Neural Markers of Responsiveness to the Environment in Human Sleep

Abstract

Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed human volunteers to classify words with lateralized hand responses while falling asleep. Using an electroencephalographic (EEG) marker of motor preparation, we show how responsiveness is modulated across sleep. These modulations are tracked using classic event-related potential analyses complemented by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which could be related to modulation in sleep depth. In REM sleep, however, this relationship was reversed. We therefore propose that, in REM sleep, endogenously generated processes compete with the processing of external input. Sleep can thus be seen as a self-regulated process in which external information can be processed in lighter stages but suppressed in deeper stages. Last, our results suggest drastically different gating mechanisms in NREM and REM sleep.

Significance statement: Previous research has tempered the notion that sleepers are isolated from their environment. Here, we pushed this idea forward and examined, across all sleep stages, the brain's ability to flexibly process sensory information, up to the decision level. We extracted an EEG marker of motor preparation to determine the completion of the sensory processing chain and explored how it is constrained by baseline and evoked neural activity. In NREM sleep, slow waves elicited by stimuli appeared to block response preparation. We also used a novel analytic approach (Lempel-Ziv complexity) and showed that the ability to process external information correlates with neural complexity. A reversal of the correlation between complexity and motor indices in REM sleep suggests drastically different gating mechanisms across sleep stages.

Keywords: EEG; NREM; REM; complexity; sensory processing; sleep.

Figure 1.

Experimental procedure. Illustration of the protocol. Different lists of animal and object words were played to participants. Participants were instructed to classify these words through left- and right-hand responses according to their semantic category (here right-hand responses for animals). Lateralized hand-response preparation involves the contralateral motor cortices, a task-dependent lateralization of brain activity that can be tracked with the EEG (Fig. 2). Different lists of words were presented to participants. A list was restricted to NREM sleep (NREM2 and NREM3, blue) and another one to REM sleep (green) while the wake list (red) was played otherwise. Changing list between wake and sleep prevents sleepers from using stimulus-response associations learned in wake to classify words in sleep. Words being novel, participants must have had access to the meaning of each word to prepare for the correct response.

Figure 2.

LRPs across sleep stages. The LRP allows monitoring the lateralization of brain activity associated with motor selection and preparation. We used it here as an index of participants' ability to process sensory information up to the semantic level and to use this information in a flexible task-dependent fashion (see Materials and Methods) (Kouider et al., 2014). Subpanels, Left, The stimulus-locked ERP computed on Cz: a, in wakefulness; b, in REM sleep; c, in light NREM sleep (blue, black curves show deep NREM sleep for comparison); d, in deep NREM sleep. Right, The corresponding stimulus-locked LRP computed on C3/C4 electrodes are plotted. Shaded areas represent the SEM computed across participants. Colored horizontal bars represent significant clusters for LRP (p_cluster < 0.05). Insets, Scalp topographies of LRP averaged over the red and blue clusters. Curves were smoothed using a Gaussian kernel (width: 50 ms for ERPs, 200 ms for LRPs) for display only (statistics were performed before smoothing). An LRP peaking over motor cortices is visible in light NREM but is absent in deeper sleep stages (deep NREM and REM sleep). RT, Response time.

Figure 3.

LRPs in light NREM, deep NREM, and REM sleep for words categorized during wakefulness. Top, Power spectra (left), stimulus-locked ERPs (middle), and stimulus-locked LRPs (right) computed in light NREM sleep for trials in which either the NREM list (blue) or the wake (purple) list was played. The wake ERP and power spectrum (red curves) are displayed for comparison. Horizontal bars represent the significant clusters for LRPs (p_cluster < 0.05). Note the presence of a similar (and slightly earlier) LRP when words categorized during wakefulness were played. Middle, Same plots for deep NREM sleep trials. No significant LRP cluster could be observed for either the NREM or the wake lists. Bottom, Power spectra (left), ERPs (middle), and LRPs (right) computed in REM sleep for trials in which either the REM list (light green) or the wake (dark green) list was played. Interestingly, for words previously categorized in wakefulness (practiced), a clear LRP was observed (p_cluster < 0.05) but not for unpracticed words (black bar represents cluster for the comparison between the LRPs for the wake and REM lists, p_cluster < 0.05). Yet, the power spectrum and ERPs for both practiced and unpracticed words are highly similar and different from wake trials (red curves). Shaded areas represent the SEM computed across participants.

Figure 4.

Lempel-Ziv complexity (LZc) across sleep stages and in relation to motor preparation indexes. a, LZc extracted over the prestimulus activity ([−1.5, 0] s) was averaged across trials scored as wakefulness, light NREM, deep NREM, and REM sleep. Error bars indicate the SEM computed across participants. LZc allowed to unambiguously separate the different vigilance states (one-way ANOVA: F₍₃₎ = 9.67, p = 2 × 10⁻⁵, N = 18 participants). Post hoc comparisons show highly significant differences with a gradual decrease in complexity: wake > REM > light NREM > deep NREM (all paired t tests, p < 0.005). b, LZc time course locked on stimulus onsets and expressed as a ratio of the baseline level ([−1.5, 0] s). Stimuli robustly modulated the complexity of the EEG signal with an initial decrease after stimulus onset (p_cluster < 0.05, except for REM sleep). The initial decrease was followed by an increase in complexity in light NREM, deep NREM, and REM sleep (p_cluster < 0.05). c, Correlation between the baseline LZc (see a) and the LRP magnitude computed across the entire night for wake (left), light NREM (middle), and REM sleep (right) trials. Correlation between the pairs of variables was assessed using the Pearson's method, which coefficients are displayed on each subplot along their significance levels. ***p < 0.005. Dotted lines indicate the linear fit between the pairs of variables. Values were z-scored across trials for each participant before being aggregated across participants. Values were binned for visual purpose (N = 50 bins on the sorted LZc values). Error bars indicate the SEM of the LRP magnitude for the corresponding bin.

Figure 5.

Local modulations of sleep rhythms in association to stimuli. a, Time-frequency decomposition of the EEG signal recorded at Cz in response to stimuli. The time-frequency decomposition was extracted for each trial in NREM sleep (light and deep) and averaged across participants (N = 18) (see Materials and Methods). Right after stimulus onset, a large increase in the low-frequency range (<6 Hz) and spindle range ([11, 16] Hz) can be observed, which correspond to slow waves and spindles evoked by stimuli. Interestingly, these sleep rhythms were suppressed later on, at the time during which a LRP was observed in light NREM sleep (Fig. 2). This decrease was confirmed in b by examining the modulation of the power (at Cz) in these 2 frequency bands (<6 Hz: slow-wave range, black curve; [11, 16] Hz: spindle range, gray curve). Horizontal bars represent the significant clusters determined across participants (p_cluster < 0.05). Shaded areas represent the SEM computed across participants. Insets, Scalp topographies of the power within the slow-wave and spindle ranges at trial onset ([0, 2] s) and during the LRP window ([2.9, 3.8] s). Power was z-scored across sensors to emphasize regional differences. The decrease associated with the LRP is centrally distributed for slow waves and sleep spindles despite their originally frontal distribution, suggesting a local suppression of sleep rhythms. c, d, Same as a, b, except for REM sleep. Note the initial broadband increase in the higher frequency range (>12 Hz) and the decrease within the theta range ([4, 8] Hz). Scalp topographies were computed by averaging power over the significant clusters (p_cluster < 0.05).

Figure 6.

Neural bistability gates sensory processing in NREM sleep. a, ERPs computed at Cz for trials in light NREM (blue curve) and deep NREM sleep (black). Two distinct potentials are clearly visible: a positivity ∼200 ms (P200) maximal at centroparietal electrodes (see scalp topography on the top right); and a negativity ∼550 ms (N550) predominant in deep NREM sleep (trials associated with slow waves) and maximal at frontal electrodes (see scalp topography on the bottom right). Shaded areas represent the SEM computed across participants (N = 18). Scalp topographies were established by averaging the voltage over windows around the two potentials of interest (see gray areas on ERP plot). These values were averaged across participants and z-scored across channels to emphasize regional differences. b, Correlations between the LRP magnitude and the P200 magnitude (left) or the N550 magnitude (right, opposite of amplitude) for trials in light NREM sleep. The P200 and N550 magnitudes were computed at Pz and Cz, respectively. Correlation between the pairs of variables was assessed using Pearson's method, with coefficients displayed on each subplot along with their significance levels. ***p < 0.005. *p < 0.05. Dotted lines indicate the linear fit between the two pairs of variables. Values were z-scored across trials for each participant before being aggregated across participants. Values were binned for visual purpose (N = 50 bins on the sorted x-axis variable). Error bars indicate the SEM of the LRP magnitude for the corresponding bin. Scalp topographies on the right represent the Pearson coefficients computed for each sensor (nonsignificant coefficients were set to 0, p > 0.05, FDR corrected for multiple comparisons). c, Same as in b for trials in deep NREM sleep. The reversal of the relationship between the LRP and the P200 from light to deep NREM paralleled with the appearance of a large N550, showing a suppressive effect on LRP magnitude.

Figure 7.

Evoked responses to sounds correlate with LRP magnitude in REM sleep. a, ERPs computed at Cz for trials in REM sleep. Two distinct potentials are again visible: a positivity ∼200 ms (P200) maximal at central electrodes (see scalp topography on the top right); and a negativity ∼500 ms (N500) maximal at parietal electrodes (see scalp topography on the bottom right). Scalp topographies were established by averaging the voltage over windows around the two potentials of interest (see gray areas on ERP plot). These values were averaged across participants and z-scored across channels to emphasize regional differences. These two potentials are quite different from the potentials described in NREM (Fig. 6a) in terms of temporal profile, amplitude, and topography. b, Correlations between the LRP magnitude and the P200 magnitude (left) or the N500 magnitude (right, opposite of amplitude) for trials in REM sleep. The P200 and N500 magnitudes were computed at Cz and Pz, respectively. Correlation between the pairs of variables was assessed using Pearson's method, with coefficients displayed on each subplot along with their significance levels. ***p < 0.005. Dotted lines indicate the linear fit between the two pairs of variables. Values were z-scored across trials for each participant before being aggregated across participants. Values were binned for visual purpose (N = 50 bins on the sorted x-axis variable). Error bars indicate the SEM of the LRP magnitude for the corresponding bin. Scalp topographies on the right represent the Pearson coefficients computed for each sensor (nonsignificant coefficients were set to 0, p > 0.05, FDR corrected for multiple comparisons). Similar correlations were obtained when focusing on practiced words (data not shown).

Figure 8.

The ability to process information is dynamically modulated within sleep cycles. Modulation of the LRP magnitude (colored dots), the LZc (black curve), and the ∂-power (gray curve) within the NREM sleep part of sleep cycles. Colors of dots (LRP magnitude) represent the proportion of light and deep NREM trials included in the corresponding bin. A classical increase in ∂-power (a proxy for slow-wave density) is observed corresponding to the transition from light to deep NREM sleep. This increase in ∂-power is accompanied by a decrease in LZc and LRP magnitude. Both LZc and LRP have the tendency to increase again at the end of the NREM cycle, paralleling the transition from deep NREM sleep to REM sleep. LRP, LZc, and δ values were estimated within each sleep cycle on fixed windows (see Materials and Methods). Sleep cycles were then binned (N = 30) so as to average cycles with different durations, and values were normalized across the entire cycle to better visualize the dynamics of each variable of interest (expressed here in arbitrary units [a.u.]).