No abstract

The sleep EEG of healthy young men was recorded during baseline and recovery sleep after 40 h of waking. To analyse the EEG topography, power spectra were computed from 27 derivations. Mean power maps of the nonREM sleep EEG were calculated for 1-Hz bins between 1.0 and 24.75 Hz. Cluster analysis revealed a topographic segregation into distinct frequency bands which were similar for baseline and recovery sleep, and corresponded closely to the traditional frequency bands. Hallmarks of the power maps were the frontal predominance in the delta and alpha band, the occipital predominance in the theta band, and the sharply delineated vertex maximum in the sigma band. The effect of sleep deprivation on EEG topography was determined by calculating the recovery/baseline ratio of the power spectra. Prolonged waking induced an increase in power in the low-frequency range (1-10.75 Hz) which was largest over the frontal region, and a decrease in power in the sigma band (13-15.75 Hz) which was most pronounced over the vertex. The topographic pattern of the recovery/baseline power ratio was similar to the power ratio between the first and second half of the baseline night. These results indicate that changes in sleep propensity are reflected by specific regional differences in EEG power. The predominant increase of low-frequency power in frontal areas may be due to a high 'recovery need' of the frontal heteromodal association areas of the cortex.

Humans have an individual profile of the electroencephalographic power spectra at the 8 to 16 Hz frequency during non-rapid eye movement sleep that is stable over time and resistant to experimental perturbations. We tested the hypothesis that this electroencephalographic "fingerprint" is genetically determined, by recording 40 monozygotic and dizygotic twins during baseline and recovery sleep after prolonged wakefulness. We show a largely greater similarity within monozygotic than dizygotic pairs, resulting in a heritability estimate of 96%, not influenced by sleep need and intensity. If replicated, these results will establish the electroencephalographic profile during sleep as one of the most heritable traits of humans.

The sleep EEG of eight healthy young men was recorded from 27 derivations during a baseline night and a recovery night after 40 h of waking. Individual power maps of the nonREM sleep EEG were calculated for the delta, theta, alpha, sigma and beta range. The comparison of the normalized individual maps for baseline and recovery sleep revealed very similar individual patterns within each frequency band. This high correspondence was quantified and statistically confirmed by calculating the Manhattan distance between all pairs of maps within and between individuals. Although prolonged waking enhanced power inthe low-frequency range and reduced power in the high-frequency range Sleep is generally regarded as a global brain process. Recently, regional aspects of sleep have gained increasing attention. Early reports that the location of the recording electrodes affects the pattern of the sleep EEG (Findji et al. 1981;Buchsbaum et al. 1982;Hori 1985) were reexamined by using contemporary methods of quantitative EEG analysis. Power spectra along the antero-posterior axis were shown to exhibit frequencyspecific and state-dependent gradients (Werth et al. 1996(Werth et al. , 1997) with a frontal predominance in the 2-Hz band during the initial part of sleep. This hyperfrontality of low-frequency activity was accentuated by sleep deprivation (Cajochen et al. 1999). It may be associated with the reduction of regional cerebral blood flow which is known to occur during slow wave sleep (Finelli et al. 2000b; for a review see Maquet 2000) as well as in the course of prolonged waking (Thomas et al. 1998(Thomas et al. , 2000. These findings support the hypothesis that sleep has a local, use-dependent, facet and that cerebral structures that had been particularly active during waking may exhibit more intensive signs of sleep (Horne 1993;Krueger and Obál 1993;Kattler et al. 1994;Benington and Heller 1995;Borbély and Achermann 2000).Studies on regional differences in the sleep EEG are typically based on the statistical analysis of the records from several subjects. This approach emphasizes the changes common to all subjects, and tends to disregard individual differences. However, individual features may be important. This was recently demonstrated in a study where the relationship between the waking and sleep EEG was investigated (Finelli et al. 2000a). Examining the individual time course of theta power in the waking EEG in the course of a 40-h sleep deprivation period revealed a correlation with the change in delta power in the nonREM sleep EEG. The results suggested that a common regulatory process might control specific parts of the waking and sleep EEG. The aim of the present analysis was to investigate the individual topographic distribution of power in non-REM sleep before and after sleep deprivation, a manipulation known to cause massive changes in the sleep EEG. SUBJECTS AND METHODSEight right-handed healthy male subjects (mean age 23 y Ϯ 0.46 SEM, range 21-25 years) participated in the study. They were selected on the bas...