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Abstract #2397

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter

Adam C Richie-Halford1, Jason Yeatman2, Noah Simon3, and Ariel Rokem4
1eScienceInstitute, University of Washington, Seattle, WA, United States, 2Graduate School of Education and Division of Developmental and Behavioral Pediatrics, Stanford University, Stanford, CA, United States, 3Department of Biostatistics, University of Washington, Seattle, WA, United States, 4Department of Psychology, University of Washington, Seattle, WA, United States

We present a novel method for the analysis of diffusion MRI tractometry data based on the sparse group lasso. It capitalizes on natural anatomical grouping of diffusion metrics, providing both accurate prediction of phenotypic information and results that are readily interpretable. We show the effectiveness of this approach in two settings. In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls and SGL automatically identifies known anatomical correlates of ALS. In a regression setting, we accurately predict “brain age” in two previous dMRI studies. We demonstrate that our approach is both accurate and interpretable.

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