AUTHOR=Mao Jun-Wei , Ye Xiao-Lai , Li Yong-Hua , Liang Pei-Ji , Xu Ji-Wen , Zhang Pu-Ming 

TITLE=Dynamic Network Connectivity Analysis to Identify Epileptogenic Zones Based on Stereo-Electroencephalography

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=10

YEAR=2016

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2016.00113

DOI=10.3389/fncom.2016.00113

ISSN=1662-5188

ABSTRACT=<p><bold>Objectives:</bold> Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of 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 EZs.</p><p><bold>Methods:</bold> SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome. A time-variant multivariate autoregressive model was constructed using a Kalman filter, and the time-variant partial directed coherence was computed. This was then used to construct a dynamic directed network model 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.</p><p><bold>Results:</bold> In all seven patients, the indicative EZs localized by the in-degree and the betweenness centrality were highly consistent with the clinically diagnosed EZs. However, the out-degree did not indicate any significant differences between nodes in the network.</p><p><bold>Conclusions:</bold> In this work, a method based on ictal SEEG signals and effective connectivity analysis localized EZs accurately. The results suggest that the in-degree and betweenness centrality may be better network characteristics to localize EZs than the out-degree.</p>