Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI

Dongren Yao; Mingxia Liu; Mingliang Wang; Chunfeng Lian; Jie Wei; Li Sun; Jing Sui; Dinggang Shen

doi:10.1007/978-3-030-35817-4_9

Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI

Dongren Yao, Mingxia Liu, Mingliang Wang, Chunfeng Lian, Jie Wei, Li Sun, Jing Sui^*, Dinggang Shen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

48 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
Editors	Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
Publisher	Springer
Pages	70-78
Number of pages	9
ISBN (Print)	9783030358167
DOIs	https://doi.org/10.1007/978-3-030-35817-4_9
Publication status	Published - 2019
Event	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 17 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11849 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/17 → 19/10/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-35817-4_9

Cite this

Yao, D., Liu, M., Wang, M., Lian, C., Wei, J., Sun, L., Sui, J., & Shen, D. (2019). Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 70-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_9

Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. / Yao, Dongren; Liu, Mingxia; Wang, Mingliang et al.
Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. ed. / Daoqiang Zhang; Luping Zhou; Biao Jie; Mingxia Liu. Springer, 2019. p. 70-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yao, D, Liu, M, Wang, M, Lian, C, Wei, J, Sun, L, Sui, J & Shen, D 2019, Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. in D Zhang, L Zhou, B Jie & M Liu (eds), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11849 LNCS, Springer, pp. 70-78, 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/17. https://doi.org/10.1007/978-3-030-35817-4_9

Yao D, Liu M, Wang M, Lian C, Wei J, Sun L et al. Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. In Zhang D, Zhou L, Jie B, Liu M, editors, Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. p. 70-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-35817-4_9

Yao, Dongren ; Liu, Mingxia ; Wang, Mingliang et al. / Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. editor / Daoqiang Zhang ; Luping Zhou ; Biao Jie ; Mingxia Liu. Springer, 2019. pp. 70-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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AB - 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.

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Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI

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