Circadian Patterns of Behavioral Seizures in the ventral Intrahippocampal Kainic Acid Mouse Model of Temporal Lobe Epilepsy
Temporal lobe epilepsy (TLE) is the most common form of epilepsy, characterized by spontaneous recurrent seizures that are largely unpredictable. Growing evidence suggests that seizure susceptibility fluctuates across the 24-hour circadian cycle, and identifying these patterns could advance seizure forecasting. The ventral intrahippocampal kainic acid (vIHKA) mouse model replicates key features of human TLE, yet circadian modulation of seizure frequency in this model has not been closely examined. This study aimed to determine whether behavioral seizures in vIHKA mice exhibit circadian clustering and to compare temporal patterns between hypersynchronous (HYP) and low-voltage high-frequency (LVHF) seizure types. Seizure onset times were visualized using circular plots and statistically analyzed using the Rayleigh test for uniformity. Results revealed a significant non-uniform distribution of seizures across the 24-hour cycle, with a mean direction corresponding to approximately 22:18 (10:18 PM). This peak falls within the dark phase of the light/dark cycle, consistent with heightened seizure susceptibility during this circadian phase and aligning with the active period in nocturnal rodents. Seizure clustering was evident at both the individual animal and group levels, demonstrating a strong influence of circadian rhythms on seizure risk across subjects. These findings establish a clear temporal structure to behavioral seizure occurrence in the vIHKA model. Establishing this circadian seizure pattern provides a quantifiable baseline against which future predictive algorithms can be benchmarked and highlights time-of-day as a meaningful variable in seizure forecasting. Future studies should extend this work by integrating circadian phase information into machine learning–based prediction models. Additionally, testing time-specific interventions, such as chronotherapy or adaptive neuromodulation, may help determine whether aligning treatment with periods of peak seizure susceptibility can improve therapeutic efficacy. Ultimately, these findings support the integration of circadian dynamics into epilepsy research, moving the field toward temporally informed, predictive approaches to epilepsy research and treatment.