Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network

Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance ima...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 67; H. 8; S. 2241 - 2252
Hauptverfasser:	Wang, Mingliang, Lian, Chunfeng, Yao, Dongren, Zhang, Daoqiang, Liu, Mingxia, Shen, Dinggang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Alzheimer's disease Automation Brain Brain mapping Brain modeling Cognitive ability Convolution Dementia Dementia disorders Dependence Diagnosis Feature extraction Functional magnetic resonance imaging hub detection Hubs Long short-term memory Machine learning Magnetic resonance imaging Medical imaging Modelling Network hubs neural network Neural networks Neurodegenerative diseases Neuroimaging Recurrent neural networks resting-state functional MRI Sequences Sliding Spatial-temporal dependency Time series Time series analysis
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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