Adolescent depression recognition and symptom prediction based on multi-view EEG network: an exploratory study.

Authors

Yanli Zhao, Haitao Chen, Xiaoxiao Ma, Xiaoyue Li, Kai Li, Zihao Wei, Jiaqi Song, Yan Chen, Wen Xin, Guimei Yin, Shuping Tan

Published in

Frontiers in psychiatry. Volume 17. Pages 1908969. Epub Jul 31, 2026.

Abstract

Adolescent depression diagnosis currently relies primarily on subjective self-report questionnaires, with a notable lack of objective neurobiological biomarkers. This study aimed to compare the diagnostic performance of traditional psychological scales with a newly developed Multi-view Adaptive Graph Convolutional Network (MVA-GCN) based on resting-state electroencephalography (EEG), and to exploratorily examine the associations between MVA-GCN-derived brain network features and psychological resilience in adolescents.
Resting-state EEG data were collected from 44 adolescents with major depressive disorder (MDD) and 30 healthy controls (HCs). The MVA-GCN model integrated three parallel connectivity views-phase-locking value (PLV), Pearson correlation coefficient (PCC), and phase lag index (PLI)-via an adaptive fusion mechanism. We compared the classification performance of questionnaire-based machine learning models against the MVA-GCN, and further examined the correlations between model-highlighted network features and clinical symptom measures, including depression, anxiety, loneliness, rumination, and resilience, with age and sex included as covariates.
Questionnaire-based machine learning models achieved a mean classification accuracy of 86.43%, whereas the MVA-GCN attained 99.84% accuracy in the present dataset. Occipital and fronto-central regions contributed most to the model's predictions. Increased gamma-band functional connectivity and reduced alpha-band power were identified as potential electrophysiological correlates of group differences. Exploratory correlational analyses at the nominal level (p < 0.05, uncorrected) suggested potential group-specific patterns in brain-resilience associations. A post-hoc deviation index analysis showed that, within the MDD group, greater deviation from the healthy reference brain-resilience association pattern was significantly correlated with more severe rumination, self-rated depression, clinician-rated depression severity, loneliness, and self-rated anxiety (all FDR q < 0.05).
In this exploratory sample, the MVA-GCN demonstrated promising proof-of-concept discriminative capability for adolescent depression compared with traditional self-report scales. However, the brain-resilience association findings were not statistically significant after correction for multiple comparisons and should be interpreted with caution. These results suggest that MVA-GCN-derived network features warrant further investigation in larger, independent cohorts, but do not yet support their use as a clinically validated diagnostic tool.

PMID:
42601901
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.