Neural Regeneration Research ›› 2022, Vol. 17 ›› Issue (9): 2014-2021.doi: 10.4103/1673-5374.332161

Previous Articles     Next Articles

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).

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