Abstract
Brain connectivity network (BCN) is a non-directed graph which represents the relationship between brain regions. Traditional feature extractor methods has been applied to identify diseases related to brains such as Alzheimer’s disease (AD) with BCN. However, the feature extraction capabilities of these methods are insufficient to collect various topological properties of non-directed graphs. They can only take into consider the local topological properties or frequent sub-graph topological properties. To explicitly exploit these properties, this paper proposes a BCN based fully-supervised Graph Convolution Network (GCN) to select features and classify disease automatically, skipping the step of manual feature selection and keeping all information in the brain connectivity network. Extensive experiments show that we outperform other method in classifying different stages of AD. Furthermore, our method can find the most relevant brain regions to classify different stages of AD, showing better interpretation ability compared to traditional methods.
P. Gu and X. Xu–These authors contributed equally to this work.
This work was supported by the General Program of National Natural Science Foundation of China (NSFC) (Grant No. 61806147).
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
Similar content being viewed by others
References
Bi, X.A., Shu, Q., Sun, Q., et al.: Random support vector machine cluster analysis of resting-state fMRI in Alzheimer’s disease. PloS One 13(3), e0194479 (2018)
Borgwardt, K.M., Kriegel, H.P.: Shortest-path kernels on graphs. In: ICDM, pp. 8-pp. IEEE (2005)
Jie, B., Liu, M., Zhang, et al.: Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis. IEEE TIP 27(5), 2340–2353 (2018)
Kaiser, M.: A tutorial in connectome analysis: topological and spatial features of brain networks. Neuroimage 57(3), 892–907 (2011)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. preprint arXiv:1609.02907 (2016)
Machulda, M.M., Ward, H., Borowski, B., et al.: Comparison of memory fMRI response among normal, MCI, and Alzheimer’s patients. Neurology 61(4), 500–506 (2003)
Parisot, S., Ktena, S.I., Ferrante, E., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)
Raichle, M.E.: The brain’s default mode network. Annu. Rev. Neurosci. 38, 433–447 (2015)
Sarica, A., Cerasa, A., Quattrone, A.: Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front. Aging Neurosci. 9, 329 (2017)
Sarraf, S., Tofighi, G., Initiative, A.D.N., et al.: DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv (2016)
Shervashidze, N., Schweitzer, P., Leeuwen, et al.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12(Sep), 2539–2561 (2011)
Shrivastava, A., Li, P.: A new space for comparing graphs. In: International Conference on Advances in Social Networks Analysis and Mining, pp. 62–71. IEEE Press (2014)
Veličković, P., Cucurull, G., Casanova, A., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vemuri, P., Jack, C.R.: Role of structural MRI in Alzheimer’s disease. Alzheimer’s Res. Ther. 2(4), 23 (2010)
Zhu, X., Ghahramani, Z., Lafferty, et al.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International conference on Machine learning (ICML-03), pp. 912–919 (2003)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, P., Xu, X., Luo, Y., Wang, P., Lu, J. (2021). BCN-GCN: A Novel Brain Connectivity Network Classification Method via Graph Convolution Neural Network for Alzheimer’s Disease. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-92185-9_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92184-2
Online ISBN: 978-3-030-92185-9
eBook Packages: Computer ScienceComputer Science (R0)