Outputs of sMRIPost-LINC

sMRIPost-LINC outputs conform to the BIDS Derivatives specification (see BIDS Derivatives, along with the upcoming BEP 011 and BEP 012). sMRIPost-LINC generates three broad classes of outcomes:

  1. Visual QA (quality assessment) reports: One HTML per subject, that allows the user a thorough visual assessment of the quality of processing and ensures the transparency of sMRIPost-LINC operation.

  2. Atlases: Atlases selected by the user are warped to fsaverage space and converted to Freesurfer .annot format.

  3. Parcellated structural measures: Anatomical measures are summarized by region of interest (ROI) from each of the atlases.

  4. Confounds: Some confound values, including Euler numbers, are saved in a TSV file.

Layout

Assuming sMRIPost-LINC is invoked with:

smripost_linc <input_dir>/ <output_dir>/ participant [OPTIONS]

The outputs will be a BIDS Derivatives dataset of the form:

<output_dir>/
  logs/
  atlases/
  sub-<label>/
  sub-<label>.html
  dataset_description.json
  .bidsignore

For each participant in the dataset, a directory of derivatives (sub-<label>/) and a visual report (sub-<label>.html) are generated. The log directory contains citation boilerplate text. dataset_description.json is a metadata file in which sMRIPost-LINC records metadata recommended by the BIDS standard.

Visual Reports

sMRIPost-LINC outputs summary reports, written to <output dir>/smripost_linc/sub-<label>.html. These reports provide a quick way to make visual inspection of the results easy.

Parcellations and Atlases

XCP-D produces parcellated anatomical and functional outputs using a series of atlases. The individual outputs are documented in the relevant sections of this document, with this section describing the atlases themselves.

The atlases currently used in XCP-D can be separated into three groups: subcortical, cortical, and combined cortical/subcortical. The two subcortical atlases are the Tian atlas :footcite:p:`tian2020topographic` and the CIFTI subcortical parcellation :footcite:p:`glasser2013minimal`. The cortical atlases are the Glasser :footcite:p:`Glasser_2016`, the Gordon :footcite:p:`Gordon_2014`, the MIDB precision brain atlas derived from ABCD data and thresholded at 75% probability :footcite:p:`hermosillo2022precision`, and the Myers-Labonte infant atlas thresholded at 50% probability :footcite:`myers2023functional`. The combined cortical/subcortical atlases are 10 different resolutions of the 4S (Schaefer Supplemented with Subcortical Structures) atlas.

The 4S atlas combines the Schaefer 2018 cortical atlas (version v0143) :footcite:p:`Schaefer_2017` at 10 different resolutions (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 parcels) with the CIT168 subcortical atlas :footcite:p:`pauli2018high`, the Diedrichson cerebellar atlas :footcite:p:`king2019functional`, the HCP thalamic atlas :footcite:p:`najdenovska2018vivo`, and the amygdala and hippocampus parcels from the HCP CIFTI subcortical parcellation :footcite:p:`glasser2013minimal`. The 4S atlas is used in the same manner across three PennLINC BIDS Apps: XCP-D, QSIPrep_, and ASLPrep_, to produce synchronized outputs across modalities. For more information about the 4S atlas, please see https://github.com/PennLINC/AtlasPack.

Tip

You can choose to only use a subset of the available atlases by using the --atlases parameter.

fsaverage-space atlases are written out to the atlases subfolder, following BEP038. fsnative-space atlases are written out to the subject directory.

<output_dir>/
   atlases/
      dataset_description.json
      atlas-<label>/
         atlas-<label>_hemi-<L|R>_space-fsaverage_dseg.annot
         atlas-<label>_dseg.json
         atlas-<label>_dseg.tsv
   sub-<label>/[ses-<label>/]
      anat/
         sub-<label>[_ses-<label>]_hemi-<L|R>_space-fsnative_seg-<atlas>_dseg.annot
         sub-<label>[_ses-<label>]_hemi-<L|R>_space-fsnative_seg-<atlas>_dseg.json

Parcellated Structural Measures

sMRIPost-LINC outputs a set of parcellated structural measures.

Confounds

sMRIPost-LINC outputs a set of confounds that can be used to summarize data quality.