Alzheimer’s disease classification based on nonlinear high-order features and hypergraph convolutional neural network_Journal of Biomedical Engineering

Authors：

ZENG An ¹ , LUO Bairong ¹ ,  PAN Dan ² , RONG Huabin ¹ , CAO Jianfeng ¹ , ZHANG Xiaobo ³ , LIN Jing ⁴ , YANG Yang ⁵ , LIU Jun ⁶

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, P. R. China;
2. School of Electronics and Information Engineering, Guangdong University of Technology and Education, Guangzhou 510665, P. R. China;
3. School of Automation, Guangdong University of Technology, Guangzhou 510006, P. R. China;
4. Classroom Management Center, Guangdong University of Technology, Guangzhou 510006, P. R. China;
5. Information Management Department, Guangdong Provincial People’s Hospital, Guangzhou 510080, P. R. China;
6. Neurology Department, Affiliated Second Hospital of Guangzhou Medical University, Guangzhou 510260, P. R. China;

Corresponding author：

PAN Dan, Email: pandan@gpnu.edu.cn

Keywords：

Alzheimer’s disease; Classification; Functional magnetic resonance imaging data; Region of interest; Nonlinear high-order features; Hypergraph convolutional neural network

DOI：

10.7507/1001-5515.202305060

Video：

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Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that damages patients’ memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

Citation： ZENG An, LUO Bairong, PAN Dan, RONG Huabin, CAO Jianfeng, ZHANG Xiaobo, LIN Jing, YANG Yang, LIU Jun. Alzheimer’s disease classification based on nonlinear high-order features and hypergraph convolutional neural network. Journal of Biomedical Engineering, 2023, 40(5): 852-858. doi: 10.7507/1001-5515.202305060 Copy

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17.	Jie Biao, Wee C Y, Shen Dinggang, et al. Hyper-connectivity of functional networks for brain disease diagnosis. Med Image Anal, 2016, 32: 84-100.
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19.	梁旭峰, 黄雄. 基于SVR-RBF的在线情感识别研究. 电子设计工程, 2020(3): 103-108.
20.	叶小泉, 吴云峰. 基于支持向量机递归特征消除和特征聚类的致癌基因选择方法. 厦门大学学报(自然科学版), 2018, 57(5): 702-707.
21.	陈鹏, 李擎, 张德政, 等. 多模态学习方法综述. 工程科学学报, 2020, 42(5): 557-569.
22.	Jiang Jianwen, Wei Yuxuan, Feng Yifan, et al. Dynamic hypergraph neural networks// International Joint Conference on Artificial Intelligence (IJCAI). Aomen: IJCAI, 2019: 2635-2641.
23.	Yadati N, Nimishakavi M, Yadav P, et al. HyperGCN: A new method for training graph convolutional networks on hypergraphs// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Vancouver: NeurIPS, 2019: 1511–1522.
24.	曾安, 贾龙飞, 潘丹, 等. 基于卷积神经网络和集成学习的阿尔茨海默症早期诊断. 生物医学工程学杂志, 2019, 36(5): 711-719.
25.	Kingma D P, Ba J. Adam: A method for stochastic optimization. ArXiv preprint arXiv, 2014: 1412.6980.
26.	Gadgil S, Zhao Q, Pfefferbaum A, et al. Spatio-temporal graph convolution for resting-state fmri analysis// Medical Image Computing and Computer Assisted Intervention (MICCAI). Online: ISMICCAI, 2020: 528-538.

1. 戴建英, 王辉, 刘敏, 等. 阿尔茨海默病的病理模型及代谢组学研究进展. 药学服务与研究, 2021, 21: 241-247, 320.
2. Gauthier S, Rosa-Neto P, Morais J, et al. World Alzheimer Report 2021: Journey through the diagnosis of dementia. Montreal: Alzheimer’s Disease International, 2021.
3. Mcdade E, Bateman R J. Stop Alzheimer’s before it starts. Nature, 2017, 547(7662): 153-155.
4. Zhan Yafeng, Ma Jianhua, Alexander-Bloch A F, et al. Longitudinal study of impaired intra-and inter-network brain connectivity in subjects at high risk for Alzheimer’s disease. J Alzheimers Dis, 2016, 52(3): 913-927.
5. Dautricourt S, De F R, Landeau B, et al. Longitudinal changes in hippocampal network connectivity in Alzheimer’s disease. Ann Neurol, 2021, 90(3): 391-406.
6. Ju Ronghui, Hu Chenhui, Pan Zhou, et al. Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinform, 2017, 16(1): 244-257.
7. Wee C Y, Yap P T, Zhang Daoqiang, et al. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage, 2012, 59(3): 2045-2056.
8. Li H, Parikh N A, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci, 2018: 491.
9. Eslami T, Mirjalili V, Fong A, et al. ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform, 2019, 13: 70.
10. 李煜. 基于静息态功能磁共振成像的脑区划分研究及其在脑疾病中的应用. 合肥: 中国科学技术大学, 2022.
11. 蒋玉英, 陈心雨, 李广明, 等. 图神经网络及其在图像处理领域的研究进展. 计算机工程与应用, 2023, 59(7): 15-30.
12. Wen Guangqi, Cao Peng, Bao Huiwei, et al. MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput Bio Med, 2022, 142: 105239.
13. Li Xiaoxiao, Zhou Yuan, Dvornek N, et al. Braingnn: Interpretable brain graph neural network for fMRI analysis. Med Image Anal, 2021, 74: 102233.
14. Yan Chaogan, Zang Yufeng. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Neurosci, 2010, 4: 13.
15. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 2002, 15(1): 273-289.
16. 于海燕, 钱志余, 卢光明, 等. 基于功能磁共振成像 (fMRI) 的脑功能逻辑任务功能区BOLD信号研究. 生物医学工程学杂志, 2009, 26(6): 1171-1176.
17. Jie Biao, Wee C Y, Shen Dinggang, et al. Hyper-connectivity of functional networks for brain disease diagnosis. Med Image Anal, 2016, 32: 84-100.
18. 王泽文, 林敏, 周立波, 等. 一种基于模糊模型和 SVR-RBF 的航空目标空对空武器效能预测. 计算机集成制造系统-CIMS, 2020(3): 669-681.
19. 梁旭峰, 黄雄. 基于SVR-RBF的在线情感识别研究. 电子设计工程, 2020(3): 103-108.
20. 叶小泉, 吴云峰. 基于支持向量机递归特征消除和特征聚类的致癌基因选择方法. 厦门大学学报(自然科学版), 2018, 57(5): 702-707.
21. 陈鹏, 李擎, 张德政, 等. 多模态学习方法综述. 工程科学学报, 2020, 42(5): 557-569.
22. Jiang Jianwen, Wei Yuxuan, Feng Yifan, et al. Dynamic hypergraph neural networks// International Joint Conference on Artificial Intelligence (IJCAI). Aomen: IJCAI, 2019: 2635-2641.
23. Yadati N, Nimishakavi M, Yadav P, et al. HyperGCN: A new method for training graph convolutional networks on hypergraphs// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Vancouver: NeurIPS, 2019: 1511–1522.
24. 曾安, 贾龙飞, 潘丹, 等. 基于卷积神经网络和集成学习的阿尔茨海默症早期诊断. 生物医学工程学杂志, 2019, 36(5): 711-719.
25. Kingma D P, Ba J. Adam: A method for stochastic optimization. ArXiv preprint arXiv, 2014: 1412.6980.
26. Gadgil S, Zhao Q, Pfefferbaum A, et al. Spatio-temporal graph convolution for resting-state fmri analysis// Medical Image Computing and Computer Assisted Intervention (MICCAI). Online: ISMICCAI, 2020: 528-538.

Journal of Biomedical Engineering

Alzheimer’s disease classification based on nonlinear high-order features and hypergraph convolutional neural network

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