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The Journal of Neuroscience, December 15, 2002, 22(24):10941-10947
Grouping of Spindle Activity during Slow Oscillations in Human
Non-Rapid Eye Movement Sleep
Matthias
Mölle,
Lisa
Marshall,
Steffen
Gais, and
Jan
Born
Institute of Neuroendocrinology, University of Lübeck, 23538 Lübeck, Germany
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ABSTRACT |
Based on findings primarily in cats, the grouping of spindle
activity and fast brain oscillations by slow oscillations during slow-wave sleep (SWS) has been proposed to represent an essential feature in the processing of memories during sleep. We examined whether
a comparable grouping of spindle and fast activity coinciding with slow
oscillations can be found in human SWS. For negative and positive
half-waves of slow oscillations (dominant frequency, 0.7-0.8 Hz)
identified during SWS in humans (n = 13),
wave-triggered averages of root mean square (rms) activity in
the theta (4-8 Hz), alpha (8-12 Hz), spindle (12-15 Hz), and beta
(15-25 Hz) range were formed. Slow positive half-waves were linked to
a pronounced and widespread increase in rms spindle activity, averaging
0.63 ± 0.065 µV (23.4%; p < 0.001, with
reference to baseline) at the midline central electrode (Cz). In
contrast, spindle activity was suppressed during slow negative
half-waves, on average by  0.65 ± 0.06 µV at Cz (22%;
p < 0.001). An increase in spindle activity
400-500 msec after negative half-waves was more than twofold the
increase during slow positive half-waves (p < 0.001). A similar although less pronounced dynamic was observed for
beta activity, but not for alpha and theta frequencies. Discrete
spindles identified during stages 2 and 3 of non-rapid eye movement
(REM) sleep coincided with a discrete slow positive half-wave-like
potential preceded by a pronounced negative half-wave
(p < 0.01). These results provide the first
evidence in humans of grouping of spindle and beta activity during slow
oscillations. They support the concept that phases of cortical
depolarization during slow oscillations, reflected by surface-positive
(depth-negative) field potentials, drive the thalamocortical spindle
activity. The drive is particularly strong during cortical
depolarization, expressed as surface-positive field potentials.
Key words:
slow oscillations; spindle activity; human; sleep; EEG; cortical depolarization
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INTRODUCTION |
In recent years, studies in
humans and animals have accumulated converging evidence for the
hypothesis that a reprocessing of events acquired during previous
wakefulness occurs during non-rapid eye movement (REM) sleep, which
eventually supports long-term storage of respective neuronal
representations (Plihal and Born, 1997 ; Buzsáki, 1998; Gais et
al., 2000; Sutherland and McNaughton, 2000). At the level of rhythmic
activity in the brain, spindle activity (12-15 Hz) and faster
oscillatory activity have both been linked to aspects of the
consolidation process (Siapas and Wilson, 1998; Steriade and Amzica,
1998; Destexhe et al., 1999; Sejnowski and Destexhe, 2000; Gais et al.,
2002). Spindle oscillations provoke a massive
Ca2+ entry into the spindling cortical
cells, and thus could set the stage for plastic synaptic changes that
are supposed to be induced during subsequent slow-wave activity.
Notably, spindle and fast oscillatory activity in cats have been found
to be distinctly grouped by slow (<1 Hz) oscillations (Contreras and
Steriade, 1995, 1996; Contreras et al., 1997; Steriade and Amzica,
1998; Steriade, 1999). Moreover, the grouping by slow oscillations has been considered relevant for the effective generation in particular of
spindle activity and for establishing reiterative processing of
memories in non-REM sleep. At the cellular level, the slow oscillations
are built up by the rhythmic sequence of membrane depolarization and
hyperpolarization of cortical pyramidal neurons, with the
depolarization associated with depth-negative and surface-positive EEG
field potentials and, conversely, the hyperpolarizing phase associated
with a depth-positive, surface-negative EEG potential (Steriade et al.,
1994; Contreras and Steriade, 1995). Spindle activity originating
within thalamo-neocortical loops and also faster frequencies were found
to be driven during the depolarizing (surface-positive) phase of slow
waves, during which the firing of pyramidal cells is increased. In
contrast, the hyperpolarizing (surface-negative) phase is associated
with neuronal silence (Buzsáki et al., 1988; Contreras and
Steriade, 1995; Steriade et al., 1996; Destexhe et al., 1999).
A grouping of spindle activity by slow oscillations has been suggested
also to be present in the human EEG (Achermann and Borbély, 1997;
Amzica and Steriade, 1997). However, distinct slow rhythms for spindles
were revealed at a periodicity of ~4 sec and slower (i.e., at a much
longer periodicity than that of the slow oscillations displaying an
obvious peak in humans and cats at ~0.7-0.8 Hz) (Achermann and
Borbély, 1997; Marshall et al., 2000). Also, spindle activity is
known to be generally decreased with increasing delta activity in
humans, which could mask a grouping of this activity present during
slow-wave sleep (SWS) (Aeschbach and Borbély, 1993; Dijk et al.,
1993; De Gennaro et al., 2000a,b). Given the importance that has been
ascribed to the regulation of spindle and fast oscillatory activity by
slow oscillations during SWS for the possibility of memory processing
during this seemingly quiet period of sleep, we examined the temporal
dynamics between these faster rhythmic activities and slow oscillations during non-REM sleep and SWS in the human EEG, with particular reference to spindle activity. As in animals, we expected enhanced spindle activity during the depolarizing phase (i.e., surface-positive compared with surface-negative half-waves of slow oscillations).
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MATERIALS AND METHODS |
Subjects and EEG recordings. Direct current (DC) EEG
signals were recorded from 13 young, healthy subjects (six females,
seven males, aged 18-25 years) displaying regular sleep-wake rhythms and identified by interview as good sleepers. Because the present analyses relied on DC-recorded EEG signals and because restrained head
movement is a necessary prerequisite for proper artifact-free recordings of this kind, subjects were asked to practice at home sleeping through two nights on their back wearing a cervical collar before the experiment proper. Only data from subjects who displayed normal sleep patterns on the experimental night were included in the
analyses. In particular, care was taken that the first sleep cycle was
well pronounced, revealed a minimum of artifacts, and
possessed a minimum of at least 20 min of time spent in SWS [i.e., stages 3 and 4 according to the standard criteria by
Rechtschaffen and Kales (1968)]. Recording of the DC potentials was as
described previously (Marshall et al., 1998, 2000). In brief,
recordings were obtained from electrodes located at F3, the midline
frontal electrode (Fz), F4, C3, the midline central electrode
(Cz), and C4 (international 10-20 system) referenced to linked mastoid
electrodes. To attach the electrodes, clip-on sockets were fixed with
collodion to the scalp (Bauer et al., 1989). Before filling sockets and electrodes with electrode gel (Electrode Electrolyte; TECA Corp., Pleasantville, NY), the underlying scalp was punctured with a sterile hypodermic needle, to eliminate possible skin potential artifacts (Picton and Hillyard, 1972). Electrode impedance was always
<5 kohm. EEG signals were amplified (2000-fold) and analog-filtered (0-30 Hz) with a DC amplifier (Toennies DC/AC amplifier; Jaeger GmbH
and Co., KG, Hoechberg, Germany). The DC offset was
automatically corrected at a threshold of ±4 mV. With short-circuited
input, the amplifier drift, if present, was <3 µV/hr. Analog DC/EEG
signals were digitized at 100 Hz (CED 1401; Cambridge Electronics
Design, Cambridge, UK) and stored on a personal computer for
offline analysis.
Data analysis. Sleep stages (1, 2, 3, 4, and REM sleep),
awake time, and movement artifacts were scored offline for 30 sec intervals (Rechtschaffen and Kales, 1968) (see Fig. 1).
EEG data from the first sleep cycle were chosen, and analyses were
targeted at channels Fz and Cz, where spindles and slow oscillations
are most pronounced. (Analyses of the other channels did not add any substantial information.) To produce the slow-oscillation signal, first
the DC shift was eliminated with a simple feedback filter, equivalent
to a 6 dB/octave analog filter and a time constant of 1 sec
(corresponding to 0.16 Hz). Subsequently, EEG data were digitally
filtered with a low-pass finite impulse response (FIR) filter of 4 Hz
(3 dB at 4.7 Hz; 96 dB at  6 Hz). This bandpass filtering (0.16-4
Hz) was done after spectral power analysis of the EEG in all
individuals; it revealed a single broad peak indicating maximum power
at ~0.7 Hz (see Fig. 2). Exploratory
analyses performed on a more narrow frequency band of slow
oscillation of <1 Hz revealed essentially identical results.
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Figure 1.
Selection of slow-oscillation half-waves.
From top to bottom, Sleep hypnogram for
the first cycle from an individual night (W, wake;
R, REM sleep; S1-S4, non-REM sleep
stages 1-4). Dots underneath indicate time points of
the largest slow-positive and negative-oscillation half-waves chosen
for analysis (see Materials and Methods for details). DC-recorded
(0-30 Hz) original EEG signal, slow-oscillation signal (bandpass,
0.16-4 Hz), spindle activity (12-15 Hz), and spindle rms signal are
shown. Parallel vertical bars indicate a critical 20 sec
time interval used for the analyses as depicted in Figure
3A.
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To produce the spindle activity signal, a bandpass FIR filter of 12-15
Hz (3 dB at 11.3 and 15.7 Hz; 96 dB from 0 to 10 Hz and at 17 Hz)
was applied. After bandpass filtering, a root mean square (rms) signal
was calculated with a time resolution of 0.05 sec using a time window
of 0.1 sec (see Fig. 1). Basically, analog procedures were used to produce signals in the theta (4-8 Hz),
alpha (8-12 Hz), and beta (15-25 Hz) frequency bands.
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Figure 2.
Averaged spectral power density for the slow
oscillation frequency band (0.16-4 Hz) at Fz (solid
line) and Cz (dashed line). Note the peak power
density at 0.7-0.8 Hz.
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Two independent analyses were performed. For the first main analysis,
the largest positive and negative half-waves were selected from SWS
(stages 3 and 4) (see Fig. 3A)
using a thresholding procedure applied to the slow-oscillation signal.
This first analysis concentrated on wave-triggered averages. The
calculation of wave-triggered averages consisted of averaging short
windows of data (slow oscillatory activity and rms signal) time-locked
to the positive and negative peaks of half-waves, which were
numerically detected by a thresholding procedure in the
slow-oscillation band signal. The peak time of a half-wave found was
used for averaging if the following criteria were fulfilled: (1) The
beginning and end of the half-wave were two succeeding zero-crossings
of the slow-oscillation band signal separated from each other by
0.125-1 sec. (2) The peak amplitude between both zero-crossings
exceeded a threshold of at least 80 µV and 80 µV for positive and
negative half-waves, respectively. (3) The half-wave lay in a 30 sec
epoch of sleep stage 3 or 4, which was free of movement artifacts.
Threshold settings were such that in each case >50 half-waves were
found. For all detected positive and negative half-waves, the
slow-oscillation signal and the rms signal in the other frequency bands
of interest were averaged with intervals of ±1.0 sec around the peak
amplitude time. Grand mean averages across all 13 subjects were
calculated.
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Figure 3.
Steps of analysis illustrated for two 20 sec
epochs of recordings for an individual subject (same as in Fig. 1).
A, Analysis of slow oscillation-dependent changes in
spindle activity. From top to bottom,
DC-recorded (0-30 Hz) original EEG signal, slow oscillatory signal
(0.16-4 Hz) with detected slow positive and negative half-waves
indicated by a thick solid line, and spindle rms signal.
For one positive and one negative half-wave each, the peak time used
for time-locked averaging and the ±1 sec averaging interval are
indicated by a dotted line and two solid vertical
lines, respectively. B, Analysis of
spindle-dependent changes in the DC potential, exemplified on discrete
spindles that occurred during the first period of sleep stage 2 shown
in Figure 1. From top to bottom,
DC-recorded (0-30 Hz) original EEG during non-REM sleep stage 2, spindle activity filtered at 12-15 Hz, and spindle rms signal with
identified spindle periods indicated by a thick solid
line. Only the largest spindles were selected for analysis by a
thresholding procedure applied to the spindle rms signal. For one
spindle, the center time used for time-locked averaging and the ±1 sec
averaging interval are indicated by a dotted line and
two solid vertical lines, respectively.
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It was also of interest to determine whether discrete spindles,
typically most easily identified during non-REM sleep stage 2 in
humans, are associated with slow oscillation-like shifts in the DC
potential. For this second analysis, the largest sleep spindles were
selected from sleep stages 2 and 3 (see Fig. 3B) using a
thresholding procedure applied to the spindle rms signal. Thus, the
second analysis concentrated on averages of the DC potential as
triggered by discrete spindles. The respective calculation consisted of
averaging short windows of data (spindle rms and DC potential) selected
by reference of the time of occurrence of a sleep spindle. Sleep
spindles were detected numerically using a thresholding procedure,
comparable with the procedure of Schimicek et al. (1994), which
searched for sleep spindles separately in the Fz and Cz channels. The
center times of every detected sleep spindle were used to synchronize
averaging, if the following criteria were fulfilled: (1) The beginning
and end of a spindle were two succeeding threshold-crossings of the
spindle rms signal separated by 0.4-1.3 sec. (2) The spindle rms
signal between both threshold-crossings was larger than the threshold,
which was determined individually for each subject, but in any case was
>5 µV. (3) The spindle lay in a 30 sec epoch of sleep recordings
free of artifacts. More than 50 spindles were found for each
individual. For all spindles found, intervals of the DC potential
(baseline corrected) and the spindle rms signal of ±1.0 sec around the
spindle center were averaged, and grand mean averages across all 13 subjects were formed.
For a third analysis, individual cross-correlation functions between
the potential of the slow oscillatory signal and the spindle rms signal
were calculated during SWS. The slow oscillatory potential signal was
down-sampled to a time resolution of 0.05 sec. The two correlated time
series began with the first 30 sec epoch of sleep stage 3 and ended
with the last 30 sec epoch of sleep stage 4 within the first sleep
cycle. The cross-correlation was calculated for time shifts up to ±2
sec (i.e., up to 80 points with an offset of 40 points).
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RESULTS |
Table 1 summarizes the number of
averaged data segments and thresholds used for individual averaging in
the analyses of negative and positive half-waves and sleep spindles. A
relationship between slow-oscillation half-waves and spindle activity
was already visible after superimposing epochs of EEG activity filtered
in the spindle band for all positive and negative half-waves (Fig.
4A). Spindle activity
is suppressed in a 200 msec interval around the peak of negative
half-waves and enhanced around the peak of positive half-waves. This
relationship is most clearly seen in the grand means of
half-wave-related spindle rms signals (Fig. 4B). The spindle rms value in both channels was on average 1 µV (i.e., 27.8 and 30.7% for Fz and Cz, respectively) lower during the peak of
negative than during the peak of slow positive half-waves (Fz, 2.26 ± 0.16 vs 3.13 ± 0.15 µV; Cz, 2.30 ± 0.12 vs
3.32 ± 0.12 µV for negative and positive half-waves,
respectively; p < 0.001). If the first 0.2 sec period
of the averaged interval was defined as the baseline, then the decrease
in spindle activity during negative half-waves was 0.37 ± 0.07 µV at Fz and 0.65 ± 0.06 µV at Cz (p < 0.001). The increase in spindle activity during positive half-waves
was 0.41 ± 0.06 µV at Fz and 0.63 ± 0.09 µV at Cz
(p < 0.001).
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Table 1.
Mean number ± SEM of individually averaged data segments
and thresholds used in the analyses of negative and positive half-waves
and sleep spindles (n = 13)
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Figure 4.
A, top, Superimposed slow
negative (left) and positive (right)
half-waves. A, bottom, Corresponding spindle band
signals (black line, right axis) and mean spindle rms
activity (white dashed line, left axis) in a single
subject at Cz. B, Grand means (across 13 subjects) of
results from wave-triggered analysis of slow negative
(left) and positive (right) half-waves.
The mean ± SEM slow-oscillation signal (top) and
spindle rms signal (bottom) at Cz are shown.
Dashed lines indicate mean values at Fz (without SEM).
C, Grand means of beta rms signal. Note that the
temporal dynamics of beta activity were similar to spindle activity, in
particular at Cz. Vertical dotted lines indicate the
half-wave peak time used for time-locked averaging.
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Interestingly, Figure 4B also reveals a
distinct rebound enhancement after the negative half-wave-associated
suppression of the spindle rms signal peaking ~400-500 msec after
the lowest value. In both channels, the spindle rms value at this time
was even higher than that during the peak of slow positive half-waves (0.58 ± 0.1 µV at Fz and 0.80 ± 0.12 µV at Cz;
p < 0.001). Notably, the slow (positive) oscillatory
potential at this time was ~100 µV lower than at the peak time of
slow positive half-waves.
Changes in the rms signal for the adjacent beta frequency band (15-25
Hz) resulted in temporal dynamics similar to those seen for spindle
activity (Fig. 4C). However, they were much less pronounced. The only consistent changes reaching significance were an increase in
the beta rms signal during slow positive half-waves (Fz, 0.11 ± 0.04 µV; Cz, 0.21 ± 0.05 µV, with reference to baseline;
p < 0.05) and, as seen only at Cz, the rebound
increase in the rms signal after slow negative half-waves (0.37 ± 0.09 µV, with reference to the preceding trough; p < 0.01). The rms signals of the alpha (8-12 Hz) and theta (4-8 Hz)
bands did not indicate any changes similar to that found for spindle
activity. A significant increase (p < 0.01) in
the rms signal for these two bands found selectively during the
negative slope of slow negative half-waves reflects the fact that this
amplitude decrement toward the negative peak is generally steeper than
the changes toward positivity (also see Fig. 4B).
The analysis of spindle-triggered averages during sleep
stages 2 and 3 revealed a complementary relationship between sleep spindles and the concurrently obtained DC potential (Fig.
5). When averaged time-locked to the
center of spindles, the mean DC potential shows a distinct negative
peak shortly before the rise in spindle activity. The negative peak is
followed by a positive potential coinciding with the time of highest
spindle power. Thereafter, the DC potential shifts toward negativity
again. During the time of peak spindle activity, the mean DC potential
was 12-20 µV more positive than 500 msec before
(p < 0.001) and after this time (p < 0.001, at both electrodes). Also, both the
positive DC potential peak as well as the negative peaks before and
after it were significant in comparison with the potential during the
first 0.2 sec period of the averaging interval used as a baseline
(p < 0.01, except for the negative peak after
the spindle at Fz, which was p < 0.05). Interestingly,
the negative DC shift immediately preceding the rise in spindle
activity was at Fz significantly larger than the negative DC peak after
the spindle (13.8 ± 3.3 µV vs 6.9 ± 2.7 µV;
p < 0.05).
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Figure 5.
Grand means (across 13 subjects) of
sleep-spindle-dependent changes in the DC potential. The mean ± SEM spindle rms signal (top) and DC potential
(bottom) at Fz, both averaged time-locked to the spindle
center, are shown. Dashed lines indicate respective mean
values from Cz (without SEM). The vertical dotted line
indicates the spindle center time used for time-locked
averaging.
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The mean cross-correlation functions between the slow oscillatory EEG
potential and the spindle rms signal during periods of SWS are shown in
Figure 6. Although correlation
coefficients were generally of moderate size, the cross-correlation
function shows distinct and significant peaks (p < 0.005). In both channels, a positive peak value is located at a time
lag of 100 msec, indicating that changes in the spindle rms signal
tend to precede shifts in the corresponding direction of the slow
oscillatory signal. The highest cross-correlation is negative and at a
time shift of 400 msec, indicating that changes in the spindle rms
signal occur 400 msec after changes in the slow-oscillation band signal in the opposite direction. This peak coefficient probably reflects the
strong increase in spindle activity after the peak of the negative
half-wave (Fig. 4B).
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Figure 6.
Mean ± SEM cross-correlation function
between slow-oscillation signal and spindle rms signal across 13 subjects at Fz (top) and Cz (bottom).
Dashed horizontal lines indicate a p < 0.005 level of significance. The vertical dotted line
indicates the time lag of 0 sec.
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DISCUSSION |
Results show that during human SWS, rhythmic activity in the
12-15 Hz spindle frequency range becomes grouped during slow oscillations (with a dominant frequency of 0.7-0.8 Hz). Wave-triggered averages of the spindle rms signal reveal suppressed spindle activity during slow negative half-waves and enhanced activity during positive half-waves. For the adjacent beta-frequency band (15-25 Hz), a similar
although weaker modulation in the course of slow oscillations was
observed. However, there were no similar dynamics expressed for the
alpha (8-12 Hz) and theta (4-8 Hz) frequency bands. Complementing these observations, discrete spindles identified during non-REM stage 2 and stage 3 sleep were found to be preceded by a slow negative DC shift
followed by a positive DC potential during the spindle. The temporal
dynamics of these DC shifts were similar to slow oscillatory activity.
The averaged power spectrum of the DC-recorded EEG during SWS (stages 3 and 4) revealed a clear peak at ~0.7-0.8 Hz (Fig. 2). This frequency
peak, which is distinctly <1 Hz, corresponds to the slow oscillation
frequency reported previously in sleeping humans (Achermann and
Borbély, 1997; Steriade and Amzica, 1998) and cats (Steriade et
al., 1996). The small peak at ~0.25 Hz preceding the main 0.7-0.8 Hz
peak in the frequency histogram depicted in Figure 2 might indicate the
presence of a class of even slower oscillations (also see Steriade et
al., 1993a). However, inspection of individual spectra revealed that in
subjects showing a distinct peak of ~0.25 Hz, this was much less
pronounced than the main 0.7-0.8 Hz peak. This agrees well with
previous observations, in which subjects with abundant slow
oscillations showed great peaks in the 0.7-0.8 Hz range, although
peaks at somewhat longer periodicities may occur (Steriade and Amzica,
1998). The dominating frequency of 0.7-0.8 Hz also distinguishes the
waves selected for our analysis from typical delta waves defined by
faster frequencies between 1 and 4 Hz, although our spectra did not
provide safe indications for the existence of a separate delta peak.
Our algorithm detected the largest positive and negative deflections,
with a duration of 0.125-1 sec in the 0.16-4 Hz filtered signal. This resulted in averaged half-waves with a length of 0.6-0.8 sec (Fig. 4B), which corresponds to a period length of 1.2-1.6
sec (i.e., a frequency of 0.62-0.83 Hz) for the oscillation underlying
the averaged half-waves. Thus, the frequency content of the half-waves chosen for analysis here exactly fit the frequency range of 0.6-1 Hz
used in previous reports to define and establish the existence of this
slow oscillatory rhythm (Steriade and Amzica, 1998; Steriade, 1999).
Along these lines, it seems justified to consider the half-waves of our
study to be specific EEG manifestations of the slow oscillations originally described by Steriade et al. (1993a) in animals.
The animal data indicate that these slow waves have the virtue of
grouping cortically recorded spindle activity and also faster oscillations in the beta and gamma frequency range (20-60 Hz). The
present data extend this observation by showing a similar grouping of
spindle activity in humans. Gamma activity was not the focus of this
study, because it is prone to artifacts, primarily of muscular origin,
in recordings of ongoing EEG activity from the human scalp. We did
observe a significant modulation by slow oscillations of beta band
activity, which like gamma activity is considered to reflect processing
of representations. However, more distant cortical areas are involved
in the generation of beta activity, and the synchronization properties
are probably different from those of gamma activity (Kopell et al.,
2000). Thus, an intriguing question remains about whether gamma
activity displays a similar modulation.
Our result of enhanced spindle power during surface-positive half-waves
of slow oscillations is quite consistent with the underlying mechanisms
of the grouping of spindle activity during slow rhythmic activity, as
revealed in animals (Contreras et al., 1997; Destexhe et al., 1999;
Steriade, 1999). These studies indicated that slow positive potential
shifts recorded from the cortical surface correspond to extracellular
depth potentials of negative polarity and to a prolonged depolarization
of neocortical pyramidal cells. This depolarization is associated with
increased firing, which drives the generation of spindle oscillations
in thalamo-neocortical feedback loops (Contreras and Steriade, 1995;
Steriade et al., 1996; Destexhe et al., 1999). In contrast, the
surface-negative components of slow waves corresponding to
intracellular hyperpolarization were associated with neuronal silence.
This corresponds to the finding of suppressed spindle activity during
slow negative half-waves in the present study. Thus, our results show
that during non-REM SWS also in human surface EEG recordings, a
consistent relationship of spindle activity with the depolarizing and
hyperpolarizing phase of slow oscillations is distinguishable. A
similar relationship could underlie the grouping of spindles dominating
during sleep stage 2, which in previous studies was found to display a
periodicity of ~4 sec (Achermann and Borbély, 1997; Marshall et
al., 2000).
To obtain more accurate information with regard to the temporal
relationships between slow oscillations and spindle activity, cross-correlation functions were calculated that revealed overall low
coefficients with distinct and highly significant peaks of interest.
There was a positive correlation between both time series with a peak
at 100 msec, indicating that changes in spindle activity tend to
precede those in the slow oscillatory signal. At first glance, this
temporal relationship appears to be in contrast to findings in cats,
indicating that the depolarizing component (i.e., surface-positive EEG
component) of the slow oscillation is the factor driving the thalamic
generation of spindle activity via corticothalamic volleys (Steriade
and Amzica, 1998; Steriade, 1999). However, examination of Figure 4
indicates that although the slow positive half-wave starts at virtually
the same time as the increase in spindle activity, spindle activity
peaks and begins to decrease ~100 msec earlier than the slow positive
half-wave. Thus, rather than a time lag in the onset of these two
phenomena, the positive correlation at 100 msec reflects the fact
that the duration of the depolarizing phase of slow oscillations during SWS obviously exceeds the duration of triggered spindle activity, the
waning of which is regulated by separate mechanisms (Luthi and
McCormick, 1998; Sejnowski and Destexhe, 2000). An additional although
perhaps minor contribution of spindle activity to the positive EEG
deflection of the slow oscillation may be considered in light of our
finding that isolated spindles identified during non-REM sleep stages 2 and 3 coincided with a small but discrete positive DC
potential. In animals, the induction of cortical spindle-like activity
in conjunction with augmenting responses is found to be
followed by secondary longer-lasting depolarizations that
may add to the depolarizing part of the cortically generated slow oscillation (Steriade, 1993, 2001; Steriade et
al., 1998). However, it should be noted that the slow oscillation can
be recorded in athalamic animals in which spindles are absent (Steriade
et al., 1993b).
The most pronounced peak in the cross-correlation function was of
negative polarity and occurred at a time lag of 400 msec (Fig. 6). This
peak has to be linked to the interesting fact that the highest spindle
activity found in the present analysis was not that coinciding with
slow positive half-waves but rather that following the peak of slow
negative half-waves by ~400 msec (Fig. 4B). This
distinct rebound enhancement in spindle activity after slow negative
half-waves likely reflects postinhibitory rebound spike-bursts in
thalamocortical neurons, as described by Contreras and Steriade (1995).
They found that intracellularly recorded thalamocortical neurons at
almost every cycle of the slow oscillation showed burst responses after
their disfacilitation (and silenced firing) that correspond in time to
the scalp-negative (depth-positive) wave [Contreras and Steriade
(1995), their Figs. 8 and 9]. Also, the averaging of DC potentials
time-locked to the center of spindles in sleep stages 2 and 3 revealed
that the onset of spindles was preceded by a remarkably strong negative
potential shift (Fig. 5). Together, these results indicate that the
most pronounced enhancements of spindle activity occur a short time
after phases of relatively strong hyperpolarization within cortical
cells; this rebound spindle activity exceeds that observed during more depolarizing phases. This interpretation stresses the importance of
previous hyperpolarization for the synchronizing influence of slow
oscillations on spindle activity, which adds to the influence of
cortical depolarization. This view is corroborated by the observation that strong rebound spindle activity after slow negative half-waves coincides with only a moderate positive slow oscillatory potential. Also, the slow negative half-waves in our analysis showed a steeper onset and a higher (absolute) peak amplitude than the positive half-waves.
Our data show a grouping of spindle activity coinciding with negative
and positive parts of slow oscillations during human non-REM and SWS,
which presumably reflect the inhibiting and driving forces of,
respectively, hyperpolarizing and depolarizing phases of cortical cells
on thalamically generated spindle activity. beta activity displayed a
similar dynamic. Additional research is justified to clarify whether
this pattern is relevant for the supposed iterative mechanisms of
memory reprocessing during non-REM and SWS.
| |
FOOTNOTES |
Received June 17, 2002; revised Sept. 23, 2002; accepted Sept. 27, 2002.
This work was supported by a grant from the Deutsche
Forschungsgemeinschaft. We thank Dr. D. Contreras for helpful comments on a previous version of this manuscript.
Correspondence should be addressed to Dr. M. Mölle, Institute of
Neuroendocrinology, University of Lübeck, Ratzeburger Allee 160, Haus 23a, 23538 Lübeck, Germany. E-mail:
moelle{at}kfg.uni-luebeck.de.
| |
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ABSTRACT
INTRODUCTION
