This tutorial explains how we can use RecoBundles 1 to extract bundles from input tractograms.
First, we need to download a reference streamline atlas. Here, we downloaded an atlas with 30 bundles in MNI space 2 from:
For this tutorial, you can use your own tractography data or you can download a single subject tractogram from:
Let’s say we have an input target tractogram named
streamlines.trk and the atlas we
Visualizing the target and atlas tractograms before registration:
dipy_horizon "streamlines.trk" "whole_brain_MNI.trk" --random_color
To extract the bundles from the tractogram, we first need move our target tractogram to be in the same space as the atlas (MNI, in this case). We can directly register the target tractogram to the space of the atlas, using streamline-based linear registration (SLR) 3.
The following workflows require two positional input arguments;
moving .trk files. In our case, the
static input is the atlas and the
target tractogram (
Run the following workflow:
dipy_slr "whole_brain_MNI.trk" "streamlines.trk" --force
Per default, the SLR workflow will save a transformed tractogram as
Visualizing the target and atlas tractograms after registration:
dipy_horizon "moved.trk" "whole_brain_MNI.trk" --random_color
out_dir folder (e.g.,
rb_output), into which output will be placed:
For the RecoBundles workflow, we will use the 30 model bundles downloaded earlier. Run the following workflow:
dipy_recobundles "moved.trk" "bundles/*.trk" --force --mix_names --out_dir "rb_output"
This workflow will extract 30 bundles from the tractogram.
Example of extracted Left Arcuate fasciculus (AF_L) bundle (visualized with
Example of extracted Left Arcuate fasciculus (AF_L) bundle visualized along with the model AF_L bundle used as reference in RecoBundles:
Output of RecoBundles will be in native space. To get bundles in subject’s original space, run following commands:
mkdir org_output dipy_labelsbundles 'streamlines.trk' 'rb_output/*.npy' --mix_names --out_dir "org_output"
For more information about each command line, please visit DIPY website https://dipy.org/ .
If you are using any of these commands please be sure to cite the relevant papers and DIPY 4.
Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017
Yeh F.C., Panesar S., Fernandes D., Meola A., Yoshino M., Fernandez-Miranda J.C., Vettel J.M., Verstynen T. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage, 2018.
Garyfallidis et al., “Robust and efficient linear registration of white-matter fascicles in the space of streamlines”, Neuroimage, 117:124-140, 2015.
Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. “DIPY, a library for the analysis of diffusion MRI data”. Frontiers in Neuroinformatics, 1-18, 2014.