Institute of Pharmacology and Toxicology, University of Zürich, CH-8057 Zurich, Switzerland
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ABSTRACT |
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Huber, Reto, Tom Deboer, and Irene Tobler. Topography of EEG Dynamics After Sleep Deprivation in Mice. J. Neurophysiol. 84: 1888-1893, 2000. Several recent results show that sleep and sleep regulation are not only global phenomena encompassing the entire brain, but have local features. It is well established that slow-wave activity [SWA; mean electroencephalographic (EEG) power density in the 0.75-4.0 Hz band] in non-rapid eye movement (NREM) sleep is a function of the prior history of sleep and wakefulness. SWA is thought to reflect the homeostatic component of the two-process model of sleep regulation. According to this model, originally formulated for the rat and later extended to human sleep, the timing and structure of sleep are determined by the interaction of a homeostatic Process S and a circadian process. Our aim was to investigate the dynamics of SWA in the EEG of two brain regions (frontal and occipital cortex) after sleep deprivation (SD) in two of the mice strains most often used in gene targeting. C57BL/6J (n = 9) and 129/Ola (n = 8) were recorded during a 24-h baseline day, 6-h SD, and 18-h recovery. Both derivations showed a significant increase in SWA in NREM sleep after SD in both strains. In the first hour of recovery, SWA was enhanced more in the frontal derivation than in the occipital derivation and showed a faster decline. This difference resulted in a lower value for the time constant for the decrease of SWA in the frontal derivation (frontal: 10.9 ± 2.1 and 6.8 ± 0.9 h in Ola and C57, respectively; occipital: 16.6 ± 2.1 and 14.1 ± 1.5 h; P < 0.02; for each of the strains; paired t-test). Neither time constant differed significantly between the strains. The subdivision of SWA into a slower and faster band (0.75-2.5 Hz and 2.75-4.0 Hz) further highlighted regional differences in the effect of SD. The lower frequency band had a higher initial value in the frontal derivation than in the occipital derivation in both strains. Moreover, in the higher frequency band a prominent reversal took place so that power in the frontal derivation fell below the occipital values in both strains. Thus our results indicate that there may be differences in the brain in the effects of SD on SWA in mice, suggesting regional differences in the dynamics of the homeostatic component of sleep regulation. The data support the hypothesis that sleep has local, use- or waking-dependent features that are reflected in the EEG, as has been shown for humans and the laboratory rat.
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INTRODUCTION |
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The homeostatic regulation of
sleep is one of its most prevalent features among mammalian species
(Borbély and Achermann 1999; Tobler
2000
). Thus electroencephalographic (EEG) slow-wave activity
(SWA, mean EEG power density in the 0.75- to 4.0-Hz range) in
non-rapid eye movement (NREM) sleep changes as a function of the
previous sleep-waking history and may represent a measure of sleep
intensity. This feature of sleep has been consistently shown in a broad
range of species, such as humans, cat, mouse, rat, and squirrel
(Huber et al. 2000
; Larkin and Heller
1998
; Strijkstra and Daan 1998
; reviewed in
Tobler 1995
). Furthermore, in humans a night following a
nap exhibits reduced SWA compared with normal nights (Dijk et
al. 1987
; Werth et al. 1996b
). The neuronal
mechanisms leading to slow waves in the EEG have been elucidated
(Amzica and Steriade 1998
); however, their function remains unknown.
There is little doubt that the function(s) of sleep must entail
restoration, and most probably mainly for the brain (Horne 1985). Moreover, it has been proposed that sleep may not be a global phenomenon encompassing the entire brain, but that slow waves
may reflect local recovery processes (Krueger and Obál 1995
; Moruzzi 1972
). Findings in the bottlenose
dolphin contributed to this hypothesis. These animals have the capacity
to exhibit "deep" slow-wave sleep only in one brain hemisphere
while the EEG in the other hemisphere exhibits a waking pattern
(Mukhametov et al. 1977
). Furthermore, after
unihemispheric sleep deprivation, the deprived brain hemisphere showed
a larger increase of deep slow-wave sleep (Oleksenko et al.
1992
). Also birds seem to have the capacity to exhibit minor
hemispheric asymmetries in the EEG (Amlaner and Ball
1994
), but they last only a few seconds and are related to
unilateral eye-opening (Rattenborg et al. 1999
).
Sleep could be regarded as a use-dependent local phenomenon serving to
stimulate synapses insufficiently used during wakefulness to maintain
neuronal connections (Krueger and Obál 1995).
According to this hypothesis, synaptic connectivity is strengthened
locally and modulates EEG synchronization during sleep. An alternative hypothesis proposed a restoration of brain glycogen levels during sleep, which are thought to be depleted during the brain activity related to wakefulness (Benington and Heller 1995
).
Recent evidence supports the notion that a previous experience can
affect the cell firing of specific neurons during sleep (Poe et
al. 2000
). Thus in rats an experience-dependent reversal of the
phase of multiunit firing of hippocampal cells was shown in REM sleep
following running in specific tracks.
It is well known from animal studies that the EEG pattern differs
according to the position of the cortical electrodes, but most reports
have remained at the descriptive level. Spectral analysis of the EEG
recorded from a frontal and occipital derivation in the rat, or from
six bipolar derivations in humans revealed state- and
frequency-specific differences between the derivations (Schwierin et al. 1999; Werth et al.
1997
). After 24-h sleep deprivation (SD) in the rat, a larger
increase in the 1.75- to 4.5-Hz band was observed in frontal versus
occipital power (Schwierin et al. 1999
). This
differential response to SD may reflect regional, use-dependent aspects
of sleep regulation. A frontal predominance of SWA has been described
in humans (Werth et al. 1996a
), which supports the
notion that the prefrontal cortex is the site of greatest cortical
brain work during wakefulness (Horne 1993
). In addition,
local activation of the left somatosensory cortex by vibration of the
right hand in humans resulted in a shift of power in the sleep EEG of
the somatosensory cortex toward the left hemisphere (Kattler et
al. 1993
), and visual stimulation in macaque monkeys suggested
that sleep does not occur simultaneously in all cortical areas of the
brain (Pigarev et al. 1997
). Such studies are supported
by results from regional distribution of cerebral blood flow
(Gur et al. 1995
; Swanson et al. 1992
)
and positron emission tomography in humans, which reflect changes in
the functional organization of the brain by the different patterns of
regional blood flow (Braun et al. 1998
; Finelli
et al. 2000
; Gillin et al. 1996
; Maquet
et al. 1996
, 1997
).
Due to the large use of gene targeting in mice, these rodents have
become important models to investigate the mechanisms and function(s)
of sleep (Boutrel et al. 1999; Chemelli et al.
1999
; Franken et al. 2000
; Huber et al.
1999
; Tobler et al. 1996
). Our aim was to
investigate the dynamics of EEG activity of two cortical derivations
after SD in two strains of inbred mice that are most often used in gene
targeting experiments.
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METHODS |
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Mice
Adult male mice, of the inbred strain 129/Ola (Ola; n = 8) and C57BL/6J (C57; n = 9) were used. They were maintained in a 12-h light-12-h dark cycle (lights from 08:00 to 20:00 h; OSRAM Dulux EL energy saving lamp, 7 W, 10-40 lux at the level of the mice), kept individually in Macrolon cages (36 × 20 × 35 cm) and in sound-attenuated chambers. Food and water were available ad libitum. Adaptation to these conditions was at least 3 wk. Ambient temperature in the boxes during the recording days was 22.5 ± 0.4°C.
Surgery
Implantation of EEG and electromyographic (EMG) electrodes occurred under deep anesthesia (pentobarbital sodium, Nembutal, 80 mg/kg ip; volume, approximately 0.5 ml). Three gold-plated miniature screws (diameter 0.9 mm) placed over the right occipital cortex (2-3 mm lateral to the midline, 2 mm posterior to bregma), the right frontal cortex (1 mm anterior to bregma, 1 mm lateral to midline) and the cerebellum (at midline, 1 mm posterior to lambda) served as epidural EEG electrodes. Two gold wires (diameter 0.2 mm) inserted into the neck muscle served to record the EMG. The electrodes were connected to stainless steel wires that were glued to the skull with dental cement. At least 3 wk were allowed for recovery. Age at recording onset was as follows: Ola, 16.6 ± 0.8 (mean ± SE); C57, 16.6 ± 1.2 wk.
Experimental protocol and data acquisition
After a 24-h baseline recording the mice were recorded during a
6-h SD starting at light onset and the remaining 18-h recovery. SD was
performed by gentle handling (Huber et al. 2000). The
bipolar EEG recordings (occipital-cerebellum; frontal-cerebellum) and EMG signals were amplified (amplification factor ~2,000), conditioned by analog filters (high-pass filter:
3 dB at 0.016 Hz; low-pass filter:
3 dB at 40 Hz; less than
35 dB at 128 Hz), sampled with either 256 or 512 Hz, digitally filtered (EEG: low-pass FIR filter 25 Hz; EMG: band-pass FIR filter 20-50 Hz) and stored with a resolution of 128 Hz. EEG power spectra were computed for consecutive 4-s epochs
by a fast Fourier transform (FFT) routine within the frequency range of
0.25-25.0 Hz. Between 0.25 and 5.0 Hz, the values were expressed in
0.5-Hz bins and between 5.25 and 25.0 Hz in 1-Hz bins. EMG signals were
integrated over 4 s and ambient temperature inside the chambers
was recorded at 4-s intervals. All data were recorded simultaneously
and stored on optical disks. The EEG channels were calibrated with a
10-Hz sine wave, 300-µVPP signal that was recorded before each experiment.
Vigilance states and analysis
Vigilance states were determined for 4-s epochs as described
previously (Tobler et al. 1997). Epochs containing EEG
artifacts were excluded from spectral analysis (% of recording time:
Ola, 10.3 ± 2.1; C57, 7.8 ± 1.9; >85% of all EEG
artifacts were in waking). Vigilance states could always be determined.
Overall effects within a strain were analyzed by two-way ANOVA for repeated measures with factors "time" (1-h intervals) and "frequency bin" or "derivation" (frontal and occipital). Contrasts were tested by post hoc two-tailed t-tests only if a factor or interaction between factors reached significance.
Time constant of the decrease of slow-wave activity
To obtain an approximation of the time constant of the decrease
of SWA in NREM sleep, an exponential function was fitted to the first
six 1-h intervals after SD (Franken et al. 1991;
Huber et al. 2000
).
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RESULTS |
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Baseline
Invariably, independent of strain and derivation, power in the delta range was highest in NREM sleep compared with REM sleep and waking (Fig. 1). Furthermore, power in REM sleep exceeded NREM sleep within the theta range in the occipital derivation only. A peak in waking within the theta band was more prominent in the occipital derivation compared with the frontal one in both strains, but did not exceed the values observed in REM sleep (Fig. 1). Neither the 24-h mean integrated power (0.25-25.0 Hz) over the three vigilance states nor power within the slow-wave range (0.75-4.0 Hz) in NREM sleep differed between the derivations (Table 1).
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Sleep deprivation
After 6-h SD EEG power density in NREM sleep was massively enhanced in both derivations and strains, especially in the delta range (Fig. 2). In the frontal derivation the effect in the first 2 h was most prominent in the frequency bins between 0.75 and 1.5 Hz, and larger than in the occipital derivation. In the course of recovery the occipital and frontal changes affected a progressively narrowing band within the delta range in C57. A similar effect was seen in Ola in a broader frequency range. Moreover, in Ola all frequencies were initially enhanced in both derivations, and power in some of the higher frequencies remained enhanced till the end of the light period. In contrast, in C57 power in the higher frequencies in the frontal derivation was below baseline and also below power of the occipital derivation after the first 2 h of recovery (Fig. 2, right panel).
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The estimation of the time constants of the decrease of SWA in NREM sleep for the first 6 h after SD revealed a significant difference between the frontal and occipital derivation in both strains (Table 2). Thus SWA in the frontal derivation showed a faster decrease. No difference was found between the strains (2-way ANOVA was not significant for factor "strain" and interaction "strain" vs. "derivation").
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The subdivision of SWA into a slow and a faster frequency band highlighted regional differences in the effect of SD. The time course of the two bands during recovery is illustrated in Fig. 3. Thus in both strains the slow band (0.75-2.5 Hz) in the frontal derivation was initially enhanced to a higher level compared with the occipital derivation. For the fast band (2.75-4.0 Hz), this result was similar but did not reach significance. However, in contrast to the slow band, a significant reversal took place in the course of recovery. Thus after the initial increase the fast band of the frontal derivation fell below the values of the occipital one in both strains. The strains differed in the time point and duration of this reversal. Moreover, in C57 similar differences between the bands already were apparent during the baseline, with higher frontal than occipital values in the lower band at the beginning of the light period and a more rapid subsequent decline (data not shown).
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DISCUSSION |
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Vigilance state-specific differences were found in the EEG
spectra of the frontal and occipital derivation in both strains, despite the strain differences in the spectra (Fig. 1). The prominent occipital peak in theta power in REM sleep and waking as well as its
reduced appearance in the frontal derivation was similar in both
strains and comparable to data obtained from similar cortical regions
in the rat (Bringmann 1995; Schwierin et al.
1999
). Also in the rabbit, EEG recordings from the motor cortex
showed little theta activity in REM sleep or waking while in the
sensory cortex a prominent theta rhythm was recorded in both states
(Tobler et al. 1990
). In mice the occipital electrode
was over the somatosensory cortex and the hippocampus while the frontal
electrode was over the motor cortex. Therefore it can be assumed that
the occipital electrode primarily reflects the hippocampal theta rhythm
(Vertes and Kocsis 1997
).
Our main finding was the regional difference in time course of SWA in
NREM sleep. Especially the initial increase of SWA after SD was larger
in the frontal than in the occipital derivation and showed a faster
decline (Fig. 3). This topographic difference was similar in the rat,
where frequencies between 1.75 and 4.5 Hz showed a larger frontal power
increase after SD compared with occipital power (Schwierin et
al. 1999).
In humans EEG power in NREM sleep derived from anterior recordings
exceeded power of more posterior derivations in the 2-Hz band in the
first two NREM-REM sleep cycles (Werth et al. 1996a). These topographic differences support the hypotheses that sleep has
local, use-dependent features (Krueger and Obál
1995
). It is tempting to assume that the differences in EEG
power between derivations during sleep reflect differences in neuronal
activity occurring during waking in the brain region below the
recording site (frontal: motor cortex; occipital: somatosensory
cortex). This interpretation is supported by results from
Swanson et al. (1992)
, which showed increased glycogen
utilization in the somatosensory cortex after facial vibrissae
stimulation indicating a regionally increased metabolism.
The two-process model of sleep regulation describes the timing and
structure of sleep on the basis of a circadian and homeostatic process
(Borbély 1982; Daan et al. 1984
).
With this model SWA in NREM sleep was simulated successfully for a
considerable number of experimental protocols, including the rat and
mouse (e.g., Borbély and Achermann 1999
;
Franken et al. 1991
; Huber et al. 2000
).
Our results show that regional differences in the dynamics of SWA
become more apparent when the slow-wave band is subdivided into two
frequency bands. Thus the initial, larger increase of SWA in the
frontal derivation is mainly due to the contribution of frequencies
belonging to the lower delta range (0.75-2.5 Hz), whereas the faster
frontal power decline in the course of recovery can be attributed to
the higher delta range (2.75-4.0 Hz). The lower delta frequency band
corresponds to the frequency of slow cortical oscillations (<1 Hz) and
thalamic clocklike oscillations (1-4 Hz), while the higher delta
frequency band corresponds to the intrinsic delta activity of cortical
neurons (3-4 Hz) (Amzica and Steriade 1998
). Since the
former two oscillations are related to hyperpolarization of neurons
during sleep (Amzica and Steriade 1998
), our data could
indicate topographic differences of the cerebral cortex in its response
to the activity of the thalamocortical system. The time course
differences in regional EEG power in the two bands within the
"delta" range may reflect differences in the recovery process of
distinct neuronal groups.
The estimation of the time constants of the decrease of SWA resulted in
a faster SWA decline in the frontal derivation compared with the
occipital derivation, and did not differ between the two strains (Table
2). This result supports our previous lack of strain differences in
both the time constant for SWA increase and decrease in the comparison
between 129/SvJ and C57 (Huber et al. 2000). The
different time constants for the two derivations (Table 2), support a
difference in the underlying dynamics and could reflect different rates
of recovery. This is therefore a first indication of topographic
differences in the dynamics of the homeostatic Process S of the
two-process model of sleep regulation in rodents (Borbély
1982
; Daan et al. 1984
).
In general, the strain differences in SWA were minor. However, the
regional changes in the frequencies above the SWA-band were not
consistent between the strains (Fig. 2). Interestingly, in Ola, power
in these frequencies exceeded baseline in both derivations, and in the
frontal derivation they were above occipital power. In contrast, in C57
there was no increase in frequencies above 10 Hz in either derivation,
but a decrease below baseline values in frontal power. This finding in
C57 is comparable to the larger decrease after SD in similar
frequencies in the rat in a frontal derivation compared with an
occipital derivation (Schwierin et al. 1999).
It remains to be investigated whether particular brain regions are more activated in the course of sleep deprivation, leading to a different time course of power in specific frequency bands during recovery.
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ACKNOWLEDGMENTS |
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We thank Drs. A. Borbély and P. Achermann for comments on the manuscript.
This study was supported by the Swiss National Grant Foundation (3100-053005.97).
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FOOTNOTES |
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Address for reprint requests: I. Tobler, Institute of Pharmacology and Toxicology, University of Zürich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland (E-mail: tobler{at}pharma.unizh.ch).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 3 April 2000; accepted in final form 6 June 2000.
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REFERENCES |
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