ePoster

Properties and predictive potential of the pre-ictal oscillatory dynamics in an ex vivo model of seizure-like activity in the different hippocampal subregions

Lida Vagiaki, Dionysios Xydias, Maria Kefalogianni, Sotiris Psilodimitrakopoulos, Emmanouel Stratakis, Kyriaki Sidiropoulou
FENS Forum 2024(2024)
Messe Wien Exhibition & Congress Center, Vienna, Austria

Conference

FENS Forum 2024

Messe Wien Exhibition & Congress Center, Vienna, Austria

Resources

Authors & Affiliations

Lida Vagiaki, Dionysios Xydias, Maria Kefalogianni, Sotiris Psilodimitrakopoulos, Emmanouel Stratakis, Kyriaki Sidiropoulou

Abstract

Seizure-like activity (SLA) results from synchronous neuronal activity in the mouse hippocampal subregions, namely: CA1, CA3 and DG. While many studies have proved the anticonvulsant properties of the administration of anti-epileptic drugs, such as diazepam (DZP), a GABA-A receptor agonist, and carbamazepine (CBZ), a sodium channel blocker, their role in modulating the oscillatory patterns within these three regions remains unclear. Furthermore, the role of oscillatory dynamics in the pre-ictal period in predicting the emergence of a seizure events require further investigation. In our study, we used a high [K+] artificial cerebrospinal fluid (aCSF) solution to induce SLA in mouse hippocampal slices, followed by bath application of CBZ or DZP. Using spontaneous field potential recordings, we detected differential DZP and CBZ-induced changes not only in the number of spontaneous events, but also in the oscillatory patterns across the CA1, CA3 and DG regions. Imaging of neuronal activity in the ex vivo model of SLA following DZP and CBZ perfusion also revealed a subregion-dependent modulation of neuronal activity, which resembled the pattern of modulation of the oscillatory dynamics. Moreover, LFP analysis in the pre-ictal period revealed significant changes that the oscillatory profiles in this period significantly differed from the oscillatory dynamics at the start of the ictal event and in the absence of SLA. Furthermore, a classification algorithm revealed that using the oscillatory dynamics, the emergence of an ictal event can be predicted with high accuracy. Therefore, the oscillatory dynamics could serve a potential electrophysiological biomarker for predicting seizure activity.

Unique ID: fens-24/properties-predictive-potential-pre-ictal-e033856d