Abstract
In general, large-scale fMRI analysis helps to uncover functional biomarkers and diagnose neuropsychiatric disorders. However, the existence of multi-site problem caused by inter-site variation hinders the full exploitation of fMRI data from multiple sites. To address the heterogeneity across sites, we propose a novel end-to-end framework for multi-site disease prediction, which aims to build a robust population graph and denoise the message passing on it. Specifically, we decompose the fMRI feature into site-invariant and site-specific embeddings through representation disentanglement, and construct the edge of population graph through the site-specific embedding and represent each subject using its site-invariant embedding, followed by the feature propagation and transformation over the constructed population graph via graph convolutional networks. Compared to the state-of-the-art methods, we have demonstrated its superior performance of our framework on the challenging ABIDE dataset.
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Notes
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Caltech, CMU, KKI, MAX_MUN, NYU, Olin, OHSU, SDSU, SBL, Stanford, Trinity, UCLA\(_{1}\), UCLA\(_{2}\), Leuven\(_{1}\), Leuven\(_{2}\), UM\(_{1}\), UM\(_{2}\), Pittsburgh, USM and Yale.
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Acknowledgements
This research is funded by the Basic Research Project of Shanghai Science and Technology Commission (No.19JC1410101). The computation is supported by ECNU Multifunctional Platform for Innovation (001).
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Lin, Y., Yang, J., Hu, W. (2023). Denoising fMRI Message on Population Graph for Multi-site Disease Prediction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_55
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