Computational models of epileptiform activity

Abstract

We reviewed computer models that have been developed to reproduce and explain epileptiform activity. Unlike other already-published reviews on computer models of epilepsy, the proposed overview starts from the various types of epileptiform activity encountered during both interictal and ictal periods. Computational models proposed so far in the context of partial and generalized epilepsies are classified according to the following taxonomy: neural mass, neural field, detailed network and formal mathematical models. Insights gained about interictal epileptic spikes and high-frequency oscillations, about fast oscillations at seizure onset, about seizure initiation and propagation, about spike-wave discharges and about status epilepticus are described. This review shows the richness and complementarity of the various modeling approaches as well as the fruitful contribution of the computational neuroscience community in the field of epilepsy research. It shows that models have progressively gained acceptance and are now considered as an efficient way of integrating structural, functional and pathophysiological data about neural systems into "coherent and interpretable views". The advantages, limitations and future of modeling approaches are discussed. Perspectives in epilepsy research and clinical epileptology indicate that very promising directions are foreseen, like model-guided experiments or model-guided therapeutic strategy, among others.

Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

“…Computational models can be useful in many ways in the context of epilepsy [100]. They can elucidate potential mechanisms underlying the reproduced behavior of the model.…”

“…At the microscopic scale, models explicitly represent the structure and function of cells and include parameters related to morphology, as well as passive and active electrical properties [100]. …”

Computational modeling of neurostimulation in brain diseases

“…Ever since the term "dynamical diseases" was coined (Glass and Mackey, 1979;Mackey and Milton, 1987), epilepsy has been used as a classic example (Lopes da Silva et al, 2003;Milton and Jung, 2002). The number of theoretical approaches and computational studies in the field of epilepsy is vast, and we point the reader to a range of excellent reviews in the field Case and Soltesz, 2011;Lytton, 2008;Stefanescu et al, 2012;Suffczynski et al, 2008;Ullah and Schiff, 2009;Wendling, 2008;Wendling et al, 2015). In agreement with the overall trend in computational neuroscience, computational models of epileptic activity are built based on knowledge regarding the relevant underlying neural components.…”

Sequential Noise-Induced Escapes for Oscillatory Network Dynamics

“…See, for example, [2] for a review of oscillatory models in neuroscience. In the context of pathological states or disease associated brain dynamics, epilepsy serves as a classical example of the importance of oscillatory network dynamics linked to the generation and propagation of epileptiform activity, as is discussed in a recent review [34]. The particular model we study here, given in [6], is based on the work of [24], as a network model of epileptic seizures in which each node captures the behavior of a neural population.…”