SampleData

Code related to the processing of the Winawer lab sample dataset

The scripts in this directory present an example analysis of fMRI data like those often performed in the Winawer lab.

You can set the directory to put all data and analyses in by passing a path to any of the main scripts. Alternatively, if they're called with no arguments, the data directory is set to DownloadedData/ within this directory or scratch/$(whoami)/SampleData/DownloadedData if the $CLUSTER environmental variable is equal to GREENE (and thus we think we're on NYU's greene cluster); ideally this would be a general solution for different compute clusters, but we couldn't come up with one.

Main scripts:

• masterScript.sh: checks for dependencies (matlab, freesurfer license, python environment) and calls the rest of the scripts in this section, in order. Takes a very long time, so you may want to call each section separately (which will also ease debugging).
• s0_download-data.sh: downloads the DICOMs and extra files from the OSF.
• s1_preprocess-data.sh: converts data from DICOMs to NIFTIs, then defaces, preprocesses, and runs the BIDS validator on the data. Uses docker to do all this; this script takes the longest time.
• s2_addToBIDS.sh: copies over the extra files (e.g., stimulus) into the BIDS directory, then runs BIDS validator. This takes <5 minutes.
• s3_glmDenoise.sh: runs GLMdenoise on the preprocessed data (just the sfp task), estimating the response of each voxel to the log-polar grating stimuli. Uses MATLAB.
• s4_prf.sh: runs analyzePRF on the preprocessed data (just the prf task), estimating the location and size of each voxels' population receptive field (pRF). Uses MATLAB.
• s5_Benson_Atlases.sh: runs neuropythy on the data to generate the anatomical atlas of visual areas and the Bayesian retinotopic analysis (combining the anatomical atlas and the pRF results from above). Uses Docker.
• s6_vista.sh: runs vistasoft on the preprocessed data (just the prf task) to estimate the location and size of each voxels' pRF. This is similar to s4_prf.sh, but uses another algorithm to do so. Uses MATLAB and Docker.

Sub-scripts:

• completeJSONS.sh: called by s1_preprocess-data.sh to fill out the information contained in the sidecar jsons for BIDS. Script written by NYU CBI, necessary for fMRIprep.
• setup.sh: called at the beginning of each script to set environmental variables to make sure we get paths correct.
• subroutines/, contains helper scripts used by the main scripts:
  • s3_glmDenoise.m: actually calls GLMdenoise on the data, after setting the various arguments. Saves out many images for examining the quality of the fit. Uses MATLAB, python, and the Winawer Lab MRI_tools repo (see below).
  • s4_prf.m: actually calls analyzePRF on the data, after setting various arguments. Uses MATLAB and the Winawer Lab MRI_tools repo (see below).
  • s6_vista.m: actually calls vistasoft on the data, after setting various arguments. Uses MATLAB, docker, and ???.
  • subscript_rois.py: Saves out .annot files for the proposed visual areas from the Bayesian retinotopic analysis. Called at the end of s5_Benson_Atlases.sh. Uses python.

Dependencies:

• matlab (version??) must be on the system path (MATLAB 9.6?)
• ToolboxToolbox, for managing matlab dependencies. See the Winawer lab wiki for info on how to set it up.
  • Winawer lab Toolbox Registry.
• docker
• freesurfer license must be at $FREESURFER_HOME/license.txt(where $FREESURFER_HOME is an environmental variable specifying the path to the freesurfer application) or at the location specified by the environmental variable$FREESURFER_LICENSE`
• setup the winawerlab python environment, as described here (you can activate it, following step 4, but you shouldn't need to if you use masterScript.sh, because we activate it there).
• download the Winawer Lab MRI_tools repo and make sure that its BIDS folder is on your path. One way to do this, from the command line, is (note this will only last for this session, if you want to permanently add that folder to your path, add the last line to your ~/.bashrc file):

# download the repo
git clone [email protected]:WinawerLab/MRI_tools.git ~/Documents/MRI_tools
# add it to your path
export PATH="$HOME/Documents/MRI_tools/BIDS/:$PATH"

• if you're running this on your local machine, then you should be good to go.
• if you're running this on a compute cluster that's not NYU's greene, then you'll need a couple more changes: you'll need to at least chang:
  1. subroutines/utils.sh: change on_cluster() function to something that will work for your cluster (note that we use set -e pipefail, so you can't rely on catching errors).
  2. subroutines/utils.sh: check load_modules() and make sure the modules are correctly named. If your cluster uses some other command to handle packages, you'll have to change this more.
  3. setup.sh: check that the path /scratch/$(whoami) exists and that you have write access (and your cluster admin is okay with placing a lot of large files here). If not, change $SAMPLE_DATA_DIR to some other path (in the first if block at the top of the file) or pass a path to the scripts when calling them..