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Abstract #4757

Predicting stroke severity with structural connectivity network disruption as measured with the Network Modification (NeMo) Tool

Amy Kuceyeski 1 , Mark Villanueva 2 , Ashish Raj 1 , Michael O'Dell 2 , and Joan Toglia 3

1 Radiology, Weill Cornell Medical College, New York, NY, United States, 2 Rehabilitation Medicine, Weill Cornell Medical College, New York, NY, United States, 3 Occupational Therapy, Mercy College, NY, United States

The Network Modification (NeMo) Tool quantifies losses in the brain connectivity network by mapping areas of damage onto a large collection of healthy tractograms. This allows for a clinically feasible method for identifying areas that are most affected by loss of connectivity due to stroke, which can provide insight as to type and severity of functional loss. Here we hypothesized the NeMo Tools measure of connectivity disruption could better predict stroke severity, measured with NIHSS, than lesion volume. Our partial least squares regression model predicted NIHSS from baseline disconnection with accuracy of R 2 =0.75, while correlation with lesion volume was R 2 =0.28.

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