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Dallmer-Zerbe, I.

Publications and source records attributed to Dallmer-Zerbe, I..

2 recordsLinked to original sources

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↗

Amplitude entropy captures chimera-like behavior in epileptic seizure dynamics

Epilepsy is a neurological disease characterized by epileptic seizures, which commonly manifest with pronounced frequency and amplitude changes in the EEG signal. In the case of focal seizures, initially localized pathological activity spreads from a so-called "onset zone" to a wider network of brain areas. Chimeras, defined as states of simultaneously occurring coherent and incoherent dynamics in symmetrically coupled networks are increasingly invoked for characterization of seizures. In particular, chimera-like states have been observed during the transition from a normal (asynchronous) to a seizure (synchronous) network state. However, chimeras in epilepsy have only been investigated with respect to the varying phases of oscillators. We propose a novel method to capture the characteristic pronounced changes in the recorded EEG amplitude during seizures by estimating chimera-like states directly from the signals in a frequency- and time-resolved manner. We test the method on a publicly available intracranial EEG dataset of 16 patients with focal epilepsy. We show that the proposed measure, titled Amplitude Entropy, is sensitive to the altered brain dynamics during seizure, demonstrating its significant increases during seizure as compared to before and after seizure. This finding is robust across patients, their seizures, and different frequency bands. In the future, Amplitude Entropy could serve not only as a feature for seizure detection, but also help in characterizing amplitude chimeras in other networked systems with characteristic amplitude dynamics.

neuroscience↗