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Triple-network analysis of Alzheimer’s disease based on the energy landscape

INTRODUCTION: Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease i...

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Autores principales: Li, Youjun, An, Simeng, Zhou, Tianlin, Su, Chunwang, Zhang, Siping, Li, Chenxi, Jiang, Junjie, Mu, Yunfeng, Yao, Nan, Huang, Zi-Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242117/
https://www.ncbi.nlm.nih.gov/pubmed/37287802
http://dx.doi.org/10.3389/fnins.2023.1171549
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author Li, Youjun
An, Simeng
Zhou, Tianlin
Su, Chunwang
Zhang, Siping
Li, Chenxi
Jiang, Junjie
Mu, Yunfeng
Yao, Nan
Huang, Zi-Gang
author_facet Li, Youjun
An, Simeng
Zhou, Tianlin
Su, Chunwang
Zhang, Siping
Li, Chenxi
Jiang, Junjie
Mu, Yunfeng
Yao, Nan
Huang, Zi-Gang
author_sort Li, Youjun
collection PubMed
description INTRODUCTION: Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer’s disease (AD) affects the state transitions of functional networks in the resting state. METHODS: Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. RESULTS: AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects’ dynamic features are correlated with clinical index. DISCUSSION: The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.
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spelling pubmed-102421172023-06-07 Triple-network analysis of Alzheimer’s disease based on the energy landscape Li, Youjun An, Simeng Zhou, Tianlin Su, Chunwang Zhang, Siping Li, Chenxi Jiang, Junjie Mu, Yunfeng Yao, Nan Huang, Zi-Gang Front Neurosci Neuroscience INTRODUCTION: Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer’s disease (AD) affects the state transitions of functional networks in the resting state. METHODS: Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. RESULTS: AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects’ dynamic features are correlated with clinical index. DISCUSSION: The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients. Frontiers Media S.A. 2023-05-23 /pmc/articles/PMC10242117/ /pubmed/37287802 http://dx.doi.org/10.3389/fnins.2023.1171549 Text en Copyright © 2023 Li, An, Zhou, Su, Zhang, Li, Jiang, Mu, Yao, Huang and Alzheimer’s Disease Neuroimaging Initiative. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Youjun
An, Simeng
Zhou, Tianlin
Su, Chunwang
Zhang, Siping
Li, Chenxi
Jiang, Junjie
Mu, Yunfeng
Yao, Nan
Huang, Zi-Gang
Triple-network analysis of Alzheimer’s disease based on the energy landscape
title Triple-network analysis of Alzheimer’s disease based on the energy landscape
title_full Triple-network analysis of Alzheimer’s disease based on the energy landscape
title_fullStr Triple-network analysis of Alzheimer’s disease based on the energy landscape
title_full_unstemmed Triple-network analysis of Alzheimer’s disease based on the energy landscape
title_short Triple-network analysis of Alzheimer’s disease based on the energy landscape
title_sort triple-network analysis of alzheimer’s disease based on the energy landscape
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242117/
https://www.ncbi.nlm.nih.gov/pubmed/37287802
http://dx.doi.org/10.3389/fnins.2023.1171549
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