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

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 8; pp. 2241 - 2252
• Main Authors: Wang, Mingliang, Lian, Chunfeng, Yao, Dongren, Zhang, Daoqiang, Liu, Mingxia, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...