4.7 Article

Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation

期刊

NEUROIMAGE
卷 111, 期 -, 页码 147-158

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.02.018

关键词

Resting-state fMRI; Attention networks; Default network; Whole brain signal; Predictive marker of sleep deprivation; vulnerability

资金

  1. National Medical Research Council, Singapore [STaR/0004/2008]
  2. National University of Singapore (NUS)
  3. Singapore Ministry of Education [MOE2014-T2-2-016]
  4. NUS Strategic Research Award

向作者/读者索取更多资源

Significant inter-individual differences in vigilance decline following sleep deprivation exist. We characterized functional connectivity in 68 healthy young adult participants in rested wakefulness and following a night of total sleep deprivation. After whole brain signal regression, functionally connected cortical networks during the well-rested state exhibited reduced correlation following sleep deprivation, suggesting that highly integrated brain regions become less integrated during sleep deprivation. In contrast, anti-correlations in the well-rested state became less so following sleep deprivation, suggesting that highly segregated networks become less segregated during sleep deprivation. Subjects more resilient to vigilance decline following sleep deprivation showed stronger anti-correlations among several networks. The weaker anti-correlations overlapped with connectivity alterations following sleep deprivation. Resilient individuals thus evidence clearer separation of highly segregated cortical networks in the well-rested state. In contrast to corticocortical connectivity, subcortical-cortical connectivity was comparable across resilient and vulnerable groups despite prominent state-related changes in both groups. Because sleep deprivation results in a significant elevation of whole brain signal amplitude, the aforesaid signal changes and group contrasts may be masked in analyses omitting their regression, suggesting possible value in regressing whole brain signal in certain experimental contexts. (C) 2015 Elsevier Inc. All rights reserved.

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