Treatment effects in epilepsy: a mathematical framework for understanding response over time

Elanor Harrington*, Peter Kissack*, John Terry, Wessel Woldman, Leandro Junges

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.
Original languageEnglish
Article number1308501
Number of pages15
JournalFrontiers in network physiology
Volume4
DOIs
Publication statusPublished - 26 Jun 2024

Keywords

  • Epilepsy
  • Brain Network Model
  • Honeymoon Effect
  • Brain Network Ictogenicity
  • Brain Surgery
  • Anti-Seizure Medication
  • Network Physiology

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