Abstract #4295

The test-retest reliability and robustness of diffusion-MRI based tractometry

John Kruper^1,2, Jason D Yeatman^3,4, Adam Richie-Halford², David Bloom^1,2, Mareike Grotheer^5,6, Sendy Caffarra^3,4,7, Greg Kiar⁸, Iliana I. Karipidis⁹, Ethan Roy³, and Ariel Rokem^1,2

¹Department of Psychology, University of Washington, Seattle, WA, United States, ²eScience Institute, University of Washington, Seattle, WA, United States, ³Graduate School of Education, Stanford University, Stanford, CA, United States, ⁴Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, United States, ⁵Center for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg, Germany, ⁶Department of Psychiatry, University of Marburg, Marburg, Germany, ⁷Basque Center on Cognition (BCBL), Brain and Language, Donostia‐San Sebastián, Spain, ⁸Department of Biomedical Engineering, McGill University, Montreal, QC, Canada, ⁹Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States

Tractometry from diffusion MRI estimates the tissue properties along the length of major white matter tracts, using: computational tractography; tract segmentation using atlases or other classification methods; and microstructural modeling in voxels along the length of the estimated tracts. Given previous concerns about the sensitivity of dMRI-based analysis to variations in methodology, we tested: the reliability of tractometry results within individuals across measurements; and within measurement, across variations in the analysis methods. We found that although there are variations that arise from differences in tractography methods, bundle segmentation methods, microstructural modeling, and different software implementations, tractometry is overall quite robust.

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