nConsistent + effects across participants and between groups were tested using a random-effects + multiple regression analysis that included six contrast images (approachhappy, + approachneutral, approachangry, avoidhappy, avoidneutral, avoidangry) per participant. + Together, these images represented the estimated cerebral effects from 12 conditions + of the experimental design [group (PP, HC) valence (happy, neutral, angry) response + (approach, avoid)]. Standardized log-transformed testosterone and standardized + STAI state levels were included in the multiple regression analysis as condition-specific + [group (PP, HC) valence (happy, neutral, angry) response (approach, avoid)] + regressors, generating another 12 regressors per variable. \n\nAll analyses + assessed the congruency effect, reflecting task-related differences of affect-incongruent + (approachangry, avoidhappy) versus affect-congruent trials (approachhappy, avoidangry; ; ). + We considered two effects. First, to test for general effects of congruency, + we performed an analysis on the congruency effect over both groups and for each + group separately. When assessing the effects of one group explicitly, we also + tested whether those effects were specific to that group and were significantly + weaker in the other group (at p < 0.05 uncorrected) by masking the statistical + map describing the congruency effect in the first group (using multiple comparisons + correction, see below) with the statistical map describing the group congruency + contrast. Second, to test whether testosterone differentially modulated the + control of emotionally relevant actions in the groups, we performed a group congruency + contrast on the regressor parametrizing interindividual differences in testosterone + on task-related conditions. If such an interaction is present, the testosterone + modulation on the congruency effect of each group separately is considered. + In addition to whole-brain analyses, we used a volume of interest (VOI) on coordinates + previously found to be modulated by testosterone during the congruency effect + in healthy students (two 8-mm-radius spheres centered on the following MNI coordinates: x , + 30; y , 58; and z, 2; and x , 32; y , 54; and z , 8; ). \n\nThe + reported activations are corrected for multiple comparisons using familywise + error (FWE) correction. For whole-brain analyses, we made inferences at cluster + level (FWE: p < 0.05, corresponding to a cluster size of >140 on the basis + of intensity threshold, p < 0.001). For VOI analyses, we made inferences + at voxel-level (FWE corrected, p < 0.05; ; ). Anatomical inference is + drawn by superimposing SPM showing significant signal changes on structural + images of participants. For anatomical accuracy, we report only activation peaks + in gray matter. Anatomical landmarks were identified using the atlas of . Brodmann + areas (BAs) were assigned by superimposing significant SPM on the SPM anatomy + toolbox ( ) and MRIcron template ( /). \n\n\n\n### Connectivity analyses \n \nThe + aim of the following analysis was to test whether inter-regional coupling of + the aPFC (see Results) with the amygdala and other brain regions during the + congruency effect was different between the groups and modulated by testosterone. + To test for these effects, we used the psychophysiological interactions (PPIs) + method ( ). More specifically, we tested for significant differences between + the regression coefficients of each voxel over the right aPFC during the affect-incongruent + versus the affect-congruent conditions. To select voxels to be included in the + VOI, we used the following anatomical constraints ( ): for each participant, + selected voxels fell within a sphere with a radius of 4 mm around the peak voxel + corresponding to the activated cluster of the congruency effect over both groups + (coordinates: x , 30; y , 58; z , 14; see Results). Participant specific + contrast images were generated describing the PPI between the time courses of + the right aPFC VOI and affect-incongruent versus affect-congruent conditions. + Group differences and testosterone modulations on task-related coupling between + the aPFC and other regions were then assessed using a multiple regression design + on participant-specific contrast images with their corresponding testosterone + (log-transformed, standardized) and STAI state (standardized) levels as subject- + and group-specific regressors. In addition to whole-brain analyses, we assessed + significant voxel-level effects (FWE corrected for multiple comparisons, p < + 0.05) within the amygdala, defined on the Automated Anatomical Labeling atlas + ( ) using the WFU PickAtlas tool ( ). \n\n\n\n## Results \n \n### Behavioral + results \n \nFifteen psychopathic criminals (PPs; PCL-R score of 26, according + to European standards ( ; ; ) and 19 HCs (for demographics, see ) were included + in the analyses. Participants performed the task accurately and consistently + (error rates: PPs, 7.9%; HCs, 7.3%; omissions: PPs, 1.6%; HCs, 1.5%; undefined + responses: PPs, 0.9%; HCs, 0.3%; ). \n \nRTs and error rates for each group + and factor of the AA task \n \nA significant movement valence interaction + for the RTs indicated that, over groups, participants responded more slowly + during affect-incongruent (approachangry, avoidhappy) than during affect-congruent + trials (approachhappy, avoidangry; F = 10.4, p = 0.003; ). This congruency + effect replicates the behavioral results from previous fMRI studies ( ; ). + Furthermore, there were main effects of movement ( F = 26.3, p < 0.001) + and valence ( F = 28.7, p < 0.001), reflecting the slowing of avoidance + movements and responses to angry faces in general ( ). There were no significant + effects involving group, including no main effect ( p > 0.3). The congruency + effect correlated positively (without corrections for multiple comparisons) + with the PCL-R total score ( p = 0.048, R = 0.517, respectively). Excluding + anxiety from the analyses did not affect the outcomes. Moreover, when including + the neutral conditions in the analyses, the movement valence (happy, neutral, + angry) interaction for RTs remained significant ( F = 5.5, p = 0.010), + showing that neutral approachavoidance effects are intermediary compared with + happy and angry ( ). \n \nBehavioral results. Mean RTs (SEM) for the affect-congruent + and affect-incongruent conditions of the AA task for the healthy control subjects + and psychopathic offenders. The groups were significantly slower to provide + affect-incongruent responses (approachangry; avoidhappy) than affect-congruent + responses (approachhappy; avoidangry), with no significant group differences. + \n \nFor the error rates, the three-way ANCOVArm showed main effects of movement + ( F = 27.5, p < 0.001), valence ( F = 25.9, p < 0.001), and testosterone + ( F = 4.6, p = 0.040), and a valence testosterone interaction ( F = + 4.3, p = 0.047). There were no other significant effects for the error rates + ( p > 0.15). \n\nEndogenous testosterone levels [median (SD): PPs, 101 pg/ml + (70 pg/ml); HCs, 90 pg/ml (46 pg/ml)] and state anxiety levels [STAI mean (SD): + PPs, 32 (8); HCs, 32 (5)] did not differ between groups ( p > 0.4), and showed + no correlations with psychopathy (PCL-R) scores or with each other ( p > + 0.1). \n\n\n### fMRI results \n \n#### Multiple regression analyses \n \nTo + assess the two main questions of this study, we isolated cerebral structures + showing stronger responses during affect-incongruent than affect-congruent trials + (congruency effect), and cerebral structures in which the congruency effect + was modulated by testosterone levels. \n\nThe results showed a significant congruency + effect across groups in the aPFC [ROI analysis: MNI coordinates ( x , y , z ): + (30, 58, 14) and (30 58 10); p = 0.001 and 0.036; t = 4.46 and 3.43; + for further details, see ]. As expected, this effect was driven by the healthy + control group, and it was significantly weaker in the psychopathic offenders + [ p = 0.001 and 0.040; t = 4.58 and 3.40, on the congruency effect in + healthy control subjects masked implicitly by group (HC > PP) congruency interaction]. + The implicit masking demonstrates that the group congruency interaction is + also significant at p < 0.05 within the significant voxels corrected for + multiple comparisons on the HC congruency effect. The psychopathy group showed + no significant congruency effect in this region ( p > 0.3). There was also + a significant congruency effect across groups in the right superior parietal + lobule (whole-brain analysis); this effect was driven mainly by the psychopathy + group ( ). \n \nClusters showing significantly larger activity for the affect-incongruent + vs the affect-congruent conditions (emotion-control effect) \n \nCritically, + testosterone modulated the congruency effect in the aPFC differently in psychopathic + offenders and healthy control subjects (whole-brain analysis on testosterone group congruency: + MINI coordinates ( x , y , z ): (30, 58, 12); p < 0.001; t = + 5.10; for all details, see ). Post hoc analyses revealed that, in the psychopathy + group, congruency effects decreased as testosterone levels increased [MNI coordinates + ( x , y , z ): (32, 56, 10) and (30, 58, 8); p = 0.002 and 0.015; t = + 4.34 and 3.74]. The modulatory effect of testosterone on congruency was absent + in the healthy control subjects ( p 0.05; ). The whole-brain analysis also + showed an effect in the right caudate nucleus and right inferior supramarginal + gyrus, driven by reduced congruency effects as a function of testosterone in + the psychopathy group ( ; ). \n \nTestosterone modulations of the cerebral + congruency effect in psychopathic offenders and healthy control subjects. A , D , + Brain image showing testosterone-modulated congruency effects (affect-incongruentaffect-congruent) + in the psychopathic offenders in the bilateral aPFC ( A ) and right supramarginal + gyrus ( D ). B , E , Bar graphs showing the mean activation (SEM) + of the active voxels within the yellow circles per group. * p < 0.05. ns, + Not significant. C , F , Scatterplots showing the correlation of the + mean activation of active voxels within the yellow circles with testosterone + (log-transformed and standardized) for the healthy control group and the psychopathy + group. The ROI activations are presented at p < 0.05, uncorrected for visualization + purposes. There are no outliers [Mahalanobis distances D < 4.2 (cutoff + at p < 0.05; D = 7.74); ; ]. Healthy control subjects show an increased + aPFC activity for the congruency effect and no modulation by testosterone, while + in psychopathic offenders endogenous testosterone levels modulate the activity + of the aPFC and right supramarginal gyrus. \n \n\n#### Effective connectivity + analyses \n \nGiven the relevance of aPFCamygdala connectivity for implementing + emotional control as evoked by the AA task ( ), we assessed whether psychopathy + also resulted in altered connectivity along that neural pathway. Connectivity + analyses using the right aPFC [4-mm-radius sphere; central voxel from main analysis + (MNI coordinates: x , 30; y , 58; z , 14)] as the seed region on the + congruency effect indicated a significant group difference (PP > HC) with the + right amygdala ( ; ROI analysis; extent, 3 voxels; t = 3.82; p = 0.027; + MNI coordinates of local maxima: x , 32; y , 0; z , 16). When testing + effects for both groups separately, healthy control subjects showed a significant + negative coupling between the right aPFC and amygdala (ROI analysis; extent: + 3 voxels, t = 3.70; p = 0.036; MNI coordinates of local maxima: x , + 32; y , 0; z , 16), while psychopathic offenders showed no differential + connectivity effect. Post hoc testing on right amygdala voxels showing the + group interaction (threshold, p < 0.05 FWE) indicated a significant positive + correlation with testosterone over both groups (ROI analysis; extent, 1 voxel; t = + 2.29; p = 0.029; MNI coordinates of local maxima: x , 32; y , 2; z , + 16). There was no correlation between aPFCamygdala connectivity and the PCL-R + scores ( p > 0.2). \n \nGroup difference on congruency-related aPFCamygdala + connectivity. A , Brain images illustrating the congruency-related modulation + of connectivity between the right aPFC (yellow circle, axial slice) and the + right amygdala (coronal slice) for the congruency contrast. The activations + are presented at p < 0.05, uncorrected for visualization purposes. B , + Bar graph visualizing the strength of the congruency-specific change (SEM) in + aPFCamygdala connectivity for the healthy control subjects and psychopathic + offenders. There is a significant negative aPFCamygdala coupling in the healthy + control subjects, which is not present in the psychopathic offenders. \n \n\n\n\n## + Discussion \n \nThis study indicates that psychopathic offenders show reduced + aPFC activity as well as less aPFCamygdala connectivity during the control of + emotional behavior. Emotional control was measured by comparing affect-incongruent + and affect-congruent approachavoidance responses to emotional faces (congruency + effect on the AA task; ). When healthy control subjects exerted emotional control, + reaction times, aPFC activity, and aPFCamygdala anticorrelations increased, + confirming previous observations ( ). In contrast, psychopathic offenders did + not show this typical control-related pattern of aPFC activity and connectivity. + In addition, these effects were significantly modulated by endogenous testosterone. + Namely, psychopathic individuals with relatively lower testosterone levels showed + a neural activity and connectivity pattern that resembled the findings in healthy + control subjects, while this pattern was absent in those with higher testosterone + levels. This indicates that especially psychopathic individuals with high testosterone + levels have less prefrontal regulation of amygdala-driven emotional actions + when the control of emotional behavior is required. \n\n### Emotional control + in psychopathy \n \nImaging studies have illustrated an association between + psychopathy and altered processing of fear, including altered amygdala responses + ( ; ; ), attentional deficits for peripheral stimuli ( ), and moral/empathic + insensitivity ( ; ). However, psychopathic offenders also show clear impulsivity + problems ( ), for example, when control is required during emotionally provoking + situations. To address this relatively unexplored but crucial component of criminal + psychopathy, we used a paradigm requiring rule-driven control of emotional actions. + With this paradigm, it was possible to move beyond simple motor inhibition and + to target the flexible control of emotionally driven action tendencies. \n\nFirst, + the aPFC (also called BA 10) was less active in psychopathic offenders as a + function of testosterone. The aPFC is a region crucial for the control of social + emotional behavior. When aPFC functioning is temporarily disrupted, participants + have increased difficulty in overriding emotional tendencies with rule-driven + behavior ( ). Moreover, the aPFC seems especially important for integrating + and coordinating multiple cognitive processes to facilitate response selection + ( ; ). For example, transcranial magnetic stimulation-induced reduction of + aPFC functioning during the control of emotional behavior decreased activity + in brain areas associated with rule selection (posterior parietal cortex), while + both amygdala activity and automatic action tendencies increased ( ). The current + study indicates that psychopathic individuals with especially high testosterone + levels recruited the aPFC less when the control of emotional responses was needed. + This finding suggests that they have reduced coordination of rule-based behavior + with emotional information. \n\nSecond, connectivity between the aPFC and amygdala + also differed significantly between groups. Healthy control subjects showed + a negative aPFCamygdala coupling during the control of social emotional behavior, + whereas psychopathic individuals showed no significant coupling between these + regions. Evidence of anatomical connectivity alterations between these regions + in psychopathic individuals and the relation of that tract to social emotional + behavior modifications support these findings ( ). Although these results cannot + resolve the direction of these connectivity effects, a previous study ( ) using + this paradigm showed an effective connectivity modulation of emotional control + on the connection from aPFC to amygdala. Also, animal studies ( ) suggest strong + prefrontal inhibitory connections that control automatic amygdala responses. + The absence of this aPFCamygdala coupling in psychopathic offenders suggests + that in this group the aPFC has a reduced ability to inhibit amygdala-driven + responses. This study used subtle emotional provocations, but stronger emotional + events result in stronger amygdala responses, increasing the bias for automatic + emotional behavior ( ). A lack of prefrontal control likely reduces the ability + to inhibit these biases and lead to an increased expression of automatic emotional + actions even when they are not beneficial ( ; ). \n\nTestosterone administration + studies also illustrated a decoupling between the prefrontal cortex and the + amygdala, suggesting that testosterone reduces the communication between the + PFC and amygdala ( ; ; ) and, within the AA task, reduces top-down control. + The association between testosterone levels and enhanced social aggression and + dominance seeking, and reduced impulse control in the general population ( ; ; ) + supports the relevance of testosterone in this process. Even amygdala responses + to angry faces have recently been found to be enhanced after testosterone administration + and in psychopathic individuals ( ; ; ). There is a clear association between + testosterone and aggression after provocation, which has been related to reduced + activity in the orbital frontal cortex, a region just ventral of the aPFC ( + ). Interestingly, psychopathic offenders with lower testosterone levels displayed + a pattern similar to that in healthy control subjects, while the psychopathic + individuals with high testosterone levels showed less aPFC activity and aPFCamygdala + coupling. This could provide a potential vulnerability factor explaining the + difference between the goal-directed successful psychopath and the unsuccessful + psychopath with reduced impulse control ( ; ). We hypothesize that especially + psychopathic individuals with high testosterone levels fail to inhibit amygdala-driven + action tendencies using the aPFC during the control of emotional behavior.\n + \n\nEndogenous testosterone levels also modulated control-related activity in + the supramarginal gyrus and caudate nucleus of the psychopathy group. The supramarginal + gyrus was previously found to be involved during emotional control on the AA + task in a healthy student sample ( ). Previous work indicated that it plays + an important role in action organization ( ), and that psychopathic individuals + show reduced supramarginal gyrus activity compared with control subjects when + reasoning about other peoples emotional state ( ). The current findings, emphasizing + the role of supramarginal gyrus during emotional control in psychopathic offenders + with low testosterone levels, could indicate the facilitation of action preparation + in trials with affect-incongruent stimulusresponse mapping. The caudate nucleus + is important for incorporating predicted action outcome, when selecting the + most beneficial behavioral goal ( ), and has previously found to be larger in + psychopathy ( ). In light of these findings, our results suggest that psychopathic + offenders with low endogenous testosterone levels, as opposed to those with + high testosterone levels, have more interference of automatic action tendencies + and outcomes associated with the facial emotions (e.g., approachhappy) that + are opposite to the required actions during affect-incongruent trials ( ). \n\n\n### + Interpretational issues \n \nIndividuals with psychopathy have been suggested + to have difficulty recognizing emotional expressions. However, this impairment + seems quite specific to fear, rather than the emotional expressions used here + (anger and happiness; ; ). Furthermore, the groups assessed in this study + made comparable numbers of errors, suggesting that psychopathic offenders had + no special difficulty in recognizing the clear emotional expressions used in + this study. \n\nThis study used a relatively subtle manipulation to target the + emotional control system. The rationale of this choice was to detect neural + vulnerability markers without affecting behavioral performance. Psychopathic + offenders performing a more salient behavioral version of the AA task showed + reduced avoidance of angry faces ( ). In this study, angry faces evoked numerically + similar behavioral effects ( ) and, additionally, aPFC effects ( post hoc inspection + of extracted parameters). Although these observations could be interpreted as + a sign that psychopathic offenders have a tendency to approach angry faces, + those observations were not statistically significant between groups [behavioral + and aPFC group effects on angry faces: p > 0.2; p = 0.271; z = 2.54, + on the angrycongruency effect in healthy control subjects masked implicitly + by group (HC > PP) angrycongruency interaction]. Future investigation is needed + to directly test whether more provocative paradigms induce specific effects + for angry faces. A previous study ( ) using this fMRI task in participants with + genetic susceptibility for developing aggressive disorders, also found no group-specific + behavioral effects. That study suggested that alterations of the aPFCamygdala + pathway might reflect a vulnerability factor for psychopathologies. \n\nPreviously, + endogenous testosterone modulated the aPFC and aPFCamygdala coupling in a sample + of healthy students ( ). In that study, a different demographic group of healthy + control subjects similarly showed a testosterone modulation of aPFCamygdala + coupling, but no testosterone modulation of aPFC activity. This difference in + the strength of testosterone-modulatory effects might be related to between-group + differences in age (mean healthy control subjects, 41; mean students, 22; ), + educational level (staff of forensic psychiatric institute vs university students), + or general anxiety [STAI trait, lower in healthy control subjects of the current + study; mean (SD): 29 (4.4) and 34 (6.9), respectively; t = 2.605; p = + 0.014]. A limitation of this study is the modest sample size. Our focus to exclude + moderating factors of comorbid disorders (except antisocial personality disorder) + and recent drug use has the advantage that the sample is relatively homogeneous, + but future studies using larger samples are needed for replication and to define + subsamples. \n\n\n### Conclusion \n \nPsychopathic offenders showed reduced + aPFC activity and aPFCamygdala connectivity during control of emotional actions, + suggesting a decreased coordination of emotional information during rule-driven + behavior. Moreover, endogenous testosterone modulated the involvement of these + neural mechanisms. Psychopathic offenders with high testosterone levels showed + less involvement of the aPFC, aPFCamygdala connectivity, supramarginal gyrus, + and caudate nucleus, whereas psychopathic individuals with low testosterone + levels recruited the aPFC in a fashion similar to that of healthy control subjects. + These findings suggest that a lack of prefrontal control during emotional actions + may explain enhanced impulsivity in psychopathic offenders during emotionally + provoking situations. They outline a neuroendocrine model underlying impulsive + emotional behavior in psychopathy and support the relevance of assessing a potential + imbalance in testosterone function to guide treatment. 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00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:13:56 -0600 Subject: [PATCH 21/42] allow installable pyproject --- pyproject.toml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 720bfde..82eb99d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,8 +22,8 @@ participant_demographics = [ "pandas", "numpy", "pydantic", - "publang", - "openai", + "publang @ git+https://github.com/adelavega/publang.git", + "openai" ] umls_disease = [ "pandas", @@ -48,3 +48,6 @@ source = "vcs" [tool.hatch.build.hooks.vcs] version-file = "ns_pipelines/_version.py" + +[tool.hatch.metadata] +allow-direct-references = true From 0839a6ead2342bf619a8fc3e83627784524dd4bf Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:17:52 -0600 Subject: [PATCH 22/42] move test directory and remove top level __init__ --- __init__.py | 0 .../test_ParticipantDemographicsExtraction.yaml | 0 {tests => ns_pipelines/tests}/conftest.py | 0 .../tests}/data/sample_inputs/3qT3nzK9bLZ7/identifiers.json | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/ace/coordinates.csv | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/ace/metadata.json | 0 .../tests}/data/sample_inputs/3qT3nzK9bLZ7/processed/ace/text.txt | 0 .../sample_inputs/3qT3nzK9bLZ7/processed/pubget/coordinates.csv | 0 .../sample_inputs/3qT3nzK9bLZ7/processed/pubget/metadata.json | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/text.txt | 0 .../data/sample_inputs/3qT3nzK9bLZ7/source/ace/26507433.html | 0 .../data/sample_inputs/3qT3nzK9bLZ7/source/pubget/26507433.xml | 0 .../sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_000.csv | 0 .../3qT3nzK9bLZ7/source/pubget/tables/table_000_info.json | 0 .../sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_001.csv | 0 .../3qT3nzK9bLZ7/source/pubget/tables/table_001_info.json | 0 .../sample_inputs/3qT3nzK9bLZ7/source/pubget/tables/table_002.csv | 0 .../3qT3nzK9bLZ7/source/pubget/tables/table_002_info.json | 0 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a/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml b/ns_pipelines/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml similarity index 100% rename from tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml rename to ns_pipelines/tests/data/sample_inputs/8EVW7TUtC9cx/source/pubget/tables/tables.xml diff --git a/tests/test_dataset.py b/ns_pipelines/tests/test_dataset.py similarity index 100% rename from tests/test_dataset.py rename to ns_pipelines/tests/test_dataset.py diff --git a/tests/test_participant_demographics.py b/ns_pipelines/tests/test_participant_demographics.py similarity index 100% rename from tests/test_participant_demographics.py rename to ns_pipelines/tests/test_participant_demographics.py diff --git a/tests/test_word_count.py b/ns_pipelines/tests/test_word_count.py similarity index 100% rename from tests/test_word_count.py rename to ns_pipelines/tests/test_word_count.py From 585fc21abbfcd3788f2b7e525a22a46185b32d37 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:22:22 -0600 Subject: [PATCH 23/42] try underscores --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 82eb99d..901cdcf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [project] -name = "neurostore-text-extractor" +name = "neurostore_text_extractor" authors = [{name = "James Kent", email = "jamesdkent21@gmail.com"}] description = "A package for extracting text features from the NeuroStore database." readme = "README.md" From 5cdfc6d43f1614965bc0823eebc07cf3090e31ef Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:23:56 -0600 Subject: [PATCH 24/42] Revert "allow installable pyproject" This reverts commit ef0b25d14b1175aff18953735bb0a723607f6989. --- pyproject.toml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 901cdcf..116df4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,8 +22,8 @@ participant_demographics = [ "pandas", "numpy", "pydantic", - "publang @ git+https://github.com/adelavega/publang.git", - "openai" + "publang", + "openai", ] umls_disease = [ "pandas", @@ -48,6 +48,3 @@ source = "vcs" [tool.hatch.build.hooks.vcs] version-file = "ns_pipelines/_version.py" - -[tool.hatch.metadata] -allow-direct-references = true From 306e9ec44bc747c0c5f7df74b196c717a8d1a0cb Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:24:19 -0600 Subject: [PATCH 25/42] Revert "Revert "allow installable pyproject"" This reverts commit 5cdfc6d43f1614965bc0823eebc07cf3090e31ef. --- pyproject.toml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 116df4c..901cdcf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,8 +22,8 @@ participant_demographics = [ "pandas", "numpy", "pydantic", - "publang", - "openai", + "publang @ git+https://github.com/adelavega/publang.git", + "openai" ] umls_disease = [ "pandas", @@ -48,3 +48,6 @@ source = "vcs" [tool.hatch.build.hooks.vcs] version-file = "ns_pipelines/_version.py" + +[tool.hatch.metadata] +allow-direct-references = true From 693cb769d91346c50ace02cde6ddceb3bb2540f1 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:24:49 -0600 Subject: [PATCH 26/42] Revert "try underscores" This reverts commit 585fc21abbfcd3788f2b7e525a22a46185b32d37. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 901cdcf..82eb99d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [project] -name = "neurostore_text_extractor" +name = "neurostore-text-extractor" authors = [{name = "James Kent", email = "jamesdkent21@gmail.com"}] description = "A package for extracting text features from the NeuroStore database." readme = "README.md" From c3b57670c903fbe607eb02a3bcb633d706f43643 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:25:12 -0600 Subject: [PATCH 27/42] Revert "move test directory and remove top level __init__" This reverts commit 0839a6ead2342bf619a8fc3e83627784524dd4bf. --- __init__.py | 0 .../test_ParticipantDemographicsExtraction.yaml | 0 {ns_pipelines/tests => tests}/conftest.py | 0 .../data/sample_inputs/3qT3nzK9bLZ7/identifiers.json | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/ace/coordinates.csv | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/ace/metadata.json | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/ace/text.txt | 0 .../sample_inputs/3qT3nzK9bLZ7/processed/pubget/coordinates.csv | 0 .../sample_inputs/3qT3nzK9bLZ7/processed/pubget/metadata.json | 0 .../data/sample_inputs/3qT3nzK9bLZ7/processed/pubget/text.txt | 0 .../data/sample_inputs/3qT3nzK9bLZ7/source/ace/26507433.html | 0 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b/tests/test_dataset.py similarity index 100% rename from ns_pipelines/tests/test_dataset.py rename to tests/test_dataset.py diff --git a/ns_pipelines/tests/test_participant_demographics.py b/tests/test_participant_demographics.py similarity index 100% rename from ns_pipelines/tests/test_participant_demographics.py rename to tests/test_participant_demographics.py diff --git a/ns_pipelines/tests/test_word_count.py b/tests/test_word_count.py similarity index 100% rename from ns_pipelines/tests/test_word_count.py rename to tests/test_word_count.py From 8e7152f5dfbaf6c030dde49f4fc4a9f00c4ce6c7 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:25:31 -0600 Subject: [PATCH 28/42] remove init --- __init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 __init__.py diff --git a/__init__.py b/__init__.py deleted file mode 100644 index e69de29..0000000 From 20af58054a1ee8de2fde63f4e7d9ae68e9f0d4dd Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:26:05 -0600 Subject: [PATCH 29/42] remove old files --- requirements.txt | 2 -- setup.py.old | 16 ---------------- 2 files changed, 18 deletions(-) delete mode 100644 requirements.txt delete mode 100644 setup.py.old diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 6f53444..0000000 --- a/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -spacy -openai diff --git a/setup.py.old b/setup.py.old deleted file mode 100644 index 0023626..0000000 --- a/setup.py.old +++ /dev/null @@ -1,16 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name='ns_text_extraction', - version='0.0.1', - description='A package for extraction of text features using NLP/LLMs from NeuroStore articles', - author='Psychoinformatics Lab', - author_email='delavega@utexas.edu', - packages=find_packages(), - install_requires=[ - 'openai', - 'pandas', - 'scispacy', - 'pubget' - ] -) \ No newline at end of file From 5cff6be21fbaf179847036ca7c6f1b37394cd4bb Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:32:57 -0600 Subject: [PATCH 30/42] switch to version 5 --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ea525e4..94ea43d 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -21,7 +21,7 @@ jobs: uses: actions/checkout@v2 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' From 194e9b1566bcda8cff24a85598f3adc6fb813223 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:50:01 -0600 Subject: [PATCH 31/42] use editable install --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 94ea43d..ced91a8 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -26,7 +26,7 @@ jobs: python-version: '3.8' - name: Install dependencies - run: pip install .[tests,participant_demographics,word_count] + run: pip install -e .[tests,participant_demographics,word_count] - name: Test with pytest run: pytest From 6f45fba8fa681d22d663264365e17ca7b259da9b Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:54:58 -0600 Subject: [PATCH 32/42] trigger variable From b6e26b09b11d35bc0625bbb34cae84c6556b1d34 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 10:57:49 -0600 Subject: [PATCH 33/42] add fake key --- .github/workflows/test.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ced91a8..ea11475 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -29,4 +29,6 @@ jobs: run: pip install -e .[tests,participant_demographics,word_count] - name: Test with pytest + env: + OPENAI_API_KEY: "fake_key" run: pytest From 08e534a3ea0bcd677a710bbceb2d0b96c9130051 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 19:45:38 -0600 Subject: [PATCH 34/42] Update ns_pipelines/word_count/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/word_count/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 0d40e05..dd4844e 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -42,7 +42,7 @@ def __init__(self, inputs=("text",), input_sources=("pubget", "ace"), square_roo super().__init__(inputs=inputs, input_sources=input_sources) def _run(self, all_study_inputs, debug=False): - """Run the word count extraction pipeline.""" + """Run the word deviance extraction pipeline.""" # Calculate the average word count total_word_count = 0 From e8108fda943fcce97d204b555c288ab385cb0561 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 15 Nov 2024 19:46:09 -0600 Subject: [PATCH 35/42] Update ns_pipelines/participant_demographics/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/participant_demographics/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index 8dc6c60..adc2039 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -50,7 +50,6 @@ class ParticipantDemographicsExtraction(IndependentPipeline): _version = "1.0.0" # _hash_attrs = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] - # everything in __init__ is hashed with _inputs and _input_sources def __init__( self, From c366e6119485461dcbc58413ae21b88685bf6a82 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 18 Nov 2024 11:24:57 -0600 Subject: [PATCH 36/42] Update ns_pipelines/word_count/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/word_count/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index dd4844e..7aca813 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -1,5 +1,4 @@ from datetime import datetime -import hashlib import json from ns_pipelines.pipeline import IndependentPipeline, DependentPipeline From e1fcd2b0d34f5e75a1bdfcf4af9ea4bb4bd1a488 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 18 Nov 2024 11:25:06 -0600 Subject: [PATCH 37/42] Update ns_pipelines/word_count/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/word_count/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 7aca813..6dc016e 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -1,5 +1,4 @@ from datetime import datetime -import json from ns_pipelines.pipeline import IndependentPipeline, DependentPipeline From 01a70f0d47aea115f175c82551f96083d8022a94 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 18 Nov 2024 11:25:17 -0600 Subject: [PATCH 38/42] Update ns_pipelines/participant_demographics/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/participant_demographics/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/run.py index adc2039..227b399 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/run.py @@ -49,7 +49,6 @@ class ParticipantDemographicsExtraction(IndependentPipeline): """Participant demographics extraction pipeline.""" _version = "1.0.0" - # _hash_attrs = ["extraction_model", "prompt_set", "kwargs", "_inputs", "_input_sources"] def __init__( self, From ce537b8e152b22cf0c3262d07a9935c66ba74a80 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 18 Nov 2024 18:22:52 -0600 Subject: [PATCH 39/42] Update ns_pipelines/word_count/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/word_count/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 6dc016e..90d679b 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -35,7 +35,6 @@ class WordDevianceExtraction(DependentPipeline): _version = "1.0.0" def __init__(self, inputs=("text",), input_sources=("pubget", "ace"), square_root=False): - """add any pipeline configuration here (as opposed to runtime arguments like n_cpus or n_cores)""" self.square_root = square_root super().__init__(inputs=inputs, input_sources=input_sources) From 44ad3c635e9ba4c5012a3b898e85a08779831945 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 18 Nov 2024 18:23:11 -0600 Subject: [PATCH 40/42] Update ns_pipelines/word_count/run.py Co-authored-by: Alejandro de la Vega --- ns_pipelines/word_count/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/run.py index 90d679b..8bbccdf 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/run.py @@ -1,4 +1,3 @@ -from datetime import datetime from ns_pipelines.pipeline import IndependentPipeline, DependentPipeline From 35c09aaba9902cce05652951e904858e574b2997 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 21 Nov 2024 14:20:49 -0600 Subject: [PATCH 41/42] change the names --- ns_pipelines/__init__.py | 8 ++ .../participant_demographics/__init__.py | 5 + .../{run.py => model.py} | 26 +++- .../umls_disease/{run.py => model.py} | 8 +- ns_pipelines/word_count/__init__.py | 6 +- ns_pipelines/word_count/{run.py => model.py} | 4 +- ...est_ParticipantDemographicsExtractor.yaml} | 111 +++++++++--------- tests/test_participant_demographics.py | 11 +- tests/test_word_count.py | 14 +-- 9 files changed, 114 insertions(+), 79 deletions(-) rename ns_pipelines/participant_demographics/{run.py => model.py} (71%) rename ns_pipelines/umls_disease/{run.py => model.py} (99%) rename ns_pipelines/word_count/{run.py => model.py} (95%) rename tests/cassettes/test_participant_demographics/{test_ParticipantDemographicsExtraction.yaml => test_ParticipantDemographicsExtractor.yaml} (97%) diff --git a/ns_pipelines/__init__.py b/ns_pipelines/__init__.py index e69de29..4e82416 100644 --- a/ns_pipelines/__init__.py +++ b/ns_pipelines/__init__.py @@ -0,0 +1,8 @@ +from .participant_demographics import ParticipantDemographicsExtractor +from .word_count import WordCountExtractor, WordDevianceExtractor + +__all__ = [ + "ParticipantDemographicsExtractor", + "WordCountExtractor", + "WordDevianceExtractor", +] diff --git a/ns_pipelines/participant_demographics/__init__.py b/ns_pipelines/participant_demographics/__init__.py index e69de29..5a01e87 100644 --- a/ns_pipelines/participant_demographics/__init__.py +++ b/ns_pipelines/participant_demographics/__init__.py @@ -0,0 +1,5 @@ +from .model import ParticipantDemographicsExtractor + +__all__ = [ + "ParticipantDemographicsExtractor", +] diff --git a/ns_pipelines/participant_demographics/run.py b/ns_pipelines/participant_demographics/model.py similarity index 71% rename from ns_pipelines/participant_demographics/run.py rename to ns_pipelines/participant_demographics/model.py index 227b399..4d95209 100644 --- a/ns_pipelines/participant_demographics/run.py +++ b/ns_pipelines/participant_demographics/model.py @@ -31,10 +31,9 @@ def extract(extraction_model, extraction_client, text, prompt_set='', **extract_ return predictions, clean_preds -def _load_client(model_name): +def _load_client(model_name, api_key): if 'gpt' in model_name: - client = OpenAI(api_key=os.getenv('OPENAI_API_KEY')) - + client = OpenAI(api_key=api_key) else: raise ValueError(f"Model {model_name} not supported") @@ -45,7 +44,7 @@ def _load_prompt_config(prompt_set): return getattr(prompts, prompt_set) -class ParticipantDemographicsExtraction(IndependentPipeline): +class ParticipantDemographicsExtractor(IndependentPipeline): """Participant demographics extraction pipeline.""" _version = "1.0.0" @@ -56,16 +55,33 @@ def __init__( prompt_set, inputs=("text",), input_sources=("pubget", "ace"), + env_variable=None, + env_file=None, **kwargs ): super().__init__(inputs=inputs, input_sources=input_sources) self.extraction_model = extraction_model self.prompt_set = prompt_set + self.env_variable = env_variable + self.env_file = env_file self.kwargs = kwargs + def get_api_key(self): + """Read the API key from the environment variable or file.""" + if self.env_variable: + api_key = os.getenv(self.env_variable) + if api_key is not None: + return api_key + if self.env_file: + with open(self.env_file) as f: + return ''.join(f.read().strip().split("=")[1]) + else: + raise ValueError("No API key provided") + def _run(self, study_inputs, n_cpus=1): """Run the participant demographics extraction pipeline.""" - extraction_client = _load_client(self.extraction_model) + api_key = self.get_api_key() + extraction_client = _load_client(self.extraction_model, api_key) prompt_config = _load_prompt_config(self.prompt_set) if self.kwargs is not None: diff --git a/ns_pipelines/umls_disease/run.py b/ns_pipelines/umls_disease/model.py similarity index 99% rename from ns_pipelines/umls_disease/run.py rename to ns_pipelines/umls_disease/model.py index a35fe63..e3ce2b5 100644 --- a/ns_pipelines/umls_disease/run.py +++ b/ns_pipelines/umls_disease/model.py @@ -8,6 +8,8 @@ import json from pathlib import Path +from ns_pipelines.pipeline import IndependentPipeline + from spacy.language import Language @Language.component("serialize_abbreviation") def replace_abbrev_with_json(spacy_doc): @@ -170,14 +172,14 @@ def __main__(docs_path, preds_path, replace_abreviations=True, output_dir=None, # Refactor to replace abbrevations while loading preds, abbreviations = _load_preds(preds_path, docs_path, replace_abreviations, n_workers) - + if abbreviations is not None and output_dir is not None: out_name = Path(preds_path).stem.replace('_clean', '_abrv') out_path = Path(output_dir) / f'{out_name}.json' json.dump(abbreviations, out_path.open('w')) - + results = run_umls_extraction(preds, abbreviations=abbreviations) - + results_df = pd.DataFrame(results) if output_dir is not None: diff --git a/ns_pipelines/word_count/__init__.py b/ns_pipelines/word_count/__init__.py index 59f4a60..bd8d47d 100644 --- a/ns_pipelines/word_count/__init__.py +++ b/ns_pipelines/word_count/__init__.py @@ -1,6 +1,6 @@ -from .run import WordCountExtraction +from .model import WordCountExtractor, WordDevianceExtractor __all__ = [ - "WordCountExtraction", - "WordDevianceExtraction", + "WordCountExtractor", + "WordDevianceExtractor", ] diff --git a/ns_pipelines/word_count/run.py b/ns_pipelines/word_count/model.py similarity index 95% rename from ns_pipelines/word_count/run.py rename to ns_pipelines/word_count/model.py index 8bbccdf..374ad71 100644 --- a/ns_pipelines/word_count/run.py +++ b/ns_pipelines/word_count/model.py @@ -2,7 +2,7 @@ from ns_pipelines.pipeline import IndependentPipeline, DependentPipeline -class WordCountExtraction(IndependentPipeline): +class WordCountExtractor(IndependentPipeline): """Word count extraction pipeline.""" _version = "1.0.0" @@ -25,7 +25,7 @@ def _run(self, study_inputs, debug=False): return {"word_count": len(text.split())} -class WordDevianceExtraction(DependentPipeline): +class WordDevianceExtractor(DependentPipeline): """Word deviance pipeline. Count the deviance of each study from the average word count. diff --git a/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml b/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtractor.yaml similarity index 97% rename from tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml rename to tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtractor.yaml index 734c5a2..92219f1 100644 --- a/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtraction.yaml +++ b/tests/cassettes/test_participant_demographics/test_ParticipantDemographicsExtractor.yaml @@ -429,22 +429,22 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA9RUTWvbQBC9+1csc7aL5NiOrVtpaUvS0rQNBBoFMV6N5HX2Q+yuWhvj/15W/pDs - OFDoqTqI5b2ZN7NPmtn0GAORQ8KAL9BzVcnB23tXf6LRD/rmpj/vbn+t7j7+norr+e+vSnyHfsgw - 8yVxf8h6w42qJHlh9I7mltBTUI2vr+LJdDy7um4IZXKSIa2s/GBkBsNoOBpE00E02ScujODkIGGP - 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chunked @@ -2085,7 +2086,7 @@ interactions: openai-organization: - university-of-texas-at-austin-8nppuy openai-processing-ms: - - '3440' + - '3192' openai-version: - '2020-10-01' strict-transport-security: @@ -2103,7 +2104,7 @@ interactions: x-ratelimit-reset-tokens: - 325ms x-request-id: - - req_cc9175a86e95a4f55f38511697e8c115 + - req_6f88a6359206a9dda8699903a9f9dbf5 status: code: 200 message: OK diff --git a/tests/test_participant_demographics.py b/tests/test_participant_demographics.py index 1205615..03ac2d2 100644 --- a/tests/test_participant_demographics.py +++ b/tests/test_participant_demographics.py @@ -1,15 +1,18 @@ import pytest +from pathlib import Path -from ns_pipelines.participant_demographics.run import ParticipantDemographicsExtraction +from ns_pipelines import ParticipantDemographicsExtractor from ns_pipelines.dataset import Dataset @pytest.mark.vcr(record_mode="once", filter_headers=["authorization"]) -def test_ParticipantDemographicsExtraction(sample_data, tmp_path): +def test_ParticipantDemographicsExtractor(sample_data, tmp_path): """Test the word count extraction pipeline.""" - pde = ParticipantDemographicsExtraction( + pde = ParticipantDemographicsExtractor( extraction_model="gpt-4o-2024-08-06", - prompt_set="ZERO_SHOT_MULTI_GROUP_FTSTRICT_FC" + prompt_set="ZERO_SHOT_MULTI_GROUP_FTSTRICT_FC", + env_variable="API_CLIENT_OPENAI_KEY", + env_file=str(Path(__file__).parents[1] / ".env"), ) dataset = Dataset(sample_data) output_dir = tmp_path / "participant_demographics" diff --git a/tests/test_word_count.py b/tests/test_word_count.py index b9def54..438dfb6 100644 --- a/tests/test_word_count.py +++ b/tests/test_word_count.py @@ -1,11 +1,11 @@ from pathlib import Path -from ns_pipelines.word_count.run import WordCountExtraction, WordDevianceExtraction +from ns_pipelines import WordCountExtractor, WordDevianceExtractor from ns_pipelines.dataset import Dataset -def test_WordCountExtraction(sample_data, tmp_path): +def test_WordCountExtractor(sample_data, tmp_path): """Test the word count extraction pipeline.""" - wce = WordCountExtraction() + wce = WordCountExtractor() dataset = Dataset(sample_data) output_dir = tmp_path / "word_count" wce.run(dataset, output_dir) @@ -13,14 +13,14 @@ def test_WordCountExtraction(sample_data, tmp_path): # no ouputs generated wce.run(dataset, output_dir) # rerun with preference of ace - wce_ace = WordCountExtraction(input_sources=("ace", "pubget")) + wce_ace = WordCountExtractor(input_sources=("ace", "pubget")) wce_ace.run(dataset, output_dir) assert True -def test_WordDevianceExtraction(sample_data, tmp_path): +def test_WordDevianceExtractor(sample_data, tmp_path): """Test the word deviance extraction pipeline.""" - wde = WordDevianceExtraction() + wde = WordDevianceExtractor() dataset = Dataset(sample_data) output_dir = tmp_path / "word_deviance" wde.run(dataset, output_dir) @@ -28,6 +28,6 @@ def test_WordDevianceExtraction(sample_data, tmp_path): # no ouputs generated wde.run(dataset, output_dir) # rerun with preference of ace - wde_ace = WordDevianceExtraction(input_sources=("ace", "pubget")) + wde_ace = WordDevianceExtractor(input_sources=("ace", "pubget")) wde_ace.run(dataset, output_dir) assert True From 8c5237f98de3568be39aef7b12d515b9d3cf2e32 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 21 Nov 2024 18:27:05 -0600 Subject: [PATCH 42/42] work with .keys file --- .github/workflows/test.yml | 4 +++- .gitignore | 1 + .keys.example | 1 + tests/test_participant_demographics.py | 4 ++-- 4 files changed, 7 insertions(+), 3 deletions(-) create mode 100644 .keys.example diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ea11475..f03ae14 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -31,4 +31,6 @@ jobs: - name: Test with pytest env: OPENAI_API_KEY: "fake_key" - run: pytest + run: | + cp .keys.example .keys + pytest diff --git a/.gitignore b/.gitignore index a745ea2..f795f65 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,7 @@ ipython_config.py dmypy.json # Environments +.keys .env .venv env/ diff --git a/.keys.example b/.keys.example new file mode 100644 index 0000000..b4ea083 --- /dev/null +++ b/.keys.example @@ -0,0 +1 @@ +OPENAI_CLIENT_API_KEY=fake_key diff --git a/tests/test_participant_demographics.py b/tests/test_participant_demographics.py index 03ac2d2..8678bdc 100644 --- a/tests/test_participant_demographics.py +++ b/tests/test_participant_demographics.py @@ -9,10 +9,10 @@ def test_ParticipantDemographicsExtractor(sample_data, tmp_path): """Test the word count extraction pipeline.""" pde = ParticipantDemographicsExtractor( - extraction_model="gpt-4o-2024-08-06", + extraction_model="gpt-4o-mini-2024-07-18", prompt_set="ZERO_SHOT_MULTI_GROUP_FTSTRICT_FC", env_variable="API_CLIENT_OPENAI_KEY", - env_file=str(Path(__file__).parents[1] / ".env"), + env_file=str(Path(__file__).parents[1] / ".keys"), ) dataset = Dataset(sample_data) output_dir = tmp_path / "participant_demographics"