Analyzing epileptic network dynamics via time-variant partial directed coherence

Epilepsy is growingly considered as a brain network disorder. In this study, epileptiform discharges were induced by low-Mg2+ in mouse entorhinal cortex-hippocampal slices, and recorded with a micro-electrode array. Dynamic effective network connectivity was constructed by calculating the time-variant partial directed coherence (tvPDC) of signals. We proposed a novel approach to track the state transitions of epileptic networks over time, and characterized the network topology by using graphical measures. We found that the hub nodes with high degrees in the network coincided with the epileptogenic zone in previous electrophysiological findings. Two consecutive states with distinct network topologies were identified during the ictal-like discharges. The small-worldness remained at a low level at the first state but increased significantly at the second state. Our results indicate the ability of tvPDC to capture the causal interaction between multi-channel signals important in indentifying the epileptogenetic zone. Moreover, the evolution of network states extends our knowledge of the network drivers for the initiation and maintenance of ical activity, and suggests the practical value of our network clustering approach.