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Abstract #1642

Social Network Theory Applied to Resting-State FMRI Connectivity Data in the Analysis of Epilepsy Networks

Xiaohui Zhang1, Fuyuze Tokoglu2, R. Todd Constable2

1Department of Diagnostic Radiology, School of Medicine , Yale University, New Haven, CT, USA; 2Department of Diagnostic Radiology, School of Medicine, Yale University, New Haven, CT, USA

Epilepsy is a brain disorder with the essential basis of abnormal cortical and/or subcortical networks. Currently, most of the evidence of seizure networks come from ictal EEG observations. Resting-state functional connectivity studies can be helpful to localize abnormal networks and widen the array and approach of therapeutic options. This study aims to classify patients data from control subjects by characterizing the interictal epilepsy connectivity networks using social network topology in functional MRI. An average sensitivity of the 87.5% and specificity of 78.9% were achieved in the given data set.