学术文献库

The brain is a high-dimensional directional network system consisting of many regions as network nodes that influence each other. The directional influence from one region to another is referred to as directional connectivity. Epilepsy is a directional network disorder, as epileptic activity spreads from a seizure onset zone (SOZ) to many other regions after seizure onset. However, directional network studies of epilepsy have been mostly limited to low-dimensional directional networks between the SOZ and contiguous regions due to the lack of efficient methods for analyzing high-dimensional directional brain networks. To address this gap, we study high-dimensional directional networks in epileptic brains by using a clustering-enabled multivariate autoregressive state-space model (MARSS) to analyze multi-channel intracranial EEG recordings of focal seizures. This new MARSS characterizes the SOZ, nearby regions, and many other non-SOZ regions as one integrated high-dimensional directional network system with a clustering feature. Using the new MARSS, we reveal changes in high-dimensional directional brain networks throughout seizure development. We simultaneously identify directional connections and the SOZ cluster, regions most affected by SOZ activity, in different seizure periods. We found that, after seizure onset, the numbers of directional connections of the SOZ and regions in the SOZ cluster increase significantly. We also reveal that many regions outside the SOZ cluster have no changes in directional connections, although these regions' EEG data signal ictal activity. Lastly, we use these high-dimensional network results to localize the SOZ and achieve 100% true positive rates and less than 3% false positive rates for different SOZ locations.