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Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning

文献类型：期刊论文

作者Shi, Yachen9; Wang, Zan9; Chen, Pindong1,5,7; Cheng, Piaoyue9; Zhao, Kun1,4,7; Zhang, Hongxing2,10; Shu, Hao9; Gu, Lihua9; Gao, Lijuan9; Wang, Qing9
刊名BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING
出版日期2023-02-01
卷号8期号:2页码:10
ISSN号2451-9022
DOI10.1016/j.bpsc.2020.12.007
通讯作者Liu, Yong(yongliu@bupt.edu.cn) ; Zhang, Zhijun(janemengzhang@vip.163.com)
英文摘要BACKGROUND: Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD. METHODS: Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-b plaques. RESULTS: The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-b positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups. CONCLUSIONS: Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.
WOS关键词MILD COGNITIVE IMPAIRMENT ; DEFAULT NETWORK ; FUNCTIONAL CONNECTIVITY ; STRUCTURAL MRI ; DIFFERENTIAL-DIAGNOSIS ; BRAIN MRI ; STATE ; CLASSIFICATION ; RELEVANCE ; TEXTURE
资助项目National Key Projects for Research and Development Program of China[2016YFC1305800] ; National Key Projects for Research and Development Program of China[2016YFC1305802] ; National Natural Science Foundation of China[81671046] ; National Natural Science Foundation of China[81420108012] ; National Natural Science Foundation of China[81871438] ; National Natural Science Foundation of China[81801680] ; Jiangsu Provincial Medical Outstanding Talent[JCRCA2016006] ; Beijing Natural Science Funds for Distinguished Young Scholar[JQ200036] ; Science and Technology Program of Guangdong[2018B030334001] ; ADNI (National Institutes of Health)[U01 AG024904] ; Department of Defense ADNI[W81XWH-12-2-0012] ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Canadian Institutes of Health Research
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:001019672400001
出版者ELSEVIER
资助机构National Key Projects for Research and Development Program of China ; National Natural Science Foundation of China ; Jiangsu Provincial Medical Outstanding Talent ; Beijing Natural Science Funds for Distinguished Young Scholar ; Science and Technology Program of Guangdong ; ADNI (National Institutes of Health) ; Department of Defense ADNI ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Canadian Institutes of Health Research
源URL[http://ir.ia.ac.cn/handle/173211/53606]  
专题自动化研究所_脑网络组研究中心
通讯作者Liu, Yong; Zhang, Zhijun
作者单位1.Brainnetome Ctr, Beijing, Peoples R China
2.Xinxiang Med Univ, Dept Psychol, Xinxiang, Peoples R China
3.Beijing Univ Posts & Tecommun, Sch Artificial Intelligence, Beijing, Peoples R China
4.Beihang Univ, Sch Biol Sci & Med Engn, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
7.Natl Lab Pattern Recognit, Beijing, Peoples R China
8.Southeast Univ, Sch Life Sci & Technol, Key Lab Dev Genes & Human Dis, Nanjing, Peoples R China
9.Southeast Univ, Affiliated ZhongDa Hosp, Inst Neuropsychiat, Sch Med,Dept Neurol, Nanjing, Peoples R China
10.Xinxiang Med Univ, Affiliated Hosp 2, Xinxiang, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Yachen,Wang, Zan,Chen, Pindong,et al. Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning[J]. BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING,2023,8(2):10.
APA Shi, Yachen.,Wang, Zan.,Chen, Pindong.,Cheng, Piaoyue.,Zhao, Kun.,...&Zhang, Zhijun.(2023).Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning.BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING,8(2),10.
MLA Shi, Yachen,et al."Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning".BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING 8.2(2023):10.

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来源：自动化研究所

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