Zhao Cui, Huang Wei-Jie, Feng Feng, Zhou Bo, Yao Hong-Xiang, Guo Yan-E, Wang Pan, Wang Lu-Ning, Shu Ni, Zhang Xi
Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing; Department of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei Province, China.
State Key Laboratory of Cognitive Neuroscience and Learning; Center for Collaboration and Innovation in Brain and Learning Sciences; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
Neural Regen Res. 2022 Sep;17(9):2014-2021. doi: 10.4103/1673-5374.332161.
Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer's disease (AD) or amnestic mild cognitive impairment (aMCI). However, most studies examined traditional resting state functional connections, ignoring the instantaneous connection mode of the whole brain. In this case-control study, we used a new method called dynamic functional connectivity (DFC) to look for abnormalities in patients with AD and aMCI. We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant, and then used a support vector machine to classify AD patients and normal controls. Finally, we highlighted brain regions and brain networks that made the largest contributions to the classification. We found differences in dynamic function connectivity strength in the left precuneus, default mode network, and dorsal attention network among normal controls, aMCI patients, and AD patients. These abnormalities are potential imaging markers for the early diagnosis of AD.
众多研究表明，患有阿尔茨海默病（AD）或遗忘型轻度认知障碍（aMCI）的个体存在大脑功能连接异常。然而，大多数研究检测的是传统静息态功能连接，忽略了全脑的瞬时连接模式。在这项病例对照研究中，我们使用了一种名为动态功能连接（DFC）的新方法来寻找AD患者和aMCI患者的异常情况。我们为每位参与者从功能磁共振成像数据中计算动态功能连接强度，然后使用支持向量机对AD患者和正常对照进行分类。最后，我们突出显示了对分类贡献最大的脑区和脑网络。我们发现正常对照、aMCI患者和AD患者在左侧楔前叶、默认模式网络和背侧注意网络的动态功能连接强度存在差异。这些异常是AD早期诊断的潜在影像学标志物。
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