Elsevier

Journal of Neuroscience Methods

Volume 260, 15 February 2016, Pages 233-251
Journal of Neuroscience Methods

Computational Neuroscience
Review
Computational models of epileptiform activity

https://doi.org/10.1016/j.jneumeth.2015.03.027Get rights and content

Highlights

  • Neural mass, neural field, detailed network and formal models are reviewed.

  • These models are aimed at reproducing and explaining epileptic activity.

  • Biology-inspired and mathematical models have advantages and limitations.

  • Detailed network models are required to simulate some particular HFOs.

  • Neural mass/field models provided insights in partial and generalized epilepsies.

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.

Introduction

Epileptiform activity refers to brain activity recorded during epileptic phenomena that cover not only the seizure episodes but also the plethora of abnormal transient events occurring outside seizures like, for instance, interictal spikes or high-frequency oscillations in partial epilepsies.

To date, significant progress has been achieved regarding the numerous techniques aimed at recording brain activity. Epilepsy research has always benefited from these advances. Epileptiform activity can be recorded either globally using EEG/MEG, fMRI or SPECT instruments as well as very locally using intracerebral micro- and macro-electrodes (intracellular, MUAs, LFPs, iEEG) or microscopic imaging. The multiplicity of recording techniques, each characterized by its own time and space resolution, has also led to a need (i) for advanced information processing techniques aimed at extracting/describing the multimodal information conveyed by the observations, on the one side and (ii) for computational models aimed at decoding/explaining the mechanisms underlying their generation, on the other side.

This demand explains why the field of computational modeling in epilepsy has grown rapidly over the past decades. Numerous models were proposed to (i) investigate the complex pathophysiological factors leading to ictogenesis and/or epileptogenesis and often resulting from multiple causes and (ii) help the interpretation of epileptiform activity, whatever the recording technique. These models have progressively been more widely accepted and are now considered as an efficient way of integrating structural, functional and dynamical data about neural systems (coming from neurobiology, neurophysiology and neurology research) into “coherent and interpretable views”. Models have the unique ability to identify key – possibly hidden – variables and relate these variables across multiple levels of descriptions. For instance, computational models of fast ripples observed in electrophysiological signals recorded from epileptogenic zones allowed for connecting pyramidal cell abnormal firing patterns to pathological oscillations in small-scale neural networks. Another recognized virtue of models is the capacity to generate hypotheses that can be tested experimentally. For example, some models have successfully predicted the alteration of GABAb receptor-mediated responses on thalamocortical cells in the genesis of spike-wave patterns in absence seizures.

In this article we review computer models that have been developed to reproduce and explain epileptiform activity. Conversely to reviews already published (Lytton, 2008, Wendling, 2008) or available online (http://www.scholarpedia.org/article/Models_of_epilepsy) which are based on the type of modeling approach (typically, from microscopic to mesoscopic or macroscopic level) or on the mechanisms (typically excitability and synchronization), this review follows a less traditional outline in the sense that its starts from the type of epileptiform activity under study. For both interictal and ictal activity, in the context of partial and generalized epilepsies, we reviewed most of the models proposed so far and classified them according to the following taxonomy: neural mass models (NMMs), neural field models (NFMs), detailed networks and formal mathematical models. This review shows the richness and complementarity of these various modeling approaches as well as the extreme productivity of the computational neuroscience community in the field of epilepsy research. This review ends with a discussion about the advantages, limitations and future of modeling approaches along with some perspectives in epilepsy research and clinical epileptology.

Section snippets

General principles of detailed network, neural mass, neural field and formal mathematical models

The challenge faced by any modeling approach is to capture the essential features of the system under study in a simple – but not too simple – description of its behavior (defined by the model variables) under controlled conditions (defined by the model parameters). In the brain, the level of modeling, i.e. the level at which this description is elaborated, is a crucial choice and strongly depends on (i) the type of behavior to be reproduced in the model and (ii) the nature of the data it

Interictal epileptic spikes and bursts

Interictal epileptic spikes (IESs) are very often observed in human partial epilepsies as well as in most experimental models of focal epilepsy (Schwartzkroin and Wheal, 1984). During epileptogenesis, a number of experimental studies also reported the appearance of isolated epileptic spikes during the latent period (Avoli et al., 2006, Staley and Dudek, 2006). It is commonly admitted that IESs are polymorphic events (Alarcon et al., 1994). Nevertheless, to a large extent, IESs correspond to

Fast oscillations at seizure onset (gamma, high gamma, chirps)

In partial epilepsies, seizures are often characterized by the appearance of fast oscillations in LFPs or EEG signals, sometimes referred to as “chirps” (Schiff et al., 2000). Typically, in human neocortical epilepsies, rapid discharges observed at seizure onset range from 70 to 120 Hz. They constitute a characteristic electrophysiological pattern in focal seizures characterized by a noticeable increase of intracerebral EEG signal frequency (Lee et al., 2000, Wendling et al., 2003). In mesial

Discussion and perspectives

The need for integrative approaches. Epilepsy is a complex dynamical disease (Lopes da Silva et al., 2003). In fact, the term “epilepsy” refers to a wide variety of neurological syndromes and disorders. Any brain damage can potentially lead to a disruption of its activity which, in some cases, can give rise to epileptic seizures. Over the past decades, basic and clinical research has advanced our understanding of the pathophysiology of epilepsy. The general and commonly-accepted picture is that

Acknowledgement

The authors acknowledge financial support from the French “Programme de Recherche Translationnelle en Santé” (Call PRTS 2013, Project # ANR-13-PRTS-0011, title: “VIBRATIONS”). They thank Prof. O. Raineteau for providing the hippocampal slice confocal image in Fig. 1.

Glossary

EEG
electroencephalography
iEEG
intracranial or intracerebral EEG
sEEG
stereotactic EEG
ECoG
electrocorticography
LFP
local field potential
MUA
multiunit-activity
SPECT
single-photon emission computed tomography
IES
interictal epileptic spike
HFO
high-frequency oscillation
FR
fast ripple
SW
spike-wave
SE
status epilepticus
RSE
refractory status epilepticus
NMM
neural mass model
NFM
neural field model
TLE
temporal lobe epilepsy
EZ
epileptogenic zone
PSP
post-synaptic potential
EPSP
excitatory PSP
IPSP
inhibitory PSP
CA
Cornu Ammonis

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