Altered Low-beta Characteristics in Individuals With Alcohol Use Disorder: A Pilot Resting Electroencephalography...

The pathophysiological mechanisms underlying alcohol use disorder (AUD) remain unclear, and its clinical evaluation largely depends on subjective assessments lacking objective biomarkers. This study employed a case-control design incorporating resting-state electroencephalography (EEG) with power spectral analysis (PSA) and dynamic functional connectivity (dFC) to explore potential biomarkers for AUD.

Resting-state EEG data were collected from individuals diagnosed with AUD and demographically matched healthy controls (HCs), alongside comprehensive neuropsychological and behavioral evaluations. PSA quantified energy distribution across specific frequency bands, with receiver operating characteristic analysis determining its discriminatory capacity. dFC was examined using a sliding window approach and the weighted phase-lag index, followed by K-means clustering to extract dominant connectivity states across frequency bands.

After excluding cases with suboptimal EEG data, the final analytic sample comprised 25 individuals with AUD and 26 HCs. Compared to HCs, the AUD group exhibited elevated low-beta power at F1, FCz, FC1, and C3 electrode sites (10-20 EEG system), with respective area under the curve values of 0.795, 0.794, 0.806, and 0.769, indicating reliable group differentiation. Temporal profiling of functional connectivity revealed three distinct brain states: S1 (60.81%), S2 (21.05%), and S3 (18.15%). Correlations between these connectivity patterns and clinical indices were observed in the AUD cohort.

Individuals with AUD showed increased brain activity in the medial frontal gyrus and left central gyrus at rest, as well as significant low-beta frequency changes in dFC analysis. Resting EEG scans with PSA and dFC analysis could serve as potential biomarkers for detecting AUD.