Abstract #2841

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

Ceren Tozlu¹, Dylan Edwards^2,3,4, Aaron Boes⁵, K. Zoe Tsagaris⁴, Joshua Silverstein⁴, Heather Pepper Lane⁴, Mert R. Sabuncu⁶, Charles Liu⁷, and Amy Kuceyeski^1,8

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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