Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023

被引:0
作者
He, Chunying [1 ,2 ]
Hu, Xiaohua [1 ,2 ]
Wang, Muren [1 ,2 ]
Yin, Xiaolan [3 ]
Zhan, Min [1 ]
Li, Yutong [1 ,4 ]
Sun, Linjuan [1 ]
Du, Yida [1 ,4 ]
Chen, Zhiyan [1 ,4 ]
Wang, Huan [1 ,2 ]
Shao, Haibin [1 ,2 ]
机构
[1] China Acad Chinese Med Sci, Xiyuan Hosp, Dept Neurol, Beijing, Peoples R China
[2] China Acad Chinese Med Sci, Grad Sch, Beijing, Peoples R China
[3] China Acad Chinese Med Sci, Xiyuan Hosp, Dept Gastroenterol, Beijing, Peoples R China
[4] Beijing Univ Tradit Chinese Med, Grad Sch, Beijing, Peoples R China
关键词
mild cognitive impairment; bibliometric analysis; visualized analysis; CiteSpace; VOSviewer; ALZHEIMER-DISEASE; DIAGNOSIS; ASSOCIATION; BIOMARKERS; RISK; MCI;
D O I
10.3389/fnins.2024.1352129
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Background: Mild cognitive impairment is a heterogeneous syndrome. The heterogeneity of the syndrome and the absence of consensus limited the advancement of MCI. The purpose of our research is to create a visual framework of the last decade, highlight the hotspots of current research, and forecast the most fruitful avenues for future MCI research. Methods: We collected all the MCI-related literature published between 1 January 2013, and 24 April 2023, on the "Web of Science." The visual graph was created by the CiteSpace and VOSviewer. The current research hotspots and future research directions are summarized through the analysis of keywords and co-cited literature. Results: There are 6,075 articles were included in the final analysis. The number of publications shows an upward trend, especially after 2018. The United States and the University of California System are the most prolific countries and institutions, respectively. Petersen is the author who ranks first in terms of publication volume and influence. Journal of Alzheimer's Disease was the most productive journal. "neuroimaging," "fluid markers," and "predictors" are the focus of current research, and "machine learning," "electroencephalogram," "deep learning," and "blood biomarkers" are potential research directions in the future. Conclusion: The cognition of MCI has been continuously evolved and renewed by multiple countries' joint efforts in the past decade. Hotspots for current research are on diagnostic biomarkers, such as fluid markers, neuroimaging, and so on. Future hotspots might be focused on the best prognostic and diagnostic models generated by machine learning and large-scale screening tools such as EEG and blood biomarkers.
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页数:19
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共 69 条
  • [1] Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects
    Ahmed, Md Rishad
    Zhang, Yuan
    Feng, Zhiquan
    Lo, Benny
    Inan, Omer T.
    Liao, Hongen
    [J]. IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2019, 12 : 19 - 33
  • [2] A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer's Disease Using Structural MRI Images
    Ashtari-Majlan, Mona
    Seifi, Abbas
    Dehshibi, Mohammad Mahdi
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2022, 26 (08) : 3918 - 3926
  • [3] Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks
    Basaia, Silvia
    Agosta, Federica
    Wagner, Luca
    Canu, Elisa
    Magnani, Giuseppe
    Santangelo, Roberto
    Filippi, Massimo
    [J]. NEUROIMAGE-CLINICAL, 2019, 21
  • [4] The use of biomarkers for the etiologic diagnosis of MCI in Europe: An EADC survey
    Bocchetta, Martina
    Galluzzi, Samantha
    Kehoe, Patrick Gavin
    Aguera, Eduardo
    Bernabei, Roberto
    Bullock, Roger
    Ceccaldi, Mathieu
    Dartigues, Jean-Francois
    de Mendonca, Alexandre
    Didic, Mira
    Eriksdotter, Maria
    Felician, Olivier
    Froelich, Lutz
    Gertz, Hermann-Josef
    Hallikainen, Merja
    Hasselbalch, Steen G.
    Hausner, Lucrezia
    Heuser, Isabell
    Jessen, Frank
    Jones, Roy W.
    Kurz, Alexander
    Lawlor, Brian
    Lleo, Alberto
    Martinez-Lage, Pablo
    Mecocci, Patrizia
    Mehrabian, Shima
    Monsch, Andreas
    Nobili, Flavio
    Nordberg, Agneta
    Rikkert, Marcel Olde
    Orgogozo, Jean-Marc
    Pasquier, Florence
    Peters, Oliver
    Salmon, Eric
    Sanchez-Castellano, Carmen
    Santana, Isabel
    Sarazin, Marie
    Traykov, Latchezar
    Tsolaki, Magda
    Visser, Pieter Jelle
    Wallin, Asa K.
    Wilcock, Gordon
    Wilkinson, David
    Wolf, Henrike
    Yener, Goersev
    Zekry, Dina
    Frisoni, Giovanni B.
    [J]. ALZHEIMERS & DEMENTIA, 2015, 11 (02) : 195 - 206
  • [5] A Systematic Review of Cerebral Functional Near-Infrared Spectroscopy in Chronic Neurological Diseases-Actual Applications and Future Perspectives
    Bonilauri, Augusto
    Intra, Francesca Sangiuliano
    Pugnetti, Luigi
    Baselli, Giuseppe
    Baglio, Francesca
    [J]. DIAGNOSTICS, 2020, 10 (08)
  • [6] Plasma extracellular vesicles reveal early molecular differences in amyloid positive patients with early-onset mild cognitive impairment
    Cano, Amanda
    Esteban-de-Antonio, Ester
    Bernuz, Mireia
    Puerta, Raquel
    Garcia-Gonzalez, Pablo
    de Rojas, Itziar
    Olive, Claudia
    Perez-Cordon, Alba
    Montrreal, Laura
    Nunez-Llaves, Raul
    Sotolongo-Grau, Oscar
    Alarcon-Martin, Emilio
    Valero, Sergi
    Alegret, Montserrat
    Martin, Elvira
    Martino-Adami, Pamela V.
    Ettcheto, Miren
    Camins, Antonio
    Vivas, Assumpta
    Gomez-Chiari, Marta
    Tejero, Miguel angel
    Orellana, Adelina
    Tarraga, Lluis
    Marquie, Marta
    Ramirez, Alfredo
    Marti, Merce
    Pividori, Maria Isabel
    Boada, Merce
    Ruiz, Agustin
    [J]. JOURNAL OF NANOBIOTECHNOLOGY, 2023, 21 (01)
  • [7] Salience network anatomical and molecular markers are linked with cognitive dysfunction in mild cognitive impairment
    Chand, Ganesh B.
    Thakuri, Deepa S.
    Soni, Bhavin
    [J]. JOURNAL OF NEUROIMAGING, 2022, 32 (04) : 728 - 734
  • [8] Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches
    Chang, Chun-Hung
    Lin, Chieh-Hsin
    Liu, Chieh-Yu
    Huang, Chih-Sheng
    Chen, Shaw-Ji
    Lin, Wen-Cheng
    Yang, Hui-Ting
    Lane, Hsien-Yuan
    [J]. JOURNAL OF PSYCHOPHARMACOLOGY, 2021, 35 (03) : 265 - 272
  • [9] Plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL across the Alzheimer's disease continuum: A cross-sectional and longitudinal study in the AIBL cohort
    Chatterjee, Pratishtha
    Pedrini, Steve
    Doecke, James D.
    Thota, Rohith
    Villemagne, Victor L.
    Dore, Vincent
    Singh, Abhay K.
    Wang, Penghao
    Rainey-Smith, Stephanie
    Fowler, Christopher
    Taddei, Kevin
    Sohrabi, Hamid R.
    Molloy, Mark P.
    Ames, David
    Maruff, Paul
    Rowe, Christopher C.
    Masters, Colin L.
    Martins, Ralph N.
    [J]. ALZHEIMERS & DEMENTIA, 2023, 19 (04) : 1117 - 1134
  • [10] Mild cognitive impairment among rural-dwelling older adults in China: A community-based study
    Cong, Lin
    Ren, Yifei
    Wang, Yongxiang
    Hou, Tingting
    Dong, Yi
    Han, Xiaojuan
    Yin, Ling
    Zhang, Qinghua
    Feng, Jianli
    Wang, Lidan
    Tang, Shi
    Grande, Giulia
    Laukka, Erika J.
    Du, Yifeng
    Qiu, Chengxuan
    [J]. ALZHEIMERS & DEMENTIA, 2023, 19 (01) : 56 - 66