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

  • Dongren Yao
  • Mingxia LiuEmail author
  • Mingliang Wang
  • Chunfeng Lian
  • Jie Wei
  • Li Sun
  • Jing SuiEmail author
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)


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.


  1. 1.
    Zhang, D., Huang, J., Jie, B., Du, J., Tu, L., Liu, M.: Ordinal pattern: a new descriptor for brain connectivity networks. IEEE Trans. Med. Imaging 37(7), 1711–1722 (2018)CrossRefGoogle Scholar
  2. 2.
    Lian, C., et al.: Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. Med. Image Anal. 46, 106–117 (2018)CrossRefGoogle Scholar
  3. 3.
    Norman, L.J., et al.: Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: a comparative meta-analysis. JAMA Psychiatry 73(8), 815–825 (2016)CrossRefGoogle Scholar
  4. 4.
    Zhao, Y., et al.: Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks. IEEE Trans. Biomed. Eng. 65(9), 1975–1984 (2018)CrossRefGoogle Scholar
  5. 5.
    Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)CrossRefGoogle Scholar
  6. 6.
    Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)Google Scholar
  7. 7.
    Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint: arXiv:1901.00596 (2019)
  8. 8.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint: arXiv:1609.02907 (2016)
  9. 9.
    Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV, pp. 2840–2848 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Brainnetome Center & National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  5. 5.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  6. 6.National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of HealthPeking UniversityBeijingChina

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