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Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy

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  • Timothée Proix

    (Brown University
    Brown University
    U.S. Department of Veterans Affairs)

  • Viktor K. Jirsa

    (Aix Marseille Univ)

  • Fabrice Bartolomei

    (Aix Marseille Univ)

  • Maxime Guye

    (Aix Marseille Univ)

  • Wilson Truccolo

    (Brown University
    Brown University
    U.S. Department of Veterans Affairs)

Abstract

Recent studies have shown that seizures can spread and terminate across brain areas via a rich diversity of spatiotemporal patterns. In particular, while the location of the seizure onset area is usually invariant across seizures in an individual patient, the source of traveling (2–3 Hz) spike-and-wave discharges during seizures can either move with the slower propagating ictal wavefront or remain stationary at the seizure onset area. Furthermore, although many focal seizures terminate synchronously across brain areas, some evolve into distinct ictal clusters and terminate asynchronously. Here, we introduce a unifying perspective based on a new neural field model of epileptic seizure dynamics. Two main mechanisms, the co-existence of wave propagation in excitable media and coupled-oscillator dynamics, together with the interaction of multiple time scales, account for the reported diversity. We confirm our predictions in seizures and tractography data obtained from patients with pharmacologically resistant epilepsy. Our results contribute toward patient-specific seizure modeling.

Suggested Citation

  • Timothée Proix & Viktor K. Jirsa & Fabrice Bartolomei & Maxime Guye & Wilson Truccolo, 2018. "Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-02973-y
    DOI: 10.1038/s41467-018-02973-y
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    Cited by:

    1. Anton V Chizhov & Aleksei E Sanin, 2020. "A simple model of epileptic seizure propagation: Potassium diffusion versus axo-dendritic spread," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-21, April.
    2. Michael E Rule & David Schnoerr & Matthias H Hennig & Guido Sanguinetti, 2019. "Neural field models for latent state inference: Application to large-scale neuronal recordings," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-23, November.
    3. Miao Cao & Daniel Galvis & Simon J. Vogrin & William P. Woods & Sara Vogrin & Fan Wang & Wessel Woldman & John R. Terry & Andre Peterson & Chris Plummer & Mark J. Cook, 2022. "Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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