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Abstract #3306

Support Vector Machine Classification of Stroke Using Resting State Functional Connectivity

Svyatoslav Vergun1, Veena A. Nair2, Matthew Jensen3, Marcus Chacon3, Justin Sattin3, Vivek Prabhakaran2

1Medical Physics, UW-Madison, Madison, WI, United States; 2Radiology, UW-Madison, Madison, WI, United States; 3Neurology, UW-Madison, Madison, WI, United States

Multivariate pattern analysis methods have been shown successful in extracting significant information and classifying individual scans. In this work, a support vector machine classifier accurately discriminated between stroke and normal aging subjects based on their resting state functional connectivity. 50 resting state fMRI scans from 24 normal and 26 stroke subjects were preprocessed and time series from 160 functional ROIs were correlated to produce a functional connectivity matrix for each subject. Each subjects correlations were input as features into the classifier, which predicted subjects with 80% accuracy using leave-one-out cross validation. Sensorimotor network connectivity was most influential for classification.