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UMCP is a set of Python programs used to calculate connectivity metrics from a variety of neuroimaging modalities including diffusion weighted MRI (DTI/DSI), fMRI, and structural MRI. If you use this code in your research, please include the link to the github repository as a reference: https://github.com/jbrown81/umcp Please note that this code lives in perpetual beta. Users are encouraged to use their eyes and common sense to assess various output connectivity metrics for validity. "tracks" and "timeseries" functions are relatively well tested. "core" functions are a mix of supportive functions and hodgepodge functions of use to the original author. "plot_network" functions are useful for creating network visualizations but more experimental and/or catered for specific purposes. For questions contact Jesse Brown, firstname.lastname@example.org Example usage: 1) to get a functional connectivity matrix, given: - fMRI 4D data - a list of ROI .nii files, with the same dimensions + resolution as the fMRI data - optionally, a list of nuisance covariates (eg white matter, CSF timeseries, motion parameters) run_timeseries.py -f bold_4d.nii -m roi_list_21.txt -o fc_mat_21_covars -c --nuis=nuisance_regressors.txt Will output a 21x21 symmetric functional connectivity matrix as a space-delimited text file. For help, type: run_tracks.py --h Usage: run_timeseries.py -f <4d_nii_file> -m <input_masks_file> -o <output_prefix> [options] Options: -h, --help show this help message and exit -f FUNCFILE, --func=FUNCFILE read 4D BOLD fMRI data from FILENAME.nii -m MASKSFILE, --masks=MASKSFILE read mask filenames stored on separate lines in FILENAME.txt -o OUTPUT, --out=OUTPUT output file prefix -c, --corr calculate correlation matrix between all masks -p, --pcorr calculate partial correlation matrix between all masks -v, --cov calculate covariance matrix between all masks --scrub=SCRUBFILE optional: include one column file with 1 for TRs to exclude, 0 for TRs to include -n NUIS, --nuis=NUIS covary for nuisance parameter timeseries in FILENAME.txt 2) to get a structural connectivity matrix, given: - a Diffusion Toolkit/Trackvis .trk file with deterministic streamlines defined - a list of ROI .nii files, with the same dimensions + resolution as the diffusion data run_tracks.py -t tracks.trk -m masklist_21.txt -o sc_mat_21 -c -s Will output a 21x21 symmetric functional connectivity matrix as a space-delimited text file, along with corresponding 21x21 matrices for various statistics about all fibers connecting each pair of regions (average length, average curvature, etc.) For help, type: run_tracks.py --h Usage: run_tracks.py -t <input_tracks> -m <input_masks> -o <output_prefix> [options] Options: -h, --help show this help message and exit -t TRACKSFILE, --tracks=TRACKSFILE read track data from Diffusion Toolkit FILENAME.trk or DSI Studio FILENAME.txt -m MASKSFILE, --masks=MASKSFILE read mask filenames stored on separate lines in FILENAME.txt -o OUTPUT, --out=OUTPUT output file prefix -c, --cmat calculate connectivity matrix between all masks -d, --dens calculate number (density) of tracks intersecting each mask -s, --stats calculate statistics for each track group --statimg=STATIMAGE optional: calculate average track group value for diffusion metric (FA, MD, ...) from .nii file --cthrough connectmat: any part of track must hit any part of mask --dend density: either endpoint of track must hit any part of mask --maskthr=MASKTHRESH optional: threshold value for probabilistic masks --lenthr=LENTHRESH optional: length threshold for tracks --densnii for density calculation, output .nii density file instead of mask hit counts in .txt file --dsistudio if .trk file was generated with dsi_studio