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

Abstract

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.

Interictal electroencephalographic functional network topology in drug‐resistant and well‐controlled idiopathic generalized epilepsy

“…Mechanisms of action of AEDs are well characterized at a cellular level, but how those relate to antiseizure effects is incompletely understood. Our results support the notion of a common pathway of AED effects, arising through a large‐scale network effect 47 . Studies in other epilepsy cohorts have reported changes in global efficiency (the inverse of L) with topiramate but not with lamotrigine, levetiracetam, or valproate 48 .…”

Circadian distribution of epileptiform discharges in epilepsy: candidate mechanisms of variability

“…To investigate the impact of sleep and CORT on the ED likelihood during the day, we employed a phenomenological mathematical model to simulate brain excitability when perturbed by those external forces. Unlike in previous works where the variation of the brain excitability was constant [48] or perturbed by a fixed constant [49], this model describes cortical excitability as a dynamical variable that is modulated by dynamic external factors, such as sleep or CORT. We used sleep stages and CORT levels collected from healthy subjects to inform the dynamics of the variable representing the status of brain activity.…”

“…The model used in this study is based on the normal form of a subcritical Hopf bifurcation [48, 49, 57], whose co-existing states reflect two distinct types of neural activity. The first is a background state, represented by a steady-state solution in the model, whilst the second is an epileptiform state, represented by a high-amplitude oscillation.…”

“…The model used in this study is based on the normal form of a subcritical Hopf bifurcation [48,49,57…”

Computerized application for epilepsy in China: Does the era of artificial intelligence comes?

“…When dealing with crucial issues in epilepsy, such as symptomatic diagnosis, seizure onset zone (SOZ) localization, presurgical evaluation, prognosis of medication responses, etc. [5][6][7][8][9] most neurologists make comprehensive decisions based on multifaceted clinical information. In that process, medical techniques such as functional magnetic resonance imaging (fMRI) and intracranial electroencephalogram (EEG) recordings certainly help a lot, however, artificial efforts are still paid to analyze.…”