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

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.Author summary: Epilepsy is a complex disease affecting 1% of the worldwide population of which 30% of the patients are drug-resistant and seeking for alternative therapeutic methods, such as neurostimulation. Epileptic seizures can be hallmarked by preceding pre-ictal phases which are a possible window of opportunity to trigger electrical stimulation with the objective to prevent seizure initiation. Biophysiological models are an appropriate framework to understand underlying dynamics and transitions between different epileptogenic phases. In this study, we consider a typical pre-ictal regime with complex bursting-type oscillations, which can be accurately reproduced by a neural mass model. By analyzing the multiple time-scaled structure of the model, we identify the key role of the subpopulations of GABAergic interneurons. We show that appropriate brain stimulation targeting GABAergic interneurons is able to abort pre-ictal bursting, thus preventing seizures to develop.