Circadian and Sleep-Wake Modulation of EEG Functional Connectivity Across Brain States and Its Associations With Cognition in Humans

Lazar, Alpar, Lazar, Zsolt I, Santhi, Nayantara, Lo, June C, Groeger, John A. and Dijk, Derk-Jan (2026) Circadian and Sleep-Wake Modulation of EEG Functional Connectivity Across Brain States and Its Associations With Cognition in Humans. NeuroImage. ISSN 1053-8119

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Abstract

Sleep and circadian rhythms both influence human brain function, but their distinct contributions to large-scale network organisation remain unclear. We quantified the effect of brain state (wake, NREM, REM), sleep-wake history, and circadian phase on functional connectivity using over 1200 h of artefact-free EEG recorded during a 10-day forced-desynchrony protocol. We derived principal components of functional connectivity and found that both sleep-wake history and circadian phase significantly modulated functional connectivity, with sleep-wake history exerting a broader influence. Complementary cosinor analyses further demonstrated circadian rhythmicity in several NREM and REM connectivity measures. Across sleep, global connectivity increased progressively during NREM sleep, particularly in the delta and sigma bands, but declined during REM sleep. During controlled resting wakefulness, increasing time awake was associated with higher global alpha-band connectivity, which was associated with poorer concurrent cognitive performance. Connectivity during NREM sleep was also associated with subsequent cognitive performance, with lower global theta and, to a lesser extent, higher sigma connectivity linked to better performance. REM connectivity was not associated with these cognitive measures. Together, these findings reveal opposing NREM and REM trajectories in large-scale EEG functional connectivity and show that these brain-state-specific network dynamics are shaped by both sleep-wake history and circadian phase and are associated with cognitive performance.

Item Type:	Article
Uncontrolled Keywords:	das in place,de-identified participant data supporting the findings of this study are available from the corresponding author upon reasonable request and subject to institutional ethical approval and applicable data protection regulations. analysis code sufficient to reproduce the reported results is available from the first author upon reasonable request. eeg spectral and functional connectivity analyses pipeline was implemented in python and relied on the standard scientific computing stack (numpy, scipy, pandas, matplotlib). statistical analyses were conducted primarily in sas 9.4, with additional multivariable association analyses were conducted in python. reproduction of the analyses requires the corresponding software environments and package dependencies. ,/dt/atira/pure/keyword/DAStatement/das_in_place
Faculty \ School:	Faculty of Medicine and Health Sciences > School of Health Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Mental Health and Social Care (fka Lifespan Health) Faculty of Medicine and Health Sciences > Research Groups > Dementia & Complexity in Later Life
Depositing User:	LivePure Connector
Date Deposited:	27 Oct 2026 16:16
Last Modified:	29 Oct 2026 00:08
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104888
DOI:	10.1016/j.neuroimage.2026.122301

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