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

Abstract : 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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https://hal.archives-ouvertes.fr/hal-03092852
Contributor : Demian Battaglia Connect in order to contact the contributor
Submitted on : Sunday, January 3, 2021 - 9:08:33 AM
Last modification on : Wednesday, November 3, 2021 - 5:54:14 AM
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Diego Lombardo, Catherine Cassé-Perrot, Jean-Philippe Ranjeva, Arnaud Le Troter, Maxime Guye, et al.. Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation. NeuroImage, Elsevier, 2020, 222, pp.117155. ⟨10.1016/j.neuroimage.2020.117155⟩. ⟨hal-03092852⟩

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