https://doi.org/10.1140/epjs/s11734-026-02481-x
Regular Article
Frequency-specific alterations of functional brain network measures in stereo-EEG recordings of epilepsy patients
1
Research Institute of Applied Artificial Intelligence and Digital Solutions, Plekhanov Russian University of Economics, 36 Stremyannyy Pereulok, 115054, Moscow, Russia
2
Pirogov National Medical and Surgical Center, 70 Nizhnyaya Pervomayskaya Street, 105203, Moscow, Russia
a
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Received:
30
May
2026
Accepted:
19
June
2026
Published online:
3
July
2026
Abstract
Epilepsy is increasingly considered a disorder of large-scale brain network organization characterized by abnormal interactions between distributed neuronal populations. In the present study, we investigated frequency-specific alterations of functional brain network topology in patients with focal epilepsy using graph-theoretical analysis of stereo-electroencephalography (sEEG) recordings. Functional connectivity networks were reconstructed using imaginary coherence within delta, theta, alpha, beta, low-gamma, and high-gamma frequency bands. Network topology was characterized using node strength, clustering coefficient, and participation coefficient. Comparative analysis between epileptogenic zones (EZ) and non-epileptic zones (non-EZ) revealed significant frequency-dependent differences in functional network organization. In particular, EZ channels demonstrated increased node strength in beta and gamma frequency bands, indicating enhanced global functional integration, while clustering coefficient was significantly reduced in beta and gamma networks, suggesting decreased local segregation and altered local connectivity organization. Participation coefficient analysis additionally revealed changes in intermodular interactions. The strongest alterations were observed within beta and high-gamma activity, emphasizing the important role of high-frequency oscillatory interactions in epileptogenic network dynamics. The obtained results support the concept of epilepsy as a network disorder involving large-scale reorganization of functional interactions rather than isolated local abnormalities and demonstrate the usefulness of graph-theoretical analysis for investigating pathological brain network organization.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
