Cortical stability and chaos during focal seizures: insights from inference-based modeling

  • Yun Zhao
  • , David B. Grayden
  • , Mario Boley
  • , Yueyang Liu
  • , Philippa J. Karoly
  • , Mark J. Cook
  • , Levin Kuhlmann

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. Epilepsy affects millions globally, with a significant subset of patients suffering from drug-resistant focal seizures. Understanding the underlying neurodynamics of seizure initiation and propagation is crucial for advancing treatment and diagnostics. In this study, we present a novel, inference-based approach for analyzing the temporal evolution of cortical stability and chaos during focal epileptic seizures. Approach. Utilizing a multi-region neural mass model, we estimate time-varying synaptic connectivity from intracranial electroencephalography (iEEG) data collected from individuals with drug-resistant focal epilepsy. Main results. Our analysis reveals distinct preictal and ictal phases characterized by shifts in cortical stability, heightened chaos in the ictal phase, and highlight the critical role of inter-regional communication in driving chaotic cortical behaviour. We demonstrate that cortical dynamics are consistently destabilized prior to seizure onset, with a transient reduction in instability at seizure onset, followed by a significant increase throughout the seizure. Significance. This work provides new insights into the mechanisms of seizure generation and offers potential biomarkers for predicting seizure events. Our findings pave the way for innovative therapeutic strategies targeting cortical stability and chaos to manage epilepsy.

Original languageEnglish
Article number036021
JournalJournal of Neural Engineering
Volume22
Issue number3
DOIs
StatePublished - 1 Jun 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 The Author(s). Published by IOP Publishing Ltd.

Keywords

  • chaos analysis
  • epilepsy
  • neural mass model
  • stability analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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