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Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease

BACKGROUND: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer’s disease (AD). OBJECTIVE: Coactivation pattern (CAP) analysis can...

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Autores principales: Zhang, Si-Ping, Mao, Bi, Zhou, Tianlin, Su, Chun-Wang, Li, Chenxi, Jiang, Junjie, An, Simeng, Yao, Nan, Li, Youjun, 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/PMC10631782/
https://www.ncbi.nlm.nih.gov/pubmed/37946728
http://dx.doi.org/10.3389/fnins.2023.1198839
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author Zhang, Si-Ping
Mao, Bi
Zhou, Tianlin
Su, Chun-Wang
Li, Chenxi
Jiang, Junjie
An, Simeng
Yao, Nan
Li, Youjun
Huang, Zi-Gang
author_facet Zhang, Si-Ping
Mao, Bi
Zhou, Tianlin
Su, Chun-Wang
Li, Chenxi
Jiang, Junjie
An, Simeng
Yao, Nan
Li, Youjun
Huang, Zi-Gang
author_sort Zhang, Si-Ping
collection PubMed
description BACKGROUND: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer’s disease (AD). OBJECTIVE: Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD. METHODS: We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects’ clinical index was further analyzed. RESULTS: The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients’ clinical Mini-Mental State Examination assessment scale scores. CONCLUSION: This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.
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spelling pubmed-106317822023-11-09 Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease Zhang, Si-Ping Mao, Bi Zhou, Tianlin Su, Chun-Wang Li, Chenxi Jiang, Junjie An, Simeng Yao, Nan Li, Youjun Huang, Zi-Gang Front Neurosci Neuroscience BACKGROUND: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer’s disease (AD). OBJECTIVE: Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD. METHODS: We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects’ clinical index was further analyzed. RESULTS: The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients’ clinical Mini-Mental State Examination assessment scale scores. CONCLUSION: This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP. Frontiers Media S.A. 2023-10-25 /pmc/articles/PMC10631782/ /pubmed/37946728 http://dx.doi.org/10.3389/fnins.2023.1198839 Text en Copyright © 2023 Zhang, Mao, Zhou, Su, Li, Jiang, An, Yao, Li and Huang. 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
Zhang, Si-Ping
Mao, Bi
Zhou, Tianlin
Su, Chun-Wang
Li, Chenxi
Jiang, Junjie
An, Simeng
Yao, Nan
Li, Youjun
Huang, Zi-Gang
Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title_full Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title_fullStr Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title_full_unstemmed Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title_short Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease
title_sort frequency dependent whole-brain coactivation patterns analysis in alzheimer’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631782/
https://www.ncbi.nlm.nih.gov/pubmed/37946728
http://dx.doi.org/10.3389/fnins.2023.1198839
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