Machine learning methods have been widely used for early diagnosis of Alzheimer’s disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional magnetic resonance imaging (R-fMRI). However, FCNs overlook inter-region interactions, which limits application to brain disease diagnosis. To overcome this drawback, we develop a novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis. Specifically, a sliding window approach is employed to generate some R-fMRI sub-series. The correlations among these sub-series are then used to construct a series of dynamic FCNs. High-order FCNs based on the topographical similarity between each pair of the dynamic FCNs are then constructed. Afterward, a local weight clustering method is used to extract effective features of the network, and the least absolute shrinkage and selection operation method is chosen for feature selection. A support vector machine is employed for classification, and the dynamic high-order network approach is evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our experimental results demonstrate that the proposed approach not only achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers.

机器学习方法已被广泛用于通过神经影像数据的功能连接网络（FCN）分析对阿尔茨海默氏病（AD）进行早期诊断。通过基于静止状态功能磁共振成像（R-fMRI）的整个大脑的时间序列相关性，可以获得常规的低阶FCN。但是，FCN忽略了区域间的相互作用，这限制了其在脑疾病诊断中的应用。为克服此缺点，我们开发了一种新颖的框架来利用大脑区域之间的高级动态相互作用来进行早期AD诊断。具体而言，采用滑动窗口方法生成一些R-fMRI子系列。这些子系列之间的相关性随后被用来构建一系列动态FCN。然后，基于每对动态FCN之间的地形相似性构建高阶FCN。然后，使用局部权重聚类方法提取网络的有效特征，并选择最小绝对收缩和选择操作方法进行特征选择。使用支持向量机进行分类，并在阿尔茨海默氏病神经成像计划（ADNI）数据集上评估动态高阶网络方法。我们的实验结果表明，所提出的方法不仅在AD分类中取得了可喜的结果，而且还成功地识别了疾病相关的生物标记。选择最小的绝对收缩和选择操作方法进行特征选择。使用支持向量机进行分类，并在阿尔茨海默氏病神经成像计划（ADNI）数据集上评估动态高阶网络方法。我们的实验结果表明，所提出的方法不仅在AD分类中取得了可喜的结果，而且还成功地识别了疾病相关的生物标记。选择最小绝对收缩和选择操作方法进行特征选择。使用支持向量机进行分类，并在阿尔茨海默氏病神经成像计划（ADNI）数据集上评估动态高阶网络方法。我们的实验结果表明，所提出的方法不仅在AD分类中取得了可喜的结果，而且还成功地识别了疾病相关的生物标记。