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A unified framework of graph structure learning, graph generation and classification for brain network analysis

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Abstract

Recently, functional brain networks have been employed for classifying neurological disorders, such as autism spectrum disorders (ASDs). Graph convolutional networks (GCNs) have been shown to be successful in modeling applications with graph structures. However, brain network data is in general of complex structure with small sample size, and the use of GCNs on available datasets remains a big challenge. Driven by this important issue, three questions arise: 1) how to capture the critical structures of brain networks by removing noisy connections, to facilitate the following GAN and GCN; 2) how to generate graphs by generative adversarial networks (GANs) to preserve the local graph topology as well as the global data distribution; 3) how to sufficiently leverage the real and generated graphs with domain gaps for improved classification. In this paper, we proposed a three-stage framework, named BrainGC-Net, which coherently joins the power of graph pooling, GAN and GCN for brain network generation and classification. Given the original brain network with a large number of noisy connections, we propose graph pooling to enhance the important connections with a supervision scheme. Then, based on the coarsened brain networks, we propose a graph GAN model, named EG-GAN, to focus on the global data distribution in the embedding space and local graph topology in the graph space simultaneously. Finally, a domain consistent GCN model is proposed to take sufficient advantage of the two domains rather than simply merging by incorporating multiple consistent regularizations from view correlation, class correlation and sample correlation. With extensive experiments on the ASD classification problem, we validate the effectiveness of our method and it achieves consistent improvements over state-of-the-art methods on the public ABIDE dataset.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the ABIDE repository, https://fcon_1000.projects.nitrc.org/indi/abide/.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No.62076059) and the Science Project of Liaoning province (2021-MS-105).

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Correspondence to Peng Cao.

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Cao, P., Wen, G., Yang, W. et al. A unified framework of graph structure learning, graph generation and classification for brain network analysis. Appl Intell 53, 6978–6991 (2023). https://doi.org/10.1007/s10489-022-03891-9

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