Resting-State Brain Organization Revealed by Functional Covariance Networks

Zhiqiang Zhang, Wei Liao, Xi-Nian Zuo, Zhengge Wang, Cuiping Yuan, Qing Jiao, Huafu Chen, Bharat B Biswal, Guangming Lu and Yijun Liu

PLOS ONE, 2011, vol. 6, issue 12, 1-8

Abstract: 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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0028817

DOI: 10.1371/journal.pone.0028817

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