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Linear Graph Convolutional Model for Diagnosing Brain Disorders

  • Zarina RakhimberdinaEmail author
  • Tsuyoshi Murata
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

Deep learning models find an increasing application in the diagnosis of brain disorders. Designed for large scale datasets, deep neural networks (DNNs) achieve state-of-the-art classification performance on a number of functional magnetic resonance imaging (fMRI) data. While utilizing DNNs might improve the performance, the complexity of the learning function decreases the interpretability of the model. Moreover, DNNs require considerably more time to train compared to their linear predecessors. In this paper, we re-examine the use of deep graph neural networks for graph-based disease prediction in favor of simpler linear models. We present a simplified linear model, which is more than 10 times faster to train than the previous DNN counterparts. We test our model on three fMRI datasets and show that it achieves comparable or superior performance to the state-of-the-art methods.

Keywords

Graph Convolutional Network Brain functional connectivity 

Notes

Acknowledgement

This work was supported by JSPS Grant-in-Aid for Scientific Research (B)(Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).

References

  1. 1.
    Bassett, D.S., Bullmore, E.T.: Human brain networks in health and disease. Curr. Opin. Neurol. 22(4), 340 (2009)CrossRefGoogle Scholar
  2. 2.
    Bassett, D.S., Zurn, P., Gold, J.I.: On the nature and use of models in network neuroscience. Nat. Rev. Neurosci. 19(9), 566 (2018)CrossRefGoogle Scholar
  3. 3.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)CrossRefGoogle Scholar
  4. 4.
    Bullmore, E., Sporns, O.: The economy of brain network organization. Nat. Rev. Neurosci. 13(5), 336 (2012)CrossRefGoogle Scholar
  5. 5.
    Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)Google Scholar
  6. 6.
    He, T., Kong, R., Holmes, A.J., Sabuncu, M.R., Eickhoff, S.B., Bzdok, D., Feng, J., Yeo, B.T.: Is deep learning better than kernel regression for functional connectivity prediction of fluid intelligence? pp. 1–4 (2018)Google Scholar
  7. 7.
    Hsieh, T.H., Sun, M.J., Liang, S.F.: Diagnosis of schizophrenia patients based on brain network complexity analysis of resting-state fMRI. In: The 15th International Conference on Biomedical Engineering, pp. 203–206. Springer (2014)Google Scholar
  8. 8.
    Ji, C., Maurits, N.M., Roerdink, J.B.T.M.: Comparison of brain connectivity networks using local structure analysis, pp. 639–651 (2018)Google Scholar
  9. 9.
    Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings, Toulon, France, 24–26 April 2017 (2017)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  12. 12.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  13. 13.
    Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., Rueckert, D.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)CrossRefGoogle Scholar
  14. 14.
    Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Moreno, R.G., Glocker, B., Rueckert, D.: Spectral graph convolutions for population-based disease prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 177–185. Springer (2017)Google Scholar
  15. 15.
    Ventresca, M.: Using algorithmic complexity to differentiate cognitive states in fMRI. In: International Conference on Complex Networks and their Applications, pp. 663–674. Springer (2018)Google Scholar
  16. 16.
    Whitfield-Gabrieli, S., Nieto-Castanon, A.: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity 2(3), 125–141 (2012)CrossRefGoogle Scholar
  17. 17.
    Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks 97, 6861–6871 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyTokyoJapan

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