+
+
+
+
+
ROI Reference Table Overview
+
+
There are 130 ROIs in total. Below is a table showing the indication
+relation for each ROI, including the index (tind), the text
+label (roi_text_label), and the ROI identification
+(roi):
+
+
+
+
Process T1-weighted Brain MRI Data with FSL and Register to EVE
+Atlas
+
+
Ensure FSL is installed as per the instructions provided in the package README.
+
This vignette outlines the sequential steps involved in the image
+processing pipeline, designed to prepare and analyze T1-weighted brain
+MRI data effectively:
+
+-
+Image Reorientation: Adjusts the image to align
+with a standard orientation, facilitating consistent analyses and
+comparisons.
+-
+Bias Correction: Reduces scanner-induced intensity
+inhomogeneities to improve tissue contrast and measurement
+accuracy.
+-
+Brain Extraction: Isolates the brain from
+surrounding skull and other non-brain tissues, which is critical for
+accurate subsequent analyses.
+-
+Image Registration (EVE template): Aligns the MRI
+data to the EVE Atlas, ensuring that anatomical regions are correctly
+mapped and comparable across datasets.
+-
+Extraction of Intensity Data: Gathers crucial
+signal intensity information from the brain images, which is fundamental
+for detailed tissue analysis.
+-
+Segmentation: Divides the brain into White Matter
+(WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF), allowing for
+targeted studies of these distinct tissue types.
+-
+Extraction of Tissue Mask Array Data: Retrieves
+detailed spatial information about tissue distribution, essential for
+volume and location-specific studies.
+-
+Intracranial Brain Volume Calculation: Computes the
+total brain volume, providing a baseline for various comparative and
+diagnostic assessments.
+
+
By following these structured steps, users can ensure comprehensive
+processing of MRI data, facilitating robust analyses and research
+conclusions.
+
+eve_T1(fpath, outpath, fsl_path , fsl_outputtype = "NIFTI_GZ")
+
fpath: A character string specifying the path to one
+T1-weighted MRI file. The file should be in NIFTI file format (.nii.gz).
+Processing may take some time, so please be patient. For handling
+multiple MRI files, consider using parallel processing with R’s parallel
+computation packages or through high-performance computing resources to
+improve efficiency.
+
Sample outcome:
+
#> [1] 181 217 181
+
+
+
Run Partition Pipeline on Neuroimaging Data
+
+
This section describes how to utilize the pipeline for processing
+neuroimaging data through sequential application of sophisticated
+algorithms and segmentation based on Regions of Interest (ROIs).
+
+
+
Pipeline Overview:
+
+
+-
+Super-Partition: Applies Josh’s super-partition
+algorithm, which considers 3D locations to group data based on
+ROIs.
+-
+Partition Algorithm: Processes data post
+Super-Partition using the Partition algorithm,
+enhancing data reduction for highly correlated data.
+-
+Tissue Segmentation: Segments the processed data by
+tissue type within each ROI.
+
+
+
+
+
Practical Example:
+
+
To process the ROI named “inferior_frontal_gyrus”, identify the
+corresponding tind (in this example, tind = 5)
+from the Region Labels and Structures section. You’ll
+need to set up a directory to manage all processing files and datasets.
+Note that the outputs from this pipeline will not be returned directly
+but will be stored at specified locations:
+
+- Intensity data:
+/main_dir/partition/roi/thresh/tissue_type/cmb/intensities_whole.rds
+
+- Volume data:
+/main_dir/partition/roi/thresh/tissue_type/cmb/volume_whole.rds
+
+
+
+
+
+
Parallel Processing:
+
+
This function is equipped with parallel processing capabilities,
+allowing users to specify the number of cores they wish to utilize.
+Increasing the number of cores will proportionally speed up the
+Partition process, offering significant time savings for large
+datasets.
+
+# Run the partition pipeline with specified parameters
+run_partition_pipeline(
+ tind = 5,
+ nfl = list.files(
+ '/Users/jinyaotian/Downloads/pipeline_test/eve_t1',
+ full.names = TRUE
+ ),
+ main_dir = "/Users/jinyaotian/Downloads/pipeline_test",
+ tissue_type = 2,
+ ICC_thresh_vec = c(0.8, 0.9),
+ num_cores = 4,
+ suppar_thresh_vec = seq(0.7, 1, 0.01),
+ B = 2000,
+ outp_volume = TRUE
+)
+
+
+
Reduced data showcase
+
+
Intensity:
+
+
Naming convention:
+
There are two types of feature naming formats:
+
+-
+“inferior_frontal_gyrus_left_module6_reduced_var_2”:
+
+-
+First part: “inferior_frontal_gyrus_left”
+corresponds to the
roi_text_label, which refers to the
+region of interest (ROI) as outlined in the ROI Reference Table
+Overview above.
+-
+Second part: “module6” indicates that this feature
+belongs to group 6 after the Super-Partition process. Users can
+disregard this part, as it is primarily for programming purposes.
+-
+Third part: “reduced_var_2” signifies that this is
+the second reduced feature within “module6” following the Partition
+process.
+
+
+-
+“inferior_frontal_gyrus_left_V20234”: This format
+indicates that the feature has not been reduced by the Partition
+algorithm. “V20234” refers to column 20234 in the original
+high-dimensional data.
+
+
+
+
Map Reduced Feature Back to Brain Image Voxel Locations
+
+
Each voxel in a brain image corresponds to a specific feature,
+establishing a one-to-one mapping between a voxel and a feature. This
+relationship allows us to localize particular brain features to specific
+brain areas at the voxel level, enabling visualization of these
+features. However, after applying Super-Partition and Partition
+techniques, multiple brain features are aggregated into a single reduced
+feature as part of a data reduction process. The goal of the following
+function is to relate the reduced feature back to its component
+features, and subsequently identify the brain image voxels to which
+those component features are mapped.
+
For example, we want to analyze the reduced feature
+“inferior_frontal_gyrus_left_module4_reduced_var_13”.
+
+loc_df <- map_feature2_loc(feature_name = "inferior_frontal_gyrus_left_module4_reduced_var_13",
+ threshold = 0.8,
+ main_dir = "/path/to/data")
+
+
We can see reduced feature
+“inferior_frontal_gyrus_left_module4_reduced_var_13” is aggregated by
+488 features “V14942”, “V14943”, “V14894”, “V19659”, “V21519”, “V21520”,
+“V4237”, “V6245”, “V4809”, “V3634”……
+