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New Graph-Blind Convolutional Network for Brain Connectome Data Analysis

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Information Processing in Medical Imaging (IPMI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11492))

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Abstract

Human connectome provides essential insights in diagnosing many psychiatric disorders. Though machine learning methods in predicting clinical scores have been successfully applied, it is still challenging to capture the complex relation and exploit the graph structure of brain networks. In this paper, we proposed a method to address the problem by extracting the graph embeddings using graph convolutional network (GCN), and using multi-layer perceptron for the regression. Particularly, previous GCN explicitly requires pre-defined graph structures which is not clearly defined in brain connectome. To address this problem, we showed that with naive complete graph structure, GCN can get meaningful results. Meanwhile, an effective algorithm was proposed to learn the graph structure from the data, via generating random graph during training based on the small-world model. Also, the advantages of GCN over multi-layer perceptron was discussed. The experiments demonstrate that the proposed method outperform related baselines significantly on predicting depression scores.

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Acknowledgement

This work was partially supported by U.S. NSF IIS 1836945, IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956, and NIH R01 AG049371.

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA

    Yanfu Zhang & Heng Huang

  2. JD Digits, Mountain View, USA

    Heng Huang

Authors
  1. Yanfu Zhang
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  2. Heng Huang
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Corresponding author

Correspondence to Heng Huang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Albert C. S. Chung

  2. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    James C. Gee

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Paul A. Yushkevich

  4. Department of Natural Language Processing, Baidu Inc., Shenzhen, China

    Siqi Bao

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Zhang, Y., Huang, H. (2019). New Graph-Blind Convolutional Network for Brain Connectome Data Analysis. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_52

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  • DOI: https://doi.org/10.1007/978-3-030-20351-1_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20350-4

  • Online ISBN: 978-3-030-20351-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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