Frontiers in cardiovascular medicine · 2026
Research hotspots and trend of the heart-brain axis by MRI: a bibliometric analysis.
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Wang Haoran, Jia Yang, Liao Yi, Xie Linjun, Jin Wenfeng, Peng Qianyu, Xu Huayan, Xu Rong, Guo Yingkun
Аннотация
BACKGROUND: The "Heart-Brain Axis" (HBA) represents an emerging interdisciplinary domain in which cardiac dysfunction is increasingly recognized to affect brain development, particularly with the advancement of imaging technologies such as MRI. Despite growing interest, the literature remains fragmented and lacks systematic synthesis.
METHODS: We performed a bibliometric analysis of 6,446 English-language articles and reviews in the Web of Science Core Collection (1984-2025), using VOSviewer, CiteSpace, SCImago Graphica, and Excel to map knowledge structures, research hotspots, and collaborative networks. A PubMed dataset (n = 6,389; 1984-2025) provided validation, comparing annual publication trends and high-frequency keyword structures.
RESULTS: The annual publication trends showed strong concordance between the WoSCC and PubMed datasets (Pearson r = 0.988, R2 = 0.976). Keyword co-occurrence analysis identified four primary clusters: "Technology & Development", "Function & Regulation", "Risk & Pathology", and "Hemodynamics & Perfusion". In the WoSCC keyword co-occurrence network, representative high-TLS keywords included "MRI" (TLS = 2799), "functional connectivity" (TLS = 1,224), "dementia" (TLS = 1,425), and "hemodynamics" (TLS = 1,036). The United States, Germany, and the United Kingdom were the leading contributors, with prominent institutions including the University of Toronto and Harvard Medical School. Citation bursts and recent keywords such as "artificial intelligence" reflect the technological evolution of the field.
CONCLUSION: This study provides a systematic overview of HBA research trends and thematic evolution. The sustained growth in publications reflects increasing academic attention. Findings offer insights for researchers, clinicians, and policymakers, emphasizing future directions including AI integration and multi-organ network modeling.
METHODS: We performed a bibliometric analysis of 6,446 English-language articles and reviews in the Web of Science Core Collection (1984-2025), using VOSviewer, CiteSpace, SCImago Graphica, and Excel to map knowledge structures, research hotspots, and collaborative networks. A PubMed dataset (n = 6,389; 1984-2025) provided validation, comparing annual publication trends and high-frequency keyword structures.
RESULTS: The annual publication trends showed strong concordance between the WoSCC and PubMed datasets (Pearson r = 0.988, R2 = 0.976). Keyword co-occurrence analysis identified four primary clusters: "Technology & Development", "Function & Regulation", "Risk & Pathology", and "Hemodynamics & Perfusion". In the WoSCC keyword co-occurrence network, representative high-TLS keywords included "MRI" (TLS = 2799), "functional connectivity" (TLS = 1,224), "dementia" (TLS = 1,425), and "hemodynamics" (TLS = 1,036). The United States, Germany, and the United Kingdom were the leading contributors, with prominent institutions including the University of Toronto and Harvard Medical School. Citation bursts and recent keywords such as "artificial intelligence" reflect the technological evolution of the field.
CONCLUSION: This study provides a systematic overview of HBA research trends and thematic evolution. The sustained growth in publications reflects increasing academic attention. Findings offer insights for researchers, clinicians, and policymakers, emphasizing future directions including AI integration and multi-organ network modeling.