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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2022: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 pp 375–385Cite as

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13438)

Abstract

Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.

Keywords

  • Interpretation
  • Graph neural network
  • Brain networks

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Notes

  1. 1.

    http://rfmri.org/DPARSF/.

  2. 2.

    https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/.

  3. 3.

    https://www.ppmi-info.org/.

  4. 4.

    https://www.michaeljfox.org/.

  5. 5.

    http://stnava.github.io/ANTs/.

  6. 6.

    https://surfer.nmr.mgh.harvard.edu/.

  7. 7.

    https://github.com/microsoft/nni.

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Acknowledgement

This research was partly supported by the internal funds and GPU servers provided by the Computer Science Department of Emory University and the University Research Committee of Emory University. Xiaoxiao Li was supported by NSERC Discovery Grant (DGECR-2022-00430). Lifang He was supported by ONR N00014-18-1-2009 and Lehigh’s accelerator grant S00010293.

Author information

Authors and Affiliations

  1. Emory University, Atlanta, USA

    Hejie Cui, Wei Dai & Carl Yang

  2. University of California, Los Angeles, USA

    Yanqiao Zhu

  3. The University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  4. Lehigh University, Bethlehem, USA

    Lifang He

Authors
  1. Hejie Cui
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  2. Wei Dai
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  3. Yanqiao Zhu
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  4. Xiaoxiao Li
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  5. Lifang He
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  6. Carl Yang
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Corresponding author

Correspondence to Carl Yang .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Cui, H., Dai, W., Zhu, Y., Li, X., He, L., Yang, C. (2022). Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_36

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  • DOI: https://doi.org/10.1007/978-3-031-16452-1_36

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