中国神经再生研究(英文版)

中国神经再生研究(英文版) ›› 2022, Vol. 17 ›› Issue (9): 2014-2021.doi: 10.4103/1673-5374.332161

• 原著:退行性病与再生 • 上一篇    下一篇

阿尔茨海默病动态功能连接的异常表征

  

  • 出版日期:2022-09-15 发布日期:2022-03-05

Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Cui Zhao1, 2, #, Wei-Jie Huang3, 4, 5, #, Feng Feng6, 7, Bo Zhou1, Hong-Xiang Yao8, Yan-E Guo1, Pan Wang9, Lu-Ning Wang1, Ni Shu3, 4, 5, *, Xi Zhang1, *   

  1. 1Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China; 2Department of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei Province, China; 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; 4Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China; 5Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; 6Department of Neurology, First Medical Center, Chinese PLA General Hospital, Beijing, China; 7Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, China; 8Department of Radiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China; 9Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
  • Online:2022-09-15 Published:2022-03-05
  • Contact: Ni Shu, PhD, nshu@bnu.edu.cn; Xi Zhang, MD, smrc301@163.com.
  • Supported by:
    This study was supported by the National Natural Science Foundation of China, No. 81471120; and Fund Projects in Technology of the Foundation Strengthening Program of China, No. 2019-JCJQ-JJ-151 (both to XZ).

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摘要：

大量研究表明，阿尔茨海默病和遗忘型轻型认知功能障碍患者存在脑功能连接异常，但大多是基于传统的静息状态功能连接，忽略了全脑的瞬时连接模式。此次病例-对照研究使用了一种新的动态功能连接方法来寻找阿尔茨海默病和遗忘型轻型认知功能障碍患者的异常情况。基于功能磁共振成像数据计算动态功能连接强度，然后使用支持向量机（一种机器学习算法）进行分类，最后获得对分类贡献最大的大脑区域和大脑网络。结果显示正常人、遗忘型轻型认知功能障碍和阿尔茨海默病左楔前叶、默认网络和背侧注意网络的动态功能连接强度差异。这些异常特征有望成为阿尔茨海默病早期诊断的影像标志物。

https://orcid.org/0000-0002-4598-714X (Xi Zhang)

关键词: 阿尔茨海默病, 遗忘型轻型认知障碍, 动态功能连接, 支持向量机, 静息态功能磁共振成像, 默认网络, 顶额叶回, 血氧水平依赖性

Abstract: 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. 

Key words: Alzheimer’s disease, amnestic mild cognitive impairment, blood oxygen level-dependent, default mode network, dynamic functional connectivity, frontoparietal network, resting-state functional magnetic resonance imaging, support vector machine