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Modelling the role of tissue heterogeneity in epileptic rhythms

Authors

  • Marc Goodfellow,

    1. Doctoral Training Centre, Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK
    2. Centre for Interdisciplinary Computational and Dynamical Analysis (CICADA), School of Mathematics, University of Manchester, UK
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  • Peter Neal Taylor,

    1. Doctoral Training Centre, Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK
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  • Yujiang Wang,

    1. Doctoral Training Centre, Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK
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  • Daniel James Garry,

    1. Doctoral Training Centre, Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK
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  • Gerold Baier

    1. Doctoral Training Centre, Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK
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Gerold Baier, as above.
E-mail: gerold.baier@manchester.ac.uk

Abstract

Epileptic seizure activity manifests as complex spatio-temporal dynamics on the clinically relevant macroscopic scale. These dynamics are known to arise from spatially heterogeneous tissue, but the relationship between specific spatial abnormalities and epileptic rhythm generation is not well understood. We formulate a simplified macroscopic modelling framework with which to study the role of spatial heterogeneity in the generation of epileptiform spatio-temporal rhythms. We characterize the overall model dynamics in terms of spontaneous activity and excitability and demonstrate normal and abnormal spreading of activity. We introduce a means to systematically investigate the topology of abnormal sub-networks and explore its impact on spontaneous and stimulus-evoked rhythmic dynamics. This computationally efficient framework complements results from detailed biophysical models, and allows the testing of specific hypotheses about epileptic dynamics on the macroscopic scale.

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