Evolving dynamic networks: an underlying mechanism of drug resistance in epilepsy?

Research output: Contribution to journalLetterpeer-review


Colleges, School and Institutes


At least one-third of all people with epilepsy have seizures that remain poorly controlled despite an increasing number of available anti-epileptic drugs (AEDs). Often, there is an initial good response to a newly introduced AED, which may last up to months, eventually followed by the return of seizures thought to be due to the development of tolerance. We introduce a framework within which the interplay between AED response and brain networks can be explored to understand the development of tolerance. We use a computer model for seizure generation in the context of dynamic networks, which allows us to generate an ‘in silico’ electroencephalogram (EEG). This allows us to study the effect of changes in excitability network structure and intrinsic model properties on the overall seizure likelihood. Within this framework, tolerance to AEDs – return of seizure-like activity – may occur in 3 different scenarios: 1) the efficacy of the drug diminishes while the brain network remains relatively constant; 2) the efficacy of the drug remains constant, but connections between brain regions change; 3) the efficacy of the drug remains constant, but the intrinsic excitability within brain regions varies dynamically. We argue that these latter scenarios may contribute to a deeper understanding of how drug resistance to AEDs may occur.


Original languageEnglish
Pages (from-to)264-268
Number of pages5
JournalEpilepsy & Behavior
Early online date11 Apr 2019
Publication statusPublished - 1 May 2019


  • Anti-epileptic drugs (AEDs), Computational model, Drug tolerance, Drug-resistant epilepsy (DRE), Prognosis