Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
369-02-012 ISMRM Abstract

Multimodal Graph Contrastive Learning (MG-CL) for Parkinson’s Disease Diagnosis

Primary: Analysis Methods - Classification and Prediction
Secondary: Brain Function and fMRI - Functional Connectivity
369-02-012 · Imaging Parkinson’s Disease: Multimodal Biomarkers of Progression and Phenotype · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row J
Keywords: Structural Connectivity Functional Connectivity Parkinson's Disease Graph Neural Networks Contrastive Learning
Accepted
Alou Diakite1, Cheng Li2, Shanshan Wang 3
1Biomedical engineering, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Presenting Author: Shanshan Wang

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References

1. Mano, T., Kinugawa, K., Ozaki, M., Kataoka, H. and Sugie, K., 2022. Neural synchronization analysis of electroencephalography coherence in patients with Parkinson’s disease-related mild cognitive impairment. Clinical parkinsonism & related disorders, 6, p.100140.
2. Qu, G., Zhou, Z., Calhoun, V.D., Zhang, A. and Wang, Y.P., 2025. Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks. Medical Image Analysis, 103, p.103570.
3. Xia, J., Chan, Y.H., Girish, D. and Rajapakse, J.C., 2025. Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes. Medical Image Analysis, 102, p.103509.
4. Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S. and Poewe, W., 2011. The Parkinson progression marker initiative (PPMI). Progress in neurobiology, 95(4), pp.629-635.
5. Xia, J., Chen, N. and Qiu, A., 2023. Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction. Medical image analysis, 90, p.102921.
6. Yang, Y., Ye, C., Guo, X., Wu, T., Xiang, Y. and Ma, T., 2023. Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning. IEEE Transactions on Medical Imaging, 43(1), pp.108-121.
7. Li, Y., Wei, Q., Adeli, E., Pohl, K.M. and Zhao, Q., 2022, September. Joint graph convolution for analyzing brain structural and functional connectome. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 231-240). Cham: Springer Nature Switzerland.
8. Rampášek, L., Galkin, M., Dwivedi, V.P., Luu, A.T., Wolf, G. and Beaini, D., 2022. Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35, pp.14501-14515.
9. Lv, K.M., 2025. Research Progress of Multimodal Biomarkers in the Early Diagnosis of Mild Cognitive Impairment in Parkinson's Disease. Frontiers in Neurology, 16, p.1652378.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/369-02-012