Center for Chronobiology, Psychiatric University Clinic, CH-4025 Basel, Switzerland
Address correspondence to Christian Cajochen, Center for Chronobiology, Wilhelm Kleinstrasse 27, CH-4025 Basel, Switzerland. Email: christian.cajochen{at}pukbasel.ch.
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Abstract |
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Introduction |
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The function of sleep spindles is to a large extent unknown. It has been speculated that they may serve to prevent arousing stimuli from reaching the cortex (Jankel and Niedermeyer, 1985; Steriade et al., 1993
). A negative correlation between regional cerebral blood flow (rCBF) in the medial thalamus and EEG spindle activity during sleep has been reported and interpreted as reflecting the loss of consciousness and sensory awareness during sleep (Hofle et al., 1997
). After benzodiazepine intake, SWA is decreased, whereas the occurrence of sleep spindles and SFA is enhanced (Johnson et al., 1976
; Borbély et al., 1985
; Trachsel et al., 1990
; Brunner et al., 1991
). This has led to the hypothesis that the sleep-promoting action of benzodiazepines may be based on their ability to enhance SFA (Johnson et al., 1976
), which in turn prevent sensory input signals being relayed to the cortex (Jankel and Niedermeyer, 1985
; Steriade et al., 1993
). However, current concepts of sleepwake regulation still lack crucial understanding of the role of sleep spindles. There is a general consensus that SFA is under both circadian and homeostatic control (Aeschbach et al., 1997
; Dijk et al., 1997
). After sleep deprivation, SFA is reduced and shows an inverse relationship to SWA and thus to sleep pressure (Borbély et al., 1981
; Dijk et al., 1993
; Finelli et al., 2001
). However, this reduction in SFA is limited to the upper frequency range (15 Hz bin, Borbély et al., 1981
; 13.7514 Hz, Dijk et al., 1993
), whereas low-frequency spindle activity is not affected. In a nap study, where the duration of prior wakefulness varied from 2 to 20 h, a significant decrease of power density with increasing duration of prior wakefulness was observed in the 15 Hz-bin, but not in the lower SFA range (Dijk et al., 1987
). These and other findings indicate that there may be a frequency-dependent homeostatic control of SFA. Most studies have used only one or two EEG derivations (C3, C4, or a fronto-occipital bipolar derivation) to describe the effects of different sleep pressure levels on SFA (Borbély et al., 1981
; Dijk et al., 1987
, 1993
, 1997
). However, sleep spindles may not be a homogeneous group of EEG waves: their frequency-specific distribution over different brain locations was recognized as early as 1950 (Gibbs and Gibbs, 1950
). This study reported that sleep spindles with a frequency of ~12 Hz exhibit an anterior dominance, whereas spindles with a frequency of ~14 Hz were most prominent in more posterior derivations. This frequency-specific topo-graphical distribution was later confirmed by several authors (Werth et al., 1997
; Zeitlhofer et al., 1997
; Zygierewicz et al., 1999
; Finelli et al., 2001
). However, a doseresponse relationship between the amount of prior wakefulness and its repercussions on frequency- and derivation-specific SFA during NREM sleep has, to our knowledge, not been reported.
In the present study, EEG spectra during recovery sleep after 40 h of either total sleep deprivation or a 75/150 min sleepwake (nap) schedule were compared. The build-up of sleep pressure during scheduled wakefulness could be attenuated significantly by intermittent naps (Cajochen et al., 2001; Knoblauch et al., 2001
). We aimed at assessing the effect of differential levels of sleep pressure on the dynamics of EEG power density along the antero-posterior axis, in particular in the slow wave- and spindle frequency range. We hypothesized that the reciprocal homeostatic regulation of SWA and SFA depends on brain location. We further hypothesized that the response to differential sleep pressure conditions in the spindle frequency range (1216 Hz) is not uniform.
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Materials and Methods |
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Subjects were recruited via poster advertisements at the University of Basel. After successfully completing a brief telephone screening, they received detailed information on the study and three questionnaires: a morningevening-type questionnaire (Torsvall and Akerstedt, 1980), the Pittsburgh Sleep Quality Index (PSQI), and an extensive questionnaire covering sleep habits, sleep quality, life habits, physical health and medical history. Subjects with self-reported sleep complaints (PSQI score
5) as well as extreme morning or evening types (score <12 or >23) were excluded from participation. Other exclusion criteria were chronic or current major medical illness or injury, smoking, medication or drug consumption, shift work within 3 months or transmeridian travel within 1 month prior to the study, excessive caffeine consumption and excessive physical activity.
Subjects who did not fulfill any of the above exclusion criteria were invited to the laboratory and interviewed. They spent an adaptation night in the laboratory to test his or her ability to sleep in a new environment and to exclude primary sleep disorders (i.e. insomnia). A physical examination excluded medical disorders. All subjects gave signed informed consent, and the study protocol, screening questionnaires and consent form were approved by the Ethical Committee of the Cantons Basel-Stadt and Baselland.
Subjects
Ten healthy subjects (six male, four female, age range 2432 years, mean: 27.1 ± 2.3 SEM) were studied. Female subjects started the study on days 15 after the onset of menstruation in order to complete the entire study block within their follicular phase. Three female subjects used oral contraceptives. During the week preceding the study (baseline week), subjects were instructed to maintain a regular sleepwake schedule (bed and wake times within ±30 min of self-selected target time). The latter was verified by a wrist activity monitor (Cambridge Neurotechnologies®, Cambridge, UK) and sleep logs. They were also instructed to refrain from excessive physical activity, caffeine and alcohol consumption. Drug-free status was verified upon admission via urine toxicologic analysis (Drug-Screen Card Multi-6 for amphetamines, benzodiazepines, cocaine, methadone, opiates and tetrahydrocannabinol; von Minden GmbH, Moers, Germany). All 10 subjects completed the study without any complaints.
Design
Subjects underwent two study blocks in a balanced crossover design: a sleep-deprivation (SD) and a nap protocol (NP) (Fig. 1). In either protocol, subjects reported to the laboratory in the evening for an 8 h sleep episode. The timing of their sleepwake schedule was calculated in such a way that the sleep episode was centered at the midpoint of each subjects habitual sleep episode as assessed by actigraphy during the baseline week. On the next afternoon (day 1) electrodes and thermosondes were attached. After a second 8 h sleep episode (baseline night) at their habitual bedtime, a 40 h sleep deprivation under constant routine (CR) conditions or a 40 h nap protocol under constant posture conditions (near recumbent during wakefulness and supine during scheduled sleep episodes) was carried out [for details of the CR method see Cajochen et al. (Cajochen et al., 1999b
)]. In the NP protocol, subjects completed 10 alternating cycles of 75 min of scheduled sleep and 150 min of scheduled wakefulness. The light levels were <8 lux (typically 35 lux at the angle of gaze) during scheduled wakefulness and 0 lux during scheduled sleep. The protocol ended with a 8 h recovery sleep episode starting again at habitual bedtime. After a 14 week interval, the subjects started their second study block.
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Sleep was recorded polysomnographically using the VITAPORT digital ambulatory sleep recorder (Vitaport-3 digital recorder, TEMEC Instruments BV, Kerkrade, The Netherlands). Twelve EEGs, two electro-oculograms (EOG), one submental electromyogram (EMG) and one electrocardiogram (ECG) signal were recorded. All signals were on-line digitized (12 bit AD converter, 610 mV/bit; storage sampling rate at 128 Hz for the EEG) and digitally filtered at 30 Hz (fourth-order Bessel-type anti-aliasing filters, total 24 dB/Oct.) using a time constant of 1.0 s. The raw signals were stored on-line on a Flash RAM Card (Viking, Rancho Santa Margarita, USA) and downloaded off-line to a PC hard drive. EEG artifacts were detected by an automated artifact detection algorithm. This algorithm was based on a instantaneous frequency analysis, which yields the amplitude envelope and the frequency of eight band-filtered components instantaneously at a rate of eight per second. Low-frequency (as movement) artifacts, mid-frequency (as ECG interference) and high-frequency (as EMG) artifacts are detected individually if the respective instantaneous frequencies and amplitudes in the relevant frequency bands are not within preset ranges (CASA, 2000 Phy Vision BV, Kerkrade, The Netherlands). The EEGs were off-line subjected to spectral analysis using a fast Fourier transform (FFT, 10% cosine 4 s window) resulting in a 0.25 Hz bin resolution. For data reduction, artifact-free 4 s epochs were averaged over 20 s epochs. Sleep stages were visually scored on a 20 s basis (Vitaport Paperless Sleep Scoring Software) according to standard criteria (Rechtschaffen and Kales, 1968). EEG power spectra were calculated during NREM sleep in the frequency range from 0.5 to 32 Hz. Here, we only report EEG data derived from the midline (Fz, Cz, Pz, Oz) referenced against linked mastoids (A1, A2) in the range of 0.525 Hz.
Statistics
The statistical packages SAS® (Version 6.12, SAS® Institute Inc., Cary, NC) and Statistica® [Statistica for Windows, StatSoft Inc. (1995)] were used. Statistical analyses did not reveal any significant difference between the two baseline nights, neither for sleep stage measures nor for EEG power density in any of the frequency bins. Therefore, for the sake of simplicity, the two baseline nights were pooled.
In order to analyze the time course of sleep stages and EEG power density in the course of the sleep episodes, the 8 h sleep episodes were subdivided into 2 h intervals after the first occurrence of stage two (i.e. sleep onset). This resulted in the fourth 2 h interval being shorter than 2 h and of variable length for each subject and night. To correct for this, relative values for sleep stage variables (% of total sleep time) are reported. Values of each interval were compared with values of the corresponding intervals during the baseline night. In one case, artifacts considerably disturbed EEG recordings in intervals 3 and 4. This subject was excluded for the time course analysis of the visual scoring data.
In the topographical analysis of SWA, high spindle frequency activity (HSFA) and low spindle frequency activity (LSFA), a first statistical analysis with the four separate EEG derivations (Fz, Cz, Pz, Oz) did not yield consistent significant interactions between condition x derivation. Therefore, derivations were pooled in order to obtain consistent significant interaction in the ANOVA. The frontal and central derivation (Fz + Cz) and the parietal and occipital derivation (Pz + Oz) were pooled for SWA and LSFA; the frontal and occipital derivation (Fz + Oz) and the central and parietal derivation (Cz + Pz) were pooled for HSFA.
One-, two- and three-way analyses of variance for repeated measures (rANOVA) were performed. All P values derived from rANOVAs were based on HuynhFeldts (HF) corrected degrees of freedom, but the original degrees of freedom are reported. For post hoc comparisons, the Duncans multiple range test and t-tests with correction for multiple comparisons (Curran-Everett, 2000) were used.
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Results |
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Sleep during Naps
In order to test whether the subjects were able to sleep sufficiently during the NP protocol, the amount of total sleep time (TST; NREM sleep + REM sleep + stage 1) and relative sleep stages (percent of TST) during the baseline night (BL), throughout the 40 h episode of the NP protocol and during the recovery night (REC) were calculated for each subject and then averaged over subjects. Across the total of 12.5 h scheduled sleep episodes (10 naps each of 75 min duration), TST did not significantly differ from accumulated TST in the 8 h baseline sleep episode [459.27 ± 27.52 versus 434.93 ± 8.98 min; F(2,18) = 1.34, P = 0.28]. However, the proportion of sleep stages was different. The percentage of SWS was significantly higher than during baseline (19.71 ± 1.73 versus 15.99 ± 1.33%, P < 0.05), whereas REM sleep percentage was significantly reduced (15.70 ± 1.92 versus 21.59 ± 1.37%, P < 0.05, Duncans multiple range test). Details about the changes in sleep structure throughout the NP protocol are summarized in Knoblauch et al. (Knoblauch et al., 2001); spectral EEG changes will be reported elsewhere.
Recovery Nights
Table 1 summarizes all-night sleep measures (% of total sleep time) for the average baseline night and the SD and NP recovery night. A one-way rANOVA with the factor condition (BL, SD, NP) yielded a significant variation in all measures [F(2,18) > 6.0, P < 0.03] except for REM sleep, movement time (MT) and latency to REM sleep. Post hoc comparisons revealed that TST, sleep efficiency [SE; (TST/time in bed)x100], NREM sleep, SWS, stage 3 and stage 4 were significantly enhanced in the recovery night following the SD protocol compared with the baseline night at the expense of stage 1 and 2 and the arousal index (WALO + MT) (for statistics see Table 1
). WALO (wakefulness after lights off) tended to be reduced (P = 0.06). Sleep latency to stage 2 and to stage 1 was significantly reduced. SWS in the recovery night after the NP protocol was significantly reduced whereas stage 2 was significantly enhanced, and the latency to stage 2 was significantly longer (see Table 1
).
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EEG Power Spectra during NREM Sleep: All-night Absolute EEG Power Density (0.525 Hz)
All-night absolute EEG power density in each frequency bin between 0.5 and 25 Hz for the midline derivations (Fz, Cz, Pz, Oz) during NREM sleep is illustrated in Figure 2 for the average baseline night (BL) and the SD and NP recovery night. A two-way rANOVA with the factors derivation (Fz, Cz, Pz, Oz) and condition (BL, SD, NP) revealed a significant interaction in the following frequency bins: 0.55, 8.7510.25, 12.2513.25, 13.7516.5, 2424.5 and 24.7525.5 Hz (P < 0.05 for each frequency bin, bottom panel of Fig. 2
). Visual inspection of the curves indicated a prominent spindle peak in the central, parietal and occipital derivation, whereas it was less pronounced in the frontal derivation.
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EEG power density in the 0.55 Hz (SWA), in the 12.25 13.25 Hz (LSFA) and the 13.7516.5 Hz (HSFA) range were each collapsed into bands. The frequency ranges for these bands were chosen based on a significant interaction in the two-way rANOVA with the factors derivation and condition (see Fig. 2). SWA, LSFA and HSFA are plotted for each derivation and night in Figure 3
(panels 13). A two-way rANOVA with the factors derivation and condition was performed and showed a significant interaction of these two factors for SWA [F(6,54) = 16.22; P < 0.01], LSFA [F(6,54) = 7.05; P < 0.01] and HSFA [F(6,54) = 6.47; P < 0.01]. For SWA, post hoc comparisons indicated that SWA significantly decreased from Fz to Cz to Pz to Oz in BL, SD and NP (Fig. 3
, panel 1; P < 0.01, Duncans multiple range test). Compared with BL, SWA was significantly increased after SD (P < 0.01, Duncans multiple range test) and not significantly changed in NP (P > 0.05) for all derivations.
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LSFA was significantly enhanced after SD in all derivations (Fig. 3, panel 2; P < 0.01, Duncans multiple range test) except for Fz (P = 0.77). LSFA after NP showed a significant increase in the more frontal derivations Fz (P < 0.01) and Cz (P < 0.05), tended to be enhanced in Pz (P = 0.09) and was not significantly changed in Oz (P = 0.77).
HSFA
The SD and NP condition elicited opposite effects on HSFA: there was a significant increase during the NP recovery night in Cz and Pz (P < 0.01, Duncans multiple range test), whereas in the SD recovery night HSFA was significantly decreased in Cz (Fig. 3, panel 3; P < 0.05). HSFA in the frontal and occipital derivation was not significantly changed (P > 0.05).
Topography of LSFA and HSFA
For analyzing the different topographical distribution of LSFA and HSFA, values in the frontal and central derivation and in the parietal and occipital derivation were added together for LSFA [(Fz + Cz), (Pz + Oz)] and in the frontal and occipital derivation and in the central and parietal derivation for HSFA [(Fz + Oz), (Cz + Pz); see Methods]. HSFA exhibited a centro-parietal dominance in all conditions (Fig. 3, panel 3, P < 0.05, Duncans multiple range test) and LSFA a fronto-central dominance (Fig. 3
, panel 2, P < 0.05, Duncans multiple range test).
SWAHSFA Ratio
The lowest panel in Figure 3 depicts the logarithmic ratio between SWA and HSFA. A two-way rANOVA with the factors derivation and condition was performed and showed a significant interaction [F(6,54) = 6.14; P < 0.01]. Post hoc comparisons to baseline showed that the ratio was enhanced in SD and reduced in NP in all derivations (P < 0.05, Duncans multiple range test). The ratio significantly decreased from Fz to Cz to Pz and was significantly higher in Oz than in Pz for all conditions.
Time Course of Relative EEG Power Density (0.525 Hz)
In a next step, EEG power density during NREM sleep for each frequency bin and derivation in the SD and NP recovery night was expressed as a percentage of the corresponding value of the averaged baseline night for each 2 h interval (Fig. 4). A two-way rANOVA with the factor condition (BL, SD, NP) x time (intervals 14) and a paired t-test corrected for multiple comparisons (NP versus BL, SD versus BL) was performed for each derivation and frequency bin separately.
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Slow Wave Range (0.55 Hz)
There was a global increase in NREM sleep EEG power density over a broad frequency range. The largest increase in the slow wave range occurred in interval 1 (15 Hz in Fz, Cz and Pz, 0.755 Hz in Oz).
Theta Range (58 Hz)
EEG power density in the theta range was significantly increased in most of the theta bins in intervals 1 and 2 for all derivations, in interval 3 for Fz, Cz, Pz and in interval 4 for Fz and Cz. Some theta bins in intervals 3 and 4 for Oz were also significantly increased.
Alpha Range (812 Hz)
EEG power density was significantly increased in a broad part of the alpha frequency range for all derivations in intervals 1 and 2 and in the frontal and central derivation in intervals 3 and 4. A distinct peak in relative alpha activity appeared in the first two intervals in the frontal and central derivation.
Spindle Frequency Range (1216 Hz)
A bimodal pattern emerged in the spindle frequency range. While spindle frequency activity (SFA) was unchanged in interval 1, EEG power density in the upper spindle frequency range was significantly decreased in interval 2 in Fz (14.2514.5 Hz) and Cz (14.2514.75 Hz) and in interval 3 in Cz (14.25 15.25 Hz) and Pz (14.2515 Hz). A distinct peak in relative LSFA emerged in Cz, Pz and Oz during intervals 24. This increase was significant in interval 2 for Pz (1313.25 Hz), in interval 3 for Oz (1313.5 Hz) and in interval 4 for Cz and Pz (13.2513.75 Hz).
Beta range (>16 Hz)
EEG power density between 16.25 and 25 Hz was significantly increased at the beginning of the night in the fronto-central region (in interval 1 in Fz and Cz and in interval 2 in Fz).
NP Recovery
Slow Wave Range (0.55 Hz)
EEG power density in the lower slow wave range (0.752.25 Hz) was significantly decreased in interval 1 in all derivations (Fz: 11.25 Hz, Cz: 12.5 Hz, Pz: 11.25 Hz and 1.752.25 Hz, Oz: 0.751 Hz). In the intervals 24, EEG power density in this frequency range was not significantly changed. EEG power density in the higher slow wave range (>2.75 Hz) was significantly enhanced in interval 2 in Fz (33.25 Hz, 3.754 Hz, 4.255 Hz) and Cz (33.25 Hz, 4.55.5 Hz), and in interval 3 in Fz (1.55 Hz), Cz (2.755 Hz) and Pz (3.55 Hz).
Theta Range (58 Hz)
There was a significant increase in some frequency bins in the theta range in interval 2 (Fz and Cz) and in interval 3 (Fz, Cz and Pz).
Alpha Range (812 Hz)
EEG power density was significantly increased in some of the bins, particularly in the fronto-central region during interval 3.
Spindle Frequency Range (1216 Hz)
EEG power density in the low and high spindle frequency range showed similar patterns after the NP protocol either enhanced or unaffected and did not show the bimodal pattern observed after SD. The increase was significant in interval 1 for Fz (12.2515.5 Hz), for Cz (13.2515.25 Hz, 15.7516.25 Hz) and for Pz (13.515 Hz), in interval 2 for Fz (12.2516.5 Hz) and Cz (13.513.75 Hz, 14.514.75 Hz, 15.7516 Hz) and in interval 3 for Fz (12.2512.5 Hz, 12.7513.75 Hz).
Beta Range (>16 Hz)
EEG power density above 16 Hz was not significantly affected by the NP for any of the derivations.
Time Course of SWA
Figure 5 shows the time course of SWA (0.55 Hz) throughout the sleep episodes for each derivation. A two-way rANOVA with the factors condition x time (2 h intervals 14) yielded a significant interaction for all derivations [F(6,54) > 10; P < 0.05]. Post hoc comparisons revealed that during recovery sleep after SD, SWA was significantly enhanced in the 2 h intervals 1 and 2 in all derivations (P < 0.05, Duncans multiple range test). In the NP recovery night, SWA was significantly reduced in the first 2 h interval in all derivations except for Fz (Fig. 5
; P < 0.05, Duncans multiple range test). No significant change was observed in the remaining 2 h intervals.
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Discussion |
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Taken together, frontal low EEG activity and centro-parietal HSFA were the bands with a clear homeostatic regulation, and therefore represent two distinct indexes of sleep pressure during human sleep. The balance between these indexes (SWA/HSFA) represents a very sensitive marker of changes in sleep homeostatic pressure.
SWA
The observed increases in SWA after high sleep pressure are in good accordance with previous studies in which the effects of sleep deprivation on EEG power spectra have been quantified (Borbély et al., 1981; Dijk et al., 1993
). Furthermore, we could confirm recent reports (Cajochen et al., 1999a
; Finelli et al., 2001
) that this increase in SWA varies along the antero-posterior axis and shows a fronto-central predominance. The reduction of SWA in the recovery night after the nap protocol, particularly at the beginning of the night, is likely to be a result of the low level of sleep pressure. The observed decrease in SWA, although smaller, is in good accordance with a study in which the duration of prior wakefulness was reduced by a single early evening nap (Werth et al., 1996
). The observed changes in SWA confirmed the visually scored SWS findings. Interestingly, the SWA reduction after low sleep pressure did not display the corresponding frontal predominance. It may be that the degree of sleep satiation obtained in the NP protocol was not strong enough to elicit regional differences. However, the occipital predominance in the SWA response after low sleep pressure may not support this hypothesis. Another explanation may be that frontal cortical areas of the brain are particularly affected by sleep deprivation, whereas after sleep satiation the negative rebound in SWA is not confined to frontal brain areas, and rather manifests itself in more occipital brain regions. In other words, challenging the sleep homeostat by an extension of wakefulness elicits frontal deactivation, whereas challenging the sleep homeostat by a reduction of wakefulness does not result in frontal activation. PET studies have demonstrated that the decline of regional cerebral blood flow (rCBF) during SWS is most prominent in frontal cortical areas (Maquet et al., 1997
; Hofle et al., 1997
), and rCBF in the anterior cingulate and orbitofrontal cortex are negatively correlated with EEG SWA during sleep (Hofle et al., 1997
). Nevertheless, how the frontal rCBF decline during slow wave sleep is associated with the homeostatic regulation of SWS remains to be elucidated.
Spindle Frequency Activity
Previous studies have suggested that spindle frequency activity, particularly in the high frequency range, may be under homeostatic control (Borbély et al., 1981; Dijk et al., 1987
, 1993
; Aeschbach and Borbély, 1993
). Our present results are in accordance with this hypothesis. The reduction of HSFA after sleep deprivation was rather small. However, the negative peak in the shape of the generally enhanced relative spectra was outstanding. Besides the differential qualitative response of SWA and HSFA to high sleep pressure, the time course was also different. The increase of power density in the SWA range was most salient in the first 2 h interval and dissipated thereafter. The reduction in the HSFA range, on the other hand, only became evident after the second 2 h interval. This delayed response to sleep deprivation in the high spindle frequency range has also previously been described (Borbély et al., 1981
; Dijk et al., 1993
).
After the nap protocol, when sleep pressure was low, HSFA increased markedly. This increase peaked in, but was not limited to the high-frequency range, as it was for the decrease after sleep deprivation, but also covered the lower sigma frequency range. There is evidence in the literature that homeostatic regulation may be weaker for LSFA than for HSFA. LSFA did not increase with time awake in a nap study, as did HSFA (Dijk et al., 1987, 1993
). The results of an increase limited to the high spindle frequency range (13.2515 Hz) in the course of a nocturnal sleep episode (Aeschbach and Borbély, 1993
) would fit the idea that the same underlying process is present within a sleep episode (Borbély et al., 1981
). However, in a forced desynchrony study where sleep occurred at all circadian phases, low-, intermediate- and high-frequency SFA all increased with the progression of sleep, which would rather suggest homeostatic control of LSFA (Dijk et al., 1997
). Here we report for the first time a dissimilar homeostatic regulation of LSFA and HSFA after sleep loss.
Furthermore, we could show that the effects of differential levels of sleep pressure on SFA depend on brain location. We found the previously reported centro-parietal dominance of HSFA (Jankel and Niedermeyer, 1985; Jobert et al., 1992
; Zeitlhofer et al., 1997
) and could show that here, HSFA was sensitive to different levels of sleep pressure, but not in the frontal and occipital derivation. Our data confirm analyses of scalp-recorded sleep spindles with topographically distinct slow- and fast-spindle waves (Gibbs and Gibbs, 1950
; Scheuler et al., 1990
; Jobert et al., 1992
; Werth et al., 1997
; Zeitlhofer et al., 1997
; Zygierewicz et al., 1999
).
Theta and Alpha Activity
EEG theta and alpha activity was significantly increased after the nap protocol, an effect usually seen after sleep deprivation, when sleep pressure is enhanced. The decrease of SWS and SWA at the beginning of the recovery night indicates that the subjects were indeed sleep satiated at the end of the nap schedule. The extent of this sleep satiation was, however, less pronounced than the extent of the 40 h sleep deprivation compared to baseline (note: TST was not significantly enhanced in the nap protocol compared with baseline conditions, and the reduced sleep pressure at the beginning of the recovery night may be due to the higher level of accumulated SWS and the short duration of wakefulness prior to sleep). When sleep pressure was considerably diminished by an evening nap, theta and alpha activity was reduced in the first two NREM sleep episodes during post-nap nocturnal sleep (Werth et al., 1996). This reduction in theta and alpha activity was not found after our NP protocol. However, the increase of EEG activity in these frequency bands was hardly present in the NREM spectrum during the first 2 h interval, emerged slightly in interval 2 and more prominently in interval 3. This indicates that after an initial reduction of sleep pressure, there might have been an intra-night build-up of sleep pressure which led to a partial increase of EEG activity in the theta, alpha and slow wave range in the latter part of the night.
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Conclusion |
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Acknowledgments |
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References |
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