Activity-induced spontaneous spikes in GABAergic neurons suppress seizure discharges: an implication of computational modeling

Abstract

BackgroundEpilepsy, a prevalent neurological disorder, appears self-termination. The endogenous mechanism for seizure self-termination remains to be addressed in order to develop new strategies for epilepsy treatment. We aim to examine the role of activity-induced spontaneous spikes at GABAergic neurons as an endogenous mechanism in the seizure self-termination.Methods and ResultsNeuronal spikes were induced by depolarization pulses at cortical GABAergic neurons from temporal lobe epilepsy patients and mice, in which some of these neurons fired activity-induced spontaneous spikes. Neural networks including excitatory and inhibitory neurons were computationally constructed, and their functional properties were based on our studies from whole-cell recordings. With the changes in the portion and excitability of inhibitory neurons that generated activity-induced spontaneous spike, the efficacies to suppress synchronous seizure activity were analyzed, such as its onset time, decay slope and spike frequency. The increases in the proportion and excitability of inhibitory neurons that generated activity-induced spontaneous spikes effectively suppressed seizure activity in neural networks. These factors synergistically strengthened the efficacy of seizure activity suppression.ConclusionOur study supports a notion that activity-induced spontaneous spikes in GABAergic neurons may be an endogenous mechanism for seizure self-termination. A potential therapeutic strategy for epilepsy is to upregulate the cortical inhibitory neurons that generate activity-induced spontaneous spikes.

The role of substance P in epilepsy and seizure disorders

“…Imbalances cause excessive synchrony of electrical discharges from specific neurons, thus triggering a series of abnormal behaviors [ 5 ]. Inhibitory/excitatory transmission systems play a major role in such imbalances, especially the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) and the excitatory neurotransmitter glutamate [ 6 , 7 ]. Most traditional AEDs target ion channels or ionotropic receptors to decrease the excitatory tone or increase the inhibitory tone to correct imbalances [ 8 , 9 ].…”

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

“…The insights accumulated from computational studies pave the way for more targeted and effective approaches to managing epilepsy. From investigating the impact of spiking timing stimulation on frequency-specific oscillations (Quinarez et al, 2023 ) to exploring the potential of linear delayed feedback control in thalamocortical models (Zhou et al, 2020 ), these studies collectively contribute to an expanding body of knowledge that spans from the cellular level (Lu et al, 2017 ) to network dynamics (Depannemaecker et al, 2021 ). Moreover, the translation of computational findings into clinical practice has been deliberated upon by Brinkmann et al ( 2021 ), emphasizing the promising trajectory of computational seizure forecasting.…”

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

“…The insights accumulated from computational studies pave the way for more targeted and effective approaches to managing epilepsy. From investigating the impact of spiking timing stimulation on frequency-specific oscillations (Quinarez et al, 2023) to exploring the potential of linear delayed feedback control in thalamocortical models (Zhou et al, 2020), these studies collectively contribute to an expanding body of knowledge that spans from the cellular level (Lu et al, 2017) to network dynamics (Depannemaecker et al, 2021). Moreover, the translation of computational findings into clinical practice has been deliberated upon by Brinkmann et al (2021), emphasizing the promising trajectory of computational seizure forecasting.…”