Dynamics in the neural network of an in vitro epilepsy model Online publication date: Tue, 29-Aug-2017
by Bo-Wen Liu; Jun-Wei Mao; Ye-Jun Shi; Qin-Chi Lu; Pei-Ji Liang; Pu-Ming Zhang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 18, No. 2, 2017
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.
Online publication date: Tue, 29-Aug-2017
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