Brain dynamics and depressive symptoms in young adults: Evidence from EEG

Depression is a major public health concern, with a rising prevalence among adolescents and young adults. However, the neural mechanisms underlying depressive symptoms remain poorly understood. This study aimed to identify patterns of altered brain oscillatory dynamics associated with depressive symptoms in nonclinical young adults using resting-state electroencephalography (EEG). Thirty-four university students (median age = 24 years, 18 males) underwent a 32-channel resting-state EEG recording. Depressive symptoms were assessed using the Depression, Anxiety, and Stress Scales (DASS-21). EEG data were decomposed into frequency bands, a repeated-measure ANOVA and a machine learning algorithm (Boruta) were employed to identify relevant EEG predictors of depressive scores. More than one-third of participants (35 %) exhibited subclinical depressive symptoms. Analyses revealed significant differences in theta EEG activity according to depressive symptoms (F = 9.992, p = .003). Furthermore, left temporal, left frontal, and right occipital theta resulted in the most effective variables for differentiating between students with and without depressive symptoms in the machine-learning algorithm. The findings suggest that subclinical depressive symptoms in young adults are associated with reduced theta activity. This may represent an early neurophysiological marker of depressive symptoms. From a clinical perspective, these results point to early identification of neurobiological vulnerabilities. During this critical period, such identification could facilitate targeted preventive interventions and follow-up monitoring. While preliminary, these findings underscore the need for a preventive medicine approach focused on the preclinical stages of depression in young populations.