Static and dynamic intracerebral signal analysis reveals protective networks against seizures in drug-resistant focal epilepsy

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Abstract

Background

Epilepsy research increasingly emphasizes the role of brain network dynamics in seizure generation and propagation. This study explores static and dynamic functional networks in subjects with drug-resistant epilepsy, to identify mechanisms that enhance or inhibit seizure initiation. To this aim, we analyzed functional connectivity of brain networks preceding ictal minor electrical discharges and major seizures in epileptogenic and non-epileptogenic zones explored with intracerebral electrodes.

Material and methods

Stereo-electroencephalographic signals were recorded from 39 patients with focal drug-resistant epilepsy during presurgical monitoring. Static functional connectivity was analyzed using graph theory metrics, whereas dynamic connectivity through the analysis of the complexity and dwell times of brain meta-states activations.

Results

Static connectivity analysis revealed significant alterations in network centrality, integration, and segregation properties, with distinct patterns observed in resting conditions just ahead minor electrical discharges and major seizures. Specifically, network analysis before minor electrical discharges exhibited increased nodal strength and reduced betweenness centrality in the epileptogenic zone, associated with increased integration and reduced segregation in non-epileptogenic zones. Dynamic connectivity analysis showed lower complexity and longer stability of meta-states before minor electrical discharges, particularly in high-frequency signals of non-epileptogenic zones.

Conclusions

Our findings provide novel and valuable insights into the dynamic reconfiguration of brain networks before epileptic seizures, suggesting an inhibitory/protective mechanism mainly involving the non-epileptogenic zones. Understanding these network changes is pivotal for improving epilepsy treatment strategies targeting dynamic network alterations.

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