The brain functional connectome is robustly altered by lack of sleep

Tobias Kaufmann, Torbjørn Elvsåshagen, Dag Alnæs, Nathalia Zak, Per Pedersen, Linn B. Norbom, Sophia H. Quraishi, Enzo Tagliazucchi, Helmut Laufs, Atle Bjørnerud, Ulrik F. Malt, Ole A. Andreassen, Evangelos Roussos, Eugene P. Duff, Stephen M. Smith, Inge R. Groote, Lars T. Westlye

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)-a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

Original languageEnglish
Pages (from-to)324-332
Number of pages9
JournalNeuroImage
Volume127
DOIs
StatePublished - 15 Feb 2016
Externally publishedYes

Keywords

  • Circadian variability
  • FMRI-based connectivity
  • Machine learning
  • Sleep deprivation

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