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Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network
Wang, Mingliang1; Lian, Chunfeng2,3; Yao, Dongren4,5; Zhang, Daoqiang1; Liu, Mingxia2,3; Shen, Dinggang2,3,6
Source PublicationIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
ISSN0018-9294
2020-08-01
Volume67Issue:8Pages:2241-2252
Abstract

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.

KeywordSpatial-temporal dependency neural network Alzheimer's disease hub detection resting-state functional MRI
DOI10.1109/TBME.2019.2957921
WOS KeywordMILD COGNITIVE IMPAIRMENT ; CONNECTIVITY NETWORKS ; ALZHEIMERS ; PROGRESSION ; MCI
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000550653800010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40207
Collection脑网络组研究
Corresponding AuthorZhang, Daoqiang; Liu, Mingxia; Shen, Dinggang
Affiliation1.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
Recommended Citation
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.
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