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Segregated precuneus network and default mode network in naturalistic imaging

  • ZhengZheng Deng
  • JinFeng Wu
  • JiaQi Gao
  • Yang Hu
  • YiWen Zhang
  • YinShan Wang
  • HaoMing Dong
  • Zhi YangEmail author
  • XiNian Zuo
Original Article
  • 27 Downloads

Abstract

A resting-state network centered at the precuneus has been recently proposed as a precuneus network (PCUN) or “parietal memory network”. Due to its spatial adjacency and overlapping with the default mode network (DMN), it is still not consensus to consider PCUN and DMN separately. Whether considering PCUN and DMN as different networks is a critical question that influences our understanding of brain functions and impairments. Previous resting-state studies using multiple methodologies have demonstrated a robust separation of the two networks. However, since there is no gold standard in justifying the functional difference between the networks in resting-state, we still lack of biological evidence to directly support the separation of the two networks. This study compared the responses and functional couplings of PCUN and DMN when participants were watching a movie and examined how the continuity of the movie context modulated the response of the networks. We identified PCUN and DMN in resting-state fMRI of 48 healthy subjects. The networks’ response to a context-rich video and its context-shuffled version was characterized using the variance of temporal fluctuations and functional connectivity metrics. The results showed that (1) scrambling the contextual information altered the fluctuation level of DMN and PCUN in reversed ways; (2) compared to DMN, the FC within PCUN showed significantly higher sensitivity to the contextual continuity; (3) PCUN exhibited a significantly stronger functional network connectivity with the primary visual regions than DMN. These findings provide evidence for the distinct functional roles of PCUN and DMN in processing context-rich information and call for separately considering the functions and impairments of these networks in resting-state studies.

Keywords

Resting-state networks Default mode network Precuneus network Natural viewing Functional connectivity 

Notes

Funding

This study is supported by Grants from the National Science Foundation of China (Grant numbers: 81270023, 81571756, 81971682 to Z.Y., 81471740 to X.-N.Z.), the National Key R&D Program of China (Grant No.: 2018YFC2001600 to Z.Y.), the Beijing Nova Program for Science and Technology (XXJH2015B079 to Z.Y.), Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (Grant No. 20171929 to Z.Y.), Hundred-Talent Fund from Shanghai Municipal Commission of Health (Grant No. 2018BR17 to Z.Y.]), Research Fund from Shanghai Mental Health Center (13dz2260500 to Z.Y.), Beijing Municipal Science and Tech Commission (Z161100002616023, Z171100000117012 to X.-N.Z.), and the National Basic Research Program (973 Program: 2015CB351702 to X.-N.Z.).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.CAS Key Laboratory of Behavioral Science and Research Center for Lifespan Development of Mind and BrainInstitute of PsychologyBeijingChina
  3. 3.Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
  4. 4.Brain Science and Technology Research Center, Shanghai Jiao Tong UniversityShanghaiChina

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