Epilepsy Workshop | OCNS*2025

Human and animal EEG data suggest that epileptic seizures are not stationary events, but rather evolve dynamically over tens of seconds. We explore the processes linked to seizure dynamics by enhancing the Hodgkin-Huxley mathematical model with the physical laws governing ion movement. This enhancement enables us to replicate in silico the electrographic pattern of a typical human focal seizure, which consists of distinct phases: the onset of low-voltage fast activity, the tonic phase, the clonic phase, and the postictal suppression phase. Our study provides new insights into potential mechanisms of seizure initiation by inhibitory interneurons through K+ accumulation, as well as seizure termination and the postictal state via upregulation of the outward Na+/K+ pump current. The model also clarifies ionic mechanisms that may underlie a key feature of seizure dynamics: the progressive slowing of ictal discharges. Model predictions regarding the specific scaling of inter-burst intervals are validated in both in vitro and human seizure data, suggesting these mechanisms may be preserved across different models and species.

Neuroplasticity refers to functional and structural changes in brain regions in response to healthy and pathological activity. Activity dependent plasticity induced by epileptic activity can involve healthy brain regions into the epileptogenic network by perturbing their excitation/inhibition balance. In this article, we present a new neural mass model, which accounts for neuroplasticity, for investigating the possible mechanisms underlying the epileptogenic network expansion. Our multiple-timescale model is inspired by physiological calcium-mediated synaptic plasticity and pathological extrasynaptic N- methyl-D-aspartate (NMDA) dependent plasticity dynamics. The model highlights that synaptic plasticity at excitatory connections and structural changes in the inhibitory system can transform a healthy region into a secondary epileptic focus under recurrent seizures and interictal activity occurring in the primary focus. Our results suggest that the latent period of this transformation can provide a window of opportunity to prevent the expansion of epileptogenic networks, formation of an epileptic focus, or other comorbidities associated with epileptic activity.

Reference: Köksal-Ersöz E, Benquet P, Wendling F (2024) Expansion of epileptogenic networks via neuroplasticity in neural mass models. PLoS Comput Biol 20(12): e1012666. https://doi.org/10.1371/journal.pcbi.1012666

Dravet syndrome is a developmental and epileptic encephalopathy (DEE) that typically begins in the first year of life. This complex pathology is characterized by drug-resistant seizures, various comorbidi1es such as cognitive delay, and a risk of early death. Most cases are due to mutations of NaV1.1, a voltage-gated sodium channel expressed in fast-spiking (FS) inhibitory neurons. The pathological mechanism in the initial stage of the disease involves impaired function of those neurons, leading to network hyperexcitability. However, the details remain unclear.
Mutations of NaV1.1 may result in non-functional channels or channels with altered gating properties. We focus on the less studied case of altered gating, by investigating how it impairs neuronal ac1vity in the case of a specific mutation (A1783V). Using recordings in cell lines, Layer et al. (2021) showed that A1783V alters the voltage dependence of channel activation, as well as the voltage dependence and kinetics of slow inactivation. Slow inactivation is a mechanism dis1nct from the fast inactivation of sodium channels at each spike, developing much more slowly, during prolonged trains of depolariza1on. Implementing the three effects of the mutation in a conductance-based model, Layer et al. predict that altered activation has the largest impact on channel function, as it causes the most severe reduction in firing rate.
Using conductance-based models tailored to the dynamics of FS inhibitory neurons, we examine how the three alterations affect susceptibility to depolariza1on block, another firing deficit aside from frequency reduction. We look deeper into slow inactivation, exploiting the timescale difference with the rest of the system. We find that slow inactivation of mutant channels at lower voltage values than wild type channels favors depolarization block upon sustained stimulation. More precisely, shifting the steady-state voltage dependence of slow inactivation destroys the stable limit cycle of the full system corresponding to tonic spiking, and creates a stable equilibrium corresponding to depolarization block. The accelerated kinetics of slow inactivation in mutant channels hastens the transition from tonic spiking to depolarization block. These findings suggest that alterations of NaV1.1 slow inactivation should not be neglected as they might play an important pathological role, adding to the conclusions of Layer et al. on the consequences of altered NaV1.1 activation.

Electrical stimulation is an increasingly popular method to terminate epileptic seizures; yet it is not always successful. One of the potential reasons for inconsistent efficacy is that stimuli are applied empirically, without considering the underlying dynamical properties of a given seizure. In this work, we find that different seizure types have vastly different responses to controlling stimuli. We use the Taxonomy of Seizure Dynamics to model different onset dynamotypes, then determine the ability of ictal stimulation to abort seizures after they have started. Within the model, the aborting input is realized as an applied stimulus trying to force the system from a bursting state to a quiescent or resting state. This transition requires bistability, which is not present in all onset dynamotypes. We examine how topological and geometric differences in bistable phase spaces affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are (1) the presence or absence of a baseline (DC) shift and (2) the dynamotype (onset/offset bifurcations) of the burster. Generally, we find that bursters that have a DC shift are far more likely to be terminated than those without because they are not as sensitive to the phase at which stimulation occurs. Furthermore, we observe that the probability of termination varies throughout the burster’s duration and is highly correlated to its dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. We conclude that strategies for aborting seizures with ictal stimulation must account for seizure dynamotype to optimize efficacy.