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An evolving graph convolutional network for dynamic functional brain network

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Abstract

Brain networks have received extensive attention because of its important significance in understanding human brain organization and analyzing neuropsychiatric diseases. Existing methods are mostly based on the static functional brain network. However, the static brain network only considers the correlation between global signals, and cannot reflect the changes of brain information over time. In reality, the brain activities are constantly changing. Therefore, the dynamic functional brain network is proposed in order to reflect the variations of the signal. In recent years, as an important and effective method, graph convolutional network has been widely used in the analysis of brain networks. In consequence, an evolving graph convolutional network based on dynamic functional brain network is proposed. The network not only considers the neighbor node information in the current snapshot, but also considers the neighbor node information in the precursor and the subsequent time in the process of convolution. Furthermore, four kinds of convolution rules are put forward based on the evolving graph convolutional network. Alzheimer’s disease diagnosis, as a representative neuropsychiatric diseases analysis method, is used to evaluate the model, and experiments are performed on the open dataset. The experimental results show that the proposed evolving graph convolutional network can improve the diagnostic accuracy to 99.16%, which proves the superiority of the proposed method.

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Data Availability

The data that support the findings of this study are openly available in OASIS database at https://www.oasis-brains.org/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62072089, the Fundamental Research Funds for the Central Universities of China under Grant Nos. N2104001, N2116016, N2019007, N2024005-2.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, 195 Chuangxin Road, Shenyang, 110169, Liaoning, China

    Xinlei Wang, Junchang Xin & Zhongyang Wang

  2. Key Laboratory of Big Data Management and Analytics, Northeastern University, 195 Chuangxin Road, Shenyang, 110169, Liaoning, China

    Junchang Xin

  3. College of Medicine and Biological Information Engineering, Northeastern University, 195 Chuangxin Road, Shenyang, 110169, Liaoning, China

    Qi Chen & Zhiqiong Wang

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  1. Xinlei Wang
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  2. Junchang Xin
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  3. Zhongyang Wang
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Correspondence to Junchang Xin.

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Wang, X., Xin, J., Wang, Z. et al. An evolving graph convolutional network for dynamic functional brain network. Appl Intell 53, 13261–13274 (2023). https://doi.org/10.1007/s10489-022-04203-x

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