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Abstract #0249

Predicting Neurological Outcome in Neonatal Encephalopathy: A Machine Learning and Network Analysis Approach

Etay Ziv1, Olga Tymofiyeva1, Sonia L. Bonifacio2, Patrick S. McQuillen2, Donna M. Ferriero2, A James Barkovich1, Duan Xu1, Christopher P. Hess1

1Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, United States; 2Department of Pediatrics, UCSF, San Francisco, CA, United States

Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities. The ability to predict outcome early on in the perinatal period could potentially have a significant impact on subsequent treatment. Structural connectivity networks of the brain can be constructed using diffusion MRI. We hypothesize that networks derived from patients who have poor outcome may have different structure than those who have good outcome. Here we present an unbiased approach to enumerate a large set of network properties and using a combination of unsupervised and supervised learning, we demonstrate surprisingly good discrimination between good and poor neurological outcome.