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Applications on data driven dynamics in biological systems— -Domino-like transient dynamics at seizure onset in epilepsy

Abstract

We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.


个人简介
林聪萍,华中科技大学数学中心研究员,从事系统生物方面的研究。目前主要对细胞生物学、神经科学中的复杂动力学现象进行基于实验或临床数据的动力学建模,试图揭示现象背后的理论机理。