干旱区生态环境知识资源中心(Arid): Using machine learning to quantify structuralMRIneurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases

Arid

DOI	10.1002/hbm.25115
	Using machine learning to quantify structuralMRIneurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases
	Popuri, Karteek; Ma, Da; Wang, Lei; Beg, Mirza Faisal
通讯作者	Beg, MF
来源期刊	HUMAN BRAIN MAPPING
ISSN	1065-9471
EISSN	1097-0193
出版年	2020
卷号	41 期号:14 页码:4127-4147
英文摘要	Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
英文关键词	Alzheimer's disease cross-database independent validation dementia of Alzheimer's type dementia score disease progression ensemble learning longitudinal diagnostic stratification magnetic resonance imaging probabilistic classifier prognosis prediction
类型	Article
语种	英语
开放获取类型	Other Gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000544754000001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; FEATURE-SELECTION ; WHITE-MATTER ; GRAY-MATTER ; CORTICAL THICKNESS ; STRUCTURAL MRI ; NORMATIVE DATA ; IN-VIVO ; ATROPHY ; VOLUME
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324896
作者单位	[Popuri, Karteek; Ma, Da; Beg, Mirza Faisal] Simon Fraser Univ, Sch Engn Sci, ASB 8857,8888 Univ Dr, Burnaby, BC V5A 1S6, Canada; [Wang, Lei] Northwestern Univ, Feinberg Sch Med, Evanston, IL USA
推荐引用方式 GB/T 7714	Popuri, Karteek,Ma, Da,Wang, Lei,et al. Using machine learning to quantify structuralMRIneurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases[J],2020,41(14):4127-4147.
APA	Popuri, Karteek,Ma, Da,Wang, Lei,&Beg, Mirza Faisal.(2020).Using machine learning to quantify structuralMRIneurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.HUMAN BRAIN MAPPING,41(14),4127-4147.
MLA	Popuri, Karteek,et al."Using machine learning to quantify structuralMRIneurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases".HUMAN BRAIN MAPPING 41.14(2020):4127-4147.