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dwi-connectome - Computation of structural connectome from corrected DWI datasets

The dwi-connectome pipeline computes a weighted graph encoding anatomical connections between a set of brain regions from corrected DWI datasets.

To aim that, it relies on the MRtrix3 [Tournier et al., 2019] software to compute the constrained spherical deconvolution diffusion model, perform probabilistic tractography and computes a connectome using the Desikan & Destrieux atlases from FreeSurfer.

Prerequisites

You need to preprocess your DWI data and run the t1-freesurfer pipeline on your T1-weighted MRI images prior to running this pipeline.

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of FSL 6.0, MRtrix3, and Freesurfer on your computer.

Running the pipeline

The pipeline can be run with the following command line:

clinica run dwi-connectome [OPTIONS] CAPS_DIRECTORY

where:

  • CAPS_DIRECTORY is the output folder containing the results in a CAPS hierarchy
Optional parameters common to all pipelines
  • -tsv / --subjects_sessions_tsv

This flag allows you to specify in a TSV file the participants belonging to your subset. For instance, running the FreeSurfer pipeline on T1w MRI can be done using :

clinica run t1-freesurfer BIDS_PATH OUTPUT_PATH -tsv my_subjects.tsv

participant_id  session_id
sub-CLNC0001    ses-M000
sub-CLNC0001    ses-M018
sub-CLNC0002    ses-M000

Creating the TSV

To make the display clearer the rows here contain successive tabs but that should not happen in an actual TSV.

  • -wd / --working_directory

By default when running a pipeline, a temporary working directory is created. This directory stores all the intermediary inputs and outputs of the different steps of the pipeline. If everything goes well, the output directory is eventually created and the working directory is deleted.

With this option, a working directory of your choice can be specified. It is very useful for the debugging process or if your pipeline crashes. Then, you can relaunch it with the exact same parameters which will allow you to continue from the last successfully executed node. For the pipelines that generate many files, such as dwi-preprocessing (especially if you run it on multiple subjects), a specific drive/partition with enough space can be used to store the working directory.

  • -np / --n_procs

This flag allows you to exploit several cores of your machine to run pipelines in parallel, which is very useful when dealing with numerous subjects and multiple sessions. Thanks to Nipype, even for a single subject, a pipeline can be run in parallel by exploiting the cores available to process simultaneously independent sub-parts. We recommend using your_number_of_cpu - 1 for costly pipelines such as pet-surface-longitudinal.

If you do not specify -np / --n_procs flag, Clinica will detect the number of threads to run in parallel and propose the adequate number of threads to the user.

  • -cn / --caps-name

Use this option if you want to specify the name of the CAPS dataset that will be used inside the dataset_description.json file, at the root of the CAPS folder (see CAPS Specifications for more details). This works if this CAPS dataset does not exist yet, otherwise the existing name will be kept.

Number of streamlines (--n_tracks option)

The quality of the tractography and, as a result, the connectome mainly depends on the number of streamlines you can generate (the more the better). However, increasing the number of streamlines increases the need for computational resources and space to store the results. On default, 1 million streamlines are generated which represents 1 hour of computation time.

Outputs

Results are stored in the following folder of the CAPS hierarchy: subjects/<participant_id>/<session_id>/dwi/connectome_based_processing/.

The main output files are:

  • <source_file>_space-{b0|T1w}_model-CSD_diffmodel.nii.gz: Constrained spherical deconvolution (CSD) diffusion model.
  • <source_file>_space-{b0|T1w}_model-CSD_tractography.tck: The whole-brain tractography.
  • <source_file>_space-{b0|T1w}_model-CSD_parcellation-{desikan|destrieux}_connectivity.tsv: The connectivity matrix based on the Desikan or Destrieux parcellation.

Atlases available for the Connectome-based processing pipeline:

  • Desikan [Desikan et al., 2006]: This atlas is a subdivision of the cerebral cortex into gyri and contains 34 regions per hemisphere. It was built using a dataset of 40 MRI scans from which 34 cortical ROIs were manually identified in each of the individual hemispheres.
  • Destrieux [Destrieux et al., 2010]: This atlas is a subdivision of the cerebral cortex into gyri and sulci, and contains 74 regions per hemisphere. It was built on anatomical MRI of 24 healthy subjects from which 74 cortical ROIs were manually identified in each of the individual hemispheres.

The matrices are encoded as TSV files without labels for rows and columns. These labels are generated by MRtrix3 and the lookup tables should have been automatically downloaded by Clinica when the dwi-connectome pipeline has been run. More precisely, there are two lookup tables for extracting the relevant grey matter parcellations:

  • one to extract them from the default FreeSurfer segmentation (desikan_killiany), which should be in <your_clinica_folder>/clinica/resources/mappings/fs_default.txt
  • and one to extract them from the a2009s FreeSurfer, which should be in <your_clinica_folder>/clinica/resources/mappings/fs_a2009s.txt

If you cannot find these files, you can copy-paste them from here and here respectively.

Note

The full list of output files can be found in The ClinicA Processed Structure (CAPS) specifications.

Describing this pipeline in your paper

Example of paragraph for the dwi-connectome pipeline

These results have been obtained using the dwi-connectome pipeline of Clinica [Routier et al., 2021] relying on the MRtrix3 [Tournier et al., 2019] software package. Fiber orientation distributions (FOD) at highly anisotropic voxels (FA >0.7) was computed to determine the response function, which was used for constrained spherical deconvolution to accurately estimate the FOD [Tournier et al., 2007]. Then, <n_tracks> fibers with a probabilistic tracking algorithm [Tournier et al., 2010] were generated. Default parameters included minimum length 20 mm, a step size of 0.2 mm, minimum radius of curvature of 1 mm and FOD cutoff of 0.1. All voxels in the white-matter mask were used as seeds and the tracking procedure was stopped if a fiber reached a voxel outside the mask or if a stopping criterion was met (high fiber curvature or low FOD). Finally, the connectome is estimated by counting the number of tracks connecting each pair of nodes according to the [Desikan|Destrieux] parcellation.

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