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Living Preprint
NeuroLibre

Summary

Authors
Affiliations
University of Washington
University of Washington
https://doi.org/10.55458/neurolibre.00037

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess the physical properties of long-range brain connections Yeatman et al., 2012. 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 tools that extract insights from these measurements, including novel implementations of machine learning and statistical analysis methods that consider the unique structure of tractometry data Richie-Halford et al., 2021Muncy et al., 2022, as well as tools for visualization and interpretation of the results Yeatman et al., 2018Kruper et al., 2024. Taken together, these open-source software tools provide a comprehensive environment for the analysis of dMRI data.

Acknowledgments

This work was funded by National Institutes of Health grants MH121868, MH121867, and EB027585, as well as by National Science Foundation grant #1934292. Software development was also supported by the Chan Zuckerberg Initiative’s Essential Open Source Software for Science program, the Alfred P. Sloan Foundation and the Gordon & Betty Moore Foundation.

References
  1. Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One, 7(11), e49790. 10.1371/journal.pone.0049790
  2. Richie-Halford, A., Yeatman, J. D., Simon, N., & Rokem, A. (2021). Multidimensional analysis and detection of informative features in human brain white matter. PLoS Comput. Biol., 17(6), e1009136. 10.1371/journal.pcbi.1009136
  3. Muncy, N. M., Kimbler, A., Hedges-Muncy, A. M., McMakin, D. L., & Mattfeld, A. T. (2022). General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents. Neuroimage Clin, 33, 102937. 10.1016/j.nicl.2022.102937
  4. Yeatman, J. D., Richie-Halford, A., Smith, J. K., Keshavan, A., & Rokem, A. (2018). A browser-based tool for visualization and analysis of diffusion MRI data. Nat. Commun., 9(1), 940. 10.1038/s41467-018-03297-7
  5. Kruper, J., Hagen, M. P., Rheault, F., Crane, I., Gilmore, A., Narayan, M., Motwani, K., Lila, E., Rorden, C., Yeatman, J. D., & Rokem, A. (2024). Tractometry of the Human Connectome Project: resources and insights. Front. Neurosci., 18, 1389680. 10.3389/fnins.2024.1389680
A software ecosystem for brain tractometry processing, analysis, and insight
pyAFQ: Automated Fiber Quantification in Python