Brain and behavior · 2025
Imaging Diagnosis of Mild Cognitive Impairment: A Bibliometric Analysis (1999-2024).
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Xie Jianan, Li Shuya, Li Kuncheng, Sun Kai
Аннотация
PURPOSE: Imaging diagnosis of mild cognitive impairment (MCI) has garnered increasing attention due to its critical role in the early detection of Alzheimer's disease (AD) and other dementias. This study presents a bibliometric analysis to elucidate global research trends, key contributors, thematic clusters, and emerging topics within the field of MCI imaging diagnosis.
METHOD: English-language publications related to MCI imaging were retrieved from the Web of Science Core Collection (WoSCC) from January 1999 to December 2024. Bibliometric analyses were performed using VOSviewer, CiteSpace, and R-bibliometrix to evaluate co-authorship networks, institutional collaborations, journal impact, keyword co-occurrence, and burst trends.
FINDING: A total of 7,568 articles showed an average annual growth of 22.27%, with output surging after 2007 and peaking in 2024 (n = 762). The United States led in productivity and impact, ahead of China and Italy. Leading institutions were the University of California System, Vrije Universiteit Amsterdam, and the University of London, with key authors including Clifford R. Jack Jr., Ronald C. Petersen, and Philip Scheltens. Core journals were Neurology, Neuroimage, and Brain. Cluster analysis revealed four themes: functional and cognitive networks, biomarkers and pathology, structural imaging and computational diagnostics, and guidelines. Recent trendsc (AI) (e.g., machine learning, deep learning), while citation bursts indicate an evolution from early biomarker and imaging research toward current AI and multimodal imaging for improved diagnosis and risk prediction.
CONCLUSION: This bibliometric analysis provides a comprehensive overview of the evolving research landscape in MCI imaging diagnosis. The integration of advanced computational methodologies, particularly AI-powered tools, is driving precision diagnostics and personalized medicine. These advancements hold significant potential to improve early detection, stratify risk, and inform therapeutic interventions, ultimately contributing to better outcomes for individuals with MCI.
METHOD: English-language publications related to MCI imaging were retrieved from the Web of Science Core Collection (WoSCC) from January 1999 to December 2024. Bibliometric analyses were performed using VOSviewer, CiteSpace, and R-bibliometrix to evaluate co-authorship networks, institutional collaborations, journal impact, keyword co-occurrence, and burst trends.
FINDING: A total of 7,568 articles showed an average annual growth of 22.27%, with output surging after 2007 and peaking in 2024 (n = 762). The United States led in productivity and impact, ahead of China and Italy. Leading institutions were the University of California System, Vrije Universiteit Amsterdam, and the University of London, with key authors including Clifford R. Jack Jr., Ronald C. Petersen, and Philip Scheltens. Core journals were Neurology, Neuroimage, and Brain. Cluster analysis revealed four themes: functional and cognitive networks, biomarkers and pathology, structural imaging and computational diagnostics, and guidelines. Recent trendsc (AI) (e.g., machine learning, deep learning), while citation bursts indicate an evolution from early biomarker and imaging research toward current AI and multimodal imaging for improved diagnosis and risk prediction.
CONCLUSION: This bibliometric analysis provides a comprehensive overview of the evolving research landscape in MCI imaging diagnosis. The integration of advanced computational methodologies, particularly AI-powered tools, is driving precision diagnostics and personalized medicine. These advancements hold significant potential to improve early detection, stratify risk, and inform therapeutic interventions, ultimately contributing to better outcomes for individuals with MCI.