From afe0ae394d147d992feac5b90e95f89fadede891 Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 10:43:40 -0500 Subject: [PATCH] Add nipoppy tutorial --- content/resources/tutorials/nipoppy.md | 246 ++++++++++++++++++++++++- static/images/nipoppy-1.png | Bin 0 -> 74810 bytes static/images/nipoppy-2.png | Bin 0 -> 76238 bytes 3 files changed, 243 insertions(+), 3 deletions(-) create mode 100644 static/images/nipoppy-1.png create mode 100644 static/images/nipoppy-2.png diff --git a/content/resources/tutorials/nipoppy.md b/content/resources/tutorials/nipoppy.md index 68314d8..0063090 100644 --- a/content/resources/tutorials/nipoppy.md +++ b/content/resources/tutorials/nipoppy.md @@ -1,10 +1,250 @@ --- -title: Nipoppy +title: Streamlining Neuroimaging Processing with Nipoppy linkTitle: Nipoppy type: docs weight: 5 --- -[GH 70](https://github.com/ReproNim/repronim.org/issues/70) +**ReproPrinciple**: +**Actions**: +**Standards**: +**Tools**: -https://docs.google.com/document/d/1opySPBU72gnEOx98TjvhEfD5OR8QRvYcHIGXFC7bAc8/edit?tab=t.0#heading=h.q6gqw9g6emk +# Challenge + +Managing neuroimaging pipelines often involves handling complex datasets and multiple tools, all while ensuring reproducibility throughout the process. The challenge lies in: + +* **Data complexity:** Neuroimaging data can be large, multimodal, and require precise handling to maintain data integrity. +* **Workflow management:** Keeping track of different pipeline steps, tools, and configurations can be overwhelming, especially in collaborative projects. +* **Reproducibility:** Ensuring that workflows are transparent, repeatable, and compatible with community standards such as BIDS. + +# Exercise + +In this tutorial, you will learn how to use [`nipoppy`](https://nipoppy.readthedocs.io/en/latest/) to address these challenges by: + +1. **Initializing a project:** Setting up a project directory and configuring necessary files for managing data and pipelines execution on the BIDS dataset [ds004101](https://openneuro.org/datasets/ds004101/versions/1.0.1/download) from OpenNeuro. +2. **Running the pipeline:** Executing the MRIQC 23.1.0 pipeline our example dataset. +3. **Tracking progress:** Monitoring the pipeline's status and ensuring all steps are completed successfully. + +# Step-by-step guide + +## Step 0: Prerequisites + +We will utilize the dataset **ds004101** from [OpenNeuro](https://openneuro.org/), which includes structural and functional MRI data for 9 subjects (2 sessions), as our working example. The dataset can be downloaded by following instructions [here](https://openneuro.org/datasets/ds004101/versions/1.0.1/download). + +**Dataset:** BIDS dataset [ds004101](https://openneuro.org/datasets/ds004101/versions/1.0.1/download) +**Software:** + +- Python environment with Nipoppy. Instructions can be found [here](https://nipoppy.readthedocs.io/en/latest/installation.html) +- [Apptainer](https://apptainer.org/) + +## Step 1: Initializing the Nipoppy dataset + +Run the following command to create a Nipoppy dataset and populate it with the BIDS data: + +``` +nipoppy init --bids-source nipoppy_example +``` + +This command creates a folder named `nipoppy_example` with subdirectories for raw data, processed (derivatives) data, pipeline configuration files, logs: + +``` +nipoppy_example/ +├── bids +├── code +├── containers +├── derivatives +├── global_config.json +├── logs +├── manifest.tsv +├── pipelines +│ ├── bidscoin-4.3.2 +│ ├── dcm2bids-3.1.0 +│ ├── fmriprep-20.2.7 +│ ├── fmriprep-23.1.3 +│ ├── fmriprep-24.1.1 +│ ├── freesurfer-6.0.1 +│ ├── freesurfer-7.3.2 +│ ├── freesurfer_stats_and_qc-0.1.0 +│ ├── heudiconv-0.12.2 +│ ├── mriqc-23.1.0 +│ ├── qsiprep-0.24.0 +│ └── qsiprep-1.0.0rc1 +├── scratch +├── sourcedata +└── tabular +``` + +Nipoppy automatically generated a manifest file from the BIDS dataset. This file is considered the ground truth for which participants and sessions are available for processing. + +``` +participant_id visit_id session_id datatype +09114 1pre 1pre ['anat', 'func'] +09114 2post 2post ['fmap', 'func'] +09160 1pre 1pre ['anat', 'func'] +09160 2post 2post ['fmap', 'func'] +09260 1pre 1pre ['anat', 'fmap', 'func'] +09260 2post 2post ['fmap', 'func'] +09300 1pre 1pre ['anat', 'fmap', 'func'] +09300 2post 2post ['fmap', 'func'] +09380 1pre 1pre ['anat', 'fmap', 'func'] +09380 2post 2post ['fmap', 'func'] +09381 1pre 1pre ['anat', 'fmap', 'func'] +09381 2post 2post ['fmap', 'func'] +10134 1pre 1pre ['anat', 'fmap', 'func'] +10134 2post 2post ['fmap', 'func'] +10332 1pre 1pre ['anat', 'fmap', 'func'] +10332 2post 2post ['fmap', 'func'] +10570 1pre 1pre ['anat', 'fmap', 'func'] +10570 2post 2post ['fmap', 'func'] +``` + +The `nipoppy status` prints out a summary of the dataset, including the number of participants who are in BIDS or have completed a pipeline. + +``` +nipoppy status nipoppy_example +``` + +For now, the dataset only has BIDS data. + +![](/images/nipoppy-1.png) + +## Step 2: Modifying the global configuration file + +The `nipoppy init` command created the configuration file at `nipoppy_example/global_config.json`. This file needs to be updated with information specific to the dataset and your computing environment. By default, the beginning of the global config file looks like this: + +``` +{ + "DATASET_NAME": "", + "VISIT_IDS": [ + "", + "" + ], + "SESSION_IDS": [ + "", + "" + ], + "SUBSTITUTIONS": { + "[[NIPOPPY_DPATH_CONTAINERS]]": "", + "[[HEUDICONV_HEURISTIC_FILE]]": "", + "[[DCM2BIDS_CONFIG_FILE]]": "", + "[[FREESURFER_LICENSE_FILE]]": "", + "[[TEMPLATEFLOW_HOME]]": "" + }, +... +} +``` + +The `DATASET_NAME`, `VISIT_IDS`, and `SESSION_IDS` fields can be updated with the appropriate information, and the `SUBSTITUTIONS` field can be cleaned up to remove entries that will not be used in the tutorial (e.g., configuration files for BIDS conversion, FreeSurfer license file): + +``` +{ + "DATASET_NAME": "ds004101", + "VISIT_IDS": [ + "1pre", + "2post" + ], + "SESSION_IDS": [ + "1pre", + "2post" + ], + "SUBSTITUTIONS": { + "[[NIPOPPY_DPATH_CONTAINERS]]": "", + "[[TEMPLATEFLOW_HOME]]": "" + }, +... +} +``` + +There are still paths that need to be filled in for the `SUBSTITUTIONS` field: + +- ``: a central directory where container images are stored (i.e. if sharing between multiple datasets/projects). This can also be deleted, in which case the `nipoppy_example/containers` directory will be used. +- ``: either an existing directory used by TemplateFlow, or an empty directory where templates will be downloaded. + +## Step 3: Download the MRIQC Apptainer image + +Before running the pipeline, download the MRIQC container in the appropriate directory (either `nipoppy_example/containers` or the custom path specified in the global config file) using Apptainer. + +``` +apptainer build /mriqc_23.1.0.sif docker://nipreps/mriqc:23.1.0 +``` + +## Step 4: Running MRIQC on a single participant and session + +Use `nipoppy run` to run MRIQC on a single participant and session. This will take around 15 minutes. + +``` +nipoppy run --pipeline mriqc --pipeline-version 23.1.0 nipoppy_example --participant-id 09114 --session-id 1pre +``` + +Pipeline outputs are written to `nipoppy_example/derivatives/mriqc/23.1.0/output`: + +``` +nipoppy_example/derivatives/mriqc/23.1.0/output/ +├── dataset_description.json +├── logs +├── sub-09114 +│ ├── figures +│ │ ├── sub-09114_ses-1pre_desc-background_T1w.svg +│ │ └── sub-09114_ses-1pre_desc-zoomed_T1w.svg +│ └── ses-1pre +│ └── anat +│ └── sub-09114_ses-1pre_T1w.json +└── sub-09114_ses-1pre_T1w.html +``` + +Log files can be found in `nipoppy_example/logs/run/mriqc-23.1.0`. + +## Step 5: Tracking pipeline processing status + +Run `nipoppy track` to determine the MRIQC processing status for each subject and session: + +``` +nipoppy track --pipeline mriqc --pipeline-version 23.1.0 nipoppy_example +``` + +The command will create an *imaging bagel* file at `nipoppy_example/derivatives/imaging_bagel.tsv`. After running MRIQC for a single participant and session, the file looks like this: + +``` +participant_id bids_participant_id session_id pipeline_name pipeline_version pipeline_step bids_session_id status +09114 sub-09114 1pre mriqc 23.1.0 default ses-1pre SUCCESS +09114 sub-09114 2post mriqc 23.1.0 default ses-2post FAIL +09160 sub-09160 1pre mriqc 23.1.0 default ses-1pre FAIL +09160 sub-09160 2post mriqc 23.1.0 default ses-2post FAIL +09260 sub-09260 1pre mriqc 23.1.0 default ses-1pre FAIL +09260 sub-09260 2post mriqc 23.1.0 default ses-2post FAIL +09300 sub-09300 1pre mriqc 23.1.0 default ses-1pre FAIL +09300 sub-09300 2post mriqc 23.1.0 default ses-2post FAIL +09380 sub-09380 1pre mriqc 23.1.0 default ses-1pre FAIL +09380 sub-09380 2post mriqc 23.1.0 default ses-2post FAIL +09381 sub-09381 1pre mriqc 23.1.0 default ses-1pre FAIL +09381 sub-09381 2post mriqc 23.1.0 default ses-2post FAIL +10134 sub-10134 1pre mriqc 23.1.0 default ses-1pre FAIL +10134 sub-10134 2post mriqc 23.1.0 default ses-2post FAIL +10332 sub-10332 1pre mriqc 23.1.0 default ses-1pre FAIL +10332 sub-10332 2post mriqc 23.1.0 default ses-2post FAIL +10570 sub-10570 1pre mriqc 23.1.0 default ses-1pre FAIL +10570 sub-10570 2post mriqc 23.1.0 default ses-2post FAIL + +``` + + +Running `nipoppy status` again will show that one participant has completed processing for the first session: + +![](/images/nipoppy-2.png) + +The imaging bagel file can also be uploaded to the [Neurobagel digest dashboard](https://digest.neurobagel.org/), which will produce interactive visualizations of pipeline processing statuses. + +Finally, this file can be used directly as input to the [Neurobagel CLI](https://neurobagel.org/user_guide/cli/) when generating participant-level metadata about processing pipeline results. + +## Step 6 (optional): Running MRIQC on the rest of the dataset + +Use `nipoppy run` without the participant and session flags to process the rest of the dataset (in a loop). This will skip the participant-session that has previously been run successfully. + +``` +nipoppy run --pipeline mriqc --pipeline-version 23.1.0 nipoppy_example +``` + +# Conclusion + +In this tutorial, we ran a neuroimaging pipeline (MRIQC) on a small BIDS dataset, showcasing how Nipoppy keeps track of which participants and sessions are available, and which ones have been processed already. Nipoppy offers a streamlined interface for configuring, running, and tracking virtually any pipeline (including custom ones). 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