A software ecosystem for brain tractometry processing, analysis, and insight.
Authors
Affiliations (11)
Affiliations (11)
- Department of Psychology, University of Washington, Seattle, Washington, United States of America.
- eScience Institute, University of Washington, Seattle, Washington, United States of America.
- Graduate School of Education, Stanford University, Stanford, California, United States of America.
- Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, California, United States of America.
- Department of Psychology, Phillips-Universität Marburg, Marburg, Germany.
- Center for Mind, Brain and Behavior, Phillips-Universität Marburg and Justus-Liebig Universität Giessen, Marburg, Germany.
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.
Abstract
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.