Greater task-independent overall beta power differentiates depressed patients from healthy controls during an emotion word task.

BACKGROUND: The identification of depression based on electroencephalographic (EEG) measures may be at least as accurate as commonly used screening tests in clinical practice, however, less is known about how to distinguish between trait-level, endogenous and state-level, reactive subtypes of depression on a neurophysiological basis. METHODS: 20 participants were enrolled in the study - 10 healthy controls and 10 with major depressive disorder (MDD) -, who first completed a questionnaire that included demographic, treatment/disease history variables and the Beck Depression Inventory (BDI). During EEG measurement, each participant was shown words (15 in total) presented in a repetitive but randomized order and duration, which were classified into positive, negative and neutral affective categories. Both resting, task-independent and task-specific EEG patterns were analysed. RESULTS: As the permutation version of the Welch's t-statistic for each frequency band, modality, and word category showed no notable differences between groups, relevant task-specific effects could not be observed. Cluster-based permutation tests further supported the lack of localized effects. However, significant task-independent differences were observed, with Delta (Welch's t = -2.34; p = 0.0327), Theta (Welch's t = -2.19; p = 0.0426), and Beta (Welch's t = 3.02; p = 0.0082) bands showing variations between subgroups. After applying Bonferroni correction, only the Beta band remained significantly different, showing higher activity in the depression group, particularly in the parieto-occipital regions. CONCLUSIONS: Our study supports the potential of overall Beta activity as a biomarker for trait-like depression. Future research should aim to elucidate the interactions between Beta activity, depression severity, age and medication status to better tailor therapeutic interventions for individuals with MDD.