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. 2009 Feb 24;106(8):2490-4.
doi: 10.1073/pnas.0806087106. Epub 2009 Feb 6.

Reduction of scale invariance of activity fluctuations with aging and Alzheimer's disease: Involvement of the circadian pacemaker

Affiliations

Reduction of scale invariance of activity fluctuations with aging and Alzheimer's disease: Involvement of the circadian pacemaker

Kun Hu et al. Proc Natl Acad Sci U S A. .

Abstract

Human motor control systems orchestrate complex scale-invariant patterns of activity over a wide range of time scales (minutes to hours). The neural mechanisms underlying scale-invariance are unknown in humans. In rats, the master circadian pacemaker [suprachiasmatic nucleus (SCN)] is crucially involved in scale-invariant activity fluctuations over multiple time scales from minutes to 24 h. Aging and Alzheimer's disease (AD) are associated with progressive dysfunction of the SCN. Thus, if the SCN is responsible for the scale-invariant activity fluctuations in humans, we predict disturbances of scale-invariant activity fluctuations in elderly humans and even more pronounced disturbances in elderly humans with AD. To test these hypotheses, we studied spontaneous daytime activity patterns in 13 young adults (mean +/- SD: 25.5 +/- 6.1 y); 13 elderly people with early-stage AD (68.5 +/- 6.1 y) matched with 13 elderly controls (68.6 +/- 6.1 y); and 14 very old people with late-stage AD (83.9 +/- 6.7 y) matched with 12 very old controls (80.8 +/- 8.6 y). In young adults, activity exhibited robust scale-invariant correlations across all tested time scales (minutes to 8 h). The scale-invariant correlations at 1.5-8 h declined with age (P = 0.01) and were significantly reduced in the elderly (P = 0.04) and very old controls (P = 0.02). Remarkably, an age-independent AD effect further reduced the scale-invariant correlations at 1.5-8 h (P = 0.04), leading to the greatest reduction of the scale-invariant correlations in very old people with late-stage AD-resembling closely the loss of correlations at large time scales in SCN-lesioned animals. Thus, aging and AD significantly attenuate the scale invariance of activity fluctuations over multiple time scales. This attenuation may reflect functional changes of the SCN.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Seven-day continuous activity recordings and average 24-h waveforms of 5 representative individuals: a young adult, an elderly control, an elderly with early-stage AD, a very old control, and a very old late-stage AD patient. (Left) Shown are the individuals' continuous activity recordings. (Right) Shown are the same individuals' activity recordings averaged over 24 h. The individual data and average waveform of activity is expressed in arbitrary units. Black bars indicate the individual sleep episodes.
Fig. 2.
Fig. 2.
Altered scale-invariant correlations of activity fluctuations in elderly and AD subjects. Detrended fluctuation functions were obtained from activity data during the daytime between 11 a.m. and 7 p.m. (A) Representative individuals from each group. (B) Group averages. Data are shown on log–log plots. On the abscissa, n represents the time scale in hours. The detrended fluctuation functions F(n) are vertically shifted for better visualization of differences between groups. F(n) in young controls (squares) exhibits a simple power-law form over the whole range from minutes to 8 h, indicated by a straight line in the log–log plot. In contrast, there is clearly a “break point” in the log–log relationship at a time scale of ≈1.5 h in the elderly controls and in the AD subjects (see dotted vertical lines in each plot, with different scaling behaviors below and above this time scale). (C) The break point can be seen more clearly where group average F(n) divided by time scale n was plotted. The exponent obtained from the power-law fitting of F(n)/n is α′ = α − 1.
Fig. 3.
Fig. 3.
Scaling exponent α in all groups obtained from the detrended fluctuation analysis. Because there was no break point in the log–log plot in young control subjects, α was obtained in this group by fitting the detrended fluctuation function F(n) at time scales between 5 min and 8 h. Because of the break-point in the other groups, α was obtained between 90 min and 8 h. α = 0.50 indicates “white noise” or the scaling exponent in activity of experimental animals in the absence of the SCN influence (dashed line) (2). Error bars indicate standard error of the mean. Compared to young controls, the elderly control and very old control subjects had smaller α values, as indicated by *, P < 0.05. Elderly control and very old control subjects showed no significant difference. Early-stage AD subjects and age- and living condition-matched elderly controls showed no significant difference. Late-stage AD subjects have significantly smaller α than the age- and living condition-matched very old controls, as indicated by # (P < 0.05).

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