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

Zhang, Xiatian; Zheng, Sisi; Shum, Hubert P.H.; Zhang, Haozheng; Song, Nan; Song, Mingkang; Jia, Hongxiao

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.

Citation

Zhang, X., Zheng, S., Shum, H. P., Zhang, H., Song, N., Song, M., & Jia, H. (2023, November). Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI. Presented at ICONIP 2023: 2023 International Conference on Neural Information Processing, Changsha, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICONIP 2023: 2023 International Conference on Neural Information Processing
• Start Date: Nov 20, 2023
• End Date: Nov 23, 2023
• Acceptance Date: Jul 31, 2023
• Online Publication Date: Nov 26, 2023
• Publication Date: Nov 26, 2023
• Deposit Date: Aug 10, 2023
• Publicly Available Date: Nov 27, 2024
• Publisher: Springer
• Volume: 1963
• Pages: 298-312
• Series Title: Communications in Computer and Information Science
• Book Title: Neural Information Processing 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part IX
• ISBN: 9789819981373
• DOI: https://doi.org/10.1007/978-981-99-8138-0_24
• Public URL: https://durham-repository.worktribe.com/output/1714784