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Girier, G.

Publications and source records attributed to Girier, G..

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↗

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗