TRACULA (TRActs Constrained by UnderLying Anatomy) is a tool for automatic reconstruction of a set of major white-matter pathways from diffusion-weighted MRI data. As the FreeSurfer cortical parcellation and subcortical segmentation use prior knowledge on the relative positions of anatomical structures with respect to each other, TRACULA uses prior knowledge on the relative positions of white-matter pathways with respect to their surrounding anatomical structures. Whereas the anatomical segmentation/parcellation tools use this type of neighborhood information to classify voxels in the volume or vertices on the surface, TRACULA uses it to produce tractography streamlines.



TRACULA uses global probabilistic tractography with anatomical neighborhood priors. Prior distributions on the neighboring anatomical structures of each white-matter pathway are derived from a set of manually annotated training subjects. When TRACULA reconstructs a pathway of interest in a novel subject, it fits the shape of the pathway both to the fiber orientation vectors derived from the subject's diffusion MRI data, and to the anatomical neighborhood priors derived from the subject's T1 data. This obviates the need for any user interaction, such as having to define ad hoc regions of interest or thresholds on curvature and length for each pathway.

In addition to this core functionality, TRACULA provides an end-to-end pipeline that allows the user to:

Finally, TRACULA offers a dedicated stream for processing longitudinal diffusion MRI data. The longitudinal stream reconstructs the pathways of interest jointly from a subject's data at all time points, rather than processing each time point independently as if it were a cross-sectional data point.

Getting started

The best way to get started using TRACULA is by following the tutorials from the FreeSurfer training workshop:

A somewhat older version of the slide presentations from the workshop is available here:

Note that, because TRACULA relies on the underlying anatomy as derived from the FreeSurfer cortical parcellation and subcortical segmentation, these need to be generated first. This means that, before analyzing your diffusion MRI data with TRACULA, you will have to analyze your subjects' T1 data with recon-all and make sure that they have a good-quality mri/aparc+aseg.mgz. In addition to this whole-brain segmentation, the latest version of TRACULA can make use of the thalamic nuclei segmentation, to improve reconstruction of pathways that terminate in or neighbor the thalamus. Thus it is recommended that, prior to TRACULA, you also run the thalamic nuclei segmentation, which will save its output as mri/ThalamicNuclei.v12.T1.FSvoxelSpace.mgz.


All preprocessing, tractography, and post-processing steps are performed by the trac-all script. Several options for each analysis step can be set by the user in a configuration file (dmrirc file), which is passed as an argument to trac-all.

More detailed documentation is available for both trac-all and the configuration files, in case you need more customization of processing options or more information on all the outputs:

White-matter tract atlas

The TRACULA atlas is included in the FreeSurfer distribution and currently comprises 42 major pathways.

Although we refer to it as an "atlas", it is not simply an average brain where voxels have been labeled as tracts. It consists of both tractography streamlines and anatomical segmentations from individual subjects. These subjects were imaged on the MGH Connectom scanner, as part of the young adult Human Connectome Project.


TRACULA in FreeSurfer 7.3 (upcoming):

TRACULA in FreeSurfer 7.2:

TRACULA in FreeSurfer 6.0:

TRACULA in FreeSurfer 5.3:

TRACULA in FreeSurfer 5.2:

TRACULA in FreeSurfer 5.1:


If you use TRACULA, please cite:

If you use our measures of head motion, please cite:

If you use the longitudinal stream of TRACULA, please cite:

Tracula (last edited 2023-07-17 03:04:26 by AnastasiaYendiki)