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

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        Author
        Meng, Xianglian
        Wu, Yue
        Liang, Yanfeng
        Zhang, Dongdong
        Xu, Zhe
        Yang, Xiong
        Meng, Li
        Attention
        2299/25484
        Abstract
        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.
        Publication date
        2022-04-04
        Published in
        Frontiers in Psychiatry
        Published version
        https://doi.org/10.3389/fpsyt.2022.862958
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/25484
        Relations
        School of Physics, Engineering & Computer Science
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