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    Domino-like transient dynamics at seizure onset in epilepsy

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    Author
    Creaser, J; Lin, C; Ridler, T; Brown, JT; D'Souza, W; Seneviratne, U; Cook, M; Terry, JR; Tsaneva-Atanasova, K
    Date
    2020-09-01
    Source Title
    PLoS Computational Biology
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Seneviratne, Udaya; Cook, Mark; D'Souza, Wendyl
    Affiliation
    Medicine and Radiology
    Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Creaser, 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 Status
    Open Access
    URI
    http://hdl.handle.net/11343/251648
    DOI
    10.1371/journal.pcbi.1008206
    Abstract
    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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