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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)
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)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint: arXiv:1609.02907 (2016)
Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV, pp. 2840–2848 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yao, D. et al. (2019). Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds) Graph Learning in Medical Imaging. GLMI 2019. Lecture Notes in Computer Science(), vol 11849. Springer, Cham. https://doi.org/10.1007/978-3-030-35817-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-35817-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35816-7
Online ISBN: 978-3-030-35817-4
eBook Packages: Computer ScienceComputer Science (R0)
