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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, jesse.brown@ucsf.edu

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

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Neuroimaging connectivity Python functions galore

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