基于交替式超边神经元算法的阿尔茨海默症多模态脑网络融合
A Multimodal Brain Network Fusing Based on Interleaved Hyperedge Neurons Algorithm for Alzheimer’s Disease
DOI: 10.12677/CSA.2022.129224, PDF,    科研立项经费支持
作者: 潘俊任*, 荆常宏：中国科学院深圳先进技术研究院，广东 深圳
关键词: 生成对抗策略；超图；多模态融合；脑网络计算；Generative Adversarial Strategy； Hypergraph； Multimodal Fusion； Brain Network Computing
摘要: 阿尔茨海默症(Alzheimer’s Disease, AD)是一种常见于中老年人的神经退行性疾病，其病理机制至今尚未明确。通过多模态脑影像来表征脑网络在阿尔茨海默症研究中已显示出巨大的潜力和前景。然而，由于多模态数据之间的异质性，现有大多数融合算法都不能有效利用不同模态数据之间的功能–结构互补信息。为解决上述问题，本文基于超图理论和生成对抗策略，提出了交替式超边神经元算法和最优超图同态算法的多模态脑网络计算方法。具体来说，首先将功能磁共振(fMRI)和弥散张量成像(DTI)利用最优超图同态算法进行超图数据构建，再利用交替式超边神经元算法对fMRI和DTI数据的功能–结构互补特征进行深度融合，最终学习得到疾病相关的多模态脑网络。该模型的优势在于能够最大限度地挖掘疾病相关互补信息，进行多层级交替式深度融合。实验结果表明，该模型不仅能提高阿尔茨海默症早期识别性能，而且能有效检测与阿尔茨海默症相关的异常脑连接作为疾病标志物，为病理机制溯源提供基础。
Abstract: Alzheimer’s disease is a neurodegenerative disease commonly occurring in aged people, and its pathological mechanism has not been revealed. Characterizing brain networks through multi-modal brain imaging has shown great potential and promise in Alzheimer’s disease research. However, due to the heterogeneity among multimodal data, most existing multimodal fusion methods cannot effectively utilize the functional-structural complementary information between different modal data. In order to solve the above problems, based on the hypergraph theory and generative adversarial strategy, this paper proposes a multimodal brain network computing method based on the interleaved hyperedge neurons algorithm and the optimal hypergraph homomorphism algorithm. Specifically, hypergraph data is constructed from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) by using the optimal hypergraph homomorphism algorithm. Then use the interleaved hyperedge neurons algorithm to deeply fuse the functional-structural complementary information between fMRI and DTI. Finally, use this fused information to compute a multimodal brain network. The advantage of the proposed model is that it can efficiently integrate the functional and structural information provided by fMRI and DTI to extract disease-related complementary information. The experimental results show that the model can not only improve the early recognition performance of Alzheimer’s disease but also effectively detect abnormal brain connections related to Alzheimer’s disease as disease markers, providing a foundation for under-standing the pathological mechanism.
文章引用：潘俊任, 荆常宏. 基于交替式超边神经元算法的阿尔茨海默症多模态脑网络融合[J]. 计算机科学与应用, 2022, 12(9): 2203-2216. https://doi.org/10.12677/CSA.2022.129224

参考文献

[1] Daviglus, M.L., Bell, C.C., Berrettini, W., et al. (2010) National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer Disease and Cognitive Decline. Annals of Internal Medicine, 153, 176-181. [Google Scholar] [CrossRef] [PubMed]
[2] Alzheimer’s Association (2019) 2019 Alz-heimer’s Disease Facts and Figures. Alzheimer’s & Dementia, 15, 321-387. [Google Scholar] [CrossRef] 
[3] Canter, R.G., Penney, J. and Tsai, L.H. (2016) The Road to Restor-ing Neural Circuits for the Treatment of Alzheimer’s Disease. Nature, 539, 187-196. [Google Scholar] [CrossRef] [PubMed]
[4] Wang, S., Shen, Y., Hu, J., et al. (2015) Hadoop-Based Analysis for Large-Scale Click-Through Patterns in 4G Network. International Conference on Wireless Algorithms, Systems, and Ap-plications, Qufu, 10-12 August 2015, 829-835. [Google Scholar] [CrossRef] 
[5] 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] 
[6] Wang, S.Q. and Li, H.X. (2011) Quantitative Construction of Regulatory Networks Using Multiple Sources of Knowledge. 2011 International Conference on Ma-chine Learning and Cybernetics, Vol. 1, Guilin, 10-13 July 2011, 91-96. [Google Scholar] [CrossRef] 
[7] Wang, S., Hu, J., Shen, Y., et al. (2014) Modeling and Analy-sis of Gene Regulatory Networks with a Bayesian-Driven Approach. 2014 14th International Symposium on Communi-cations and Information Technologies (ISCIT), Incheon, 24-26 September 2014, 289-293. [Google Scholar] [CrossRef] 
[8] Wang, S.Q. and Li, H.X. (2012) Defining Transcriptional Net-work by Combining Expression Data with Multiple Sources of Prior Knowledge. 2012 International Conference on Sys-tem Science and Engineering (ICSSE), Dalian, 30 June-2 July 2012, 102-106. [Google Scholar] [CrossRef] 
[9] 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] 
[10] Wang, S., Shen, Y., Zeng, D., et al. (2018) Bone Age As-sessment Using Convolutional Neural Networks. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, 26-28 May 2018, 175-178. [Google Scholar] [CrossRef] 
[11] Wang, S., Wang, X., Shen, Y., et al. (2020) An Ensem-ble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity. IEEE Transactions on Sys-tems, Man, and Cybernetics: Systems, 52, 426-437. [Google Scholar] [CrossRef] 
[12] 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]
[13] 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] 
[14] Wu, K., Shen, Y. and Wang, S. (2018) 3D Convolutional Neural Network for Regional Precipitation Nowcasting. Image Signal Process, 7, 200-212. (In Chinese) [Google Scholar] [CrossRef] 
[15] 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 (ICMLA), Orlando, 17-20 December 2018, 517-523. [Google Scholar] [CrossRef] 
[16] 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. Neu-rocomputing, 333, 145-156. [Google Scholar] [CrossRef] 
[17] 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] 
[18] 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] 
[19] 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] 
[20] 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]
[21] 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] 
[22] 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] 
[23] Dai, Z., Lin, Q., Li, T., et al. (2019) Disrupted Structural and Functional Brain Networks in Alzheimer’s Disease. Neurobiology of Aging, 75, 71-82. [Google Scholar] [CrossRef] [PubMed]
[24] Dennis, E.L. and Thompson, P.M. (2014) Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease. Neuropsychology Review, 24, 49-62. [Google Scholar] [CrossRef] [PubMed]
[25] Jeon, E., Kang, E., Lee, J., et al. (2020) Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, 4-8 October 2020, 397-406. [Google Scholar] [CrossRef] 
[26] Yu, S., Wang, S., Xiao, X., et al. (2020) Multi-Scale En-hanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection. International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, 4-8 October 2020, 228-237. [Google Scholar] [CrossRef] 
[27] Baltrušaitis, T., Ahuja, C. andMorency, L.P. (2019) Multi-modal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 423-443. [Google Scholar] [CrossRef] 
[28] Munsell, B.C., Wu, G., Gao, Y., et al. (2016) Identifying Rela-tionships in Functional and Structural Connectome Data Using a Hypergraph Learning Method. International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, 17-21 October 2016, 9-17. [Google Scholar] [CrossRef] [PubMed]
[29] Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014) Generative Adversarial Nets. 28th Conference on Neural Information Processing Systems, Vol. 27, Montreal, 8-13 De-cember 2014.
[30] Wang, S., Wang, X., Hu, Y., et al. (2020) Diabetic Retinopathy Diagnosis Using Multichannel Gen-erative Adversarial Network with Semisupervision. IEEE Transactions on Automation Science and Engineering, 18, 574-585. [Google Scholar] [CrossRef] 
[31] Hu, S., Shen, Y., Wang, S., et al. (2020) Brain MR to Pet Syn-thesis via Bidirectional Generative Adversarial Network. International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, 4-8 October 2020, 698-707. [Google Scholar] [CrossRef] 
[32] Hu, S., Yu, W., Chen, Z., et al. (2020) Medical Image Recon-struction Using Generative Adversarial Network for Alzheimer’s Disease Assessment with Class-Imbalance Problem. 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, 11-14 December 2020, 1323-1327. [Google Scholar] [CrossRef] 
[33] Lei, B., Xia, Z., Jiang, F., et al. (2020) Skin Lesion Seg-mentation via Generative Adversarial Networks with Dual Discriminators. Medical Image Analysis, 64, Article ID: 101716. [Google Scholar] [CrossRef] [PubMed]
[34] Zuo, Q., Lei, B., Shen, Y., et al. (2021) Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction. Chinese Con-ference on Pattern Recognition and Computer Vision (PRCV), Beijing, 29 October-1 November 2021, 479-490. [Google Scholar] [CrossRef] 
[35] 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] 
[36] Zu, C., Gao, Y., Munsell, B., et al. (2016) Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph. International Workshop on Machine Learning in Medical Imaging, Athens, 17 October 2016, 1-9. [Google Scholar] [CrossRef] [PubMed]
[37] Liu, M., Zhang, J., Yap, P.T., et al. (2017) View-Aligned Hy-pergraph Learning for Alzheimer’s Disease Diagnosis with Incomplete Multi-Modality Data. Medical Image Analysis, 36, 123-134. [Google Scholar] [CrossRef] [PubMed]
[38] Shao, W., Peng, Y., Zu, C., et al. (2020) Hypergraph Based Mul-ti-Task Feature Selection for Multimodal Classification of Alzheimer’s Disease. Computerized Medical Imaging and Graphics, 80, Article ID: 101663. [Google Scholar] [CrossRef] [PubMed]
[39] Aviles-Rivero, A.I., Runkel, C., Papadakis, N., et al. (2022) Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification. arXiv preprint arXiv:2204.02399.
[40] 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] 
[41] Wang, S.Q. (2009) A Variational Approach to Nonlinear Two-Point Boundary Value Problems. Computers & Mathematics with Applications, 58, 2452-2455. [Google Scholar] [CrossRef] 
[42] 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] 
[43] 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] 
[44] 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] 
[45] 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 (ICMIPE), Shenzhen, 22-24 November 2019, 1-5. [Google Scholar] [CrossRef] 
[46] Ji, J., Liu, J., Han, L. and Wang, F. (2021) Estimating Effective Connectivity by Recurrent Generative Adversarial Networks. IEEE Transactions on Medical Imaging, 40, 3326-3336. [Google Scholar] [CrossRef] 
[47] Yan, C. and Zang, Y. (2010) DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI. Frontiers in Systems Neuroscience, 13, Article No. 13. [Google Scholar] [CrossRef] [PubMed]
[48] Wang, J., Wang, X., Xia, M., et al. (2015) GRETNA: A Graph Theoretical Network Analysis Toolbox for Imaging Connectomics. Frontiers in Human Neuroscience, 9, Article No. 386. [Google Scholar] [CrossRef] [PubMed]
[49] Cui, Z., Zhong, S., Xu, P., et al. (2013) PANDA: A Pipeline Toolbox for Analyzing Brain Diffusion Images. Frontiers in Human Neuroscience, 7, Article No. 42. [Google Scholar] [CrossRef] [PubMed]
[50] Jiang, J., Wei, Y., Feng, Y., et al. (2019) Dynamic Hypergraph Neural Networks. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao (China), 10-16 August 2019, 2635-2641. [Google Scholar] [CrossRef] 
[51] Zhang, L., Wang, L., Gao, J., et al. (2021) Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Medical Image Analysis, 72, Article ID: 102082. [Google Scholar] [CrossRef] [PubMed]
[52] Dyrba, M., Grothe, M., Kirste, T., et al. (2015) Multimodal Analysis of Functional and Structural Disconnection in Alzheimer’s Disease Using Multiple Kernel SVM. Human Brain Mapping, 36, 2118-2131. [Google Scholar] [CrossRef] [PubMed]
[53] Songdechakraiwut, T., Shen, L. and Chung, M. (2021) Topological Learning and Its Application to Multimodal Brain Network Integration. International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, 27 September-1 October 2021, 166-176. [Google Scholar] [CrossRef] [PubMed]