An application of dynamical directed connectivity of ictal intracranial EEG recordings in seizure onset zone localization

Abstract

BackgroundIdentification of the seizure onset zone (SOZ) is a challenging task in epilepsy surgery. Patients with epilepsy have an altered brain network, allowing connectivity-based analyses to have a great potential in SOZ identification. We investigated a dynamical directed connectivity analysis utilizing ictal intracranial electroencephalographic (iEEG) recordings and proposed an algorithm for SOZ identification based on grouping iEEG contacts. New methodsGranger Causality was used for directed connectivity analysis in this study. The intracranial contacts were grouped into visually detected contacts (VDCs), which were identified as SOZ by epileptologists, and non-resected contacts (NRCs). The intragroup and intergroup directed connectivity for VDCs and NRCs were calculated around seizure onset. We then proposed an algorithm for SOZ identification based on the cross-correlation of intragroup outflow and inflow of SOZ candidate contacts. ResultsOur results revealed that the intragroup connectivity of VDCs (VDC→VDC) was significantly larger than the intragroup connectivity of NRCs (NRC→NRC) and the intergroup connectivity between NRCs and VDCs (NRC→VDC) around seizure onset. We found that the proposed algorithm had 90.1 % accuracy for SOZ identification in the seizure-free patients. Comparison with existing methodsThe existing connectivity-based methods for SOZ identification often use either outflow or inflow. In this study, SOZ contacts were identified by integrating outflow and inflow based on the cross correlation between these two measures. ConclusionsThe proposed group-based dynamical connectivity analysis in this study can aid our understanding of underlying seizure network and may be used to assist in identifying the SOZ contacts before epilepsy surgery.