Circadian phenotype impacts the brain's resting-state functional connectivity, attentional performance, and sleepiness

Abstract

Introduction: Functional connectivity (FC) of the human brain's intrinsically connected networks underpins cognitive functioning and disruptions of FC are associated with sleep and neurological disorders. However, there is limited research on the impact of circadian phenotype and time of day on FC.

Study objectives: The aim of this study was to investigate resting-state FC of the default mode network (DMN) in Early and Late circadian phenotypes over a socially constrained day.

Methods: Thirty-eight healthy individuals (14 male, 22.7 ± 4.2 years) categorized as Early (n = 16) or Late (n = 22) using the Munich ChronoType Questionnaire took part. Following a 2-week baseline of actigraphy coupled with saliva samples for melatonin and cortisol rhythms, participants underwent testing at 14:00 hours, 20:00 hours, and 08:00 hours the following morning. Testing consisted of resting-state functional magnetic resonance imaging (fMRI), a structural T1 scan, attentional cognitive performance tasks, and self-reported daytime sleepiness. Seed-based FC analysis from the medial prefrontal and posterior cingulate cortices of the DMN was performed, compared between groups and linked with behavioral data.

Results: Fundamental differences in the DMN were observed between Early and Late circadian phenotypes. Resting-state FC of the DMN predicted individual differences in attention and subjective ratings of sleepiness.

Conclusion: Differences in FC of the DMN may underlie the compromised attentional performance and increased sleepiness commonly associated with Late types when they conform to a societally constrained day that does not match their intrinsic circadian phenotype.

Keywords: attentional performance; circadian phenotype; circadian rhythms; default mode network; resting-state functional magnetic resonance imaging (fMRI); sleep; sleepiness.

Figure 1.

Linear relationships between corrected mid-sleep on free days (MSF_sc) and biological phase markers to validate circadian phenotyping. (a) Dim light melatonin onset (DLMO), (b) Sleep onset, (c) Time of peak cortisol concentration, (d) Wake-up time. MSF_sc is displayed as time of day (hours) on the x-axis. Statistical analysis was carried out using linear regression analysis. Significance (****p < 0.0001) and R² values are shown in the bottom right corner.

Figure 2.

rs-FC of the Default Mode Network between ECP and LCP. z-transformed connectivity maps show significant clusters (FWE-corrected p < 0.05 at voxel level and subsequent cluster level) and t-score scales for each contrast are shown in the center. Overall results from each seed are shown in a/d with results from each time point (hours) represented in b/c and e/f. (a) Summary results from the PCC seed with diurnal variations between circadian phenotype groups plotted in (b) and (c). (d) Summary results from mPFC seed with diurnal variations between circadian phenotype groups plotted in (e) and (f). Significant regions at the whole group level are represented in grayscale. Regions higher in ECPs (ECPs > LCPs) are shown in red and regions higher in LCPs (LCPs > ECPs) in green. Statistical analysis for (a) and (d) was carried out using a flexible factorial model in SPM12. Two-way ANOVA was used to analyze group and time of day differences in (b), (c), (e), and (f). *p < 0.05, ***p < 0.001, ****p < 0.0001.

Figure 3.

Nonlinear regression curves to show diurnal variations in sleepiness, PVT and Stroop performance. (a) Subjective sleepiness score measured with the Karolinska Sleepiness Scale. (b) PVT performance (reaction time in seconds), (c) Stroop performance (reaction time in seconds) for Early circadian phenotypes (white) and Late circadian phenotypes (gray). Clock time of test (hours) is shown on the x axis for each parameter. Statistical analysis was carried out using two-way ANOVA. Post-hoc multiple comparison tests were run to determine group and time of day effects. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 4.

Summary of predictive analysis using rs-FC to predict attentional performance and subjective daytime sleepiness (black boxes). Solid arrows indicate the predictive effects of rs-FC on attentional performance (PVT and Stroop task) and sleepiness variables for models using data from seeds in the mPFC and PCC. Dotted lines and red boxes indicate where time of day or the interaction of time of day and rs-FC was also found to be a significant factor.