The suprachiasmatic nucleus functions beyond circadian rhythm generation

Abstract

We recently discovered that human activity possesses a complex temporal organization characterized by scale-invariant/self-similar fluctuations from seconds to approximately 4 h-(statistical properties of fluctuations remain the same at different time scales). Here, we show that scale-invariant activity patterns are essentially identical in humans and rats, and exist for up to approximately 24 h: six-times longer than previously reported. Theoretically, such scale-invariant patterns can be produced by a neural network of interacting control nodes-system components with feedback loops-operating at different time scales. However such control nodes have not yet been identified in any neurophysiological model of scale invariance/self-similarity in mammals. Here we demonstrate that the endogenous circadian pacemaker (suprachiasmatic nucleus; SCN), known to modulate locomotor activity with a periodicity of approximately 24 h, also acts as a major neural control node responsible for the generation of scale-invariant locomotor patterns over a broad range of time scales from minutes to at least 24 h (rather than solely at approximately 24 h). Remarkably, we found that SCN lesion in rats completely abolished the scale-invariant locomotor patterns between 4 and 24 h and significantly altered the patterns at time scales <4 h. Identification of the control nodes of a neural network responsible for scale invariance is the critical first step in understanding the neurophysiological origin of scale invariance/self-similarity.

Fig. 1

Similarity of scale-invariant patterns of activity fluctuations in humans and rats. (A) Correlation analysis and (B) magnitude analysis (nonlinear properties) in the activity recordings throughout 10 day protocols in the laboratory in a representative individual human, a representative individual rat (upper plots), and in the group averages (lower plots). The phase randomized surrogate data (red line) was generated from the rat data during DD by performing a Fourier transform, preserving the amplitudes of the Fourier transform but randomizing the phases, and then performing an inverse Fourier transform. Such surrogate data has the same correlation properties as the original data, but contains no nonlinear information embedded in the Fourier phases. Data are shown on log-log plots. On the abscissa, n represents the time scale in hours. The detrended fluctuation functions F(n) and F_mag(n) are depicted on the ordinates in A and B respectively. Note, for both humans and rats, before averaging, the data of each subject were normalized to account for individual differences in the standard deviation of activity. The lines depicting the best fit to the data are vertically shifted for better visualisation, but the slope remains the same. The dashed line indicates the exponent 0.5 which occurs with a random signal (white noise). The power-law form of F(n) and F_mag(n) with α and α_mag >0.5 indicate long-range correlations and nonlinearities in activity data. Virtually identical scaling exponents in humans and rats (α=0.90±0.04 [SD] and α_mag=0.79±0.04 for human, α=0.89±0.04, and α_mag=0.79±0.02 for rats during LD, and α=0.85±0.06 and α_mag=0.75±0.04 during DD) indicate the same scale-invariant patterns in the two species. Scaling exponents are obtained by fitting data in the range from ~16 minute to 24 hour.

Fig. 2

Circadian and ‘ultradian’ rhythms of activity in rats are virtually abolished by SCN lesion. (A) 10-day continuous activity recordings of a representative control rat and a representative SCN-lesioned rat (SCNx) both throughout 12-hour light-dark cycles (LD) and under constant darkness (DD). In all plots, blue indicates control rats in LD; black, control rats in DD; red, SCNx rats in LD; and green, SCNx rats in DD. (B) The left panel shows power spectral densities of the individuals’ activity recordings from A, and the right panel shows the same individuals’ 10-day continuous activity recordings averaged over the estimated circadian period (i.e., ~24 h) after aligning the data according to the onset of darkness (circadian time, CT = 0 h). (C) The group average power spectral densities and average 24 hour activity waveforms of control rats and SCNx rats. The individual data and average waveform of activity is expressed in arbitrary units. For better clarity and to avoid overlap, power spectral curves are vertically offset. Note, the abscissa of the power spectrum is a log scale with time (thus higher frequencies appear to the left of lower frequencies). As seen in the power spectra in B and C, control rats exhibit a sharp peak in activity at ~24 hour with additional peaks at precise harmonics of 24 h, namely at 12, 8, 6, 4.8, 4, and 3.4 hour. The circadian rhythm and its harmonics were almost completely abolished by SCN-lesions.

Fig. 3

SCN lesion abolishes scale-invariant patterns of activity at time scales above 4 h. (A) Correlation analysis and (B) magnitude analysis (nonlinear properties) in the activity recordings throughout 10 day protocols in the laboratory in representative individual control and SCN lesioned rats (upper plots), and in the group averages (lower plots). Data are shown on log-log plots. On the abscissa, n represents the time scale in hours. The detrended fluctuation functions F(n) and F_mag(n) are vertically shifted for a better visualization of differences between control and SCN-lesioned rats (SCNx). (C) The F(n) ratio and (D) the F_mag(n) ratio of control/SCN-lesioned rats obtained from the group averages. Both ratios exhibit clearly a crossover point at a time scale of ~4 hour (see dotted vertical lines on each plot), which is caused by the different scaling behaviors of SCNx rats below and above 4 hour. At time scales larger than 4 h, the F(n) and F_mag(n) of SCNx rats, greatly differ from those of control rats, displaying a scaling behavior of white noise characterized by the scaling exponent α₂≈0.5 and α_mag2≈0.5. At time scales less than ~4 h, the SCN-lesioned rats still exhibit scale-invariant correlations and nonlinear patterns, although the scaling exponents are statistically larger than those of control rats.