Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Abstract

The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as long-term memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.

Keywords: EEG–functional MRI; consciousness; long-range correlations; multi-modal; resting state.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

BOLD signals gradually shift to short-range temporal correlations in the descent to deep sleep. (A, Left) Probability distributions for H (all gray matter voxels and all subjects) for all sleep stages and a water phantom. The probability distributions for the fluctuation function fitting error (R2) are shown in Inset. (A, Center) Probability distributions for by sleep stage (all gray matter voxels and subjects). (A, Right) Mean H (including phantom data for comparison) and by sleep stage. Light blue dashed lines indicate , the value corresponding to exponentially decaying autocorrelation. (B) Spatial maps of average H and .

Fig. 2.

Deep NREM sleep stages are characterized by specific spatial patterns of decreased H and increased variance. (A) Main effect of sleep stage on H and maps of statistically significant differences between wakefulness and N2 sleep and between wakefulness and N3 sleep. Results are presented overlaid onto an anatomical image and rendered on a 3D cortical surface. The blue dotted lines over the 3D rendering depict the DMN identified with ICA. Modified from ref. . Maps are thresholded at P < 0.05 [family wise error (FWE) cluster corrected]. (B) Main effect of sleep stage on (signal variance) and maps of statistically significant differences between wakefulness and N2 sleep and between wakefulness and N3 sleep. Results are presented overlaid onto an anatomical image and rendered on a 3D cortical surface. The dotted lines over the 3D rendering depict the visual (light green), auditory (yellow), and sensory motor RSNs (light blue) found with ICA. Modified from ref. . Maps are thresholded at P < 0.05 (FWE cluster corrected).

Fig. 3.

Relationship between RSNs and patterns of change in temporal properties of the fMRI BOLD signal during deep sleep. (A) Maps of statistically significant differences in H and (for N2 and N3 sleep vs. wakefulness) rendered together in a 3D cortical surface for comparison (overlapping regions are colored in yellow). (B) Rendering of maps representing six well-established RSNs (thresholded at ): default mode, dorsal attention, executive control, visual, sensorimotor, and auditory. (C) Spatial correlation between maps of statistically significant differences in (Left) H and (Right) and the RSNs revealed with ICA (for N2 and N3 sleep). Results were compared with 1,000 surrogate H and maps with preserved first-order statistics (obtained by phase randomization), and an empirical P value was obtained by counting the instances of smaller correlation using the randomized versions (*P < 0.05, corrected), resulting in significant correlation of attention networks, DMNs, and visual RSNs with H significance maps during N2 and N3 sleep.

Fig. 4.

EEG Δ-power predicts changes in BOLD fMRI temporal properties. (A) Statistical significance maps for the correlation between EEG δ-power (averaged across all channels) and H and BOLD variance. Maps are thresholded at P < 0.05 (FWE cluster corrected). (B) Scatter plots of H and BOLD variance vs. δ-power for three regions located in the DMN and three sensory regions (all selected from SI Appendix, Tables S9.1 and S9.2): right inferior frontal gyrus and orbital part (ORBinf; 44, 38, −6), right inferior parietal cortex (IPC; 52, −38, 56), left angular gyrus (ANG; −40, −60, −42), left calcarine sulcus (Cal; −2, −80, 12), left Heschl’s gyrus (Heschl; −52, −10, 10), and left paracentral lobule (PCL; −2, −20, 68). Monotonous dependence was quantified with Spearman rank correlation [ρ; i.e., the linear correlation between ranked variables, which was in all cases significant ]; x, y, z indicate coordinates in Montreal Neurological Institute space.