Search bioRxiv⌕ Search

Biology subjects

Chvojka, J.

Publications and source records attributed to Chvojka, J..

2 recordsLinked to original sources

Interictal activity fluctuations follow rather than precede seizures on multiple time scales in a mouse model of focal cortical dysplasia

The unpredictability of seizure occurrence is a major debilitating factor for people with epilepsy. A seizure forecasting system would greatly improve their quality of life. Successful seizure forecasting necessitates a comprehensive understanding of the factors influencing seizure timing at multiple temporal scales. In this study, we investigated multiscale properties of interictal epileptiform discharges (IEDs) and seizure parameters in a highly realistic mouse model of focal cortical dysplasia-related epilepsy. We analyzed the properties evolution at four timescales, ranging from epilepsy progression and seizure clusters to circadian and peri-ictal changes. We discovered that the FCD-related epilepsy syndrome was progressive in terms of interictal activity rate and seizure characteristics. Sixty percent of seizures occurred in clusters. During the clusters, the seizure duration, seizure power, and IED rate were increasing. Circadian rhythm influenced seizure occurrence with the peak seizure probability at 4 p.m. under a standard 12/12 light dark cycle with lights-on at 6 a.m. Peri-ictal analysis revealed no significant change in IED rate preceding individual seizures; however, a consistent two-peak pattern of IED elevation was observed following seizures. Specifically, an initial peak in IED rate emerged 5-10 minutes post-seizure, returning to baseline within two hours, followed by a secondary peak 6-12 hours later, which again subsided to baseline levels in 24-48 hours. This pattern could be fitted with a sum of three exponentials. Using the three-exponential pattern, we simulated IED rate fluctuations in each animal. The smoothed simulated IED rates showed good agreement with the smoothed real recorded IED rates, suggesting that the cumulative effect of post-ictal IED patterns can account for long-term fluctuations in IED rate. Our results indicate that, in our model of FCD-related epilepsy, consistent IED rate fluctuations follow rather than precede individual seizures. Therefore, fluctuations in IED rate can be viewed as a reflection of cyclic seizure occurrence. This implies that either IED rate fluctuations or accurate seizure records may be equally valuable for seizure risk forecasting. HighlightsO_LIFCD-related epilepsy model displays a progressive nature and fluctuations between high and low seizure risk. C_LIO_LISeizures occur with higher probability in the day time which corresponds to sleep-related seizures commonly occurring in human patients with FCD. C_LIO_LIIED rate increases significantly after seizures, indicating a postictal effect rather than a preictal one, with the post-ictal phase displaying a two-peak pattern of fast and slow IED rate increase. C_LIO_LILong-term changes in the IED rate could be attributed to the time-dependent cumulative effect of the two-peak seizure-related increase in IED rate. C_LI

neuroscience↗

Ion Dynamics Underlying the Seizure Delay Effect of Low-Frequency Electrical Stimulation

The biological mechanisms underlying the spontaneous and recurrent transition to seizures in the epileptic brain are still poorly understood. As a result, seizures remain uncontrolled in a substantial proportion of patients. Brain stimulation is an emerging and promising method to treat various brain disorders, including drug-refractory epilepsy. Selected stimulation protocols previously demonstrated therapeutic efficacy in reducing the seizure rate. The stimulation efficacy critically depends on chosen stimulation parameters, such as the time point, amplitude, and frequency of stimulation. This study aims to explore the neurobiological impact of 1Hz stimulation and provide the mechanistic explanation behind its seizure-delaying effects. We study this effect using a computational model, a modified version of the Epileptor-2 model, in close comparison with such stimulation effects on spontaneous seizures recorded in vitro in a high-potassium model of ictogenesis in rat hippocampal slices. In particular, we investigate the mechanisms and dynamics of spontaneous seizure emergence, the seizure-delaying effect of the stimulation, and the optimal stimulation parameters to achieve the maximal anti-seizure effect. We show that the modified Epileptor-2 model replicates key experimental observations, and captures seizure dynamics and the anti-seizure effects of low-frequency electrical stimulation (LFES) observed in hippocampal slices. We identify the critical thresholds in the model for seizure onset and determine the optimal stimulation parameters - timing, amplitude, and duration - that exceed specific thresholds to delay seizures without triggering premature seizures. Our study highlights the central role of sodium-potassium pump dynamics in terminating seizures and mediating the LFES effect. Author SummaryThis study investigates the mechanisms by which low-frequency electrical stimulation can suppress epileptic seizures. Epilepsy patients often do not respond to pharmacological treatment, necessitating alternative approaches, such as brain stimulation. Using a combination of computational modeling and in vitro experiments on rat hippocampal slices, we examine how periodic stimulation at 1 Hz influences seizure occurrence. Our results show that carefully timed low-frequency stimulation can delay seizure onset by modulating neuronal excitability, largely through the action of the Na-K-pump that maintains ion homeostasis. We employ a modified version of the Epileptor 2 model to reproduce the protective effects seen experimentally. By systematically varying stimulation parameters, we identify conditions that effectively delay seizures, helping to explain the antagonistic effects of stimulation observed by previous studies. Overall, this work advances our understanding of how low-frequency electrical stimulation interacts with intrinsic neuronal mechanisms to prevent seizures, thus offering a potential target for more effective neuromodulation strategies in drug-resistant epilepsy.

neuroscience↗