Domino-like transient dynamics at seizure onset in epilepsy
AuthorCreaser, J; Lin, C; Ridler, T; Brown, JT; D'Souza, W; Seneviratne, U; Cook, M; Terry, JR; Tsaneva-Atanasova, K
Source TitlePLoS Computational Biology
PublisherPUBLIC LIBRARY SCIENCE
AffiliationEngineering and Information Technology
Medicine (St Vincent's)
Document TypeJournal Article
CitationsCreaser, J., Lin, C., Ridler, T., Brown, J. T., D'Souza, W., Seneviratne, U., Cook, M., Terry, J. R. & Tsaneva-Atanasova, K. (2020). Domino-like transient dynamics at seizure onset in epilepsy. PLOS COMPUTATIONAL BIOLOGY, 16 (9), https://doi.org/10.1371/journal.pcbi.1008206.
Access StatusOpen Access
The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. 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.
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