Module: connectivity/decompose

Divide a tractogram into its various connections using a brain parcellation(labels). The hdf5 output format allows to store other information required for connectivity, such as the associated labels.

Inputs

	Type	Description	Pattern
meta	map	Groovy Map containing sample information e.g. [ id:'sample1', single_end:false ]
trk	file	Tractogram to decompose.	*.{trk, tck, vtk, fib, dpy}
labels	file	brain parcellation. Labels must have 0 as background. Volumes must have isotropic voxels.	*.nii.gz

Outputs

	Type	Description	Pattern
meta	map	Groovy Map containing sample information e.g. [ id:'sample1', single_end:false ]
hdf5	file	Output hdf5 file where each bundles is a group with key’LABEL1_LABEL2’. The array_sequence format cannot be stored directly in a hdf5, so each group is composed of ‘data’, ‘offsets’ and ‘lengths’ from the array sequence. The ‘data’ is stored in VOX/CORNER for simplicity and efficiency.	*__decomposed.h5
labels_list	file	Save the labels list as text file.	*__labels_list.txt
versions	file	File containing software versions	versions.yml

Tools

	Description	Homepage	DOI
scilpy	The Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox.	https://github.com/scilus/scilpy.git

Keywords

nifti
connectivity
decompose
scilpy

Authors

@ThoumyreStanislas

Maintainers

@ThoumyreStanislas