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Developing a quantitative algorithm for predicting seizure outcome following anterior temporal lobectomy (ATL) in temporal lobe epilepsy (TLE) patient would constitute a significant advance for presurgical decision making. In this project, we tested the ability of topographic properties extracted from presurgical resting-state (rsfMRI) data to predict surgical outcome, using two separate maching learning classification methods (support vector machine, SVM, and random forest RF).
Machine Learning Prediction of Seizure Outcome with Presurgical Resting-State fMRI Data
Medicine and Health Sciences
He, Xiaosong; Pustina, Dorian; Sperling, Michael R; Sharan, Ashwini; and Tracy, Joseph I, "Machine Learning Prediction of Seizure Outcome with Presurgical Resting-State fMRI Data" (2016). Department of Neurosurgery Posters. 7.