Computational modeling of seizure onset patterns to underpin their underlying mechanisms

Abstract In the study of epilepsy, it is of crucial importance to understand the transition from interictal into ictal activities (ictogenesis). Different mechanisms have been suggested for the generation of ictal activity; yet, it remains unclear whether different physiological mechanisms underly different seizure onset patterns. Herein, by implementing a computational model that takes into account some of the most relevant physiological events (e.g., depolarization block, collapse, and recovery of inhibitory activities) and different scenarios of imbalanced excitatory-inhibitory activities, we explored if seizures with different onset patterns stem from different underlying mechanisms. Our model revealed that depending on the excitation level, seizures could be generated due to both enhancement and collapse of inhibition for specific range of parameters. Successfully reproducing some of the commonly observed seizure onset patterns, our findings indicated that different onset patterns can arise from different underlying mechanisms. Significance Statement Various seizure onset patterns have been reported; however, it yet remains unknown whether seizures with distinct onset patterns originate from different underlying mechanisms. The common belief on seizure generation focuses on the imbalance between synaptic excitation and inhibition which has led to the identification of distinct and, in some cases, even contradictory mechanisms for seizure initiation. In this study, by incorporating some of these various physiological mechanisms in a unified framework, we reproduced some commonly observed seizure onset patterns. Our results suggest the existence of different mechanisms responsible for the generation of seizures with distinct onset patterns which can enhance our understanding of seizure generation mechanisms with significant implications on developing therapeutic measures in seizure control.