The critical dynamics of hippocampal seizures

Gregory Lepeu, Ellen Maren, Kristina Slabeva, Cecilia Friedrichs-Maeder, Markus Fuchs, Werner J. Z’Graggen, Claudio Pollo, Kaspar A. Schindler, Antoine Adamantidis, Timothée Proix and Maxime O. Baud ()
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Gregory Lepeu: University of Bern
Ellen Maren: University of Bern
Kristina Slabeva: University of Bern
Cecilia Friedrichs-Maeder: University of Bern
Markus Fuchs: University of Bern
Werner J. Z’Graggen: University of Bern
Claudio Pollo: University of Bern
Kaspar A. Schindler: University of Bern
Antoine Adamantidis: University of Bern
Timothée Proix: University of Geneva
Maxime O. Baud: University of Bern

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Epilepsy is defined by the abrupt emergence of harmful seizures, but the nature of these regime shifts remains enigmatic. From the perspective of dynamical systems theory, such critical transitions occur upon inconspicuous perturbations in highly interconnected systems and can be modeled as mathematical bifurcations between alternative regimes. The predictability of critical transitions represents a major challenge, but the theory predicts the appearance of subtle dynamical signatures on the verge of instability. Whether such dynamical signatures can be measured before impending seizures remains uncertain. Here, we verified that predictions on bifurcations applied to the onset of hippocampal seizures, providing concordant results from in silico modeling, optogenetics experiments in male mice and intracranial EEG recordings in human patients with epilepsy. Leveraging pharmacological control over neural excitability, we showed that the boundary between physiological excitability and seizures can be inferred from dynamical signatures passively recorded or actively probed in hippocampal circuits. Of importance for the design of future neurotechnologies, active probing surpassed passive recording to decode underlying levels of neural excitability, notably when assessed from a network of propagating neural responses. Our findings provide a promising approach for predicting and preventing seizures, based on a sound understanding of their dynamics.

Date: 2024
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DOI: 10.1038/s41467-024-50504-9

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