@Article{cmes.2023.028732,
AUTHOR = {Qiankun Zuo, Junhua Hu, Yudong Zhang, Junren Pan, Changhong Jing, Xuhang Chen, Xiaobo Meng, Jin Hong},
TITLE = {Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2129--2147},
URL = {http://www.techscience.com/CMES/v137n3/53736},
ISSN = {1526-1506},
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 is
estimated to regularize the latent space learned by the graph encoder, which can make the learning process stable
and the learned representation robust. Also, the transformer generator is devised to map the node representations
into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.
The typical topological properties and discriminative features can be preserved entirely. Furthermore, the generated
brain functional networks improve the prediction performance using different classifiers, which can be applied to
analyze other cognitive diseases. Attempts on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset
demonstrate that the proposed model can generate good brain functional networks. The classification results show
adding generated data can achieve the best accuracy value of 85.33%, sensitivity value of 84.00%, specificity value of
86.67%. The proposed model also achieves superior performance compared with other related augmented models.
Overall, the proposed model effectively improves cognitive disease diagnosis by generating diverse brain functional
networks.},
DOI = {10.32604/cmes.2023.028732}
}