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A bibliometric and visual analysis of research dynamics in fMRI applied to mild cognitive impairment: Hotspots, frontiers, and trends
BIAN Xudong  CHEN Zhengguang  LI Xiaozhen  ZHONG Liqun  GUO Jing 

Cite this article as: BIAN X D, CHEN Z G, LI X Z, et al. A bibliometric and visual analysis of research dynamics in fMRI applied to mild cognitive impairment: Hotspots, frontiers, and trends[J]. Chin J Magn Reson Imaging, 2026, 17(3): 85-92, 98. DOI:10.12015/issn.1674-8034.2026.03.012.


[Abstract] Early identification and intervention for mild cognitive impairment (MCI) are of great significance in delaying the progression of dementia. Functional magnetic resonance imaging (fMRI), with its advantages of non-invasiveness, reproducibility, and in vivo visualization of brain function, has become a core neuroimaging technique for exploring functional brain changes in MCI. This study retrieved relevant literature on the application of fMRI in MCI research from the Web of Science (WOS) Core Collection and the China National Knowledge Infrastructure (CNKI), covering the period from database inception to September 1, 2025. Using bibliometric methods, the software Citespace 6.1.R6 and VOSviewer 1.6.18 were employed to systematically reveal the global development trends, collaboration patterns, knowledge structure, research hotspots, and emerging frontiers in the field of fMRI research on MCI. A total of 4132 articles were included, comprising 4014 from WOS and 118 from CNKI. The annual number of publications showed a steady upward trend. In the WOS database, China ranked first in the number of publications (1684 articles, accounting for 41.95%, including 52 articles from the Taiwan region), while the United States had the highest citation count (74 881 citations). The number of publications in CNKI was relatively limited, indicating a need for improvement in both research quantity and quality. This review demonstrates that fMRI is widely applied in MCI research. Resting-state functional brain network analysis, multimodal imaging integration, and deep learning models represent current research hotspots and frontiers. Future efforts should focus on establishing interdisciplinary, cross-regional, and international collaborative networks to promote knowledge sharing and resource complementarity, thereby addressing core scientific challenges in the early diagnosis and intervention of MCI and fostering sustained innovation and high-quality development in this field.
[Keywords] mild cognitive impairment；functional magnetic resonance imaging；magnetic resonance imaging；visualization analysis；bibliometrics

BIAN Xudong1   CHEN Zhengguang1*   LI Xiaozhen1   ZHONG Liqun1   GUO Jing2  

1 Department of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China

2 Department of Acupuncture and Moxibustion, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100011, China

Corresponding author: CHEN Z G, E-mail: guangchen999@sina.com

Conflicts of interest   None.

Received  2025-11-12
Accepted  2026-02-22
DOI: 10.12015/issn.1674-8034.2026.03.012
Cite this article as: BIAN X D, CHEN Z G, LI X Z, et al. A bibliometric and visual analysis of research dynamics in fMRI applied to mild cognitive impairment: Hotspots, frontiers, and trends[J]. Chin J Magn Reson Imaging, 2026, 17(3): 85-92, 98. DOI:10.12015/issn.1674-8034.2026.03.012.

[1]
LISS J L, SELERI ASSUNÇÃO S, CUMMINGS J, et al. Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (mci and dementia) in primary care: a review and synthesis[J]. J Intern Med, 2021, 290(2): 310-334. DOI: 10.1111/joim.13244.
[2]
XUE J, LI J, LIANG J, et al. The prevalence of mild cognitive impairment in China: a systematic review[J/OL]. Aging Dis, 2018, 9(4): 706 [2025-11-12]. https://dx.doi.org/10.14336/AD.2017.0928. DOI: 10.14336/AD.2017.0928.
[3]
ALFEO F, LANCIANO T, ABBATANTUONO C, et al. Cognitive, emotional, and daily functioning domains involved in decision-making among patients with mild cognitive impairment: a systematic review[J/OL]. Brain Sci, 2024, 14(3): 278 [2025-11-12]. https://dx.doi.org/10.3390/brainsci14030278. DOI: 10.3390/brainsci14030278.
[4]
KIM S G, OGAWA S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals[J]. J Cereb Blood Flow Metab, 2012, 32(7): 1188-1206. DOI: 10.1038/jcbfm.2012.23.
[5]
YANG C, GAO X, LIU N, et al. Convergent and distinct neural structural and functional patterns of mild cognitive impairment: a multimodal meta-analysis[J]. Cereb Cortex, 2023, 33(14): 8876-8889. DOI: 10.1093/cercor/bhad167.
[6]
DU Q, ZHAO R, WAN Q, et al. Protocol for conducting bibliometric analysis in biomedicine and related research using citespace and vosviewer software[J/OL]. STAR Protoc, 2024, 5(3): 103269 [2025-11-12]. https://dx.doi.org/10.1016/j.xpro.2024.103269. DOI: 10.1016/j.xpro.2024.103269.
[7]
VAN ECK N J, WALTMAN L. Software survey: Vosviewer, a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2): 523-538. DOI: 10.1007/s11192-009-0146-3.
[8]
AMRAPALA A, SABÉ M, SOLMI M, et al. Neuropsychiatric disturbances in mild cognitive impairment: A scientometric analysis[J/OL]. Ageing Res Rev, 2023, 92: 102129 [2025-11-12]. https://dx.doi.org/10.1016/j.arr.2023.102129. DOI: 10.1016/j.arr.2023.102129.
[9]
KNOWLSON C, DEAN A, DOHERTY L, et al. Recruitment patterns in multicentre randomised trials fit more closely to price's law than the pareto principle: A review of trials funded and published by the United Kingdom Health Technology Assessment Programme[J/OL]. Contemp Clin Trials, 2022, 113: 106665 [2025-11-12]. https://dx.doi.org/10.1016/j.cct.2021.106665. DOI: 10.1016/j.cct.2021.106665.
[10]
OWENS C D, PINTO C B, MUKLI P, et al. Neurovascular coupling, functional connectivity, and cerebrovascular endothelial extracellular vesicles as biomarkers of mild cognitive impairment[J]. Alzheimers Dement, 2024, 20(8): 5590-5606. DOI: 10.1002/alz.14072.
[11]
JIANG J, LI C, LU J, et al. Using interpretable deep learning radiomics model to diagnose and predict progression of early ad disease spectrum: A preliminary [18F]FDG PET study[J]. Eur Radiol, 2024, 35(5): 2620-2633. DOI: 10.1007/s00330-024-11158-9.
[12]
CHI X, FAN X, FU G, et al. Research trends and hotspots of post-stroke cognitive impairment: a bibliometric analysis[J/OL]. Front Pharmacol, 2023, 14: 1184830 [2025-11-12]. https://dx.doi.org/10.3389/fphar.2023.1184830. DOI: 10.3389/fphar.2023.1184830.
[13]
ZHOU R, XIAO L, XIAO W, et al. Bibliometric review of 1992–2022 publications on acupuncture for cognitive impairment[J/OL]. Front Neurol, 2022, 13: 1006830 [2025-11-12]. https://dx.doi.org/10.3389/fneur.2022.1006830. DOI: 10.3389/fneur.2022.1006830.
[14]
HUANG L, XU G, HE J, et al. Bibliometric analysis of functional magnetic resonance imaging studies on acupuncture analgesia over the past 20 years[J]. J Pain Res, 2021, 14: 3773-3789. DOI: 10.2147/JPR.S340961.
[15]
GAUTHIER C J, FAN A P. BOLD signal physiology: models and applications[J]. Neuroimage, 2019, 187: 116-127. DOI: 10.1016/j.neuroimage.2018.03.018.
[16]
LI H, DONG L, LIU J, et al. Abnormal characteristics in disorders of consciousness: a resting-state functional magnetic resonance imaging study[J/OL]. Brain Res, 2025, 1850: 149401 [2025-11-12]. https://dx.doi.org/10.1016/j.brainres.2024.149401. DOI: 10.1016/j.brainres.2024.149401.
[17]
XI Q, ZHAO X, WANG P, et al. Spontaneous brain activity in mild cognitive impairment revealed by amplitude of low-frequency fluctuation analysis: A resting-state fMRI study[J]. Radiol Med, 2012, 117(5): 865-871. DOI: 10.1007/s11547-011-0780-8.
[18]
LIU L, JIANG H, WANG D, et al. A study of regional homogeneity of resting-state functional magnetic resonance imaging in mild cognitive impairment[J/OL]. Behav Brain Res, 2021, 402: 113103 [2025-11-12]. https://dx.doi.org/10.1016/j.bbr.2020.113103. DOI: 10.1016/j.bbr.2020.113103.
[19]
PU Z, HUANG H, LI M, et al. Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: A functional near-infrared spectroscopy study[J/OL]. Neuroimage, 2025, 310: 121130 [2025-11-12]. https://dx.doi.org/10.1016/j.neuroimage.2025.121130. DOI: 10.1016/j.neuroimage.2025.121130.
[20]
BRANDES LOURENÇO R, MACHADO DE CAMPOS B, RIZZI L, et al. Functional connectome analysis in mild cognitive impairment: comparing Alzheimer's disease continuum and suspected non-Alzheimer pathology[J]. Brain Connect, 2022, 12(9): 774-783 . DOI: 10.1089/brain.2021.0154.
[21]
TANG F, ZHU D, MA W, et al. Differences changes in cerebellar functional connectivity between mild cognitive impairment and Alzheimer's disease: A seed-based approach[J/OL]. Front Neurol, 2021, 12: 645171 [2025-11-12]. https://dx.doi.org/10.3389/fneur.2021.645171. DOI: 10.3389/fneur.2021.645171.
[22]
XIONG J, YU C, SU T, et al. Altered brain network centrality in patients with mild cognitive impairment: an fMRI study using a voxel-wise degree centrality approach[J]. Aging (Albany NY), 2021, 13(11): 15491-15500 . DOI: 10.18632/aging.203105.
[23]
YOTSUYA C, WATANABE K, KASAI S, et al. Altered connectivity among the triple brain networks in patients with mild cognitive impairment: A source-based morphometry study with a large elderly population[J]. Jpn J Radiol, 2025, 43(9): 1434-1444 . DOI: 10.1007/s11604-025-01791-9.
[24]
ZHANG D, YUE J, NIU H, et al. Altered default mode network connectivity in mild cognitive impairment: insights from resting-state fMRI studies[J]. Curr Alzheimer Res, 2025, 22(2): 83-91 . DOI: 10.2174/0115672050352061250328055829.
[25]
SONG Y, XU W, CHEN S, et al. Functional MRI-specific alterations in salience network in mild cognitive impairment: An ALE meta-analysis[J/OL]. Front Aging Neurosci, 2021, 13: 695210 [2025-11-12]. https://dx.doi.org/10.3389/fnagi.2021.695210. DOI: 10.3389/fnagi.2021.695210.
[26]
HOJJATI S H, EBRAHIMZADEH A, BABAJANI-FEREMI A. Identification of the early stage of Alzheimer's disease using structural MRI and resting-state fMRI[J/OL]. Front Neurol, 2019, 10: 904 [2025-11-12]. https://dx.doi.org/10.3389/fneur.2019.00904. DOI: 10.3389/fneur.2019.00904.
[27]
KAMAGATA K, ANDICA C, TAKABAYASHI K, et al. Association of MRI indices of glymphatic system with amyloid deposition and cognition in mild cognitive impairment and Alzheimer disease[J/OL]. Neurology, 2022, 99(24): e2648-e2660 [2025-10-09]. https://www.neurology.org/doi/10.1212/WNL.0000000000201300. DOI: 10.1212/WNL.0000000000201300.
[28]
ZHANG T, LIAO Q, ZHANG D, et al. Predicting MCI to AD conversation using integrated sMRI and rs-fMRI: Machine learning and graph theory approach[J/OL]. Front Aging Neurosci, 2021, 13: 688926 [2025-11-12]. https://dx.doi.org/10.3389/fnagi.2021.688926. DOI: 10.3389/fnagi.2021.688926.
[29]
LU Q, WANG Y, QU B, et al. Structural and functional projections of the nucleus basalis of meynert and their changes after cognitive training in individuals with mild cognitive impairment[J/OL]. CNS Neurosci Ther, 2024, 30(12): e70194 [2025-11-12]. https://dx.doi.org/10.1111/cns.70194. DOI: 10.1111/cns.70194.
[30]
YANG C C, HUANG K Y, HSU J L, et al. Effects of intermittent theta-burst stimulation on cognition and glymphatic system activity in mild cognitive impairment and very mild Alzheimer's disease: A randomized controlled trial[J/OL]. J Neuroeng Rehabil, 2025, 22(1): 195 [2025-11-12]. https://dx.doi.org/10.1186/s12984-025-01738-1. DOI: 10.1186/s12984-025-01738-1.
[31]
DING J, ZOU L, XU R, et al. The research of dapagliflozin on cognitive function in middle-aged and older patients with type 2 diabetes mellitus and mild cognitive impairment: A 36-week prospective parallel control study[J/OL]. Eur J Pharmacol, 2025, 1002: 177819 [2025-11-12]. https://dx.doi.org/10.1016/j.ejphar.2025.177819. DOI: 10.1016/j.ejphar.2025.177819.
[32]
ZHANG J, YANG C, WEI D, et al. Long-term efficacy of Chinese medicine bushen capsule on cognition and brain activity in patients with amnestic mild cognitive impairment[J/OL]. Pharmacol Res, 2019, 146: 104319 [2025-11-12]. https://dx.doi.org/10.1016/j.phrs.2019.104319. DOI: 10.1016/j.phrs.2019.104319.
[33]
GHAFOORI S, SHALBAF A. Predicting conversion from MCI to AD by integration of rs-fMRI and clinical information using 3D-convolutional neural network[J]. Int J Comput Assist Radiol Surg, 2022, 17(7): 1245-1255 . DOI: 10.1007/s11548-022-02620-4.

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