Dynamic functional connectivity states characterize NREM sleep and wakefulness

Abstract

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow-wave activity. We collected both eyes-closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow-wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.

Keywords: DFC; EEG; fMRI; sleep; slow-wave activity.

Figure 1

Dynamic functional connectivity states of k‐means clustering when k = 4. (a) The cluster centroids from k‐means clustering. The (b) mean dwell time (MDT) and (c) frequency of expression for the four states during wakefulness, N1, N2, and N3 sleep (error bars refer to standard errors). AtN, attention network; AuN, auditory network; CN, cerebellar network; CON, cingulo‐opercular task control network; DMN, default mode network; FPN, frontoparietal task control network; SCN, subcortical network; SMN, somatomotor network; SN, salience network; VN, visual network [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Results of the Kruskal–Wallis test of MDT and frequency of expression across k values [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

The number of transitions between DFC states across k values (error bars refer to standard errors)

Figure 4

State 1 and State 2 inferred from k‐means clustering (k = 4) temporally predominate during wakefulness and N3 sleep stages and significantly correlate with slow‐wave activity [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 5

The correlations between MDT/frequency of expression and slow‐wave activity across k values [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 6

Validation of the results obtained from the Kruskal–Wallis test of MDT/frequency of expression across k‐values using different preprocessing parameters and parcellation schemes [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 7

Validation of the number of transitions between DFC states across k values using different preprocessing parameters and parcellation schemes (error bars refer to standard errors)

Figure 8

Validation of the correlations between MDT/frequency of expression and slow‐wave activity using different preprocessing parameters and parcellation schemes [Color figure can be viewed at http://wileyonlinelibrary.com]