干旱区生态环境知识资源中心(Arid): Predicting diagnosis 4 years prior to Alzheimer's disease incident

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DOI	10.1016/j.nicl.2022.102993
	Predicting diagnosis 4 years prior to Alzheimer's disease incident
	Qiu, Anqi; Xu, Liyuan; Liu, Chaoqiang; Alzheimer's Disease Neuroimaging Initiative
通讯作者	Qiu, AQ
来源期刊	NEUROIMAGE-CLINICAL
ISSN	2213-1582
出版年	2022
卷号	34
英文摘要	This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at similar to 80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.
英文关键词	Brain morphology Recurrent neural network Graph convolutional neural network Amyloid burden Structural magnetic resonance imaging Cognition
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000790409500006
WOS关键词	MILD COGNITIVE IMPAIRMENT ; GRAY-MATTER ; MRI ; CONVERSION ; DEMENTIA ; NETWORK ; ATROPHY ; MCI
WOS类目	Neuroimaging
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393865
推荐引用方式 GB/T 7714	Qiu, Anqi,Xu, Liyuan,Liu, Chaoqiang,et al. Predicting diagnosis 4 years prior to Alzheimer's disease incident[J],2022,34.
APA	Qiu, Anqi,Xu, Liyuan,Liu, Chaoqiang,&Alzheimer's Disease Neuroimaging Initiative.(2022).Predicting diagnosis 4 years prior to Alzheimer's disease incident.NEUROIMAGE-CLINICAL,34.
MLA	Qiu, Anqi,et al."Predicting diagnosis 4 years prior to Alzheimer's disease incident".NEUROIMAGE-CLINICAL 34(2022).