Epilepsy & behavior : E&B · 2026
Resting-state EEG signatures of slow and fast sodium channel blockers: Spectral and complexity insights in focal epilepsy.
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Bamdad Sobhan, Masoumian Fatima Nasrollahzadeh, Oliveri Loredana, Martellino Chiara, Atanasio Giorgia, Tilenni Diana, Lagana Angelina, Labate Angelo
Аннотация
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: Significant group differences were observed across spectral and complexity measures. The Fast-SCB group exhibited widespread reductions in PSD compare to both HCs and Slow-SCB, particularly in theta, alpha, and beta bands (p < 0.05), with marked decreases in band power (e.g., beta: 5.67 ± 0.68 vs. 10.08 ± 1.15 μV2/Hz in HCs; p < 0.01). In contrast, the Slow-SCB group showed more selective reductions, primarily in gamma bands (gamma: 1.09 ± 0.10 vs. 1.89 ± 0.20 μV2/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.
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: Significant group differences were observed across spectral and complexity measures. The Fast-SCB group exhibited widespread reductions in PSD compare to both HCs and Slow-SCB, particularly in theta, alpha, and beta bands (p < 0.05), with marked decreases in band power (e.g., beta: 5.67 ± 0.68 vs. 10.08 ± 1.15 μV2/Hz in HCs; p < 0.01). In contrast, the Slow-SCB group showed more selective reductions, primarily in gamma bands (gamma: 1.09 ± 0.10 vs. 1.89 ± 0.20 μV2/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.