A Triple-Network Dynamic Connection Study in Alzheimer's Disease

Xianglian Meng, Yue Wu, Yanfeng Liang, Dongdong Zhang, Zhe Xu, Xiong Yang, Li Meng

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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.
Original languageEnglish
Article number862958
Number of pages10
JournalFrontiers in Psychiatry
Early online date4 Apr 2022
Publication statusE-pub ahead of print - 4 Apr 2022


  • Psychiatry
  • Alzheimer's disease
  • large-scale brain networks
  • triple-network
  • functional connectivity
  • dynamic cross-network interaction


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