Auditory category learning in striatum and cortex using 7T functional MRI

Processing and analyzing tone-learning fMRI data collected at the University of Pittsburgh's 7T MRI Center.

Manuscript details

Currently in revision. Preprint to come shortly!

Data availability

Data will be uploaded to OpenNeuro.

Processing pipeline

Dicom conversion: ./01_dicom_conversion/

1. Peek at the dicom .tsv file using initialize_dicoms_heudiconv.sh
2. Create heuristic.py based on your MRI sequences
3. Convert dicoms to .nii using convert_dicoms_heudiconv.sh

Image denoising: ./02_denoising/

1. Run dwi_denoise on newly converted BIDS-formatted NIfTI files

MRI preprocessing: ./03_fmriprep/

1. Preprocess anatomical and functional MRI with run_fmriprep.sh

(Note: this runs using a Singularity image, so may need to create that first)

Behavior Behavioral data conversion: ./04_behavior/

1. Run convert_behav_to_bids.py to get psychopy outputs into BIDS-compatible format
2. Run behavioral analysis notebook

Masking: ./05_masking/

1. Create grey matter mask for searchlight using make_gm_mask.py
2. Create participant-specific region-of-interest masks

Univariate analysis: ./06_univariate/

1. Run univariate_analysis.py
2. Run group_level.ipynb for group-level GLM and output maps/figures

Representational similarity analysis: ./07_rsa/

1. Create event-specific beta estimates
2. Run region-based RSA using atlas masks (see masking)
3. Compute group-level RSA statistics for cortical and striatal networks