multimodalPipeline

0. Create the following folder structure:

.../data/imagen/subject#/
.../data/imagen/subject#/anat/
.../data/imagen/subject#/dwi/
.../data/imagen/subject#/facesfMRI/
.../data/imagen/subject#/gcafMRI/
.../data/imagen/subject#/multimodalConn/
.../data/imagen/subject#/restfMRI/
.../data/imagen/group/
.../data/imagen/subjectLists/

1. Perform fMRI preprocessing by running fMRIanalysis/preprocess.py
2. Perform parcellation by running fMRIanalysis/parcellation_*.py
3. Generate parcel time courses by running fMRIanalysis/gen_tc_parcel.py
4. Perform activation detection by using code in bayesianRegressionBN and covarianceEstimationBN
5. Perform DWI preprocessing by running dMRIanalysis/preprocess.txt
6. Perform EPItoDWI alignment by running fMRI2dMRIalignment/alignment.txt
7. Perform tractography by running dMRIanalysis/*_tractography/*_tractography.txt
8. Perform multimodal connectivity estimation by using code in covarianceEstimationBN/gaussianGraphicalModel