CASIA OpenIR  > 脑网络组研究
A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity
Yao, Dongren1,2,3,4,5; Sui, Jing1,2,5,6,7,8; Wang, Mingliang9; Yang, Erkun3,4; Jiaerken, Yeerfan; Luo, Na1,2; Yap, Pew-Thian3,4; Liu, Mingxia3,4; Shen, Dinggang10,11,12
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2021-04-01
Volume40Issue:4Pages:1279-1289
Abstract

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.

KeywordFunctional magnetic resonance imaging Convolution Diseases Fuses Brain modeling Neuroimaging White matter Brain connectivity graph convolutional network triplet classification
DOI10.1109/TMI.2021.3051604
Indexed BySCI
Language英语
Funding ProjectUnited States National Institutes of Health (NIH)[AG041721] ; United States National Institutes of Health (NIH)[MH108560] ; United States National Institutes of Health (NIH)[AG053867] ; United States National Institutes of Health (NIH)[EB022880] ; Natural Science Foundation of China[61773380] ; Natural Science Foundation of China[82022035] ; Beijing Municipal Science and Technology Commission[Z181100001518005] ; China Postdoctoral Science Foundation[BX20200364] ; NIH[MH117017]
Funding OrganizationUnited States National Institutes of Health (NIH) ; Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission ; China Postdoctoral Science Foundation ; NIH
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000637532800016
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification人工智能+医疗
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44231
Collection脑网络组研究
Corresponding AuthorSui, Jing; Liu, Mingxia; Shen, Dinggang
Affiliation1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
3.Univ North Carolina UNC, Dept Radiol, Chapel Hill, NC 27599 USA
4.Univ North Carolina UNC, BRIC, Chapel Hill, NC 27599 USA
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
6.Triinst Centerfor Translat Res Neuroimaging & Dat, Atlanta, GA 30303 USA
7.Georgia State Univ, Georgia Inst Technol, Atlanta, GA 30303 USA
8.Emory Univ, Atlanta, GA 30303 USA
9.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China
10.ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China
11.Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai 200030, Peoples R China
12.Korea Univ, Dept Artificial Intelligence, Seoul 02841, South Korea
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yao, Dongren,Sui, Jing,Wang, Mingliang,et al. A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2021,40(4):1279-1289.
APA Yao, Dongren.,Sui, Jing.,Wang, Mingliang.,Yang, Erkun.,Jiaerken, Yeerfan.,...&Shen, Dinggang.(2021).A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity.IEEE TRANSACTIONS ON MEDICAL IMAGING,40(4),1279-1289.
MLA Yao, Dongren,et al."A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity".IEEE TRANSACTIONS ON MEDICAL IMAGING 40.4(2021):1279-1289.
Files in This Item: Download All
File Name/Size DocType Version Access License
A Mutual Multi-Scale(2425KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yao, Dongren]'s Articles
[Sui, Jing]'s Articles
[Wang, Mingliang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yao, Dongren]'s Articles
[Sui, Jing]'s Articles
[Wang, Mingliang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yao, Dongren]'s Articles
[Sui, Jing]'s Articles
[Wang, Mingliang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.