Meng Xianglian, Wu Yue, Liang Yanfeng, Zhang Dongdong, Xu Zhe, Yang Xiong, Meng Li
School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China.
School of Basic Medical Sciences, Jiamusi University, Jiamusi, China.
Front Psychiatry. 2022 Apr 4;13:862958. doi: 10.3389/fpsyt.2022.862958. eCollection 2022.
Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.
阿尔茨海默病（AD）与大规模脑网络的异常组织和功能有关。我们应用组独立成分分析（Group ICA）在25例AD患者、60例轻度认知障碍（MCI）患者和60例认知正常（CN）受试者中构建由突显网络（SN）、中央执行网络（CEN）和默认模式网络（DMN）组成的三重网络。为了探究动态功能网络连接性（dFNC），我们使用基于k均值聚类的Group ICA分析（GDA-k均值）研究了受试者的动态时变三重网络相互作用。通过方差分析，三组（AD、MCI、CN）中特定脑状态的网络相互作用指数均值（meanNII）显示出显著差异。为了验证研究结果的稳健性，采用支持向量机（SVM），以meanNII、性别和年龄为特征进行分类。该方法在区分AD与CN、AD与MCI以及MCI与CN时，准确率分别为95%、94%和77%。在我们的研究中，结果表明三重网络功能相互作用的动态特征有助于研究AD的潜在病理生理学。它为AD脑动力学失调提供了有力证据。
