4.7 Article

Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Journal

NEUROIMAGE
Volume 222, Issue -, Pages -

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2020.117155

Keywords

Dynamic functional connectivity; Resting-state; fMRI; Sleep deprivation; Cognitive decline; Cognitive challenge model; Attention

Funding

  1. Mission for Interdisciplinarity of the CNRS (Infiniti program 2017-2018, BrainTime)
  2. EU Innovative Training Network i-CONN [H2020 ITN 859937]
  3. Agency of Research of innovation (ANII, Uruguay) [POS EXT 2015 1 123495]
  4. European Community's Seventh Framework Program for the Innovative Medicine Initiative [115009]
  5. European Union's Horizon 2020 Framework Program for Research and Innovation [785907]

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Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.

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