Informace o publikaci

Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase-shifts

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PŘIBYLOVÁ Lenka ŠEVČÍK Jan ECLEROVÁ Veronika KLIMEŠ Petr BRÁZDIL Milan MEIJER Hil

Rok publikování 2024
Druh Článek v odborném periodiku
Časopis / Zdroj Network Neuroscience
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://doi.org/10.1162/netn_a_00351
Doi http://dx.doi.org/10.1162/netn_a_00351
Klíčová slova Very High-Frequency Oscillations; Ultra-Fast Oscillations; Neuronal Network Model; Phase-Shifting Synchrony; Bifurcations; Epilepsy
Přiložené soubory
Popis Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.
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