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Individual Functional Network Abnormalities Mapping via Graph Representation-Based Neural Architecture Search

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14178))

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Abstract

Prenatal alcohol exposure (PAE) has garnered increasing attention due to its detrimental effects on both neonates and expectant mothers. Recent research indicates that spatio-temporal functional brain networks (FBNs), derived from functional magnetic resonance imaging (fMRI), have the potential to reveal changes in PAE and Non-dysmorphic PAE (Non-Dys PAE) groups compared with healthy controls. However, current deep learning approaches for decomposing the FBNs are still limited to hand-crafted neural network architectures, which may not lead to optimal performance in identifying FBNs that better reveal differences between PAE and healthy controls. In this paper, we utilize a novel graph representation-based neural architecture search (GR-NAS) model to optimize the inner cell architecture of recurrent neural network (RNN) for decomposing the spatio-temporal FBNs and identifying the neuroimaging biomarkers of subtypes of PAE. Our optimized RNN cells with the GR-NAS model revealed that the functional activation decreased from healthy controls to Non-Dys PAE then to PAE groups. Our model provides a novel computational tool for the diagnosis of PAE, and uncovers the brain’s functional mechanism in PAE.

Q. Li and H. Dai—Equal contribution.

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Notes

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Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (Grant No. 62206024).

Author information

Authors and Affiliations

  1. Beijing Normal University, Beijing, 100875, China

    Qing Li & Xia Wu

  2. University of Georgia, Athens, GA, 30602, USA

    Haixing Dai, Lin Zhao, Zhengliang Liu, Zihao Wu & Tianming Liu

  3. The University of Sydney, Sydney, 2006, Australia

    Jinglei Lv

  4. Emory University, Atlanta, GA, 30322, USA

    Claire Coles & Xiaoping Hu

  5. University of Texas at Arlington, Arlington, TX, 76013, USA

    Dajiang Zhu

Authors
  1. Qing Li
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  2. Haixing Dai
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  4. Lin Zhao
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  5. Zhengliang Liu
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  7. Xia Wu
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  9. Xiaoping Hu
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  10. Tianming Liu
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Corresponding authors

Correspondence to Claire Coles , Xiaoping Hu , Tianming Liu or Dajiang Zhu .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Li, Q. et al. (2023). Individual Functional Network Abnormalities Mapping via Graph Representation-Based Neural Architecture Search. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_6

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  • DOI: https://doi.org/10.1007/978-3-031-46671-7_6

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  • Online ISBN: 978-3-031-46671-7

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