Jennifer Creaser: Domino-like transients at seizure onset
Channel: NorthWestBiomathsDataScienceSeminars
Date: 2021-03-29

North West Seminar Series of Mathematical Biology and Data Science 

Wednesday, 24th March 2021 (hosted by Ivo Siekmann) 


https://www.cms.livjm.ac.uk/APMSeminar/


Abstract:
Epilepsy is a serious neurological condition in which people experience recurrent brain seizures. The current classification of seizure fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset. Current thinking identifies seizures as the initiation and recruitment of populations of neurons in distinct but interconnected brain regions. Brain activity can thus be modelled as a network in which each node represents one brain region. To understand how patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which remains elusive. We propose seizure onset to be the sequential “domino-like” recruitment of nodes within the network to an active state. We consider a phenomenological network model of seizure initiation in which each node has two stable states, quiescent and active (oscillatory). The transition between the two, we call an escape, is driven by an external random ‘noisy’ input. When several such systems are coupled, these escapes can spread sequentially in the manner of a domino effect. We use this model to understand and characterise the timings of sequential domino-like cascades of activity across motif networks. We then apply this modelling framework to larger networks and introduce heterogeneous node excitability and coupling strengths to capture spatiotemporal patterns of seizure onset. Finally, intracranial recordings of epileptic seizures show a progression towards synchronization as brain regions are recruited and the seizures evolve. We extend the model to include phase-amplitude dependent coupling and explore the interplay between synchronisation and cascades of noise-induced escapes.