Simulated shift work in rats perturbs multiscale regulation of locomotor activity

Abstract

Motor activity possesses a multiscale regulation that is characterized by fractal activity fluctuations with similar structure across a wide range of timescales spanning minutes to hours. Fractal activity patterns are disturbed in animals after ablating the master circadian pacemaker (suprachiasmatic nucleus, SCN) and in humans with SCN dysfunction as occurs with aging and in dementia, suggesting the crucial role of the circadian system in the multiscale activity regulation. We hypothesized that the normal synchronization between behavioural cycles and the SCN-generated circadian rhythms is required for multiscale activity regulation. To test the hypothesis, we studied activity fluctuations of rats in a simulated shift work protocol that was designed to force animals to be active during the habitual resting phase of the circadian/daily cycle. We found that these animals had gradually decreased mean activity level and reduced 24-h activity rhythm amplitude, indicating disturbed circadian and behavioural cycles. Moreover, these animals had disrupted fractal activity patterns as characterized by more random activity fluctuations at multiple timescales from 4 to 12 h. Intriguingly, these activity disturbances exacerbated when the shift work schedule lasted longer and persisted even in the normal days (without forced activity) following the shift work. The disrupted circadian and fractal patterns resemble those of SCN-lesioned animals and of human patients with dementia, suggesting a detrimental impact of shift work on multiscale activity regulation.

Keywords: circadian misalignment; multiscale regulation; shift work.

Figure 1.

A week of representative activity recordings from a normal work animal and a shift work animal. The level of activity was expressed in arbitrary unit (arb. units). Shaded bars indicate the dark phase of LD cycles. The striped blocks denote the periods of simulated work, i.e. ZT 2–10 h for the shift work animal and ZT 14–22 h for the normal work animal. (Online version in colour.)

Figure 2.

Effects of shift work on mean activity levels and 24-h activity rhythms. Mean activity level during workdays (a,d) and during weekends (b,e) and 24-h rhythm amplitude during weekends (c,f) were calculated in each week. Results of shift work animals are shown in (a–c) and results of normal work animals in (d–f). In each individual, the mean activity level was normalized to the mean level of the data throughout the protocol (four weeks for the normal work group and five weeks for the shift work group). The amplitude of 24-h activity rhythm in each weekend was estimated from the normalized power spectrum with total power equal to one.

Figure 3.

Mean daily activity profiles during workdays for shift work animals (a) and normal work animals (c). Mean activity during the light and dark transitions during workdays for shift work animals (b) and normal work animals (d). Shaded areas indicated the dark phase of LD cycles. The striped blocks denoted the periods of simulated work. To obtain the mean daily activity pattern, data for each animal in each day were first normalized to the daily mean level. Then averaged daily patterns were obtained from five workdays. The mean activity levels were obtained from five 30-min windows near the light and dark transitions. Asterisks ‘*’ indicate a significant dependence on week (p < 0.05). For normal work animals, there were no significant changes in their activity responses to light–dark (p > 0.86) or dark–light transitions (p > 0.32) throughout the protocol. (Online version in colour.)

Figure 4.

Disrupted long-range correlations in shift work animals. Fluctuation functions F(n) of shift work animals and a normal work animals during workdays (a,b) and during weekends (c,d). The dashed curve is the fitting line derived from SCN-lesion animals [7]. On the abscissa, n represents the timescale in hours. The fluctuation functions were vertically shifted for better visualization of differences between conditions. There was a ‘break point’ at a timescale of approx. 4 h for shift work animals, which was marked by dashed vertical lines in each plots. The break point can be seen more clearly where F(n) divided by timescale n was plotted, where β = α − 1 (b,d). Scaling exponent α₂ was obtained by fitting F(n) at timescales between 4 and 12 h (Region II), whereas scaling exponent α₁ was obtained by fitting F(n) at timescales between approximately 0.5 and 4 h (Region I). Note that fewer points were plotted in the figures for a better visualization. (e) The degree of disruption of long-range correlations was quantified by Δα = α₁ − α₂. Δα = 0 indicates a consistent long-range correlations over the two regions while the larger Δα indicates more disrupted correlations with more difference in correlations between two timescale regions. Results of control animals with simulated effects of missing data corresponding to work schedule were presented for comparison. The scaling exponents were significantly different between two timescale regions for shift work animals during workdays and weekends (p < 0.01, indicated by ‘*’). (f) Disruption of correlation patterns in each week. The disruption in the shift work animals was much more severe in Week 3–5 as compared with the first two weeks. Asterisks ‘*’ indicate a significant disruption in fractal patterns (i.e. Δα different from 0; p < 0.01). Plus ‘+’ indicates a significant changes in Δα between the two consecutive weeks (p < 0.05). (Online version in colour.)