MedElevator
Войти в исследования Psychiatry investigation · 2026
Artificial Intelligence in Multimodal Neuroimaging Research on Depression: A Bibliometric Analysis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Bo, Qiu Xingchen, Lin Jian, Lian Yin, Gong Chunzhu, Yu Qianqian
Аннотация
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 (2001-2025) 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).
RESULTS: Annual 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.
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.
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 (2001-2025) 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).
RESULTS: Annual 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.
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.