Frontiers in Computational Neuroscience (Oct 2016)
Dynamic network connectivity analysis to identify the epileptogenic zones based on stereo-electroencephalography
Abstract
ObjectivesAccurate localization of the epileptogenic zones (EZs) is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing the EZs.MethodsSEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the time-variant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristics of the dynamic network and to find the important nodes in it. ResultsIn all seven patients, the indicative EZs localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZs. However, the out-degree did not indicate significant difference between nodes in the network.ConclusionsIn this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.
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