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Abstract #4295

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

John Kruper1,2, Jason D Yeatman3,4, Adam Richie-Halford2, David Bloom1,2, Mareike Grotheer5,6, Sendy Caffarra3,4,7, Greg Kiar8, Iliana I. Karipidis9, Ethan Roy3, and Ariel Rokem1,2
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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