Zuo Qiankun, Hu Junhua, Zhang Yudong, Pan Junren, Jing Changhong, Chen Xuhang, Meng Xiaobo, Hong Jin
School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Comput Model Eng Sci. 2023 Aug 3;137(3):2129-2147. doi: 10.32604/cmes.2023.028732.
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.
从脑功能网络中获取的拓扑连接信息可为痴呆症的诊断和分析带来新的见解。脑功能网络适合于搭建异常连接与痴呆症之间的关联。然而，获取大量脑功能网络数据具有挑战性，这阻碍了数据驱动模型在痴呆症诊断中的广泛应用。在本研究中，提出了一种带有Transformer的新型分布正则化对抗图自动编码器（DAGAE），以生成新的虚假脑功能网络来扩充脑功能网络数据集，提高数据驱动模型的痴呆症诊断准确率。具体而言，估计标签分布以正则化图编码器学习到的潜在空间，这可以使学习过程稳定且学习到的表示具有鲁棒性。此外，设计了Transformer生成器，通过探索高度相关的远距离脑区的长期依赖性，将节点表示映射为节点到节点的连接。典型的拓扑属性和判别特征可以完全保留。此外，生成的脑功能网络使用不同的分类器提高了预测性能，可应用于分析其他认知疾病。在阿尔茨海默病神经影像倡议（ADNI）数据集上的尝试表明，所提出的模型可以生成良好的脑功能网络。分类结果显示，添加生成的数据可以实现最佳准确率值85.33% , 灵敏度值84.00% ，特异性值86.67% 。与其他相关的增强模型相比，所提出的模型也具有优越的性能。总体而言，所提出的模型通过生成多样的脑功能网络有效地改善了认知疾病的诊断。
