Elsevier

NeuroImage

Volume 32, Issue 1, 1 August 2006, Pages 283-292
NeuroImage

Functional EEG topography in sleep and waking: State-dependent and state-independent features

https://doi.org/10.1016/j.neuroimage.2006.03.017Get rights and content

Abstract

Power spectra in the non-rapid eye movement sleep (NREMS) electroencephalogram (EEG) have been shown to exhibit frequency-specific topographic features that may point to functional differences in brain regions. Here, we extend the analysis to rapid eye movement sleep (REMS) and waking (W) to determine the extent to which EEG topography is determined by state under two different levels of sleep pressure. Multichannel EEG recordings were obtained from young men during a baseline night, a 40-h waking period, and a recovery night. Sleep deprivation enhanced EEG power in the low-frequency range (1–8 Hz) in all three vigilance states. In NREMS, the effect was largest in the delta band, in W, in the theta band, while in REMS, there was a peak in both the delta and the theta band. The response of REMS to prolonged waking and its pattern of EEG topography was intermediate between NREMS and W. Cluster analysis revealed a major topographic segregation into three frequency bands (1–8 Hz, 9–15 Hz, 16–24 Hz), which was largely independent of state and sleep pressure. To assess individual topographic traits within each state, the differences between pairs of power maps were compared within (i.e., for baseline and recovery) and between individuals (i.e., separately for baseline and recovery). A high degree of intraindividual correspondence of the power maps was observed. The frequency-specific clustering of power maps suggests that distinct generators underlie EEG frequency bands. Although EEG power is modulated by state and sleep pressure, basic topographic features appear to be state-independent.

Introduction

Typical changes in the electroencephalogram (EEG) occur at the transition between waking and sleep, as well as at the transition between the major sleep states non-rapid eye movement sleep (NREMS) and rapid eye movement sleep (REMS). In fact, the scoring of human sleep is based to a large extent on differences in the amplitude and frequency of the EEG (Rechtschaffen and Kales, 1968). An increase in homeostatic sleep pressure induced by extended waking also gives rise to state-specific changes in the EEG. In NREMS, slow-wave activity (SWA; power in the 0.75- to 4.5-Hz band) is increased, whereas activity in the spindle frequency range (12–15 Hz) is reduced (Borbély et al., 1981, Dijk et al., 1990, Dijk et al., 1993, Finelli et al., 2001b, Knoblauch et al., 2002, Curcio et al., 2003; reviewed in De Gennaro and Ferrara, 2003, Borbély and Achermann, 2005). In waking, power in the theta range (5–8 Hz) is enhanced with progression of extended wakefulness (Cajochen et al., 1995, Cajochen et al., 1999, Aeschbach et al., 1997, Dumont et al., 1999, Finelli et al., 2000a, Strijkstra et al., 2003). The close association between SWA in NREMS and the duration of previous waking has led to the establishment of the two-process model of sleep regulation (Borbély, 1982, Daan et al., 1984, Borbély and Achermann, 2005).

Recording the EEG simultaneously from multiple sites made it possible to compute topographic power distributions and thereby to gain new insights into the dynamics of sleep. Specifically, the NREMS-REMS cycles were shown to be reflected by shifts in the power gradients along the antero-posterior axis (Werth et al., 1996). Cluster analysis of the NREMS EEG power maps revealed a segregation into different frequency bands corresponding closely to the traditional frequency bands (i.e., delta, theta, alpha, sigma, and beta) (Finelli et al., 2001b). This segregation appeared to be impervious to increased sleep pressure on the basis of both mean data (Finelli et al., 2001b) and individual maps (Finelli et al., 2001a). Therefore, different generators might underlie the frequency bands in the NREMS EEG. In addition, the increase in power in the low-frequency range in NREMS induced by prolonged waking was largest over frontal regions (Cajochen et al., 1999, Finelli et al., 2001b), whereas the corresponding decrease in power in the sigma band was most pronounced over the head vertex (Finelli et al., 2001b, Knoblauch et al., 2003). The power ratio recovery/baseline exhibited a topographic pattern similar to the power ratio between the first and second half of the baseline night (Finelli et al., 2001b). Thus, changes in sleep propensity are reflected in specific regional effects on EEG power. Nevertheless, these changes do not affect the topographical power distribution.

Functional neuroimaging studies with positron emission tomography (PET) complement these findings by showing that the prefrontal cortex is among the brain regions exhibiting the largest reduction of regional cerebral blood flow (rCBF) during NREMS (Maquet et al., 1997, Finelli et al., 2000b, Dang-Vu et al., 2005). Interestingly, a frontal deactivation could also be observed during REMS (Maquet et al., 1996, Finelli et al., 2000b), as well as in wakefulness after 24 h of sleep deprivation (Thomas et al., 2000). Thus, in addition to state-specific patterns of brain activation and deactivation, those and other imaging studies (reviewed in Maquet, 2000 and Nofzinger, 2005) highlight the existence of state-independent functional features of brain dynamics.

The main objective of this study was to explore and compare the functional topography of the EEG in all three vigilance states, NREMS, REMS and waking, and to investigate the response to sleep deprivation. Complementary to sleep data, waking data provide additional information to better interpret the causes of regional changes in the sleep EEG. The identification of state-dependent and state-independent features provides new insights into EEG dynamics.

Section snippets

Design of experiment

Eight right-handed, healthy male volunteers (mean age 23 years, range 21–25 years) participated in the study. After a baseline night (23:00 to 07:00 h), preceded by an adaptation night, subjects were kept awake for 40 h. Subsequent recovery sleep started at 23:00 h, and subjects were allowed to sleep 12 h. During the sleep deprivation period, subjects remained in the sleep laboratory and the surrounding area under constant supervision. The wake EEG was recorded at 3-h intervals starting at

Results

Sleep deprivation induced the typical changes in the sleep variables including a shortening of sleep latency, a reduction of waking after sleep onset and stage 1 and an increase of slow wave sleep, total sleep time and NREMS (Table 1, see also Table 1 in Finelli et al. 2000a). REMS and tonic REMS were not affected by sleep deprivation.

EEG power maps: state-independence and state-specificity

This is the first combined analysis of EEG topography of sleep (NREMS and REMS) and waking under conditions of normal and increased sleep pressure. The present study demonstrated that distinct frequency-dependent topographies of EEG power are present not only in NREMS (Buchsbaum et al., 1982, Zeitlhofer et al., 1993, Werth et al., 1997a, Finelli et al., 2001b) but also in waking and REMS. The question therefore arose to what extent the regional distribution of power is state-specific.

The

Acknowledgments

We thank Harry Baumann for his help with the experiment. The study was supported by the Swiss National Science Foundation grant 3100A0-100567.

References (57)

  • W. Dement et al.

    Cyclic variations in EEG during sleep and their relation to eye movements, body motility, and dreaming

    Electroencephalogr. Clin. Neurophysiol.

    (1957)
  • D.J. Dijk et al.

    Dynamics of electroencephalographic sleep spindles and slow wave activity in men: effect of sleep deprivation

    Brain Res.

    (1993)
  • L.A. Finelli et al.

    Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep

    Neuroscience

    (2000)
  • L.A. Finelli et al.

    Individual ‘fingerprints’ in human sleep EEG topography

    Neuropsychopharmacology

    (2001)
  • A. Gosselin et al.

    Total sleep deprivation and novelty processing: implications for frontal lobe functioning

    Clin. Neurophysiol.

    (2005)
  • G. Hofer-Tinguely et al.

    Sleep inertia: performance changes after sleep, rest and active waking

    Brain Res. Cogn. Brain Res.

    (2005)
  • V. Knoblauch et al.

    Human sleep spindle characteristics after sleep deprivation

    Clin. Neurophysiol.

    (2003)
  • H. Laufs et al.

    EEG-correlated fMRI of human alpha activity

    NeuroImage

    (2003)
  • M. Moosmann et al.

    Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy

    NeuroImage

    (2003)
  • E.A. Nofzinger

    Neuroimaging and sleep medicine

    Sleep Med. Rev.

    (2005)
  • C. Roth et al.

    Alpha activity in the human REM sleep EEG: topography and effect of REM sleep deprivation

    Clin. Neurophysiol.

    (1999)
  • A.M. Strijkstra et al.

    Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram

    Neurosci. Lett.

    (2003)
  • P. Tassi et al.

    Sleep inertia

    Sleep Med. Rev.

    (2000)
  • L. Torsvall et al.

    Sleepiness on the job: continuously measured EEG changes in train drivers

    Electroencephalogr. Clin. Neurophysiol.

    (1987)
  • E. Werth et al.

    Spindle frequency activity in the sleep EEG: individual differences and topographical distribution

    Electroencephalogr. Clin. Neurophysiol.

    (1997)
  • D. Aeschbach et al.

    Two circadian rhythms in the human electroencephalogram during wakefulness

    Am. J. Physiol.

    (1999)
  • J.L.R. Andersson et al.

    Brain Networks affected by synchronized sleep visualized by positron emission tomography

    J. Cereb. Blood Flow Metab.

    (1998)
  • A.A. Borbély

    A two process model of sleep regulation

    Hum. Neurobiol.

    (1982)
  • Cited by (0)

    View full text