Derivatives & Preprocessing 

derivatives/
├── fmriprep/           # fMRIPrep preprocessed data (all sessions)
├── atlases/            # Group-level reference atlases in template space
├── anatomical_rois/    # Subject-specific anatomically-defined ROIs
├── functional_rois/    # Subject-specific functionally-defined ROIs
├── mriqc/              # MRIQC output (currently empty — not yet run)
├── fmriprepFR/         # Earlier fMRIPrep run (legacy — to be deprecated)
└── QA/                 # Legacy QA images (likely superseded by MRIQC)

Target Pipeline 

sourcedata (DICOMs, raw behavioral)
    │
    ▼
BIDS raw (NIfTI + JSON + events TSV)
    │
    ├──▶ MRIQC (quality metrics, outlier detection)
    │
    ├──▶ fMRIPrep (preprocessing: registration, distortion correction, confounds)
    │
    ├──▶ atlases/ (reference parcellations in template space)
    │
    ├──▶ anatomical_rois/ (subject-specific anatomical segmentations)
    │
    ├──▶ functional_rois/ (subject-specific task-defined ROIs)
    │
    └──▶ [downstream analysis stages TBD]
  • MRIQC and fMRIPrep can likely run in parallel, producing complementary QA output.
  • Long-term goal: a single fmriprep/ output directory (the current fmriprepFR/ and QA/ directories are legacy and will be deprecated).

Atlases

Group-level reference atlases stored at the template level (no per-subject data). Each atlas has a dataset_description.json and an atlas-<label>_description.json sidecar with provenance, license, and citation info.

Schaefer 2018

Local-global cortical parcellation (Schaefer et al., 2018, Cerebral Cortex). Available in 7-network and 17-network solutions at 8 granularities (100, 200, 300, 400, 500, 600, 800, 1000 parcels). All files are in MNI152NLin2009cAsym space at 2 mm resolution, matching fMRIPrep output.

derivatives/atlases/
├── dataset_description.json
├── atlas-Schaefer2018_description.json
└── tpl-MNI152NLin2009cAsym/
    └── anat/
        ├── tpl-MNI152NLin2009cAsym_atlas-Schaefer2018_seg-7n_scale-100_res-2_dseg.nii.gz
        ├── tpl-MNI152NLin2009cAsym_atlas-Schaefer2018_seg-7n_scale-100_res-2_dseg.tsv
        ├── ...  (7n × 8 scales + 17n × 8 scales = 32 NIfTI + 32 TSV)
        └── tpl-MNI152NLin2009cAsym_atlas-Schaefer2018_seg-17n_scale-1000_res-2_dseg.tsv

Entity key: seg-7n / seg-17n = network solution, scale-100 = number of parcels, res-2 = 2 mm isotropic resolution.

Anatomical ROIs

Subject-specific ROIs derived from structural imaging (e.g., hippocampal subfield segmentations, custom FreeSurfer-based masks). Stored per-subject (and optionally per-session if the definition is session-specific).

derivatives/anatomical_rois/
├── dataset_description.json
└── sub-03/
    └── anat/
        ├── sub-03_seg-hippsubfields_dseg.nii.gz     # Full subfield parcellation
        ├── sub-03_seg-hippsubfields_dseg.tsv         # Label lookup table
        ├── sub-03_label-CA1_mask.nii.gz              # Individual subfield mask
        └── sub-03_label-CA1_mask.json                # {"Type": "ROI", "Sources": [...]}

Functional ROIs

Subject-specific ROIs derived from task-based or resting-state analyses (e.g., localizer contrasts, seed-based connectivity maps). Each mask’s JSON sidecar documents the contrast, threshold, and statistical criteria.

derivatives/functional_rois/
├── dataset_description.json
└── sub-03/
    └── ses-01/
        └── func/
            ├── sub-03_ses-01_task-localizer_space-MNI152NLin2009cAsym_label-FFA_mask.nii.gz
            ├── sub-03_ses-01_task-localizer_space-MNI152NLin2009cAsym_label-FFA_mask.json
            └── ...

Minimally Preprocessed Version

Documentation forthcoming.

GLMSingle Output

Documentation forthcoming.