Title: A Bayesian approach for mapping epileptic brain networks
Authors: Tingting Zhang - University of Virginia (United States) [presenting]
Abstract: The brain regions and the influences exerted by each region over another, called directional connectivity, form a directional network. We study normal and abnormal directional brain networks of epileptic patients using their intracranial EEG (iEEG) data, which are multivariate time series recordings of many small brain regions. We propose a high-dimensional state-space multivariate autoregression (SSMAR) for iEEG data to model the brain as a dynamics system. We assume that the underlying brain network has a cluster structure and develop a Bayesian framework to estimate the proposed high-dimensional model, identify clusters of densely-connected brain regions, and map epileptic patients' brain networks in different seizure stages. We show that the new method is robust to various deviations from the model assumptions, low iEEG sampling frequency, and data noise. Applying the developed Bayesian approach to an epileptic patient's iEEG data, we reveal the patient's network changes at the seizure onset and the unique connectivity of the seizure onset zone (SOZ), where seizures start and spread to other normal regions. Using this network result, our method has the potential to assist clinicians to localize the SOZ.