National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
CISTIB Centre for Computational Imaging & Simulation technologies in Biomedicine, School of Computing and the School of Medicine, University of Leeds, Leeds, UK.
Brain Imaging Behav. 2021 Feb;15(1):276-287. doi: 10.1007/s11682-019-00255-9.
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应用于通过对神经影像学数据的功能连接网络（FCNs）分析来进行阿尔茨海默病（AD）的早期诊断。传统的低阶 FCNs 是通过基于静息态功能磁共振成像（R-fMRI）的整个大脑的时间序列相关性来获得的。然而，FCNs 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。为了克服这一缺点，我们开发了一种新的框架，以利用大脑区域之间的高级动态相互作用进行早期 AD 诊断。具体来说，采用滑动窗口方法生成一些 R-fMRI 子序列。然后，使用这些子序列之间的相关性来构建一系列动态 FCNs。然后，基于每个动态 FCN 之间的地形相似性构建高阶 FCNs。之后，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，并在阿尔茨海默病神经影像学倡议（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 分类方面取得了有希望的结果，而且还成功地识别了与疾病相关的生物标志物。
