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

文献类型：期刊论文

作者Wang, Mingliang1; Lian, Chunfeng2,3; Yao, Dongren4,5; Zhang, Daoqiang1; Liu, Mingxia2,3; Shen, Dinggang2,3,6
刊名IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
出版日期2020-08-01
卷号67期号:8页码:2241-2252
ISSN号0018-9294
关键词Spatial-temporal dependency neural network Alzheimer's disease hub detection resting-state functional MRI
DOI10.1109/TBME.2019.2957921
通讯作者Zhang, Daoqiang(dqzhang@nuaa.edu.cn) ; Liu, Mingxia(mxliu@med.unc.edu) ; Shen, Dinggang(dgshen@med.unc.edu)
英文摘要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 imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.
WOS关键词MILD COGNITIVE IMPAIRMENT ; CONNECTIVITY NETWORKS ; ALZHEIMERS ; PROGRESSION ; MCI
资助项目National Natural Science Foundation of China[61732006] ; National Natural Science Foundation of China[61876082] ; National Natural Science Foundation of China[61861130366] ; National Natural Science Foundation of China[61703301] ; Royal Society-Academy of Medical Sciences Newton Advanced Fellowship[NAF\R1\180371] ; Fundamental Research Funds for the Central Universities[NP2018104] ; National Key R&D Program of China[2018YFC2001600] ; National Key R&D Program of China[2018YFC2001602] ; National Institutes of Health[AG041721] ; National Institutes of Health[EB022880]
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000550653800010
资助机构National Natural Science Foundation of China ; Royal Society-Academy of Medical Sciences Newton Advanced Fellowship ; Fundamental Research Funds for the Central Universities ; National Key R&D Program of China ; National Institutes of Health
源URL[http://ir.ia.ac.cn/handle/173211/40206]  
专题自动化研究所_脑网络组研究中心
通讯作者Zhang, Daoqiang; Liu, Mingxia; Shen, Dinggang
作者单位1.Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, MIIT Key Lab Pattern Anal & Machine Intelligence, Nanjing 211106, Peoples R China
2.Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA
3.Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA
4.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
5.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
6.Korea Univ, Dept Brain & Cognit Engn, Seoul 02841, South Korea
推荐引用方式
GB/T 7714		 Wang, Mingliang,Lian, Chunfeng,Yao, Dongren,et al. Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2020,67(8):2241-2252.
APA Wang, Mingliang,Lian, Chunfeng,Yao, Dongren,Zhang, Daoqiang,Liu, Mingxia,&Shen, Dinggang.(2020).Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,67(8),2241-2252.
MLA Wang, Mingliang,et al."Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 67.8(2020):2241-2252.

入库方式： OAI收割

来源：自动化研究所

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