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Sara Bosticardo edited this page Sep 17, 2025 · 8 revisions

What's new in COMMIT v2.4.0 🚀

  • Multicompartment model for pathological connectome

    Introduced a lesion compartment to relax the assumption of constant microstructural properties along streamlines. Streamline weights are now rescaled according to estimated tissue damage, enabling more realistic estimates in pathological conditions.

What's new in COMMIT v2.3.0

  • Added support for Windows (requires the pthread-win32 library)

  • Operator module

    The operator.pyx module is no longer compiled at runtime. The build_operator() method no longer has the build_dir argument.

What's new in COMMIT v2.2.0

This tutorial illustrates the new features/improvements implemented in the latest version of COMMIT (v2.2.0).

  • Verbosity

    Implemented a new logging system with many different features including different types of progress bars and logging levels at module level. Here you can find the official documentation of the logging module.

  • Regularization

    Changed regularization implementation to be more flexible and allow for different types of regularizations.

What's new in COMMIT v2.0.0

  • Redundancy reduction

We introduce a novel streamline representation to reduce redundancy in tractography. This is performed in two steps:

  1. Clustering of the streamlines based on their average euclidean distance, by setting a threshold blur_clust_thr (in mm). By setting blur_clust_groupby, it can exploit anatomical information deriving from a nifti image containing cortical/subcortiical GM parcellation. This will cluster streamlines connecting the same regions based on their average distance using parallel computation (fast). If blur_clust_groupby is not set, the clustering will be performed single thread on the whole brain (slow).
  2. Changing each streamline internal representation by setting blur_core_extent (in mm) and blur_gauss_extent (in mm). We suggest to set blur_core_extent equal to blur_clust_thr and blur_gauss_extent=1.

Note

step 1 can be skipped if previously performed using other softwares such as MRtrix or dipy.

  • Parallel computation

    We added the possibility to import a tractogram using parallel threads. This is particularly useful when dealing with large tractograms, as it can significantly reduce the time required to import the data. The following code shows how to import a tractogram in parallel using 10 threads by setting n_threads=10. If this parameter is not set, tht method will try to use all the available threads.

    Running with n_threads>1 will create a temporary folder named temp which will be deleted at the end of the process. If you want to keep the temporary folder, set keep_temp=True (default is False).

from commit import trk2dictionary
trk2dictionary.run(
    filename_tractogram = 'demo01_fibers.tck',
    filename_peaks      = 'peaks.nii.gz',
    filename_mask       = 'WM.nii.gz',
    fiber_shift         = 0.5,
    n_threads           = 10,
    blur_clust_thr      = 2,
    blur_clust_groupby  = None,
    blur_core_extent    = 2,
    blur_gauss_extent   = 1.2,
    keep_temp           = False,
    peaks_use_affine    = True
)

  • License

COMMIT is now released under a proprietary license which allows free use for non-commercial purposes. If you are interested in using COMMIT for commercial purposes, please contact Alessandro Daducci at alessandro.daducci@univr.it. You can read the full license text here.

Diffusion Imaging and Connectivity Estimation (DICE) lab

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