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

Francis Xiatian Zhang, Sisi Zheng, Hubert P. H. Shum, Haozheng Zhang, Nan Song, Mingkang Song and Hongxiao Jia
Proceedings of the 2023 International Conference on Neural Information Processing (ICONIP), 2023

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

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 distancebased 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.

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Citations

BibTeX

@inproceedings{zhang23correlation,
 author={Zhang, Francis Xiatian and Zheng, Sisi and Shum, Hubert P. H. and Zhang, Haozheng and Song, Nan and Song, Mingkang and Jia, Hongxiao},
 booktitle={Proceedings of the 2023 International Conference on Neural Information Processing},
 series={ICONIP '23},
 title={Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI},
 year={2023},
 month={11},
 pages={298--312},
 numpages={15},
 doi={10.1007/978-981-99-8138-0_24},
 isbn={978-981-99-8137-3},
 publisher={Springer},
 location={Changsha, China},
}

RIS

TY  - CONF
AU  - Zhang, Francis Xiatian
AU  - Zheng, Sisi
AU  - Shum, Hubert P. H.
AU  - Zhang, Haozheng
AU  - Song, Nan
AU  - Song, Mingkang
AU  - Jia, Hongxiao
T2  - Proceedings of the 2023 International Conference on Neural Information Processing
TI  - Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
PY  - 2023
Y1  - 11 2023
SP  - 298
EP  - 312
DO  - 10.1007/978-981-99-8138-0_24
SN  - 978-981-99-8137-3
PB  - Springer
ER  - 

Plain Text

Francis Xiatian Zhang, Sisi Zheng, Hubert P. H. Shum, Haozheng Zhang, Nan Song, Mingkang Song and Hongxiao Jia, "Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI," in ICONIP '23: Proceedings of the 2023 International Conference on Neural Information Processing, pp. 298-312, Changsha, China, Springer, Nov 2023.

Supporting Grants

The Engineering and Physical Sciences Research Council
Northern Health Futures Hub (NortHFutures)
EPSRC Digital Health Hub Pilot Scheme (Ref: EP/X031012/1): £4.17 million, Co-Investigator (PI: Prof. Abigail Durrant)
Received from The Engineering and Physical Sciences Research Council, UK, 2023-2026
Project Page

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Last updated on 17 February 2024
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