Visual-affective decoding enhances neurofunctional objective and generalizable signatures in unseen adolescent depression diagnosis

Adolescent major depressive disorder is a highly prevalent and debilitating condition, yet objective and generalizable neurofunctional signatures remain elusive. Existing neuroimaging approaches largely rely on resting-state fMRI, or controlled tasks often lack ecological validity and generalizable signatures of depression-related neural dysfunctions. This study proposes a Visual–Affective Decoding (VAD) framework integrating cross-subject visual and affective decoding from whole-brain fMRI acquired during naturalistic movie viewing.

A dual-pathway anomaly detection model trained exclusively on healthy controls establishes normative brain function and identifies MDD through deviations from this norm. In 200 adolescents (112 MDD, 88 HC), VAD achieved an AUC of 0.87, 95.56% sensitivity, 83.89% accuracy, and 72.22% specificity. Using the same anomaly threshold, it generalized to an independent external adolescent cohort (70 MDD, 68 HC), achieving an AUC of 0.81 with an accuracy of 75.36%, supporting its cross-site generalizability.

VAD further revealed interpretable neurofunctional signatures, including blunted emotional reactivity, attenuated visual representation, and affective patterns consistent with emotional rumination. Region-level contribution analyses identified dissociable visual- and affective-sensitive cortical regions, consistent with dysfunctions in visual, default-mode, salience, and higher-order associative networks. These findings demonstrate that VAD provides objective, interpretable, and generalizable neurofunctional signatures for adolescent depression diagnosis and clinically relevant biomarker development.

This work was supported by National Natural Science Foundation of China (62576251), Beijing Research Ward Excellence Program (BRWEP2024W072120106), National Natural Science Foundation of China (62376198, 62406225, 82471576), National Key Research and Development Program under Grant 2025ZD0219200, the Capital Medical University Laboratory for Clinical Medicine, Beijing High-Level Innovation and Entrepreneurship Talent Support Program-Dengfeng Project (G202512066), and Research Fund from Beijing Anding Hospital (YG202302), the Shanghai Municipal Commission of Economy and Informatization under the AI for Science "Hundred Teams, Hundred Projects" Program (No. 2025-GZL-RGZN-BTBX-02020), the Capital Medical University Scientific Research Cultivation Fund, China (No.PYZ25179). These authors contributed equally: Jingxu Liu, Ke Liu. School of Computer Science and Technology, Tongji University, Shanghai, China Jingxu Liu, Mei Tang, Yichen Liu, Le Xu, Dehua Liu, Liang Hu, Lei Zhu, Duoqian Miao & Qi Zhang Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China International & VIP Health Center, Peking University Shenzhen Hospital, Shenzhen, China State Information Center, Beijing, China Correspondence to Duoqian Miao, Qi Zhang or Zhi Yang.

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