ArticleNeuroimage. Reports2026
Distributed EEG power asymmetries across frequencies in PTSD: Introducing the dynamic EEG power differential (DEPD) index.
Article in Neuroimage. Reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Hemispheric EEG power asymmetry is most often studied as frontal alpha asymmetry (FAA), but evidence suggests that asymmetry effects may be distributed across scalp regions and frequency bands, particularly in PTSD. We introduce the Dynamic EEG Power Differential (DEPD) index to quantify whole-scalp, multi-band, inter- and intra-hemispheric power differentials using time-resolved spectral estimates. Resting-state, eyes-closed EEG (32-channel) was recorded for 3 min in individuals with PTSD (n = 10) and healthy controls (n = 10). DEPD was computed as log power differences for all inter-hemispheric and intra-hemispheric electrode pairings across eight frequency bands (spanning delta to gamma) using both absolute and relative power. Group differences were tested nonparametrically with multiple-comparison control; within PTSD, DEPD features were related to symptom severity (PCL-5 scores). Homologous inter-hemispheric effects analogous to conventional asymmetry metrics were observed but did not predominate. The most prominent group- and severity-related effects were non-homologous inter- and intra-hemispheric differentials spanning anterior-posterior and fronto-parietal/fronto-occipital relationships and were strongly frequency-dependent. DEPD features derived from absolute versus relative power showed minimal overlap. Hemispheric power imbalance in controls and PTSD appears distributed and frequency-specific, and differences between PTSD and controls, as well as association with PTSD symptom severity, is better captured by these distributed inter- and intra-hemispheric power differentials at multitude frequency bands, compared with homologues asymmetries (particularly alpha band). DEPD provides a scalable framework for extracting distributed, frequency-resolved hemispheric power-differential features from resting EEG and may support future identification of candidate EEG markers in adequately powered validation studies.
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