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The Journal of Neuroscience, June 1, 1999, 19(11):4595-4608
Spatiotemporal Analysis of Local Field Potentials and Unit
Discharges in Cat Cerebral Cortex during Natural Wake and Sleep
States
Alain
Destexhe,
Diego
Contreras, and
Mircea
Steriade
Laboratoire de Neurophysiologie, Faculté de Médecine,
Université Laval, Québec G1K 7P4, Canada
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ABSTRACT |
The electroencephalogram displays various oscillation patterns
during wake and sleep states, but their spatiotemporal distribution is
not completely known. Local field potentials (LFPs) and multiunits were
recorded simultaneously in the cerebral cortex (areas 5-7) of
naturally sleeping and awake cats. Slow-wave sleep (SWS) was characterized by oscillations in the slow (<1 Hz) and delta (1-4 Hz)
frequency range. The high-amplitude slow-wave complexes consisted in a
positivity of depth LFP, associated with neuronal silence, followed by
a sharp LFP negativity, correlated with an increase of firing. This
pattern was of remarkable spatiotemporal coherence, because silences
and increased firing occurred simultaneously in units recorded within a
7 mm distance in the cortex. During wake and rapid-eye-movement
(REM) sleep, single units fired tonically, whereas LFPs
displayed low-amplitude fast activities with increased power in fast
frequencies (15-75 Hz). In contrast with the widespread synchronization during SWS, fast oscillations during REM and wake periods were synchronized only within neighboring electrodes and small
time windows (100-500 msec). This local synchrony occurred in an
apparent irregular manner, both spatially and temporally. Brief periods
(<1 sec) of fast oscillations were also present during SWS in between
slow-wave complexes. During these brief periods, the spatial and
temporal coherence, as well as the relation between units and LFPs, was
identical to that of fast oscillations of wake or REM sleep. These
results show that natural SWS in cats is characterized by slow-wave
complexes, synchronized over large cortical territories, interleaved
with brief periods of fast oscillations, characterized by local
synchrony, and of characteristics similar to that of the sustained fast
oscillations of activated states.
Key words:
fast oscillations; slow oscillations; arousal; sleep; spatiotemporal properties; cerebral cortex; coherence
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INTRODUCTION |
The slow (< 1 Hz) and delta (1-4
Hz) oscillations appear in the electroencephalogram (EEG) and local
field potentials (LFPs) during slow-wave sleep (SWS). The distinction
between these two sleep rhythms was demonstrated in intracellular
studies from animals showing that the slow oscillation groups delta
waves (Steriade et al., 1993a ,b) and in EEG recordings showing
different dynamics of the two oscillations in humans (Achermann and
Borbély, 1997). Under ketamine-xylazine anesthesia, the
depth-negative (surface-positive) EEG components of slow oscillations
are associated with cellular depolarization, whereas depth-positive
(surface-negative) components are associated with cellular
hyperpolarization (Steriade et al., 1994; Contreras and Steriade,
1995). In natural SWS, extracellularly recorded neurons fire in
coincidence with the depth-negative component of slow-wave complexes,
whereas the depth-positive component is associated with neuronal
silence (Steriade et al., 1996; Amzica and Steriade, 1997). Similar
relations were found for delta waves in the intact brain (Buzsáki
et al., 1988) or isolated cortex (Frost et al., 1966). Thus it seems
that slow-wave complexes are characterized by alternating periods of
neuronal silence and periods of increased firing. These modulations of
firing rate are also in agreement with analyses of the temporal
patterns of discharge in the neocortex, which reported that bursts of
action potentials and long periods of silence appear exclusively during
SWS (Hubel, 1959; Evarts, 1964; Steriade et al., 1974).
By contrast, during aroused states, cortical cells fire tonically
(Hubel, 1959; Evarts, 1964; Steriade et al., 1974), and the EEG is
dominated by low-amplitude fast activity in the  - (15-75 Hz)
frequency range. Experimentally, fast oscillations can be evoked in
awake animals using stimuli requiring attentive behavior (Lopes da
Silva et al., 1970; Freeman and Van Dijk, 1987; Rougeul et al.,
1979; Bouyer et al., 1981; Murthy and Fetz, 1992), in response
to visual stimuli in cats (Eckhorn et al., 1988; Gray et al., 1989), as
well as during auditory responses in humans (Ribary et al., 1991). They
also occur spontaneously during rapid-eye-movement (REM) sleep
in humans (Llinás and Ribary, 1993). The relationship of fast
oscillations with unit discharge consistently showed that unit
discharges are correlated with negative deflections in the depth EEG
(Eckhorn et al., 1988; Gray and Singer, 1989; Murthy and Fetz, 1992;
Steriade et al., 1996).
In anesthetized preparations, slow-frequency oscillations are
characterized by large-scale coherence, whereas fast oscillations display more local coherence (Bullock and McClune, 1989;
Steriade and Amzica, 1996). Fast oscillations are
synchronized locally in both space and time, as shown by the very
restricted cortical areas and time windows in which coherent fast
oscillations appear (Eckhorn et al., 1988; Gray et al., 1989; Steriade
and Amzica, 1996). It is however not known whether the same conclusions
also apply to oscillations occurring in waking or naturally sleeping animals.
In this paper, we have investigated the spatiotemporal patterns of
oscillations in cat suprasylvian cortex during waking and natural
sleep. The field potentials and the discharge of units were compared,
as a function of time, distance, and behavioral state of the animal.
Parts of this paper have been published previously (Destexhe et al.,
1998b).
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MATERIALS AND METHODS |
Multisite field potentials and unit discharges were recorded
from the cerebral cortex of cats and subsequently analyzed. The experimental procedures and methods for data analysis are described successively.
Experimental procedures
Preparation, anesthetics, and chronic implantation.
Four adult cats weighting 2.5-3.5 kg were used in the present study.
Chronic implantation of recording and stimulating electrodes was
performed under ketamine (15 mg/kg, i.m.), followed by pentobarbital
(25 mg/kg, i.p.). Cats were implanted in a stereotaxic position, and four screws were placed protruding from the dental cement to hold the
cat's head rigid without pain or pressure during recording sessions.
Two teflon-insulated wires were inserted in the neck muscles to record
the electromyogram (EMG), and two silverball electrodes were cemented
into the supraorbital cavity for recording the electrooculogram (EOG).
Stainless steel screws were anchored to the bone overlaying the
pericruciate and the suprasylvian cortex of the right hemisphere to
monitor the gross EEG. The bone overlaying the left suprasylvian cortex
was removed, and the exposed dura was covered with a plate of acrylic
with holes for the passage of the recording and stimulating electrodes.
Buprenorphine (0.03 mg/kg, i.m.) was given every 12 hr during the first
2 postoperative days to prevent pain after surgery. The antibiotic
bicillin was injected intramuscularly for 3 d after surgery.
Recordings started 1 week after implantation. Animals slept ad
libitum between experimental sessions.
Recording and stimulation. All recordings reported here were
obtained from cat suprasylvian gyrus (areas 5-7) by means of stainless
steel bipolar coaxial electrodes (0.1 mm in diameter) inserted
perpendicularly to the cortical surface, with the ring placed on the
pia and the tip inserted at a depth of 0.8-1.0 mm. In some cases,
extracellular units and LFPs were recorded using monopolar tungsten
electrodes with tip resistances from 1 to 5 M . Both coaxial and
tungsten electrodes were aligned along the anteroposterior axis of the
gyrus in arrays of eight equally spaced electrodes with an
interelectrode distance of 1 mm (Fig. 1,
top). The electrodes could be moved independently and were
lowered through the holes of an acrylic plate. For bipolar recordings,
the polarity was adjusted such that the sharp potentials of slow-wave
complexes are negative. For monopolar recordings, the reference
electrode was placed in the neck muscles.
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Figure 1.
Multisite local field potentials in cat cerebral
cortex during natural wake and sleep states. Top, Eight
bipolar electrodes (interelectrode distance of 1 mm) were inserted into
the depth (1 mm) of areas 5-7 of cat neocortex. ES,
Ectosylvian gyrus; M, marginal gyrus; PC,
postcruciate gyrus; SS, suprasylvian gyrus. LFPs
(LFPs), the decay of correlation with distance
(Spatial correlation), and autocorrelations
(Temporal correlation) are shown for three different
states. A, During the wake state (AWAKE),
LFPs were characterized by low-amplitude fast activities in the
- frequency range (15-75 Hz). Correlations decayed steeply with
distance and time. B, During SWS, the LFPs were
dominated by large-amplitude slow-wave complexes recurring at a slow
frequency (< 1 Hz) and displaying high coherence. Slow-wave complexes
of higher frequency (1-2 Hz) were also present and displayed more
moderate coherence (asterisk). Correlations stayed high
for large distances (Spatial correlation) but decayed
steeply with time (Temporal correlation).
C, During REM sleep, LFPs and correlations had
characteristics similar to those during wake periods.
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Data acquisition. Signals were recorded on an eight-channel
digital recorder (Instrutech, Mineola, NY) with an internal sampling rate of 11.8 kHz per channel and four-pole Bessel filters. For LFPs,
data were digitized off-line at 250 Hz using the Igor software package
(Wavemetrics; analog-to-digital board from GW Instruments; low-pass
filter of 100 Hz). Units were digitized off-line at 10 kHz, and spike
sorting and discrimination were performed with the DataWave software
package (DataWave Technologies; filters were 300 Hz high-pass and 5 kHz
low-pass). Data were transferred to a Unix workstation for analysis
(see below).
Data analysis
Data analyses were based on LFPs recorded at equidistant sites
and represented by:
where r1 ...
rn are n equidistant cortical
sites and obey the relation ri+1 = ri +  r, where
r is the interelectrode distance (1 mm in this case).
LFPs were normalized by subtraction of their average value and division
by their SD.
Temporal correlations. Temporal correlations, or
cross-correlations, are linear estimators to measure temporal
variations of coherence of a signal. They were computed according to
the relation:
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(1)
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where the correlation expresses the average of the normalized
LFP
v(ri,tj)
at site ri and time
tj, multiplied by the normalized LFP
v(rj,tk +  ) at site rj and time
tk + .
Cij() varies between  1 and +1.
Efficient algorithms based on fast Fourier transforms were used to
evaluate Cij() (Press et al.,
1986).
The autocorrelation Cii() is obtained
by setting i = j.
Cii() measures how a signal is
temporally coherent with itself: its value stays close to unity as long
as the signal is correlated; it oscillates for periodic oscillations
and decays toward zero for irregular signals.
Spatial correlations. The spatial correlation was computed
according to the relation:
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(2)
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where
v(ri,tk)
is the normalized LFP at site ri and time
tk. In this case, the correlation is
expressed as the time average of the product of every possible pair of
sites, combined for all pairs with the same intersite distance
(x). C(x) is included between 1 and
+1.
This quantity measures the coherence of the spatial portrait of the
system: if all variables
v(ri,tk)
are irregular in time but coherent in space (similar signals in
different sites ri), then
C(x) will be close to unity. This is indeed the
case for slow-wave sleep (see Results). Other oscillation types, such
as fast oscillations, may show variations of coherence in both time and
space (see Results). In this case, it is necessary to use both temporal
and spatial correlations to characterize these two aspects
independently. A closely related but differentmeasure of spatial
coherence is seen by representing the peak of the cross-correlation as
a function of distance. Using either procedure yielded no appreciable difference for the signals studied here.
Space constant. The representation of
C(r) as a function of distance r was
used to measure the decay of correlation with distance in the
anteroposterior axis of the suprasylvian cortex. Because the
decay of spatial correlations was always smooth toward zero, a
first-order decaying exponential term was used to fit the correlation data:
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(3)
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where the correlation space constant  is a measure of the
spatial extent of the coherence of a spatially homogeneous phenomenon, similar to the coherence measure based on power spectra (Bullock and
McClune, 1989).
In some cases, a biexponential fit was performed:
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(4)
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where 1 and 2 are two space
constants and A is a constant factor determining the
relative amplitude of the two exponentials.
Time-dependent correlations. In some cases, it was necessary
to display temporal variations of coherence. In this case, the time was
sliced into consecutive time windows of fixed duration, and
Cij() or C(r) was
computed within each window (all signals were renormalized in each
window). The values of correlations were then represented as a function
of the time value corresponding to the beginning of each window.
Spatiotemporal maps. A useful representation of multisite
signals is a spatiotemporal map consisting of successive snapshots of
the distribution of electrical activity across the cortex. Snapshots
were generated by assigning a gray spot to the instantaneous value of
LFP at each electrode and arranging spots along a vertical line
(anteroposterior axis is bottom-to-top). Successive snapshots were then arranged horizontally in columns, defining a map in which LFP
is represented as a function of space and time. This map offers a
compact representation of the spatiotemporal dynamics of the system, in
which synchronous events appear as vertical straight lines and oblique
lines stand for propagating waves or phase shifts.
Time-dependent power spectra. In addition to displaying
variations of spatial and temporal coherence, the signals also
displayed significant variations of oscillation frequency. To
characterize this aspect, we evaluated power spectra in successive time
windows, similar to correlations. The power spectrum was
evaluated using fast Fourier transforms (see Press et al., 1986), and
the relative power in a given frequency band (power within that
frequency band divided by the total power) was represented as a
function of time. Fourier transforms were also used for digital
filtering, using either square or Hamming windows (Press et al., 1986),
with no measurable effect on the present results.
Wave-triggered averages. Wave-triggered averages consist in
averaging short windows of data selected by reference to the time of
occurrence of a given trigger waveform. Negative peaks of LFPs were
used as the trigger and were detected numerically using a thresholding
procedure. The peak was calculated such that only one peak was present
within an interval of the same order as the oscillation cycle
(200-1000 msec for SWS; 10-40 msec for fast oscillations). The
signals in a given time window around the peak were then retained for averaging.
In some cases, multiunit discharges were averaged by reference to
negative peaks of the LFPs. This type of wave-triggered average
estimates the average pattern of firing correlated with the LFP
negativity. To estimate the significance of these estimations, we
always compared wave-triggered averages with a control value. The
control consisted in repeating the same procedure using randomly shuffled spikes, in which the same number of spikes was distributed randomly across the time axis. This control eliminates spurious estimates attributable to poor statistics or artifacts.
Spike-triggered averages. To detect correlations between
spikes and LFPs, a similar averaging procedure was performed in which spike events were used as the reference time to select the windows for
averaging. In this case, spike-triggered averages estimate the LFP
waveform preferentially associated to spike events. Because spikes are
usually highly variable, it is necessary to use large number of spikes
to yield statistically significant results. In this paper, all
spike-triggered averages were compared with a control analysis based on
randomly shuffled spikes (see above), thus providing an estimate of the
statistical significance of the results.
All analyses were performed using C programs based on a library of
numerical algorithms (Press et al., 1986) and were run on a Sparc 20 workstation (Sun Microsystems, Mountain View, CA).
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RESULTS |
Two oscillation types were considered in this study. "Slow
oscillations" or "slow-wave complexes" refer to oscillations in the slow (< 1 Hz) and delta (1-4 Hz) frequency range, whereas "fast
oscillations" refer to oscillations in the - (15-75 Hz) frequency range. We first describe the spatiotemporal properties of
these two oscillation types in awake, SWS, and REM sleep. We then
investigate the relation between unit firing and field potentials for
each type of oscillation. Finally, brief periods of fast oscillations during slow-wave sleep are analyzed using the same methods.
Spatial and temporal correlations during natural wake and
sleep states
Multisite LFPs were recorded using a set of eight equidistant
bipolar electrodes (interelectrode distance of 1 mm; see Fig. 1,
top). Wake and sleep states were identified using the
following criteria: for wake, low-amplitude fast activity in LFPs and
high EOG and high EMG activity; for SWS, LFPs dominated by
high-amplitude slow-waves and low EOG and EMG activity present; for REM
sleep, low-amplitude fast LFP activity, high EOG activity, and
abolition of EMG activity. During waking and attentive behavior, LFPs
were characterized by low-amplitude fast (15-75 Hz) activity (Fig. 1A, left). During SWS, LFPs were
dominated by high-amplitude slow-wave complexes occurring at a
frequency of <1 Hz (Fig. 1B, left).
Slow-wave complexes of higher frequency (1-4 Hz; Fig.
1B, asterisk) and spindle waves (7-14 Hz;
data not shown) were also present in SWS. During periods of REM sleep,
the activity was similar to that of waking periods (Fig. 1C,
left).
Autocorrelations evaluated over long periods (20 sec) for each state of
the animal showed a relative steep decay toward zero (Fig. 1,
right), indicating that the LFP activity is very irregular despite the dominant frequencies characteristic to each state. This is
contrary to the pronounced rhythmicity that can appear in
autocorrelations calculated for small time windows (data not shown).
For long periods of time, the autocorrelations showed similar behavior
for SWS, REM, and waking states and, therefore, cannot be used to
distinguish these states.
By contrast, correlations represented as a function of distance
displayed marked differences between awake, REM, and SWS (Fig. 1,
middle). SWS displays slow-wave complexes of a remarkable
spatiotemporal coherence, as indicated by the high values of spatial
correlations for large distances, in contrast with the steeper decline
of spatial correlations with distance during waking and REM sleep.
Spatial correlations were evaluated in different animals and during
different wake and sleep episodes in the same animals, and the data
grouped together are shown in Figure 2.
Correlations calculated in consecutive windows of 20 sec in a total
recording time of 2 hr (in the same animal) are shown in Figure
2A. There was a clear clustering of curves
corresponding to SWS and that obtained during awake and REM, indicating
that the differences in spatial correlation shown in Figure 1 are
robust. Figure 2B shows spatial correlations
evaluated for long periods of time, from 2.5 to 17 min in different
animals. Here again, there was a clear clustering of the spatial
correlations according to different wake and sleep episodes.
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Figure 2.
Spatial correlations during wake and sleep states.
A, Stationarity of spatial correlations in a 2 hr
recording in the same animal. Each solid line represents
the correlations calculated in consecutive windows of 20 sec in a total
recording time of 2 hr. Wake and REM periods were indistinguishable,
but SWS displayed significantly higher correlations. B,
Spatial correlations calculated from long periods of time in different
animals. Lines indicate several periods of wake, SWS,
and REM sleep in the same animal (length of each period, 2.5 and 15 min
for wake state; 11, 15, and 17 min for SWS; and 3, 8, and 10 min for
REM). The symbols indicate the spatial correlations
obtained for two other animals (squares, 5 min of SWS
and 4 min of wake; circles, 8 min of SWS and 4 min of
REM). All data sets were obtained using the eight-electrode setup shown
in the top of Figure 1, except for the data set shown by
circles that was obtained with four electrodes
(interelectrode distance of 1 mm in all cases).
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The evaluation of the correlation space constant for three animals (see
Materials and Methods) led to values of = 3.7 ± 0.4 mm
(mean ± SD) for waking periods (n = 8), = 13.0 ± 1.2 mm for SWS (n = 15), and = 3.1 ± 0.5 mm for REM sleep (n = 11). These values
show that the characteristic correlation space constant is much larger
in SWS compared with that during fast oscillations. A biexponential
fitting (see Materials and Methods) provided better fits for waking
periods (1 = 10.6 ± 0.6 mm; 2 = 0.03 ± 0.01 mm) and for REM sleep (1 = 7.5 ± 0.5 mm; 2 = 0.03 ± 0.01 mm) but not during SWS
sleep (1 = 16.1 ± 1.5 mm; 2 = 13.3 ± 0.9 mm). The small value of 2 during fast
oscillations betrays a sharp initial drop of correlations, which is
followed by a more progressive decay similar to that of SWS.
This analysis shows that SWS, awake, and REM states are distinguished
by the behavior of correlations with distance. SWS is spatially
coherent and is characterized by high values of correlations across
cortical distances of several millimeters. Wake and REM sleep show less
spatial coherence, with correlations decaying steeply with distance.
Local coherence of fast oscillations
The more local coherence of fast oscillations contrasted with the
large-scale synchrony of slow waves. This phenomenon was investigated
in more detail by monitoring the evolution of local correlations as a
function of time. The maximum of the peak of the cross-correlation
between two neighboring sites was evaluated within successive time
windows of 100 msec duration (2 sec for SWS to have a similar number of
oscillation cycles). The representation of the maximal correlation as a
function of time is shown in Figure 3.
During waking periods (Fig. 3A), neighboring electrodes were occasionally synchronized, as shown by correlations close to 1, but
only for short periods of time (100-500 msec). On the other hand,
distant electrodes displayed lower correlation values (Fig. 3A, pair 1-4), as did the "shuffled"
signal (Fig. 3A, Sh.).
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Figure 3.
Fast oscillations are coherent locally in space
and time. LFP recordings in the suprasylvian gyrus
(LFPs; locations similar to that of electrodes 1-4 in
Fig. 1, with a 1 mm interelectrode distance) are shown together with
the maximal cross-correlation (Correlations) calculated
between pairs of electrodes (1-2, 2-3, 3-4, and 1-4 pairs)
Sh., The control correlation obtained between electrode
1 and the same signal taken 20 sec later. A, Fast
oscillations during wake periods. Neighboring electrodes were
occasionally synchronized, as shown by correlations close to 1, but
only for short periods of time (100-500 msec). B,
Period of slow-wave sleep with the number of oscillation cycles similar
to that in A (note the difference in the time scale). In
this case, correlations between neighboring electrodes stayed close to
unity, and the synchrony extended the entire recorded area.
C, Period of REM sleep. Fast oscillations had a dynamics
similar to that in A, consisting in brief periods of
synchrony between neighboring electrodes, occurring irregularly and
within short time windows. Correlations were calculated in successive
time windows of 100 msec for A and C and
2 sec for B.
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Local correlations had different properties during SWS (Fig.
3B); correlations between neighboring electrodes tended to
stay close to 1, whereas distant electrodes displayed lower, although still high, correlations (Fig. 3B, pair
1-4). These features are in agreement with the high values
of spatial correlations evidenced above. During REM sleep (Fig.
3C), fast oscillations displayed locally correlated dynamics
similar to that in the awake animal.
These results indicate that fast oscillations are characterized by
brief periods of synchrony between neighboring electrodes, occurring
irregularly and within short time windows, by contrast to SWS in which
slow-wave complexes always appear coherently over large distances.
Occasionally, bursts of fast oscillations were coherent across large
distances (Fig. 4A).
This synchrony was also apparent in spatiotemporal maps of activity and
in the high value of correlations at a 7 mm distance (Fig.
4A). However, these events were only occasionally
seen during REM sleep periods, and the most typical portrait of fast
oscillations is rather incoherent spatiotemporally (Fig.
4B), with significant correlations only appearing
between neighboring sites and within restricted time windows.
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Figure 4.
Fast oscillations are occasionally coherent across
large cortical distances. LFPs from eight recording electrodes are
shown during fast oscillations (LFPs; signal filtered
between 15 and 75 Hz). Spatiotemporal maps for the same period of
activity are shown below the recordings (Maps).
Spatiotemporal maps were constructed by representing space
(y-axis) and voltage (gray
level) against time (x-axis). The
gray scale ranged from white (100 µV
and below) to black (0 µV and above) in 10 levels. The
correlation decay with distance calculated during the same period of
time is shown on the right (Spatial
correlation). A, Coherent burst,
Fast oscillations were occasionally synchronized across large
distances, as shown by the vertical black-white
stripes in the spatiotemporal maps and the high values of
correlations at a 7 mm distance. This coherent burst was recorded
during REM sleep. B, Incoherent burst, In
most instances, synchrony was present only between neighboring
electrodes and during restricted time windows (as shown in Fig. 3).
This local synchrony of fast oscillations can also be seen by the local
patterns of black-white stripes in the spatiotemporal
maps. In this case, correlations decayed steeply with distance. The
latter type of activity represents the pattern observed most frequently
during wake and REM periods.
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These results show that fast oscillations are characterized by local
synchrony but may be occasionally coherent over large distances. Fast
oscillations seem therefore to be characterized by a succession of
coherent events occurring locally both in space and time.
Spatial coherence during the transition between wake and
sleep states
The classical electrographic criteria to assess the transition
between wake and sleep states are the patterns of electrical activity
(as recorded by EEGs or LFPs), the level of muscular tone (as recorded
by the EMG), and the presence of ocular movements (detected by the
EOG). Here we show that the spatial coherence varies in accordance with
these classic criteria.
The time course of LFPs, EOGs, and EMGs are shown during the transition
between wake and sleep states in Figure
5. Waking periods were characterized by
muscular tonus, ocular movements, and low-amplitude fast LFP activity
(Fig. 5, AWAKE). SWS was characterized by muscular tonus, no
ocular movements, and LFP activity dominated by high-amplitude
slow-wave complexes (Fig. 5, SWS). REM sleep was
characterized by the absence of muscular tonus, prominent eye
movements, and low-amplitude fast activity in LFPs (Fig. 5, REM). In addition to these classic criteria, the
spatial coherence was evaluated by the correlation space constant ,
the robustness of which was shown in Figure 2. During SWS, the
correlation space constant was characterized by significantly higher
values than that in wake and REM sleep and stayed high for the entire
duration of SWS, as detected by LFP, EOG, and EMG.
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Figure 5.
Spatial coherence during the transition between
wake and sleep states. Top, The depth LFP recorded in
the suprasylvian gyrus during a period of 16 min, consisting of ~2
min of wake followed by ~8 min of SWS and ~6 min of REM sleep, is
shown. The presence of ocular movements (EOG) and of muscular tonus
(EMG) were monitored and are indicated by horizontal
bars. Middle, The relative power of 0.1-4 and
15-75 Hz frequency bands are represented during the same period at
eight different cortical sites (as shown in the top of
Fig. 1). Bottom, The space constant of the decay of
correlations with distance is shown. SWS activity is characterized by a
marked increase of spatial coherence compared with that of wake and REM
periods. Power spectra and spatial correlations were calculated in
successive windows of 16.4 sec (4096 points).
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Slow-wave complexes are preceded by a generalized
neuronal silence
LFPs and units were separated by standard procedures (see
Materials and Methods). During the waking state, units tended to discharge tonically, similar to previous observations (Hubel, 1959;
Evarts, 1964; Steriade et al., 1974). The relation between units and
LFP was not evident at first sight, although there was a tendency to
discharge during LFP negativity (see below). During SWS, the pattern of
discharge was more phasic and characterized by periods of silences and
of increased firing, as reported previously (Evarts, 1964; Steriade et
al., 1974). Positive deflections of slow-wave complexes were almost
always associated with a neuronal silence in all units, whereas
negative deflections tended to be correlated with a brief increase of
firing (Fig. 6A). REM
sleep displayed activity patterns similar to that in awake animals.
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Figure 6.
Relation between simultaneously recorded multiunit
discharges and field potentials during slow-wave complexes.
A, Individual slow-wave complexes were detected
numerically during SWS and were aligned with respect to the negative
peak of the LFPs (LFPs). The multiunit discharges
detected in the same electrode were aligned similarly
(Units). B, Wave-triggered averages of
field potentials and multiunit discharges are shown. The averaged field
potentials (LFPs, avg) were constructed
by averaging the LFP over the eight electrodes and over 210 detected
slow-wave complexes. The resulting averaged LFP consisted in a slow
positivity followed by a sharp negativity. The corresponding multiunit
discharges were averaged similarly (Units,
avg) and displayed a drop of firing rate correlated with
LFP positivity, followed by an increase of firing during the LFP
negativity. The same wave-triggered average did not show any modulation
of firing rate if performed on randomly shuffled spikes
(Control). C, Spatial profile of
the relation between units and LFPs is presented. Local field
potentials, averaged over 210 slow-wave complexes, are shown for each
electrode (LFPs). The corresponding wave-triggered
averages of multiunit discharge at each electrode are shown
(Units). Slow-wave complexes consisted in a widespread
drop of firing, correlated with LFP positivity, followed by a
synchronized increase of firing, correlated with LFP negativity. These
events were synchronous over the entire extent of the cortical area
recorded (7 mm). Data in A-C are from
the same animal.
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The temporal modulations of unit firing during SWS were investigated by
calculating wave-triggered averages. Superposition of LFPs and unit
activity showed that slow-wave complexes almost invariably correlate
with a silence in the units (Fig. 6A). Wave-triggered averages calculated over 210 complexes revealed a marked modulation of
firing rate associated with slow-wave complexes (Fig.
6B, Units, avg), whereas the
same procedure performed with randomly shuffled spikes did not show any
pattern (Fig. 6B, Control). This
analysis demonstrates a drop of firing rate in close correspondence to the depth positivity of slow-wave complexes, whereas depth negativity is associated with an increase of neuronal firing (Fig.
6B), in agreement with previous intracellular
observations (Contreras and Steriade, 1995; Steriade et al., 1996).
The same analysis was also performed as a function of distance (Fig.
6C). Wave-triggered averages performed simultaneously between the LFP and cells at each electrode showed that the depth positivity of slow-wave complexes corresponded to a concerted silence
in almost all units, whereas the depth negativity was correlated with
an increased firing. Only one unit was not correlated with LFP (Fig.
6C, unit 6). In other experiments
with less electrodes, all units were correlated with LFP (data not
shown). This analysis shows that SWS complexes are characterized by a
generalized decrease of firing occurring over large cortical distances,
followed by an increased firing occurring in rebound to each period of silence.
Increased firing probability during the negative field potential of
fast oscillations
During wake and REM sleep, units fired tonically, and their
relation with LFPs was not evident at first sight. We performed the
same wave-triggered average procedures during fast oscillations of
waking periods using the peak negativity of LFP to trigger the
averaging procedure. Wave-triggered averages computed from a total of
467 events indeed showed that units were significantly correlated with
LFPs (Fig. 7A,
Units, avg), whereas the same procedure applied
to randomly shuffled spikes did not reveal any pattern (Fig.
7A, Control). This analysis therefore
demonstrates a marked increase of firing in correspondence with the
depth-negative component of fast oscillations, as shown previously in
anesthetized animals (Eckhorn et al., 1988; Gray and Singer, 1989;
Murthy and Fetz, 1992; Steriade et al., 1996). The same conclusions
were also obtained from fast oscillations of REM sleep (Fig.
7B).
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Figure 7.
Relation between simultaneously recorded multiunit
discharges and field potentials during fast oscillations of wake and
REM sleep. A, Relation between local field potentials
(LFPs, avg) and multiunit discharges
(Units, avg) in periods of wake. Signals
were filtered between 15 and 75 Hz, and the peak negativities of field
potentials were detected. The LFP waveform shown was obtained by
averaging over a total of 467 detected events from eight electrodes.
The corresponding wave-triggered average of multiunit discharges
displayed a marked increase of firing correlated with the LFP
negativity. The same analysis performed on randomly shuffled spikes did
not show any pattern (Control). B,
Same analysis during periods of REM sleep in the same animal. In this
analysis, 1721 detected events were used to compute the averaged
LFP. During REM sleep, similar to wake states, cells tended to fire in
relation with the negativity of the field potentials during fast
oscillations, whereas no increase of firing was seen in the control.
Data in A and B are from the same
animal.
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However, the existing correlations between units and LFPs during waking
or REM sleep were only seen for signals emanating from the same
electrode. Attempts to find correlations between the LFP of one
electrode and units recorded by other electrodes, at a distance of 4 mm
and more, were unsuccessful during wake periods (Fig.
8A). On the other hand,
a significant correlation was evident during SWS (Fig.
8B), in agreement with the large-scale correlations
found in Figure 6. During REM sleep, no correlations were present over
large distances (Fig. 8C).
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Figure 8.
Correlations over large distances are present
during slow-wave sleep but not during wake or REM sleep. Wave-triggered
averages were computed from LFPs and from cells at a distance of >4
mm. LFP negativities from electrode 8 (LFP-8,
avg) were detected to average units from electrodes 1 to
4 (Units 1-4, avg; the same procedures
that were used in wave-triggered averages and the same data as in Fig.
6 for SWS and Fig. 7 for wake and REM sleep). A, In
periods of wake, there was no visible relation between
LFP-8 and units 1-4. B,
During SWS in the same animal, the positivity/negativity complex was
correlated with a decrease/increase of firing in units.
C, No detectable relation was seen for REM sleep (same
animal), similar to results in A. All control
traces display the same procedure based on randomly shuffled
spikes.
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The relations between extracellular unit discharges and LFP activity
can also be detected from the analysis of spike events. Spike-triggered
averages of LFPs computed in waking periods show that spikes occurred
preferentially in correspondence with a depth-negative component of the
field potential (Fig. 9A,
LFPs, avg), whereas randomly shuffled spikes
yielded a flat line (Fig. 9A, Control). A
similar relation was obtained during REM sleep (Fig. 9B). By contrast, performing the same spike-triggered averaging procedure for
periods of SWS yielded an average LFP consisting of a broad depth-negative deflection, followed by a slow depth-positive component (Fig. 9C, LFPs, avg), whereas no
pattern was obtained using randomly shuffled spikes (Fig.
9C, Control).
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Figure 9.
Relation between extracellular unit discharges and
local field potentials as detected from spike events. Spike-triggered
averages of local field potentials were first computed at each
individual electrode. These spike-triggered averages were then averaged
to yield a single curve, shown here for various states.
A, In wake states, individual spikes were correlated
with the negativity of the local field potentials (LFPs,
avg; 5506 spikes processed). B, A similar
relation was obtained during REM sleep (LFPs,
avg; 19491 spikes processed). C, During
slow-wave sleep, the average LFP events corresponding to spikes
consisted of a broad negative deflection, followed by a slow positive
deflection (LFPs, avg; 34244 spikes
processed; note different time scale). In all cases, the same analysis
based on randomly shuffled spikes did not evidence any preferred
pattern (Control).
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This analysis therefore confirms the above conclusions; during wake and
REM sleep, cortical neurons tend to fire in correspondence with the
depth-negative component of fast oscillations recorded by the same
electrode. By contrast, during SWS, neuronal firing is coherent across
large distances and consists in periods of decreased and increased
firing, correlated with the depth-positive and depth-negative component
of slow-wave complexes, respectively.
Periods of fast oscillations occur during slow-wave sleep
Although SWS is clearly dominated by high-amplitude slow-wave
activity, at closer scrutiny, it seems that SWS also contains brief
periods of low-amplitude fast oscillations. In LFPs, examining the
distribution of dominating frequencies using fine time windows (~0.5
sec) reveals that periods of slow-wave complexes, with high power in
low-frequency (0.1-4 Hz) bands, alternate with periods dominated by
frequencies in the - range (15-75 Hz) (Fig.
10). It thus seems that SWS is composed
of slow-wave complexes separated by brief periods of fast oscillations,
in agreement with previous observations (Steriade et al., 1996).
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Figure 10.
Fine structure of local field potentials during
slow-wave sleep. The LFPs at eight cortical sites (top
curves; same experiment described in Fig. 1), the relative
power of low-frequency (0.1-4 Hz) and fast-frequency (16-75 Hz)
components (middle curves), and the space constant of
correlation decay with distance (bottom curve) are shown
for a 20 sec period of slow-wave sleep. Power spectra and spatial
correlations were calculated in successive windows of 0.512 sec (128 points). Slow-wave complexes (single asterisk) were
synchronous over the eight electrodes, whereas brief periods of fast
oscillations (double asterisks) had lower spatial
coherence.
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Investigating the correlation decay as a function of distance based on
multisite LFPs (see above) reveals that periods of slow-wave complexes
display high coherence (Fig. 10, single asterisk), whereas
the coherence is markedly diminished during brief periods of fast
oscillations (Fig. 10, double asterisks). This local
analysis of coherence suggests that the brief periods of fast
oscillations occurring during SWS have characteristics similar to that
of the "sustained" fast oscillations of wake or REM sleep episodes.
To characterize further this aspect, we investigated these brief
periods of fast oscillations in more detail (Fig.
11). The correlation between
neighboring sites showed that fast oscillations of SWS are
characterized by correlations that fluctuate and stay high between
neighboring sites for time windows of ~100-500 msec (Fig.
11B), similar to the fast oscillations in wake and
REM sleep (see Fig. 3A,C). To
investigate whether this similitude also extended to the relation
between LFP and unit activity, we analyzed long periods of SWS by
artificially removing slow-wave complexes and removing the
corresponding spikes from the multiunit signals. Performing
wave-triggered averages of unit discharges showed that the units tended
to fire with higher probability during the depth-negative LFPs (Fig.
11C). Spike-triggered averages similarly showed a
correlation with the LFP negativity of fast oscillations (Fig.
11D). On the other hand, random shuffling of spikes
destroyed these relationships (Fig. 11C,D,
Control). This analysis therefore shows that the
brief periods of fast oscillations of SWS have a relation between units and LFPs similar to that of the sustained fast oscillations of wake and REM sleep periods.
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Figure 11.
Fast oscillations during slow-wave sleep have
characteristics similar to that during wake and REM sleep.
A, A brief period of fast oscillations (dashed
horizontal line) during slow-wave sleep. B,
Dynamics of correlations during the period of fast oscillations shown
in A, analyzed similarly as described in Figure 3. Fast
oscillations displayed local patterns of synchrony, within short time
windows and between neighboring electrodes, similar to that of wake and
REM periods. C, Relation between neuronal firing and
local field potentials. Slow-wave complexes were artificially removed
from LFPs in a period of 11 min of slow-wave sleep. The corresponding
spikes were also removed from multiunit discharges. The resulting LFPs
and multiunit discharges were then analyzed similarly as described in
Figure 7 (390 events processed). The wave-triggered averaging procedure
shows that the negative LFP of fast oscillations of SWS
(LFPs, avg) was correlated with an
increase of firing (Units). The same analysis based on
randomly shuffled spikes did not show any pattern
(Control). D, Spike-triggered
averages calculated similarly as described in Figure 9 (4011 spikes
processed). Spikes were correlated with the LFP negativity of fast
oscillations (LFPs, avg), similar to that
of wake and REM sleep, but did not show any preferred pattern if spikes
were randomly shuffled (Control).
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Finally, the relation between field potentials and cellular events
suggested by the present analysis is summarized in Figure 12. Slow-wave complexes are correlated
with periods of decreased neuronal firing, followed by periods of
increased firing, and the same pattern is seen coherently over large
cortical distances (>7 mm). By contrast, fast oscillations have their
depth-negative LFP component correlated with an increased probability
of firing in units adjacent to the recording electrode, but no relation was apparent with more distant units. Brief periods of fast
oscillations with similar characteristics also appear during SWS.
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Figure 12.
Schematic representation of the relationship
between local field potentials and unit discharges during wake and
sleep states. Top, During slow-wave sleep, slow-wave
complexes were correlated with phasic-firing activity. Periods of
neuronal silence coincided with depth positivity in the LFP, whereas
depth-negative components occurred in coincidence with increased firing
in units. The occurrence of periods of decreased and increased firing
was synchronous over large cortical distances (>7 mm).
Bottom, During fast oscillations, units discharged more
tonically, with an increased probability of firing during the
depth-negative component of the LFP. The coherence extended over short
distances (~1-2 mm), and unit activity was correlated only with the
nearby LFP. This pattern was seen for fast oscillations of wake and REM
sleep, as well as for brief periods of fast oscillations occurring
during SWS.
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DISCUSSION |
In this paper, we have investigated the spatial and temporal
distribution of fast and slow oscillation types occurring during natural wake and sleep states, as well as their relation with unit
discharges. We summarize here the results obtained, relate them to
previous approaches, and discuss possible mechanisms and physiological
consequences of these findings.
Spatial and temporal aspects of wake and sleep oscillations
The findings of the present study can be summarized as follows:
(1) slow-wave complexes of natural sleep are coherent across a distance
of several millimeters in the cortex and are correlated with a
concerted decreased and/or increased firing in units; (2) fast
oscillations of wake and REM sleep are characterized by less global and
more fluctuating coherence, and their depth-negative EEG components are
correlated with an increased probability of unit firing; and (3) SWS
contains brief periods of fast oscillations whose spatiotemporal
properties are similar to that of the sustained fast oscillations of
activated states. We consider each of these points successively.
(1) We found that the slow-wave complexes of natural sleep display high
spatial coherence. It was shown previously that low-frequency EEG
components have higher coherence than do fast frequencies in rabbits
and rats (Bullock and McClune, 1989) as well as in humans (Achermann
and Borbély, 1998; but see Bullock et al., 1995). The present
results are in agreement with this finding and further show that the
spatial coherence of oscillations, as quantified by the correlation
space constant, varies across wake and sleep states in parallel to
classical criteria of LFP, EOG, and EMG changes (Fig. 5). This suggests
that the global versus local level of coherence of LFPs is not only
characteristic of the frequency band but also parallels the state of
the animal.
However, it has been argued that the coherence estimated from LFPs or
EEGs may reflect spurious correlations attributable to the filtering
properties of extracellular space (Srinivasan et al., 1998). It may
therefore be that the low-pass filtering nature of extracellular space
(Nunez, 1981) induces artificial correlations selectively for
low-frequency events. However, two arguments demonstrate that this is
not the case here. First, fast oscillations may occasionally display
high coherence similar to that of low-frequency oscillations (Fig.
4A). Second, a consistent relation between LFP and
cell firing extends to considerable cortical distances for slow-wave
complexes but not for fast oscillations (Fig. 8).
We also found that slow-wave complexes display a striking correlation
with unit firing; depth-LFP negativities are associated with increased
firing, whereas depth-EEG positivities are simultaneous with a
decreased firing, consistent with intracellular recordings during
ketamine-xylazine anesthesia (Steriade et al., 1993a; Contreras and
Steriade, 1995). These observations are also consistent with previous
findings for delta waves in various preparations (Frost et al., 1966;
Ball et al., 1977; Buzsáki et al., 1988). The same relations
between the two main LFP components and sequences of depolarization and
hyperpolarization were observed recently in intracellular recordings
performed in awake and naturally sleeping cats (Steriade et al., 1999).
In addition, the present analysis indicates that the periods of
increased and decreased firing, occurring during slow-wave complexes,
are coherent over several millimeters in the cortex (Fig. 6).
(2) During waking and REM sleep, we found here that the coherence in
the - frequency band was highly fluctuating both in space and
time, as neighboring electrodes typically synchronized for short
periods of time (100-500 msec; see Figs. 3, 4). Similar spatiotemporal
properties of local coherence were observed for fast oscillations in
anesthetized animals, both for spontaneous oscillations (Steriade and
Amzica, 1996) and for oscillations evoked by sensory stimuli (Eckhorn
et al., 1988; Gray et al., 1989, 1992).
In wake and REM sleep periods, the discharge of units was related to
the depth negativity of LFPs, similar to the fast oscillations occurring in anesthetized animals (Eckhorn et al., 1988; Gray and
Singer, 1989; Murthy and Fetz, 1992; Steriade et al., 1996). However,
we could not detect any significant correlation between LFP and units
from distant electrodes (Fig. 8), in agreement with the local
correlation displayed by LFPs. This is also in agreement with findings
in anesthetized animals that evidenced that the correlation between
distant cells was state dependent and was only present when the EEG
displayed slow-wave activity (Contreras and Steriade, 1997).
(3) The fact that brief periods of fast oscillations are present
between slow-wave complexes was shown previously in cat suprasylvian cortex during ketamine-xylazine anesthesia (Contreras and Steriade, 1995) and natural sleep (Steriade et al., 1996). We found here that the
spatial and temporal coherence of these brief periods of fast
oscillations, as well as their relation with unit discharges, is
indistinguishable from that of the sustained fast oscillations of wake
and REM sleep. However, the present analysis only investigated oscillations over distances up to 7 mm in the cerebral cortex, and
further experiments are required to demonstrate that the above conclusions also apply to larger cortical distances.
Mechanisms of coherent cortical oscillations
The presence of a widespread decrease of firing suggests that
slow-wave complexes of natural sleep are generated by a generalized disfacilitation in the cortex, followed by a rebound of the network, as
found intracellularly during ketamine-xylazine anesthesia (Contreras and Steriade, 1995). The synchrony of the slow oscillation of ketamine-xylazine anesthesia is disrupted by the sectioning of intracortical connections (Amzica and Steriade, 1995) and is resistant to thalamic lesions (Steriade et al., 1993b), which suggested that it
is generated intracortically. It is possible that similar mechanisms
underlie the slow-wave complexes of natural sleep, although no precise
biophysical mechanism has been proposed yet.
However, the intracortical synchrony is restored a few hours after the
sectioning of intracortical connections (Amzica and Steriade, 1995).
This may suggest that thalamocortical mechanisms may play an important
role in establishing large-scale coherence. A recent computational
model proposed a mechanism for large-scale synchrony based on
thalamocortical loops (Destexhe et al., 1998a). It is possible that
both intracortical and thalamocortical interactions play an important
role in the large-scale coherence of SWS. These possibilities should be
addressed by future computational models.
Intracortical mechanisms were advanced to explain the synchrony of fast
oscillations in the cerebral cortex on the basis of networks of
inhibitory interneurons (Buzsáki and Chrobak, 1995; Traub et al.,
1996). However, the question of how the same thalamocortical circuits
are capable of generating low-frequency oscillations with large-scale
coherence, as well as fast oscillations with local coherence, is still
open. A model was proposed to explain these variations of coherence on
the basis of two-dimensional networks of excitatory and inhibitory
neurons subject to pacemaker inputs of various frequencies (Destexhe
and Babloyantz, 1991). Although this model displayed low-frequency
oscillations with large coherence and fast oscillations with low
coherence, it did not include the important role of corticothalamic
feedback. More accurate models of thalamocortical circuits are needed
to investigate mechanisms for the coexistence of oscillations of
different levels of coherence.
Possible physiological consequences
The observation that highly coherent slow-wave patterns alternate
with brief periods of low-coherent fast oscillations can be interpreted
in three possible ways. First, it may be that fast oscillations
constitute a background activity always present in thalamocortical
networks and that this activity is regularly interrupted by slow-wave
events. Slow-waves would therefore sculpt spatiotemporally coherent
events into a background of low-coherent fast oscillations.
A second possibility is that the fast oscillations occur in rebound to
slow-wave events. Slow-waves are likely to be associated with a
widespread hyperpolarization in cortical cells, followed by a rebound
depolarization, as occurs in neocortical neurons during
ketamine-xylazine anesthesia (Contreras and Steriade, 1995). At the
cellular level, fast oscillations may occur following depolarization in
neocortical cells attributable to intrinsic mechanisms (Llinás et
al., 1991; Nuñez et al., 1992; Gutfreund et al., 1995; Gray and
McCormick, 1996; Steriade et al., 1998). It is thus possible that short
periods of fast oscillations appear during the rebound depolarization
as a consequence of intrinsic ionic mechanisms in cortical cells.
A third possibility is that SWS iterates a cyclic process, in which
brief periods of processing similar to that of the awake state
alternate with highly synchronized network events. If the network
rebounds synchronously after a period of silence, pyramidal cells
should receive strong EPSPs followed by IPSPs, which form an ideal
signal to trigger a massive calcium entry in the dendrites (Contreras
et al., 1997). Because calcium is implicated in various forms of
plasticity and long-term cellular changes (for review, see Ghosh and
Greenberg, 1995), it is possible that slow-wave complexes are important
to establish permanent changes in the network. The fast oscillations
could reflect recalled events experienced previously, which are
"imprinted" in the network via synchronized network events that
appear as slow-wave complexes in the EEG.
| |
FOOTNOTES |
Received Dec. 7, 1998; revised Feb. 12, 1999; accepted March 10, 1999.
This research was supported by the Medical Research Council of Canada
and the Human Frontier Science Program.
Correspondence should be addressed to Dr. A. Destexhe, Laboratoire de
Neurophysiologie, Faculté de Médecine, Université Laval, Québec G1K 7P4, Canada.
Dr. Contreras's present address: Department of Neuroscience,
University of Pennsylvania, School of Medicine, Philadelphia, PA 19104.
| |
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TOP
ABSTRACT
INTRODUCTION
