Title: Dynamics in the neural network of an in vitro epilepsy model

Authors: Bo-Wen Liu; Jun-Wei Mao; Ye-Jun Shi; Qin-Chi Lu; Pei-Ji Liang; Pu-Ming Zhang

Addresses: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China ' School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China ' Department of Neurology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China ' Department of Neurology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China ' School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China ' School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: Epilepsy is growingly considered as a brain network disorder. In this study, epileptiform discharges induced by low-Mg2+ were recorded with a microelectrode 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, and characterised the network topology by using graphical measures. We found that the network hub nodes coincided with the epileptogenic zone in previous electrophysiological findings. Two network states with distinct topologies were identified during the ictal-like discharges. The small-worldness significantly increased at the second state. Our results indicate the ability of tvPDC to capture the causal interaction between multi-channel signals important in identifying 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.

Keywords: epilepsy; microelectrode array; dynamic network; graph theory; granger causality; tonic-clonic; network analysis; hippocampus; entorhinal cortex; small-worldness; low-Mg Mg2+.

DOI: 10.1504/IJDMB.2017.086459

International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.2, pp.125 - 143

Received: 22 Apr 2017
Accepted: 03 May 2017

Published online: 29 Aug 2017 *

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