fMRI based Diagnosis of Epilepsy using Functional Differences of Brain

Abstract

Epilepsy is one of the major neurobiological disorders and it can induce asymmetries of brain regional activation and connectivity patterns in epileptic patients as compared to healthy controls. These asymmetries can discriminate epileptic patients from healthy controls. This needs robust biomarkers that could find these asymmetries that can be achieved with the help functional magnetic resonance imaging (fMRI) data due to its high spatial resolution. In this thesis, we present two novel biomarkers, Dissimilarity of Activity (DoA) and inter subject blood oxygenated level dependent (BOLD) signal asymmetries (iBSA) that can capture asymmetries of activity by finding abnormalities in BOLD signal. We used functional connectivity analysis to find underlying connectivity patterns of regions among each other. Combining DoA and iBSA biomarkers with functional connectivity analysis of regions yields a feature set that can represent all asymmetries of connectivity and activation pattern of regions. We used these features to discriminate healthy controls from epileptic patients with the help of Support Vector Machines (SVMs). We achieved classification accuracy of 87.4% on 50-50 split for training and testing data. Results achieved by our proposed classification model are better than any other classification model on this dataset in current literature.