Resting-state EEG signatures of slow and fast sodium channel blockers: Spectral and complexity insights in focal epilepsy
BACKGROUND: This study examined how slow- and fast-acting sodium-channel-blocker (SCB) antiseizure medications (ASMs) are associated with differences in normal and interictal resting-state EEG in people with focal epilepsy (FEs) compared with healthy controls (HCs). METHODS: The study included 44 FEs receiving Slow-SCB ASMs, 44 receiving Fast-SCB ASMs, and 54 HCs. Resting-state, eyes-closed EEG was recorded and preprocessed to remove artifacts. Power spectral density (PSD), band powers, and neuronal complexity (Katz fractal dimension, Shannon entropy, and Poincaré indexes) were computed. Group differences were statistically assessed, with p-values corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR). RESULTS: /Hz in HCs; p < 0.01). Topographic analyses indicated global alterations in Fast-SCB versus spatially restricted changes in Slow-SCB. Complexity measures were significantly lower in Fast-SCB compared with both groups in alpha and beta bands (p < 0.05), while both patient groups demonstrated comparable reductions in gamma relative to HCs. CONCLUSION: These findings provide insights into how different SCB-class medications are associated with distinct EEG activity patterns across frequency bands, and suggest that neuronal complexity may serve as a potential biomarker for distinguishing FEs from HCs.
Authors
- Sobhan Bamdad (ORCID: https://orcid.org/0000-0001-9478-1685)
- Angelo Labate (ORCID: https://orcid.org/0000-0002-8827-7324)
- Chiara Martellino
- Giorgia Atanasio (ORCID: https://orcid.org/0009-0003-7179-5525)
- Diana Tilenni
- Loredana Oliveri
- Angelina Lagana
- Fatima Nasrollahzadeh Masoumian
Institutions
- Università di Camerino (IT)
- University of Messina (IT)
- Montreal Neurological Institute and Hospital (CA)
- University of Palermo (IT)
Publication Details
- Journal
- Epilepsy & Behavior
- Published
- 2026-04-22
- DOI
- https://doi.org/10.1016/j.yebeh.2026.111062
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00