Qiankun Zuo1,4, Junhua Hu2, Yudong Zhang3,*, Junren Pan4, Changhong Jing4, Xuhang Chen5, Xiaobo Meng6, Jin Hong7,8,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2129-2147, 2023, DOI:10.32604/cmes.2023.028732
Abstract The topological connectivity information derived from the brain functional network can bring new insights for
diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation
between abnormal connectivities and dementia disorders. However, it is challenging to access considerable
amounts of brain functional network data, which hinders the widespread application of data-driven models in
dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with
transformer is proposed to generate new fake brain functional networks to augment the brain functional network
dataset, improving the dementia diagnosis accuracy of data-driven models. Specifically, the label distribution… More >
Graphic Abstract
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