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.
阿尔茨海默症（AD）是一种不可逆转的大脑神经退化性疾病，会损害患者记忆力和认知能力。因此，AD诊断具有重要意义。大脑感兴趣区域（ROI）之间往往是多个区域以非线性的方式协同交互，充分利用此类非线性高阶交互特征有助于提高AD诊断分类的准确性。为此，提出基于非线性高阶特征提取和三维超图神经网络相结合的AD计算机辅助诊断框架。首先针对ROI数据使用基于径向基函数核的支持向量机回归模型训练出基估计器，再通过基于基估计器的递归特征消除算法提取功能性磁共振成像（fMRI）数据中的非线性高阶特征，进而将特征构造成超图，最后基于fMRI数据的四维时空特性搭建超图卷积神经网络模型来进行分类。阿尔茨海默症神经影像倡议（ADNI）数据库上的实验结果表明，所提框架在AD/正常对照（NC）分类任务上的效果相较于Hyper Graph Convolutional Network（HyperGCN）框架提高了8%，相较于传统二维线性特征提取方法提高了12%。综上，本文框架在AD分类效果上较主流深度学习方法有所提升，可为AD计算机辅助诊断提供有效依据。.
Keywords: Alzheimer’s disease; Classification; Functional magnetic resonance imaging data; Hypergraph convolutional neural network; Nonlinear high-order features; Region of interest.