Automated Fiber Quantification#
Tractography based on diffusion weighted MRI (dMRI) is used to find the major white matter fascicles (tracts) in the living human brain. The health of these tracts is an important factor underlying many cognitive and neurological disorders.
Tissue properties may vary systematically along each tract: different populations of axons enter and exit the tract, and disease can strike at local positions within the tract. Because of this, quantifying and understanding diffusion measures along each fiber tract (the tract profile) may reveal new insights into white matter development, function, and disease that are not obvious from mean measures of that tract ([Yeatman2012]).
pyAFQ is a software package focused on automated delineation of the major fiber tracts in individual human brains, and quantification of the tissue properties within the tracts. To learn more about the software please refer to the Table of Contents.
Citing our work#
If you use this software in your work, please consider citing the papers that describe the method [Yeatman2012], the specific implementation [Kruper2021], as well as the underlying modeling and tractography implementations in DIPY [Garyfallidis2014]. If you use the RecoBundles method, please also cite the original paper describing that method [Garyfallidis2018].
Jason D Yeatman, Robert F Dougherty, Nathaniel J Myall, Brian A Wandell, Heidi M Feldman. Tract profiles of white matter properties: automating fiber-tract quantification. PloS One, 7: e49790. https://doi.org/10.1371/journal.pone.0049790
Kruper, J. D. Yeatman, A. Richie-Halford, D. Bloom, M. Grotheer, S. Caffarra, G. Kiar, I. I. Karipidis, E. Roy, B. Q. Chandio, E. Garyfallidis, and A. Rokem. Evaluating the reliability of human brain white matter tractometry. Aperture, in press, 2021. doi: https://doi.org/10.1101/2021.02.24.432740
Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014) Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8:8. doi: https://doi.org/10.3389/fninf.2014.00008
Garyfallidis E, Côté MA, Rheault F, Sidhu J, Hau J, Petit L, Fortin D, Cunanne S, Descoteaux M. Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage. 2018 170:283-295. https://doi.org/10.1016/j.neuroimage.2017.07.015.
Work on this software is supported through grant 1RF1MH121868-01 from the National Institutes for Mental Health / The BRAIN Initiative and by a grant from the Gordon & Betty Moore Foundation, and from the Alfred P. Sloan Foundation to the University of Washington eScience Institute, by a CRCNS grant (NIH R01EB027585) to Eleftherios Garyfallidis and to Ariel Rokem , and by NSF grant 1551330 to Jason Yeatman.