基于对抗图自编码的阿尔兹海默症脑网络分析
Adversarial Graph Autoencoder for Brain Network Analysis in Alzheimer’s Disease
DOI: 10.12677/JISP.2022.114019, PDF,    科研立项经费支持
作者: 左乾坤*, 荆常宏：中国科学院深圳先进技术研究院，广东 深圳；薛 冰：马来亚大学计算机学院，马来西亚 吉隆坡
关键词: 图生成器；对抗学习；结构–功能脑连接；Graph Generator； Adversarial Learning； Structural-Functional Brain Connectivity
摘要: 阿尔兹海默症(Alzheimer’s Disease, AD)的不同阶段会发生结构或功能连接的改变。这些基于连接的特征可以大大提高疾病诊断的准确性，并能给出疾病的成因解释。如何有效融合结构和功能影像来挖掘不同模态之间的互补信息仍然是一个挑战。本文提出了一种对抗图自编码器模型，来提取脑连接特征用于AD分析。具体地说，将扩散张量成像(Diffusion Tensor Imaging, DTI)和功能磁共振成像(functional Magnetic Resonance Imaging, fMRI)相结合，构建每个受试者的图结构数据。图编码器(生成器)将图数据转换为潜在表征。同时，利用fMRI数据估计潜在分布，对图编码器进行正则化约束，以保证良好的潜在表征。为了保证潜在表征的稳定性，图解码器从潜在表征中恢复图数据。最后，将潜在表征送给分类器，使其具有疾病类别信息。实验结果表明，该模型比其他相关模型具有更高的预测精度。总体而言，该方法可以重建AD早期的结构–功能连接，分析异常的脑连接并用于AD的早期诊疗研究。
Abstract: Alterations in the structural or functional connectivity take place at different stages of Alzheimer’s Disease (AD). These connectivity-based features can greatly improve the disease diagnosis accuracy and explain the causes of the disease. How to effectively fuse structural and functional images for exploring complementary information remains challenging. This paper proposes an adversarial graph autoencoder model to extract connectivity-based features for AD analysis. Specifically, Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) are combined to construct graph data for each subject. The graph encoder (generator) transforms the graph data into a latent representation. Meanwhile, the fMRI data is utilized to estimate the latent distribution, which can regularize the graph encoder to ensure good latent representation. To ensure the latent representation is stable, the graph decoder regains the graph data from the latent representation. Finally, the latent representation is sent to the classifier to make it class-discriminative. Experimental results demonstrate that the proposed model can achieve higher prediction accuracy than other related models. Generally, this method can reconstruct the structural-functional connectivity and analyze abnormal brain connections for early AD study.
文章引用：左乾坤, 薛冰, 荆常宏. 基于对抗图自编码的阿尔兹海默症脑网络分析[J]. 图像与信号处理, 2022, 11(4): 191-201. https://doi.org/10.12677/JISP.2022.114019

参考文献

[1] Cope, T.E., Rittman, T., Borchert, R.J., et al. (2018) Tau Burden and the Functional Connectome in Alzheimer’s Disease and Progressive Supranuclear Palsy. Brain, 141, 550-567.
[Google Scholar] [CrossRef] [PubMed]
[2] Alzheimer’s Association (2019) 2019 Alzheimer’s Disease Facts and Figures. Alzheimer’s & Dementia, 15, 321-387.
[Google Scholar] [CrossRef] 
[3] Li, Y., Liu, J., Tang, Z. and Lei, B. (2020) Deep Spatial-Temporal Feature Fusion from Adaptive Dynamic Functional Connectivity for MCI Identification. IEEE Transactions on Medical Imaging, 39, 2818-2830.
[Google Scholar] [CrossRef] 
[4] Franzmeier, N. and Dyrba, M. (2017) Functional Brain Network Architecture May Route Progression of Alzheimer’s Disease Pathology. Brain, 140, 3077-3080.
[Google Scholar] [CrossRef] [PubMed]
[5] Pereira, J.B., Van Westen, D., Stomrud, E., et al. (2018) Abnormal Structural Brain Connectome in Individuals with Preclinical Alzheimer’s Disease. Cerebral Cortex, 28, 3638-3649.
[Google Scholar] [CrossRef] [PubMed]
[6] Wang, S.Q., Li, X., Cui, J.L., et al. (2015) Prediction of Myelopathic Level in Cervical Spondylotic Myelopathy Using Diffusion Tensor Imaging. Journal of Magnetic Resonance Imaging, 41, 1682-1688.
[Google Scholar] [CrossRef] [PubMed]
[7] Wang, S., Shen, Y., Zeng, D., et al. (2018) Bone Age Assessment Using Convolutional Neural Networks. 2018 International Conference on Artificial Intelligence and Big Data, Chengdu, 26-28 May 2018, 175-178.
[Google Scholar] [CrossRef] 
[8] Hong, J., Feng, Z., Wang, S.H., et al. (2020) Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning. Frontiers in Neurology, 11, Article ID: 584682.
[Google Scholar] [CrossRef] [PubMed]
[9] Wang, S., Wang, X., Shen, Y., et al. (2020) An Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 426-437.
[Google Scholar] [CrossRef] 
[10] Wang, S., Shen, Y., Shi, C., et al. (2018) Skeletal Maturity Recognition Using a Fully Automated System with Convolutional Neural Networks. IEEE Access, 6, 29979-29993.
[Google Scholar] [CrossRef] 
[11] Zhang, Y., Wu, J., Liu, Y., et al. (2020) MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation from T1-Weighted Magnetic Resonance Images. IEEE Journal of Biomedical and Health Informatics, 25, 526-535.
[Google Scholar] [CrossRef] 
[12] Wang, S., Hu, Y., Shen, Y., et al. (2018) Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning. International Journal of Neural Systems, 28, Article ID: 1750036.
[Google Scholar] [CrossRef] 
[13] Lei, B., Liang, E., Yang, M., et al. (2022) Predicting Clinical Scores for Alzheimer’s Disease Based on Joint and Deep Learning. Expert Systems with Applications, 187, Article ID: 115966.
[Google Scholar] [CrossRef] 
[14] Zeng, D., Wang, S., Shen, Y., et al. (2017) A GA-Based Feature Selection and Parameter Optimization for Support Tucker Machine. Procedia Computer Science, 111, 17-23.
[Google Scholar] [CrossRef] 
[15] Wu, K., Shen, Y. and Wang, S. (2018) 3D Convolutional Neural Network for Regional Precipitation Nowcasting. Image Signal Process, 7, 200-212.
[Google Scholar] [CrossRef] 
[16] Wang, S.Q. and Li, H.X. (2011) Quantitative Construction of Regulatory Networks Using Multiple Sources of Knowledge. 2011 International Conference on Machine Learning and Cybernetics, Vol. 1, 91-96.
[Google Scholar] [CrossRef] 
[17] Wang, S.Q. and Li, H.X. (2012) Random Network Based Dynamic Analysis for Biochemical Reaction System. Advanced Science Letters, 10, 554-558.
[Google Scholar] [CrossRef] 
[18] Zhang, H., Sun, Y., Zhao, M., et al. (2019) Bridging User Interest to Item Content for Recommender Systems: An Optimization Model. IEEE Transactions on Cybernetics, 50, 4268-4280.
[Google Scholar] [CrossRef] 
[19] Wang, S.Q. and Li, H.X. (2012) Defining Transcriptional Network by Combining Expression Data with Multiple Sources of Prior Knowledge. 2012 International Conference on System Science and Engineering, Dalian, 30 June-2 July 2012, 102-106.
[Google Scholar] [CrossRef] 
[20] Wang, S., Hu, J., Shen, Y., et al. (2014) Modeling and Analysis of Gene Regulatory Networks with a Bayesian-Driven Approach. 2014 14th International Symposium on Communications and Information Technologies, Incheon, 24-26 September 2014, 289-293.
[21] Wang, S., Shen, Y., Hu, J., et al. (2015) Hadoop-Based Analysis for Large-Scale Click-Through Patterns in 4G Network. In: Xu, K. and Zhu, H.J., Eds., International Conference on Wireless Algorithms, Systems, and Applications, Springer, Cham, 829-835.
[Google Scholar] [CrossRef] 
[22] Wang, T., Shi, F., Jin, Y., et al. (2016) Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer’s Disease: A Diffusion MRI Study with DTI and HARDI Models. Neural Plasticity, 2016, Article ID: 2947136.
[Google Scholar] [CrossRef] [PubMed]
[23] Wang, S., Wang, H., Shen, Y., et al. (2018) Automatic Recognition of Mild Cognitive Impairment and Alzheimer’s Disease Using Ensemble Based 3D Densely Connected Convolutional Networks. 2018 17th IEEE International Conference on Machine Learning and Applications, Orlando, 17-20 December 2018, 517-523.
[Google Scholar] [CrossRef] 
[24] Wang, H., Shen, Y., Wang, S., et al. (2019) Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease. Neurocomputing, 333, 145-156.
[Google Scholar] [CrossRef] 
[25] Wang, S., Wang, H., Cheung, A.C., et al. (2020) Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease. In: Wani, M.A., et al., Eds., Deep Learning Applications, Springer, Berlin, 53-73.
[Google Scholar] [CrossRef] 
[26] Yu, W., Lei, B., Wang, S., et al. (2022) Morphological Feature Visualization of Alzheimer’s Disease via Multidirectional Perception GAN. IEEE Transactions on Neural Networks and Learning Systems, 1-15.
[Google Scholar] [CrossRef] 
[27] Yu, W., Lei, B., Ng, M.K., et al. (2021) Tensorizing GAN with High-Order Pooling for Alzheimer’s Disease Assessment. IEEE Transactions on Neural Networks and Learning Systems, 33, 4945-4959.
[Google Scholar] [CrossRef] 
[28] Lei, B., Yu, S., Zhao, X., et al. (2021) Diagnosis of Early Alzheimer’s Disease Based on Dynamic High Order Networks. Brain Imaging and Behavior, 15, 276-287.
[Google Scholar] [CrossRef] [PubMed]
[29] Yu, S., Wang, S., Xiao, X., et al. (2020) Multi-Scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection. In: Martel, A.L., et al., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 228-237.
[Google Scholar] [CrossRef] 
[30] Pan, J., Lei, B., Shen, Y., et al. (2021) Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer’s Disease Analysis. In: Ma, H.M., et al., Eds., Chinese Conference on Pattern Recognition and Computer Vision, Springer, Cham, 467-478.
[Google Scholar] [CrossRef] 
[31] Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014) Generative Adversarial Nets. Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, 8-13 December 2014, 2672-2680.
[32] Wang, S., Wang, X., Hu, Y., et al. (2020) Diabetic Retinopathy Diagnosis Using Multichannel Generative Adversarial Network with Semisupervision. IEEE Transactions on Automation Science and Engineering, 18, 574-585.
[Google Scholar] [CrossRef] 
[33] Huang, J., Zhou, L., Wang, L. and Zhang, D. (2020) Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis. IEEE Transactions on Medical Imaging, 39, 2541-2552.
[Google Scholar] [CrossRef] 
[34] Zuo, Q., Lei, B., Shen, Y., et al. (2021) Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction. In: Ma, H.M., et al., Eds., Chinese Conference on Pattern Recognition and Computer Vision, Springer, Cham, 479-490.
[Google Scholar] [CrossRef] 
[35] Pan, J., Lei, B., Wang, S., et al. (2021) DecGAN: Decoupling Generative Adversarial Network Detecting Abnormal Neural Circuits for Alzheimer’s Disease.
[36] Hu, S., Yuan, J. and Wang, S. (2019) Cross-Modality Synthesis from MRI to PET Using Adversarial U-Net with Different Normalization. 2019 International Conference on Medical Imaging Physics and Engineering, Shenzhen, 22-24 November 2019, 1-5.
[Google Scholar] [CrossRef] 
[37] Hu, S., Shen, Y., Wang, S., et al. (2020) Brain MR to Pet Synthesis via Bidirectional Generative Adversarial Network. In: Martel, A.L., et al., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 698-707.
[Google Scholar] [CrossRef] 
[38] Yu, B., Zhou, L., Wang, L., et al. (2020). Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis. IEEE Transactions on Medical Imaging, 39, 2339-2350.
[CrossRef] 
[39] Hu, S., Lei, B., Wang, S., et al (2021) Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis. IEEE Transactions on Medical Imaging, 41, 145-157.
[Google Scholar] [CrossRef] 
[40] Hu, S., Yu, W., Chen, Z., et al. (2020) Medical Image Reconstruction Using Generative Adversarial Network for Alzheimer Disease Assessment with Class-Imbalance Problem. 2020 IEEE 6th International Conference on Computer and Communications, Chengdu, 11-14 December 2020, 1323-1327.
[Google Scholar] [CrossRef] 
[41] Wang, S., Chen, Z., You, S., et al. (2022) Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network. Neural Computing and Applications, 34, 8657-8669.
[Google Scholar] [CrossRef] 
[42] Hong, J., Yu, S.C.H. and Chen, W. (2022) Unsupervised Domain Adaptation for Cross-Modality Liver Segmentation via Joint Adversarial Learning and Self-Learning. Applied Soft Computing, 121, Article ID: 108729.
[Google Scholar] [CrossRef] 
[43] You, S., Shen, Y., Wu, G., et al. (2022) Brain MR Images Super-Resolution with the Consistent Features. 2022 14th International Conference on Machine Learning and Computing, Guangzhou, 18-21 February 2022, 501-506.
[Google Scholar] [CrossRef] 
[44] You, S., Lei, B., Wang, S., et al. (2022) Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. IEEE Transactions on Neural Networks and Learning Systems, 1-13.
[Google Scholar] [CrossRef] 
[45] Dharejo, F.A., Zawish, M., Deeba, F., et al. (2022) Multimodal-Boost: Multimodal Medical Image Super-Resolution Using Multi-Attention Network with Wavelet Transform. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-14.
[Google Scholar] [CrossRef] 
[46] Hu, B., Lei, B., Shen, Y., et al. (2021) A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. In: Ma, H.M., et al., Eds., Chinese Conference on Pattern Recognition and Computer Vision, Springer, Cham, 263-274.
[Google Scholar] [CrossRef] 
[47] Hu, B., Shen, Y., Wu, G., et al. (2022) SRT: Shape Reconstruction Transformer for 3D Reconstruction of Point Cloud from 2D MRI. 2022 14th International Conference on Machine Learning and Computing, Guangzhou, 18-21 February 2022, 507-511.
[Google Scholar] [CrossRef] 
[48] Kariyawasam, R.S., Song, M.K.K., Kodali, P., et al. (2022) Automated Medical Image Based Anatomical Point Cloud Generation for Collaborative Real-Time Augmented Reality Applications. Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, Vol. 12037, 172-179.
[Google Scholar] [CrossRef] 
[49] Wang, S.Q. and He, J.H. (2007) Variational Iteration Method for Solving Integro-Differential Equations. Physics Letters A, 367, 188-191.
[Google Scholar] [CrossRef] 
[50] Wang, S.Q. and He, J.H. (2008) Variational Iteration Method for a Nonlinear Reaction-Diffusion Process. International Journal of Chemical Reactor Engineering, 6, 1-8.
[Google Scholar] [CrossRef] 
[51] Wang, S.Q. (2009) A Variational Approach to Nonlinear Two-Point Boundary Value Problems. Computers & Mathematics with Applications, 58, 2452-2455.
[Google Scholar] [CrossRef] 
[52] Mo, L.F. and Wang, S.Q. (2009) A Variational Approach to Nonlinear Two-Point Boundary Value Problems. Nonlinear Analysis: Theory, Methods & Applications, 71, e834-e838.
[Google Scholar] [CrossRef] 
[53] Weiner, M.W., Veitch, D.P., Aisen, P.S., et al. (2015) Impact of the Alzheimer’s Disease Neuroimaging Initiative, 2004 to 2014. Alzheimer’s & Dementia, 11, 865-884.
[Google Scholar] [CrossRef] [PubMed]
[54] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., et al. (2002) Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. Neuroimage, 15, 273-289.
[Google Scholar] [CrossRef] [PubMed]
[55] Lu, H., Plataniotis, K.N. and Venetsanopoulos, A.N. (2006) Multilinear Principal Component Analysis of Tensor Objects for Recognition. 18th International Conference on Pattern Recognition, Vol. 2, 776-779.
[56] Atwood, J. and Towsley, D. (2016) Diffusion-Convolutional Neural Networks. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, 5-10 December 2016, 1-9.
[57] Xu, L., Wu, X., Li, R., et al. (2016) Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers. Journal of Alzheimer’s Disease, 51, 1045-1056.
[Google Scholar] [CrossRef] 
[58] Berron, D., Van Westen, D., Ossenkoppele, R., et al. (2020) Medial Temporal Lobe Connectivity and Its Associations with Cognition in Early Alzheimer’s Disease. Brain, 143, 1233-1248.
[Google Scholar] [CrossRef] [PubMed]