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Resting-State Brain Organization Revealed by Functional Covariance Networks

BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale netw...

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Autores principales: Zhang, Zhiqiang, Liao, Wei, Zuo, Xi-Nian, Wang, Zhengge, Yuan, Cuiping, Jiao, Qing, Chen, Huafu, Biswal, Bharat B., Lu, Guangming, Liu, Yijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236756/
https://www.ncbi.nlm.nih.gov/pubmed/22174905
http://dx.doi.org/10.1371/journal.pone.0028817
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author Zhang, Zhiqiang
Liao, Wei
Zuo, Xi-Nian
Wang, Zhengge
Yuan, Cuiping
Jiao, Qing
Chen, Huafu
Biswal, Bharat B.
Lu, Guangming
Liu, Yijun
author_facet Zhang, Zhiqiang
Liao, Wei
Zuo, Xi-Nian
Wang, Zhengge
Yuan, Cuiping
Jiao, Qing
Chen, Huafu
Biswal, Bharat B.
Lu, Guangming
Liu, Yijun
author_sort Zhang, Zhiqiang
collection PubMed
description BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.
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spelling pubmed-32367562011-12-15 Resting-State Brain Organization Revealed by Functional Covariance Networks Zhang, Zhiqiang Liao, Wei Zuo, Xi-Nian Wang, Zhengge Yuan, Cuiping Jiao, Qing Chen, Huafu Biswal, Bharat B. Lu, Guangming Liu, Yijun PLoS One Research Article BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale. Public Library of Science 2011-12-13 /pmc/articles/PMC3236756/ /pubmed/22174905 http://dx.doi.org/10.1371/journal.pone.0028817 Text en Zhang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhang, Zhiqiang
Liao, Wei
Zuo, Xi-Nian
Wang, Zhengge
Yuan, Cuiping
Jiao, Qing
Chen, Huafu
Biswal, Bharat B.
Lu, Guangming
Liu, Yijun
Resting-State Brain Organization Revealed by Functional Covariance Networks
title Resting-State Brain Organization Revealed by Functional Covariance Networks
title_full Resting-State Brain Organization Revealed by Functional Covariance Networks
title_fullStr Resting-State Brain Organization Revealed by Functional Covariance Networks
title_full_unstemmed Resting-State Brain Organization Revealed by Functional Covariance Networks
title_short Resting-State Brain Organization Revealed by Functional Covariance Networks
title_sort resting-state brain organization revealed by functional covariance networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236756/
https://www.ncbi.nlm.nih.gov/pubmed/22174905
http://dx.doi.org/10.1371/journal.pone.0028817
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