{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T05:47:46Z","timestamp":1791870466470,"version":"4.3.4"},"reference-count":40,"publisher":"Korean Neuropsychiatric Association","issue":"10","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012151","name":"Sanming Project of Medicine in Shenzhen","doi-asserted-by":"publisher","award":["SZZYSM202311020"],"award-info":[{"award-number":["SZZYSM202311020"]}],"id":[{"id":"10.13039\/501100012151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["psychiatryinvestigation.org"],"crossmark-restriction":true},"short-container-title":["Psychiatry Investig"],"published-print":{"date-parts":[[2026,10,25]]},"abstract":"<jats:p>Objective To conduct a systematic bibliometric review of artificial intelligence (AI)-based multimodal neuroimaging research in depression, mapping its evolution, collaborative networks, and intellectual foundations to guide future translation.Methods Records were retrieved from Web of Science Core Collection (search date: 1 September 2025) using terms for depression, neuroimaging modalities, and AI\/machine learning (ML)\/deep learning. English, peer-reviewed articles\/reviews (2001\u20132025) were screened; 1,232 studies were retained after deduplication and filtering. Analyses included annual output trends, co-authorship networks (countries\/institutions\/authors), keyword co-occurrence clustering with burst detection, and co-citation\/bibliographic coupling using VOSviewer (v1.6.20) and Bibliometrix (v4.5.1).Results Annual publications rose from &lt;20\/year to 202 in 2024; 2025 counts (n=173) reflect January\u2013August only. The international network spans 42 countries\/regions linked by 320 co-authorship ties (total link strength=1,228; density of approximately 0.37). USA, China, and the UK constitute the epistemic core, with expanding participation from India, Brazil, and Iran. Institutional and author overlays indicate a small-world, hierarchical organization (205 institutions; 10 collaboration communities; author network 168 researchers) characterized by a productive core and long tail. Six keyword clusters delineate a functional workflow: 1) biomarkers\/phenotypes, 2) functional connectivity, 3) machine-learning\/computational approaches, 4) multimodal\/precision psychiatry, 5) diagnostic classification\/algorithm development, and 6) structural imaging\/comorbidity. Co-citation and coupling reveal a progression from early ML and connectivity foundations to deep\/graph models, multimodal fusion, explainability, and treatment-oriented prediction.Conclusion AI-based multimodal neuroimaging in depression has evolved into a coherent, translationally focused field. Current trends emphasize model validation, interpretability, and cross-site generalization, supported by expanding international collaboration. Future priorities include harmonized data sharing, explainable modeling, and theory-informed clinical translation.<\/jats:p>","DOI":"10.30773\/pi.2026.0059","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:28:42Z","timestamp":1790062122000},"page":"1219-1231","update-policy":"https:\/\/doi.org\/10.30773\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Multimodal Neuroimaging Research on Depression: A Bibliometric Analysis"],"prefix":"10.30773","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2201-3504","authenticated-orcid":true,"given":"Bo","family":"Wang","sequence":"first","affiliation":[{"name":"Research and Teaching Department, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital), Shenzhen, China"},{"name":"Research and Teaching Department, Pingshan Hospital Affiliated to Guangzhou University of Chinese Medicine, Shenzhen, 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