ISTART-MID: Monetary Incentive Delay Task Data and Analyses

This repository contains code related to our in prep project related to the Monetary Incentive Delay task processing (cf. Knutson et al., 2000, Neuroimage), substance use, and reward sensitivity (grant: R03-DA046733; grant report: Sazhin et al., 2020, JPBS). All hypotheses and analysis plans were pre-registered on AsPredicted in fall semester 2019 and data collection commenced on shortly thereafter. Imaging data will be shared via OpenNeuro when the manuscript is posted on bioRxiv.

A few prerequisites and recommendations

• Understand BIDS and be comfortable navigating Linux
• Install FSL
• Install miniconda or anaconda

Notes on repository organization and files

• Raw DICOMS (an input to heudiconv) are private and only accessible locally (Smith Lab Linux: /data/sourcedata). These files will have already been de-identified and converted to BIDS under a separate repository (DVS-Lab/istart-data)
• 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. These files/folders include parts of bids and derivatives.
• Tracked folders and their contents:
  • code: analysis code
  • templates: fsf template files used for FSL analyses
    • Activation models: Model 1 is standard Model 2 includes a regressor for the right eyeball from exploring possible cocerns about niquist ghosting
    • PPI models: Psychophysiological interaction
  • masks: images used as masks, networks, and seed regions in analyses
  • derivatives: derivatives from analysis scripts, but only text files (re-run script to regenerate larger outputs)

Internal: Keeping up with analyses on an ongoing basis

As we're collecting data, we must analyze it on an ongoing basis for the sake of quality assurance and identifying and correcting potential problems.

Step 1: Checking your inputs

Before you do anything, you should make sure all your inputs are there. The person managing the preprocessing should have taken care of these steps under the DVS-Lab/istart-data repository:

1. Conversion to BIDS, defacing, and MRIQC (prepdata.sh)
2. Preprocessing with fmriprep (fmriprep.sh)
3. Creation of confound EVs (MakeConfounds.py)
4. Reward sensitivity 'measures in istart-mid/code'

Step 2: Creating 3-column files for FSL

1. go to the correct location: cd /data/projects/istart-mid
2. run script: bash code/run_gen3colfiles.sh
3. run python Extract_Eyeballphys.py if you want to run model2

Step 3: Running the analyses

1. go to the correct location on the Smith Lab Linux box: cd /data/projects/istart-mid
2. run scripts with nohup (prevents process from hanging up if you close your computer or lose your connection):

• nohup bash code/run_L1stats.sh > nohup_L1stats.out & (wait till this is done before running L2stats.sh)
• nohup bash code/run_L2stats.sh > nohup_L2stats.out &

3. review *.out logs from nohup. (if no errors, delete them. if errors, report on Asana and raise at lab meeting)

External: Basic commands to reproduce our all of our analyses (under construction)

Note: this section is still under construction. It is intended for individuals outside the lab who might want to reproduce all of our analyses, from preprocessing to group-level stats in FSL. As a working example from a different study, please see https://github.com/DVS-Lab/srndna-trustgame

# get code and data (two options for data)
git clone https://github.com/DVS-Lab/istart-mid
cd  istart-mid

rm -rf bids # remove bids subdirectory since it will be replaced below
# can this be made into a sym link?

datalad clone https://github.com/OpenNeuroDatasets/ds003745.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

# run statistics
bash code/run_L1stats.sh
bash code/run_L2stats.sh
bash code/run_L3stats.sh

Acknowledgments

This work was supported, in part, by grants from the National Institutes of Health (R03-DA046733 to DVS and R15-MH122927 to DSF). DVS was a Research Fellow of the Public Policy Lab at Temple University during the preparation of the manuscript (2019-2020 academic year).