中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review

文献类型:期刊论文

作者Chen, Pindong1,2,3; Zhang, Shirui4; Zhao, Kun4; Kang, Xiaopeng1,2; Rittman, Timothy3; Liu, Yong1,2,4,5
刊名BRAIN RESEARCH
出版日期2024-01-15
卷号1823页码:13
ISSN号0006-8993
关键词Neurodegenerative diseases Alzheimer's disease Heterogeneity Subtype Data-driven
DOI10.1016/j.brainres.2023.148675
通讯作者Liu, Yong(yongliu@bupt.edu.cn)
英文摘要Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
WOS关键词ALZHEIMERS-DISEASE ; CLINICAL CHARACTERISTICS ; FRONTOTEMPORAL DEMENTIA ; CORTICAL THICKNESS ; DEFINED SUBTYPES ; COMPOSITE SCORE ; BRAIN ATROPHY ; PATTERNS ; CLUSTERS ; MEMORY
资助项目Fundamental Research Funds for the Central Universities[2021XD-A03] ; Beijing Nat- ural Science Funds for Distinguished Young Scholars[JQ20036] ; National Natural Science Foundation of China[62333002] ; National Natural Science Foundation of China[82172018]
WOS研究方向Neurosciences & Neurology
语种英语
出版者ELSEVIER
WOS记录号WOS:001125335800001
资助机构Fundamental Research Funds for the Central Universities ; Beijing Nat- ural Science Funds for Distinguished Young Scholars ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/54931]  
专题自动化研究所_脑网络组研究中心
通讯作者Liu, Yong
作者单位1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Univ Cambridge, Dept Clin Neurosci, Cambridge, England
4.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China
5.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714
Chen, Pindong,Zhang, Shirui,Zhao, Kun,et al. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review[J]. BRAIN RESEARCH,2024,1823:13.
APA Chen, Pindong,Zhang, Shirui,Zhao, Kun,Kang, Xiaopeng,Rittman, Timothy,&Liu, Yong.(2024).Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review.BRAIN RESEARCH,1823,13.
MLA Chen, Pindong,et al."Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review".BRAIN RESEARCH 1823(2024):13.

入库方式: OAI收割

来源:自动化研究所

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