Epileptic brain networks as detected by time-variant effective connectivity and graph analysis of high-density EEG data

The surgical resection of the epileptogenic zone (EZ) may be the only therapeutic option for reducing seizures in focal drug-resistant epilepsy. Connectivity analysis using electroencephalographic (EEG) data is an influential methodology to centralize the epileptogenic zone (EZ) for the most correct possible surgical resection. The time evolution is one of the important factor to investigate the directional communication of network nodes. The directional influence between a given pair of signals can be detriment using Granger causality extensions. Direct Transfer function (DTF) and Adaptive Direct Transfer Function (ADTF) are two extensions, which can be used for time invariant and time variant signal flow analyses respectively (Wilke et al., 2011). In this study, we primarily aim to further investigate the brain connectivity in epileptic patients by evaluating the time-variant causal interaction patterns among hdEEG source time series using ADTF.