Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection

  • Shuangzhi Yu
  • Guanghui Yue
  • Ahmed Elazab
  • Xuegang Song
  • Tianfu Wang
  • Baiying LeiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)


Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), which is also the best time for treatment. However, existing methods only consider neuroimaging features learned from group relationships instead of the subjects’ individual features. Such methods ignore demographic relationships (i.e., non-image information). In this paper, we propose a novel method based on multi-scale graph convolutional network (MS-GCN) via inception module, which combines image and non-image information for MCI detection. Specifically, since the brain has the characteristics of high-order interactions, we first analyze the dynamic high-order features of resting functional magnetic resonance imaging (rs-fMRI) time series and construct a dynamic high-order brain functional connectivity network (DH-FCN). To get more effective features and further improve the detection performance, we extract the local weighted clustering coefficients from the original DH-FCN. Then, gender and age information are combined with the neuroimaging data to build a graph. Finally, we perform the detection using the MS-GCN, and validate the proposed method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental results demonstrate that our proposed method can achieve remarkable MCI detection performance.


Multi-scale graph convolutional network Mild cognitive impairment detection rs-fMRI Dynamic high-order brain functional connectivity network 


  1. 1.
    Alzheimer’s Association: 2018 Alzheimer’s disease facts and figures. Alzheimers Dement. 14(3), 367–425 (2018)Google Scholar
  2. 2.
    Petersen, R.C., et al.: Current concepts in mild cognitive impairment. Arch. Neurol. 58(12), 1985–1992 (2001)CrossRefGoogle Scholar
  3. 3.
    Huettel, S.A., Song, A.W., McCarthy, G.: Sinauer Associates Sunderland. Functional Magnetic Resonance Imaging, MA (2004)Google Scholar
  4. 4.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186 (2009)CrossRefGoogle Scholar
  5. 5.
    Wee, C.-Y., Yap, P.-T., Zhang, D., Wang, L., Shen, D.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219(2), 641–656 (2014)CrossRefGoogle Scholar
  6. 6.
    Hart, B., et al.: A longitudinal model for functional connectivity networks using resting-state fMRI. NeuroImage 178, 687–701 (2018)CrossRefGoogle Scholar
  7. 7.
    Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)CrossRefGoogle Scholar
  8. 8.
    Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)CrossRefGoogle Scholar
  9. 9.
    Kazi, A., et al.: InceptionGCN: receptive field aware graph convolutional network for disease prediction. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 73–85. Springer, Cham (2019). Scholar
  10. 10.
    Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., He, Y.: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front. Hum. Neurosci. 9, 386 (2015)Google Scholar
  11. 11.
    Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shuangzhi Yu
    • 1
  • Guanghui Yue
    • 1
  • Ahmed Elazab
    • 1
  • Xuegang Song
    • 1
  • Tianfu Wang
    • 1
  • Baiying Lei
    • 1
    Email author
  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina

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