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Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification.

Methods

We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification.

Results

The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD.

Conclusion

Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks.

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Availability of data and material

Data are publicly available.

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Funding

This research was supported by the National Natural Science Foundation of China (No.62076059) and the Science Project of Liaoning province (2021-MS-105).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, China

    Lanting Li, Guangqi Wen, Peng Cao & Jinzhu Yang

  2. Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China

    Lanting Li, Guangqi Wen, Peng Cao & Jinzhu Yang

  3. Alibaba A.I. Labs, Hangzhou, China

    Xiaoli Liu

  4. Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada

    Osmar R. Zaiane

Authors
  1. Lanting Li
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  2. Guangqi Wen
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  3. Peng Cao
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  4. Xiaoli Liu
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  5. Osmar R. Zaiane
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  6. Jinzhu Yang
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Corresponding author

Correspondence to Peng Cao.

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Li, L., Wen, G., Cao, P. et al. Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis. Int J CARS 18, 663–673 (2023). https://doi.org/10.1007/s11548-022-02780-3

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  • DOI: https://doi.org/10.1007/s11548-022-02780-3

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