ArticlePsychiatry investigation2026
Artificial Intelligence in Multimodal Neuroimaging Research on Depression: A Bibliometric Analysis.
Article in Psychiatry investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo 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.
methodsRecords 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 (2001-2025) 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).
resultsAnnual publications rose from <20/year to 202 in 2024; 2025 counts (n=173) reflect January-August 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.
conclusionAI-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.