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Prediction of Cannabis Addictive Patients with Graph Neural Networks

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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

Neurological research is closely intertwined with public health issues, and artificial intelligence (AI) holds substantial potential in this domain. This study aims to investigate the enhancement of brain imaging classification performance in diverse populations using Graph Neural Networks (GNN) and its variants. Brain activity data are sourced from public neuroimaging databases, including functional Magnetic Resonance Imaging (fMRI) data of cannabis addicts and a healthy control group. Our results show that, compared to the healthy control group, cannabis addicts exhibit significant alterations in functional connectivity in certain brain regions. With the application of AI tools, we can distinguish the two groups based on brain imaging. We observed a significant improvement in brain imaging classification performance, and this model has achieved an accuracy rate of approximately 80%. These AI tools’ robust generalizability and vast developmental potential were also highlighted. These findings not only provided a novel perspective on the role of AI in brain imaging studies but also suggested potential new strategies for addressing public health issues.

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Authors and Affiliations

  1. School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Shulin Wen, Shihao Yang, Ting Liao & Feng Liu

  2. Department of Computer Information and Decision Management, Paul and Virginia Engler College of Business, West Texas A&M University, Canyon, TX, 79016, USA

    Xinglong Ju

Authors
  1. Shulin Wen
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  2. Shihao Yang
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  3. Xinglong Ju
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  4. Ting Liao
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  5. Feng Liu
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Corresponding author

Correspondence to Feng Liu .

Editor information

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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Wen, S., Yang, S., Ju, X., Liao, T., Liu, F. (2023). Prediction of Cannabis Addictive Patients with Graph Neural Networks. 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_26

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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