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Abstract #2024

Along-Tract Statistics Allow for Enhanced Tractography Analysis

John B. Colby1,2, Lindsay Soderberg1, Catherine Lebel1, Ivo D. Dinov1,3, Paul M. Thompson1,2, Elizabeth R. Sowell1

1Department of Neurology, UCLA, Los Angeles, CA, United States; 2Interdepartmental Program for Biomedical Engineering, UCLA; 3Department of Statistics, UCLA

Despite the large amount of within-tract variability in diffusion imaging indices like FA, the vast majority of neuroscience applications still utilize a traditional tract-averaged approach for their deterministic tractography analyses. Here, we lay out a straightforward workflow for conducting one type of within-tract analysis that attains an economical balance between accessibility and improved modeling ability. We then demonstrate the advantages of this approach over traditional tract-averaged methods by looking at both within-subject and between-group examples.