ISTART-data: Data Management and Preprocessing

This repository contains the final code for managing and processing all of the data in our ISTART project. The data will eventually be placed on OpenNeuro. More information about the project can be found in Sazhin et al., 2020, Journal of Psychiatry and Brain Science.

A few prerequisites and recommendations

• Understand BIDS and be comfortable navigating Linux
• Install FSL
• Install miniconda or anaconda
• Install PyDeface: pip install pydeface
• Make singularity containers for heudiconv (version: 0.9.0), mriqc (version: 0.16.1), and fmriprep (version: 20.2.3).

Notes on repository organization and files

• Raw DICOMS (an input to heudiconv) are only accessible locally (Smith Lab Linux: /data/sourcedata)
• Some of the contents of this repository are not tracked (.gitignore) because the files are large and we do not yet have a nice workflow for datalad. Note that we only track key text files in bids and derivatives.
• Tracked folders and their contents:
  • code: analysis code
  • derivatives: stores derivates from our scripts
  • bids: contains the standardized "raw" in BIDS format (output of heudiconv)
  • stimuli: psychopy scripts and matlab scripts for delivering stimuli and organizing output

Downloading Data and Running Preprocessing

# get data via datalad (TO DO)
git clone https://github.com/DVS-Lab/istart-data
cd istart-data
datalad clone https://github.com/OpenNeuroDatasets/XXXXXX.git bids
# the bids folder is a datalad dataset
# you can get all of the data with the command below:
datalad get sub-*

# run preprocessing and generate confounds and timing files for analyses
bash code/run_fmriprep.sh
python code/MakeConfounds.py --fmriprepDir="derivatives/fmriprep"
bash code/run_gen3colfiles.sh

Acknowledgments

This work was supported, in part, by grants from the National Institutes of Health (R03-DA046733 to DVS. DVS was a Research Fellow of the Public Policy Lab at Temple University during the preparation of the manuscript (2019-2020 academic year).