Computational Neuroscience in Epilepsy

Abstract

Over the last years, decisive experimental data have been obtained concerning the biophysical mechanisms and ion channels properties important for seizure generation. Computational models have succeeded in proposing plausible mechanisms to explain the sudden emergence of hypersynchronized oscillations at ∼3 Hz (or 5-10 Hz in some species), which are associated with "spike-andwave" complexes in the electroencephalogram (EEG). The underlying mechanisms of such seizures involve thalamocortical loops, the particular oscillatory properties of thalamic neurons, and the particular biophysical properties of some receptor types (such as the GABA B receptor). Here, we overview these mechanisms step by step, starting from the genesis of hypersynchronized oscillations by thalamic circuits. We next consider how cortical circuits can generate spike-and-wave EEG patterns. These mechanisms are then merged together in thalamocortical loops, where we emphasize the central role played by the "feedback" projections from cortex to thalamus. If for some reason the corticothalamic feedback becomes too strong, thalamic circuits can switch to a slower and hypersynchronized oscillatory mode, which in turn entrains the whole thalamocortical system into hypersynchronized oscillations with spike-and-wave EEG patterns. We suggest that the key to explain absence seizures is this switching mechanism of thalamic circuits, induced by exceedingly strong corticothalamic feedback. Such a switch was identified in experiments in vitro, in which oscillatory properties could be controlled by stimulating corticothalamic fibers. According to this mechanism, absence seizures result from anomalously high cortical excitability with a physiologically intact thalamus. I IntroductionAbsence epileptic seizures (also called "petit-mal") are characterized in humans by a sudden appearance of ∼3 Hz large-amplitude oscillations in the electroencephalogram (EEG) (Fig. 1). These generalized oscillations have a sudden onset, and the seizure seems to invade the entire cerebral cortex nearly simultaneously. The typical pattern of the oscillation consists of one or several sharp deflections ("spikes") followed by a surface-positive "wave". Spike-and-wave patterns of similar characteristics are also seen in a number of experimental models in cats, rats, mice and monkeys. Fig. 1 hereLike many other pathologies, absence epilepsy can result from the disturbance of mechanisms of synaptic transmission (reviewed in Gloor and Fariello, 1988;Crunelli and Leresche, 2002). In particular, disturbing inhibitory interactions has been found to be extremely effective for generating seizures, sometimes with contrasting effects (reviewed in Sperk et al., 2004). In thalamus and cortex, inhibitory transmission essentially uses γ-aminobutyric acid (GABA) as transmitter, and operates through two main receptor Destexhe A, In: Computational Neuroscience in Epilepsy, (Ed. Soltesz I and Staley K), Elsevier, 2008 2 types, called GABA A and GABA B . These two receptors mediate fast and slow ...

A model for the propagation of seizure activity in normal brain tissue

“…Generally speaking, our models describe an interesting variety of network dynamics in relation to the propagation of strong incoming perturbations. The studies on the dynamics of “crises” in networks of neuron models are a recent developing field ( Computational Neuroscience in Epilepsy , 2008; Naze, Bernard, & Jirsa, 2015; Rich, Hutt, Skinner, Valiante, & Lefebvre, 2020; Depannemaecker et al, 2021). Another approach proposes to study computationally how a network can be stabilized by the means of an external modulatory stimulus and internal noise, and consequently, avoid the seizure-like regime (Rich et al, 2020).…”

“…It has then been observed that the neuronal activity does not necessarily correspond to synchronized spikes of the whole neuron population, as previously modeled ( Computational Neuroscience in Epilepsy , 2008) and can be modeled at different scales from cellular to whole-brain levels(Depannemaecker, Destexhe, Jirsa, & Bernard, 2021; Depannemaecker et al, 2020). In fact, it turns out that the dynamics of neural networks during seizures are more complex (Jiruska et al, 2013), and the mechanisms of propagation at different scales are poorly understood.…”