Dynamics of sleep-wake cyclicity in developing rats

^{Program in Behavioral and Cognitive Neuroscience, Department of Psychology, University of Iowa, Iowa City, IA 52242;}^{Department of Psychiatry, McLean Hospital, Harvard Medical School, 115 Mill Street, Belmont, MA 02478; and}^{Neurobiology Research Unit 15A3, Sepulveda Ambulatory Care Center, Veterans Administration Greater Los Angeles Healthcare System, North Hills, CA 94313}

^{To whom correspondence should be addressed at: Department of Psychology, University of Iowa, E11 Seashore Hall, Iowa City, IA 52242. E-mail:}ude.awoiu@grebmulb-kram.

Edited by H. Eugene Stanley, Boston University, Boston, MA, and approved August 26, 2005

Received 2005 Jul 25

Abstract

Adult mammals cycle between periods of sleep and wakefulness. Recent assessments of these cycles in humans and other mammals [Lo, C. C., Amaral, L. A. N., Havlin, S., Ivanov, P. Ch., Penzel, T., Peter, J. H. & Stanley, H. E. (2002) Europhys. Lett. 57, 625-631 and Lo, C. C., Chou, T., Penzel, T., Scammell, T. E., Strecker, R. E., Stanley, H. E. & Ivanov, P. Ch. (2004) Proc. Natl. Acad. Sci. 101, 17545-17548] indicate that sleep bout durations exhibit an exponential distribution, whereas wake bout durations exhibit a power-law distribution. Moreover, it was found that wake bout distributions, but not sleep bout distributions, exhibit scale invariance across mammals of different body sizes. Here we test the generalizability of these findings by examining the distributions of sleep and wake bout durations in infant rats between 2 and 21 days of age. In agreement with Lo et al., we find that sleep bout durations exhibit exponential distributions at all ages examined. In contrast, however, wake bout durations also exhibit exponential distributions at the younger ages, with a clear power-law distribution only emerging at the older ages. Further analyses failed to find substantial evidence either of short- or long-term correlations in the data, thus suggesting that the durations of current sleep and wake bouts evolve through time without memory of the durations of preceding bouts. These findings further support the notion that bouts of sleep and wakefulness are regulated independently. Moreover, in light of recent evidence that developmental changes in sleep and wake bouts can be attributed in part to increasing forebrain influences, these findings suggest the possibility of identifying specific neural circuits that modulate the changing complexity of sleep and wake dynamics during development.

Keywords: atonia, renewal process, Markov process, development

Abstract

As members of a diurnal species, adult humans experience sleep as a prolonged period of rest during the night; however, sleep actually occurs as a series of discrete bouts interrupted by bouts of wakefulness. What is perhaps most striking during the first several months after birth, when circadian influences on behavioral state are not well established, is the brevity of these bouts of sleep and wakefulness (1). In newborn rats at 2 days of age (P2), these cycles are astonishingly rapid: The average bout lengths of nuchal muscle atonia (indicative of sleep) and high nuchal muscle tone (indicative of wakefulness) are only 15 s and 5 s, respectively (2). Over the next week, bout lengths increase significantly as forebrain mechanisms exert increasing modulatory control over brainstem mechanisms controlling sleep and wakefulness (2).

Recently, Lo et al. (3) analyzed the distributions of sleep and wake bouts in human adults. They found that, whereas sleep bouts exhibited an exponential distribution [such that the frequency distribution f(t) of bout durations of duration t was proportional to e^{, where τ is the characteristic time scale], wake bouts exhibited a power-law distribution [such that}f(t) ≈ t^{, where α is a characteristic power-law exponent]. In a subsequent report (}4), these findings were extended to cats, rats, and mice. From this comparative analysis, Lo and colleagues found that the exponential time scale τ for sleep bout durations increased with body size, thus possibly implicating a constitutional variable (e.g., metabolic rate) in the regulation of sleep bouts. In contrast, the power-law exponent α for wake bout durations did not vary across species.

Although establishing the generalizability of these results by examining more species will be important, data from developing animals could provide additional critical information. In particular, in analyzing the sleep and wake bout durations of P2 and P8 rats, we found that both are better captured by exponential, not power-law, distributions (2). If wake bout durations do not exhibit power-law behavior in early infancy, then such behavior may develop gradually as the neural components of sleep and wakefulness are elaborated. In addition, because the precise nature of these distributions critically shapes the models that we adopt to describe the temporal dynamics of sleep and wakefulness (3), establishing the statistical properties of these bout durations across development becomes important. Therefore, using archival and newly collected data from rats at P2, P8, P10, P14, and P21, we assess here the statistical behavior of sleep and wake bout durations. By demonstrating a pronounced developmental transition in wake bout durations, important new information is provided that can help shape models of sleep-wake behavior and point to specific neural processes responsible for developmental shifts in the statistical properties of sleep-wake behavior.

Acknowledgments

We thank Carl Anderson for many helpful ideas. This work was supported by National Institute of Mental Health Grants MH50701 and MH66424 (to M.S.B.).

Acknowledgments

Notes

This paper was submitted directly (Track II) to the PNAS office.

Abbreviation: EMG, electromyogram.

Notes

This paper was submitted directly (Track II) to the PNAS office.

Abbreviation: EMG, electromyogram.

References

1. Kleitman, N. & Engelmann, T. G. (1953) J. Appl. Physiol.6, 269-282. [[PubMed]
2. Karlsson, K. Æ., Kreider, J. C. & Blumberg, M. S. (2004) Neuroscience123, 575-582. [[PubMed]
3. Lo, C. C., Amaral, L. A. N., Havlin, S., Ivanov, P. Ch., Penzel, T., Peter, J. H. & Stanley, H. E. (2002) Europhys. Lett.57, 625-631. [PubMed]
4. Lo, C. C., Chou, T., Penzel, T., Scammell, T. E., Strecker, R. E., Stanley, H. E. & Ivanov, P. Ch. (2004) Proc. Natl. Acad. Sci. USA101, 17545-17548.
5. Seelke, A. M. H., Karlsson, K. Æ., Gall, A. J. & Blumberg, M. S. (2005) Eur. J. Neurosci.22, 911-920.
6. Oppenheim, A. V. & Schafer, R. W. (1975) Digital Signal Processing (Prentice-Hall, Englewood Cliffs, NJ).
7. Gervasoni, D., Lin, S. C., Ribeiro, S., Soares, E. S., Pantoja, J. & Nicolelis, M. A. (2004) J. Neurosci.24, 11137-11147.
8. Corner, M. A. & Bour, H. L. (1984) Exp. Brain Res.54, 66-72. [[PubMed]
9. Gramsbergen, A(1976) Dev. Psychobiol.9, 501-515. [[PubMed][Google Scholar]
10. Karlsson, K. Æ. & Blumberg, M. S. (2002) Behavioral Neurosci.116, 912-917. [[PubMed]
11. Seelke, A. M. H. & Blumberg, M. S. (2005) Behav. Neuroscience19, 603-611.
12. Karlsson, K. Æ. & Blumberg, M. S. (2005) Neuroscience130, 275-283.
13. Karlsson, K. Æ., Gall, A. J., Mohns, E. J., Seelke, A. M. H. & Blumberg, M. S. (2005) PLoS Biol.3, 891-901. [PubMed]
14. Holditch-Davis, D., Edwards, L. J. & Helms, R. W. (1998) Physiol. Behav.63, 311-318. [[PubMed]
15. Borbely, A. A. & Achermann, P. (2000) in Principles and Practice of Sleep Medicine, eds. Kryger, M. H., Roth, T. & Dement, W. C. (Saunders, Philadelphia), pp. 377-390.
16. Gregory, G. G. & Cabeza, R. (2001) J. Neurophysiol.88, 2589-2597. [[PubMed]
17. Anderson, C. M., Mandell, A. J., Selz, K. A., Terry, L. M., Wong, C. H., Robinson, S. R., Robertson, S. S. & Smotherman, W. P. (1998) Brain Res.787, 351-357. [[PubMed]
18. Lowen, S. B. & Teich, M. C. (1993) Phys. Rev. E47, 992-1001. [[PubMed]
19. Lowen, S. B. & Teich, M. C. (1993) IEEE Trans. Inf. Theory39, 1669-1671. [PubMed]
20. Frank, M. G. & Heller, H. C. (1997) Am. J. Physiol.273, R472-R478. [[PubMed]
21. Aston-Jones, G., Chen, S., Zhu, Y. & Oshinsky, M. L. (2001) Nat. Neurosci.4, 732-738. [[PubMed]
22. Blumberg, M. S., Middlemis-Brown, J. E. & Johnson, E. D. (2004) Behav. Neurosci.118, 1253-1261.
23. Frank, M. G., Morrissette, R. & Heller, H. C. (1998) Am. J. Physiol.44, R148-R157. [[PubMed]