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Graph Diffusion Reconstruction Network for Addictive Brain-Networks Identification

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

Functional Magnetic Resonance Imaging(fMRI) can reveal complex patterns of brain functional changes. The exploration of addiction-related brain connectivity can be more precise with fMRI data. However, it is still difficult to obtain addiction-related brain connectivity effectively from fMRI data due to the complexity and non-linear characteristics of brain connections. Therefore, this paper proposed a Graph Diffusion Reconstruction Network (GDRN), which could capture addiction-related brain connectivity from fMRI data of addicted rats. The diffusion reconstruction module effectively maintained the unity of data distribution by reconstructing the training samples. This module enhanced the ability to reconstruct nicotine addiction-related brain networks. Experiments on the nicotine addiction rat dataset show that the proposed model can effectively explore nicotine addiction-related brain connectivity.

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Acknowledgements

This work was supported by the National Natural Science Foundations of China under Grant 62172403, the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211 and Shenzhen Key Basic Research Project under Grant JCYJ20200109115641762.

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Changhong Jing, Changwei Gong, Zuxin Chen & Shuqiang Wang

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  1. Changhong Jing
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  2. Changwei Gong
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  3. Zuxin Chen
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  4. Shuqiang Wang
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Correspondence to Shuqiang Wang .

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Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Jing, C., Gong, C., Chen, Z., Wang, S. (2023). Graph Diffusion Reconstruction Network for Addictive Brain-Networks Identification. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_12

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