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Abstract #1886

Functional and structural connectivity predict MS patients’ impairment level using an ensemble model applied with a machine learning method

Ceren Tozlu1, Keith Jamison1, Susan Gauthier1,2,3, and Amy Kuceyeski1,4
1Department of Radiology, Weill Cornell Medicine, New York City, NY, United States, 2Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York City, NY, United States, 3Department of Neurology, Weill Cornell Medicine, New York City, NY, United States, 4Brain and Mind Research Institute, Weill Cornell Medicine, Ithaca, NY, United States

No study to date has performed a rigorous analysis of the relative contributions of multi-modal imaging data including the brain’s functional (FC) and structural connectivity (SC) in the task of classifying high and low adapting MS patients for a deeper understanding of the connectome-level mechanism contributing to variability in MS-related impairment. We built a machine learning based ensemble model that can accurately classify MS patients as high and low adapters (AUC> 0.626). We observed that SC and FC networks can be used to identify the most discriminative regions and to accurately classify MS patients regarding their impairment level.

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