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 | |
发表期刊 | IEEE TRANSACTIONS ON MEDICAL IMAGING |
ISSN | 0278-0062 |
2021-04-01 | |
卷号 | 40期号:4页码:1279-1289 |
摘要 | 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. |
关键词 | Functional magnetic resonance imaging Convolution Diseases Fuses Brain modeling Neuroimaging White matter Brain connectivity graph convolutional network triplet classification |
DOI | 10.1109/TMI.2021.3051604 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | United 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] |
项目资助者 | United States National Institutes of Health (NIH) ; Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission ; China Postdoctoral Science Foundation ; NIH |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000637532800016 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+医疗 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44231 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Sui, Jing; Liu, Mingxia; Shen, Dinggang |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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. |
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