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
Inputs
Section titled “Inputs”Input 1
Section titled “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 |
Outputs
Section titled “Outputs”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
Section titled “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
Section titled “versions”Format : path(versions.yml)
| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| versions.yml | file | File containing software versions | True | versions.yml |
Arguments (see process.ext)
Section titled “Arguments (see process.ext)”| Type | Description | Default | Choices | |
|---|---|---|---|---|
| no_pruning | boolean | If set, will NOT prune on length. Length criteria in min_len, max_len. | False | |
| no_remove_loops | boolean | If set, will NOT remove streamlines making loops. Angle criteria based on max_angle | False | |
| no_remove_outliers | boolean | If set, will NOT remove outliers using QuickBundle. Criteria based on —outlier_threshold. | False | |
| no_remove_curv | boolean | If set, will NOT remove streamlines that deviate from the mean curvature. Threshold based on max_curv. | False | |
| min_len | number | Pruning minimal segment length. | 20.0 | |
| max_len | number | Pruning maximal segment length. | 200.0 | |
| outlier_threshold | number | Outlier removal threshold when using hierarchical QuickBundle. | 0.6 | |
| max_angle | number | Maximal winding angle over which a streamline is considered as looping. | 330 | |
| max_curv | number | Clustering threshold for centroids curvature filtering with QuickBundle. | 10 | |
| single_thread | boolean | If true, the command will be run in single-threaded mode. By default, the command will use multiple threads based on the number of CPUs allocated to the task. | False |
| Description | DOI | |
|---|---|---|
| scilpy | The Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox. |
Authors
Section titled “Authors”Last updated : 2026-05-12