J Integr Neurosci. 2025 Oct 30;24(10):42821. doi: 10.31083/JIN42821.
ABSTRACT
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
PMID:41200986 | DOI:10.31083/JIN42821
