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Abstract #2841

Predicting response to motor therapy in chronic stroke patients based on clinical and connectivity measurements using Machine Learning

Ceren Tozlu1, Dylan Edwards2,3,4, Aaron Boes5, K. Zoe Tsagaris4, Joshua Silverstein4, Heather Pepper Lane4, Mert R. Sabuncu6, Charles Liu7, and Amy Kuceyeski1,8

1Department of Radiology, Weill Cornell Medical College, New York City, NY, United States, 2Moss Rehabilitation Research Institute, Elkins Park, Elkins Park, PA, United States, 3Edith Cowan University, Joondalup, Australia, 4Burke Neurological Institute, White Plains, NY, United States, 5Iowa Neuroimaging and Noninvasive Brain Stimulation Laboratory, Departments of Pediatrics, Neurology & Psychiatry, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, United States, 6School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States, 7USC Neurorestoration Center, Los Angeles, CA; and Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States, 8Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States

Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The main objective of this study was to use machine learning methods to predict a chronic stroke individual’s motor function after 6 weeks of intervention from demographic, neurophysiological and imaging measurements. Our main finding was that Elastic-net outperformed Support Vector Machine, Artificial Neural Network, Random Forest, and Classification and Regression Trees in predicting post-intervention Fugl-Meyer Assessment. The addition of structural dysconnectivity measurements to the demographic and neurophysiological data did not improve the performance of the methods.

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