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Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1963))

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Abstract

Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design efforts to predict treatment response from rs-fMRI remain limited due to difficulties in understanding the current brain state and the underlying mechanisms driving the observed patterns, which limited the clinical application of rs-fMRI. To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss. This approach results in a more expressive framework that captures brain dynamic features at different scales and enables more accurate prediction of treatment response. Our experiments on the chronic pain and depersonalization disorder datasets demonstrate that our proposed method outperforms current methods in different scenarios. To the best of our knowledge, we are the first to explore the integration of distance-based and correlation-based neural similarity into graph learning for treatment response prediction.

This research is supported in part by the Beijing Hospitals Authority Youth Program (ref: QML20191901), Beijing Hospitals Authority Clinical Medicine Development of Special Funding (ref: ZYLX202129), Beijing Hospitals Authority’s Ascent Plan (ref: DFL20191901), Training Plan for High Level Public Health Technical Talents Construction Project (ref: TTL-02-40), Research Cultivation Program of Beijing Municipal Hospital (ref: PZ2023032), EPSRC NortHFutures project (ref: EP/X031012/1).

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  • 26 November 2023

    A correction has been published.

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Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China

    Sisi Zheng, Nan Song, Mingkang Song & Hongxiao Jia

  2. Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China

    Sisi Zheng, Nan Song, Mingkang Song & Hongxiao Jia

  3. Department of Computer Science, Durham University, Durham, UK

    Francis Xiatian Zhang, Hubert P. H. Shum & Haozheng Zhang

Authors
  1. Francis Xiatian Zhang
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  2. Sisi Zheng
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  3. Hubert P. H. Shum
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  4. Haozheng Zhang
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  5. Nan Song
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  6. Mingkang Song
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  7. Hongxiao Jia
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Corresponding authors

Correspondence to Hubert P. H. Shum or Hongxiao Jia .

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Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Zhang, F.X. et al. (2024). Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_24

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  • DOI: https://doi.org/10.1007/978-981-99-8138-0_24

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