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.
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
Notes
- 1.
Source code at https://github.com/zarina-aniraz/linear-graph-convolution.
- 2.
- 3.
http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html.
- 4.
http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html.
- 5.
The Hamming norm \(\Vert \mathbf {v}\Vert _H\) is defined as the number of non-zero entries of vector \(\mathbf {v}\).
References
Bassett, D.S., Bullmore, E.T.: Human brain networks in health and disease. Curr. Opin. Neurol. 22(4), 340 (2009)
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)
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)
Bullmore, E., Sporns, O.: The economy of brain network organization. Nat. Rev. Neurosci. 13(5), 336 (2012)
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)
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)
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)
Ji, C., Maurits, N.M., Roerdink, J.B.T.M.: Comparison of brain connectivity networks using local structure analysis, pp. 639–651 (2018)
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)
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)
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)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
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)
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)
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)
Whitfield-Gabrieli, S., Nieto-Castanon, A.: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity 2(3), 125–141 (2012)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks 97, 6861–6871 (2019)
Acknowledgement
This work was supported by JSPS Grant-in-Aid for Scientific Research (B)(Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rakhimberdina, Z., Murata, T. (2020). Linear Graph Convolutional Model for Diagnosing Brain Disorders. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_65
Download citation
DOI: https://doi.org/10.1007/978-3-030-36683-4_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36682-7
Online ISBN: 978-3-030-36683-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)