A software ecosystem for brain tractometry processing, analysis, and insight

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements. These include novel machine learning and statistical analysis methods adapted to the characteristic structure of tract-based data. We benchmark the performance of these statistical analysis methods in different datasets and analysis tasks, including hypothesis testing on group differences and predictive analysis of subject age. We also demonstrate that computational advances implemented in the software offer orders of magnitude of acceleration. Taken together, these open-source software tools—freely available at https://tractometry.org—provide a transformative environment for the analysis of dMRI data.Author summary: The human brain is a highly inter-connected system. Information about the environment and about internal states is effectively distributed and integrated through neural pathways that rapidly transmit signals between distant brain regions through large nerve fiber bundles. Measurements of diffusion MRI (dMRI) are sensitive to the trajectory of these nerve fiber pathways within the brain, and to their physical properties. We developed a suite of scalable open-source software tools that process dMRI data and delineate brain pathways and connections within it, quantifying the physical properties of brain tissue along the length of each pathway in an individualized manner. We demonstrate that the software is extensible to a variety of new studies, and offers useful approaches for visualization and statistical analysis, including novel machine learning tools. We also demonstrate that novel computational methods that we developed offer substantial speed-up, offering scalability for new large-scale datasets.