Neural mass modeling of slow-fast dynamics of seizure initiation and abortion

TL;DR

This paper uses neural mass models to analyze the slow-fast dynamics of seizure initiation and termination, revealing how specific stimulation frequencies can potentially abort seizures based on tissue properties.

Contribution

It provides a detailed analysis of the multi-scale neural dynamics involved in seizures and identifies stimulation parameters that could be effective in seizure control.

Findings

Intermediate stimulation frequencies (>20 Hz) can abort seizures.

Seizure dynamics depend on synaptic timescales and stimulation parameters.

Neural mass models can mimic LFP signals and inform therapeutic protocols.

Abstract

Epilepsy is a dynamic and complex neurological disease affecting about 1% of the worldwide population, among which 30% of the patients are drug-resistant. Epilepsy is characterized by recurrent episodes of paroxysmal neural discharges (the so-called seizures), which manifest themselves through a large-amplitude rhythmic activity observed in depth-EEG recordings, in particular in local field potentials (LFPs). The signature characterizing the transition to seizures involves complex oscillatory patterns, which could serve as a marker to prevent seizure initiation by triggering appropriate therapeutic neurostimulation methods. To investigate such protocols, neurophysiological lumped-parameter models at the mesoscopic scale, namely neural mass models, are powerful tools that not only mimic the LFP signals but also give insights on the neural mechanisms related to different stages of seizures. Here, we analyze the multiple time-scale dynamics of a neural mass model and explain the underlying structure of the complex oscillations observed before seizure initiation. We investigate population-specific effects of the stimulation and the dependence of stimulation parameters on synaptic timescales. In particular, we show that intermediate stimulation frequencies (>20 Hz) can abort seizures if the timescale difference is pronounced. Those results have the potential in the design of therapeutic brain stimulation protocols based on the neurophysiological properties of tissue.