Waking experience modulates sleep need in mice

Abstract

Background: Homeostatic regulation of sleep is reflected in the maintenance of a daily balance between sleep and wakefulness. Although numerous internal and external factors can influence sleep, it is unclear whether and to what extent the process that keeps track of time spent awake is determined by the content of the waking experience. We hypothesised that alterations in environmental conditions may elicit different types of wakefulness, which will in turn influence both the capacity to sustain continuous wakefulness as well as the rates of accumulating sleep pressure. To address this, we compared the effects of repetitive behaviours such as voluntary wheel running or performing a simple touchscreen task, with wakefulness dominated by novel object exploration, on sleep timing and EEG slow-wave activity (SWA) during subsequent NREM sleep.

Results: We find that voluntary wheel running is associated with higher wake EEG theta-frequency activity and results in longer wake episodes, as compared with exploratory behaviour; yet, it does not lead to higher levels of EEG SWA during subsequent NREM sleep in either the frontal or occipital derivation. Furthermore, engagement in a touchscreen task, motivated by food reward, results in lower SWA during subsequent NREM sleep in both derivations, as compared to exploratory wakefulness, even though the total duration of wakefulness is similar.

Conclusion: Overall, our study suggests that sleep-wake behaviour is highly flexible within an individual and that the homeostatic processes that keep track of time spent awake are sensitive to the nature of the waking experience. We therefore conclude that sleep dynamics are determined, to a large degree, by the interaction between the organism and the environment.

Keywords: Behaviour; EEG; Exploratory behaviour; Mice; Operant behaviour; Running-wheel activity; Sleep homeostasis; Slow-wave activity; Wakefulness.

Fig. 1

The effect of voluntary wheel running on wakefulness and sleep. a Photograph of a mouse home cage, fitted with a running wheel. EEG recordings were acquired continuously in the freely moving animals while in the home cage. b Top: outline of the experiment. In both experimental conditions, animals were first kept awake by providing novel objects for the last 3 h of the light period (ZT9–12). Subsequently, between ZT12–15, mice had access to running wheels (RW condition) or were kept awake by providing novel objects without running wheel access (EW condition). EEG recordings were acquired and analysed over the entire 24-h period between ZT0–24 in either condition. Bottom: time course of RW activity during the experiment, shown in 5-min bins. Note that the first 3 h of the dark period (ZT12–15) is dominated by spontaneous wheel running in the RW condition only. c Hypnogram of a representative mouse during the two experimental conditions, RW (top) and EW (bottom). The plots depict colour-coded EEG slow-wave activity (wakefulness: green, NREM: blue, REM sleep: red) with a 4-s epoch resolution, shown as % of the mean SWA over the 24-h period. d Wake EEG spectra during the wheel running ‘RW’ and the exploratory wakefulness ‘EW’ condition. Mean values, SEM. Horizontal bars depict frequency bins where differences between the RW and EW spectra were statistically significant (p < 0.05). e Relative wake EEG spectra during the exploratory wakefulness (ZT12–15) for frontal and occipital EEG derivations, expressed as percentage of EEG power during wake between ZT9–12 (100%). Significant differences from ZT9–12 are shown as horizontal lines (top: frontal, bottom: occipital). f Latency to the first consolidated sleep episode > 1 min (RW, 109.6 ± 42.6; EW, 62.9 ± 30.1 min, p = 0.04, Wilcoxon rank sum test). The dots indicate individual mice, black line connects dots signifying average values across animals

Fig. 2

The effect of voluntary wheel running on sleep EEG spectra and SWA. a Average NREM EEG spectra of the first hour of recovery sleep after the wheel running (RW) and exploratory wakefulness (EW) conditions. Shaded areas depict standard errors of the mean. Horizontal lines denote frequency bins where EEG power was different between RW and EW conditions (black: p < 0.05, grey: p < 0.1, Wilcoxon rank sum tests). b Cortical EEG slow-wave activity (0.5–4 Hz EEG band) during recovery NREM sleep after RW and EW. Points represent average SWA of individual animals shown in hourly bins, depicted as percentage of average SWA of preceding baseline day (frontal EEG: mixed-model ANOVA (factors hour, condition) on log-transformed data showed statistically significant effect of hour (F (5, 40)=27.97, p < 0.0001), but no effect of condition (F (1, 8)< 1, p = 1) and no statistically significant interaction (F (5, 40)=0.93, p = 0.47)). Occipital EEG: data were not normally distributed, yet neither transformed nor original data yielded a statistically significant effect of condition or a significant interaction between hour and condition (mixed-model ANOVA on log-transformed data: statistically significant effect of hour (F (5, 50)=31.6, p < 0.0001) yet no effect of experimental condition (F (1, 10)=2.6, p = 0.14) or interaction (F (5, 50)=1.2, p = 0.33); the same test on non-transformed data: significant effect of hour (F (5, 50)=33.7, p < 0.0001), no significant effect of experimental condition (F (1, 10)=1.4, p = 0.15) and no significant interaction (F (5, 50)=1.13, p = 0.36))

Fig. 3

Effect of operant behaviour on sleep and NREM EEG spectra. a Outline of the experiment. In condition 1, mice perform the TS task for ad libitum duration from the light onset. In condition 2 (EW), mice are kept awake with novel objects for the corresponding duration to the TS performance. b Total wake durations from before the respective experimental manipulation (TS or EW) until sleep onset afterwards. Points depict individual animal averages, black line depicts the mean across animals (p = 0.6, n.s., Wilcoxon signed rank test). c Average NREM EEG spectra of the first hour of recovery sleep after TS and EW conditions. Shaded areas depict standard errors of the mean. Horizontal lines denote frequency bins where EEG power was different between TS and EW conditions (black: p < 0.05, grey: p < 0.1, Wilcoxon rank sum tests). d EEG slow-wave activity (0.5–4 Hz EEG band) during recovery NREM sleep after TS and EW. Dots represent the average SWA of individual animals shown in hourly bins, depicted as percentage of average SWA of preceding baseline day (frontal EEG: repeated measures ANOVA (factors hour, condition) on log-transformed data revealed statistically significant effect of hour (F (7, 28)=32.6, p < 0.0001), and a significant interaction between condition and hour (F (7, 28)=4.7, p < 0.01)). Tukey post hoc tests revealed a significant difference between TS and EW for hours 1 and 2 (hours 1–6: p = 0.0009, 0.003, 0.45 0.29, 0.42, 0.22). Occipital EEG: significant effect of hour (F (7, 28)=19.08, p < 0.0001), significant interaction between hour and condition (F (7, 28)=4.75, p < 0.01) and a significant effect of condition (F (1, 4)=15.09, p < 0.05). Tukey post hoc testing revealed significant differences between TS and EW as follows: hours 1–6: p = 0.0005, 0.002, 0.1, 0.07, 0.09, 0.009