Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional MRI | |
Dongren Yao1,2,3; Mingxia Liu3; Mingliang Wang3,4; Chunfeng Lian3; Jie Wei3,5; Li Sun6; Jing Sui1,2; Dinggang Shen3 | |
2019 | |
会议名称 | International Workshop on Graph Learning in Medical Imaging |
会议日期 | 2019/09/17 |
会议地点 | Shen Zhen |
摘要 | Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constructing FCs, which may limit the analysis to a single spatial scale (i.e., a fixed graph) determined by the template. Also, previous methods usually ignore the underlying high-order (e.g., triplet) association among subjects. To this end, we propose a multi-scale triplet graph convolutional network (MTGCN) for brain functional connectivity analysis with rs-fMRI data. Specifically, we first employ multi-scale templates for coarse-to-fine ROI parcellation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method. |
收录类别 | EI |
七大方向——子方向分类 | 医学影像处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44819 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Jing Sui; Dinggang Shen |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Science 3.Department of Radiology and BRICUniversity of North Carolina at Chapel Hill 4.College of Computer Science and TechnologyNanjing University of Aeronautics and Astronautics 5.School of Computer ScienceNorthwestern Polytechnical University 6.National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of HealthPeking University |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Dongren Yao,Mingxia Liu,Mingliang Wang,et al. Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional MRI[C],2019. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Yao2019_Chapter_Trip(914KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论