Brain graph synthesis for multi-site autism spectrum disorder identification

Wenqi Li; Huifang Huang

doi:10.1117/12.2681310

8 June 2023 Brain graph synthesis for multi-site autism spectrum disorder identification

Wenqi Li, Huifang Huang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073Z (2023) https://doi.org/10.1117/12.2681310
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Recently, machine learning attracts more attention on autism spectrum disorder (ASD) identification based on resting-state functional magnetic resonance imaging (rs-fMRI). Most studies on ASD identification use rs-fMRI data from multiple imaging sites to increase sample size, but they suffer from the data heterogeneity among sites. Besides, most ASD identification studies use features simply extracted from brain connections, ignoring the topological structure of brain. To address these issues, we propose a brain graph synthesis model based on generative adversarial network (GAN), which transforms data from the source domain to the target domain, solving the data heterogeneity among sites. Specifically, the generator and discriminator are designed for graph structured data and propose topological losses to construct the cycle consistency loss for maintaining the original topological structure of the reconstructed brain graph. We carry out experiments on six tasks using the open database Autism Brain Imaging Data Exchange (ABIDE) for ASD identification. Experimental findings demonstrate that our model can solve the problem of data heterogeneity effectively and achieve satisfying performance on multi-site ASD identification.

Citation Download Citation

Wenqi Li and Huifang Huang "Brain graph synthesis for multi-site autism spectrum disorder identification", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073Z (8 June 2023); https://doi.org/10.1117/12.2681310

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