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.

Keywords : nifti, connectivity, decompose, scilpy

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Inputs

Input 1

Format : tuple val(meta), path(trk), path(labels)

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

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Outputs

hdf5

Format : tuple val(meta), path(*__decomposed.h5)

	Type	Description	Mandatory	Pattern
meta	map	Groovy Map containing sample information e.g. [ id:'sample1', single_end:false ]	True
*__decomposed.h5	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.	True	*__decomposed.h5

labels_list

Format : tuple val(meta), path(*__labels_list.txt)

	Type	Description	Mandatory	Pattern
meta	map	Groovy Map containing sample information e.g. [ id:'sample1', single_end:false ]	True
*__labels_list.txt	file	Save the labels list as text file.	True	*__labels_list.txt

versions

Format : path(versions.yml)

	Type	Description	Mandatory	Pattern
versions.yml	file	File containing software versions	True	versions.yml

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Tools

	Description	DOI
scilpy	The Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox.

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Authors

• @ThoumyreStanislas

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Last updated : 2025-10-30