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

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.

State-Dependent Effective Connectivity in Resting-State fMRI

“…The brain has multiple time scales of neural dynamics (Zhigalov et al, 2015 ; Yano et al, 2016 ; Williams et al, 2018 ; Koksal Ersoz et al, 2020 ; Spitmaan et al, 2020 ). In the multiple time scale perspective, we can view the current framework as a method to explore the slow dynamics of the fast time scale neural dynamics.…”

Canard solutions in neural mass models: consequences on critical regimes

“…As noticed in [48], system (2a)-(2h) is a three-time-scale system written in fast form with (v 3 , y 8 ) being fast, (v 0 , y 5 , v 1 , y 6 ) slow and (v 2 , y 7 ) super-slow variables. Köksal Ersöz et al [48] have focused on electrophysiological pre-ictal bursting patterns recorded in human patients just before the onset of seizure.…”

“…As noticed in [48], system (2a)-(2h) is a three-time-scale system written in fast form with (v 3 , y 8 ) being fast, (v 0 , y 5 , v 1 , y 6 ) slow and (v 2 , y 7 ) super-slow variables. Köksal Ersöz et al [48] have focused on electrophysiological pre-ictal bursting patterns recorded in human patients just before the onset of seizure. Pre-ictal bursting patterns are characterized by fast oscillatory discharges (which will be referred as spikes) followed by a slower oscillation (a simulated pattern with the parameter set in Table 1 is exemplified in Fig.…”

Perturbations both trigger and delay seizures due to generic properties of slow-fast relaxation oscillators

“…We acknowledge our analysis relied on reduced planar models. However, we plan to make advantage of recent methodologies computing isochrons of high dimensional systems [48] to extend our approach to different high dimensional models as [17,[19][20][21]49]. In general, the high dimensionality of these models permits to describe more accurately seizures initiation and termination [14,50].…”

“…During the last decade, there has been an increasing number of models approaching epilepsy through slow-fast time scales [15][16][17][18][19][20][21]. Recently, the slow-fast dynamics has been proposed to explain the role of the interictal epileptiform discharges (IEDs) in the generation of seizures [22].…”