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Node Based Row-Filter Convolutional Neural Network for Brain Network Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11012)

Abstract

Brain network plays an important role in the diagnosis of brain diseases. Recently, applying convolutional neural networks (CNNs) in brain network has attracted great interests. Although traditional convolution can capture all the nearest neighbors of a point in Euclidean space, it may miss the nearest neighbors of the node in the graph (i.e., in topological space). Hence, how to design a meaningful convolutional operator is a major challenge for brain network classification. Accordingly, in this paper, we propose a node based row-filter convolutional neural network, named NRF-CNN, for brain network classification. The proposed NRF-CNN can learn the high-order representation for brain network without losing important structure information. Specifically, we first introduce the concept of node neighbor within one step. Then, we define a novel row-filter convolutional operator, which can effectively capture local pattern of graph by applying a row scanning on adjacent matrix. Next, we adopt a structure preserved pooling to enrich the node representation by multiply input adjacent matrix and the node representation. Further, we stack several row-filter convolutional layers and structure preserved pooling layers to capture feature representation with more complex information. Finally, we fuse all features learned from each layer by linear weighting. To evaluate the effectiveness of our approach, we compare it with state-of-the-art methods in brain network classification on three real brain network datasets. The experimental results demonstrate that our approach outperforms the others, showing better capacity in capturing meaningful and discriminative representations for brain networks.

Keywords

Brain network Graph Node based Row-Filter Convolutional neural network 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61473149, 61422204, and 61732006).

References

  1. 1.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  2. 2.
    Jie, B., Zhang, D., Wee, C.Y., Shen, D.: Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum. Brain Mapp. 35(7), 2876 (2014)CrossRefGoogle Scholar
  3. 3.
    Fei, F., Jie, B., Zhang, D.: Frequent and discriminative subnetwork mining for mild cognitive impairment classification. Brain Connect. 4(5), 347–360 (2014)CrossRefGoogle Scholar
  4. 4.
    Rubinov, M., et al.: Small-world properties of nonlinear brain activity in schizophrenia. Hum. Brain Mapp. 30(2), 403–416 (2009)CrossRefGoogle Scholar
  5. 5.
    Sacchet, M.D., Prasad, G., Foland-Ross, L.C., Thompson, P.M., Gotlib, I.H.: Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front. Psychiatry 6, 21 (2015)CrossRefGoogle Scholar
  6. 6.
    Verma, S., Zhang, Z.L.: Hunt for the unique, stable, sparse and fast feature learning on graphs. In: Advances in Neural Information Processing Systems, pp. 87–97 (2017)Google Scholar
  7. 7.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  8. 8.
    Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1993–2001 (2016)Google Scholar
  9. 9.
    Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
  10. 10.
    Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, pp. 2224–2232 (2015)Google Scholar
  11. 11.
    Wang, S., He, L., Cao, B., Lu, C.T., Yu, P.S., Ragin, A.B.: Structural deep brain network mining. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 475–484. ACM (2017)Google Scholar
  12. 12.
    Luo, Z., Liu, L., Yin, J., Li, Y., Wu, Z.: Deep learning of graphs with Ngram convolutional neural networks. IEEE Trans. Knowl. Data Eng. 29(10), 2125–2139 (2017)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    INDI Homepage. http://fcon_1000.projects.nitrc.org/. Accessed 12 Nov 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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