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

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A Triple-Network Dynamic Connection Study in Alzheimer's Disease. / Meng, Xianglian; Wu, Yue; Liang, Yanfeng; Zhang, Dongdong; Xu, Zhe; Yang, Xiong; Meng, Li.

In: Frontiers in Psychiatry, Vol. 13, 862958, 04.04.2022.

Research output: Contribution to journalArticlepeer-review

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Meng, Xianglian ; Wu, Yue ; Liang, Yanfeng ; Zhang, Dongdong ; Xu, Zhe ; Yang, Xiong ; Meng, Li. / A Triple-Network Dynamic Connection Study in Alzheimer's Disease. In: Frontiers in Psychiatry. 2022 ; Vol. 13.

Bibtex

@article{f22efe4aee8e42d5af82e1fbec6d781e,
title = "A Triple-Network Dynamic Connection Study in Alzheimer's Disease",
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.",
keywords = "Psychiatry, Alzheimer's disease, large-scale brain networks, triple-network, functional connectivity, dynamic cross-network interaction",
author = "Xianglian Meng and Yue Wu and Yanfeng Liang and Dongdong Zhang and Zhe Xu and Xiong Yang and Li Meng",
note = "{\textcopyright} 2022 Meng, Wu, Liang, Zhang, Xu, Yang and Meng. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/ ",
year = "2022",
month = apr,
day = "4",
doi = "10.3389/fpsyt.2022.862958",
language = "English",
volume = "13",
journal = "Frontiers in Psychiatry",
issn = "1664-0640",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - A Triple-Network Dynamic Connection Study in Alzheimer's Disease

AU - Meng, Xianglian

AU - Wu, Yue

AU - Liang, Yanfeng

AU - Zhang, Dongdong

AU - Xu, Zhe

AU - Yang, Xiong

AU - Meng, Li

N1 - © 2022 Meng, Wu, Liang, Zhang, Xu, Yang and Meng. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/

PY - 2022/4/4

Y1 - 2022/4/4

N2 - 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.

AB - 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.

KW - Psychiatry

KW - Alzheimer's disease

KW - large-scale brain networks

KW - triple-network

KW - functional connectivity

KW - dynamic cross-network interaction

U2 - 10.3389/fpsyt.2022.862958

DO - 10.3389/fpsyt.2022.862958

M3 - Article

VL - 13

JO - Frontiers in Psychiatry

JF - Frontiers in Psychiatry

SN - 1664-0640

M1 - 862958

ER -