Exploring the Functional Connectivity of Resting-state EEG in Adolescent Major Depressive Disorder

This study aimed to explore the potential relationship between resting-state brain network attributes and adolescent major depressive disorder (MDD), with a focus on understanding how resting-state electroencephalogram (EEG) network features correlate with Hamilton Depression Rating Scale (HAMD) scores, and to identify potential physiological biomarkers for predicting HAMD scores in adolescents with MDD.

Adolescent MDD presents unique neurodevelopmental challenges, yet the neurophysiological correlates of symptom severity remain poorly characterized. This study investigated resting-state EEG network topology and its relationship with HAMD scores in adolescent MDD, aiming to identify potential neural biomarkers for depression severity.

MDD patients exhibited significantly enhanced frontal-parietal connectivity compared with healthy controls (HC) (p < 0.05, false discovery rate (FDR)-corrected). HAMD scores correlated positively with coefficient (Clu) (r = 0.401), global efficiency (Ge) (r = 0.408), and local efficiency (Le) (r = 0.402), while showing a negative correlation with characteristic path length (Cpl) (r = -0.408; all PFDR < 0.05). The regression model achieved strong prediction accuracy (R² = 0.38, p < 0.001; root mean square error (RMSE) = 2.83), and network features distinguished MDD from HC with 94% classification accuracy.

These preliminary findings deepen our understanding of adolescents with MDD and suggest that resting-state brain network attributes in the alpha band may serve as a potential physiological biomarker for predicting HAMD scores.