Laboratoire de Neurophysiologie, Faculté de Médecine, Université Laval, Quebec G1K 7P4, Canada
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ABSTRACT |
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Grenier, François, Igor Timofeev, and Mircea Steriade. Focal Synchronization of Ripples (80-200 Hz) in Neocortex and Their Neuronal Correlates. J. Neurophysiol. 86: 1884-1898, 2001. Field potentials from different neocortical areas and intracellular recordings from areas 5 and 7 in acutely prepared cats under ketamine-xylazine anesthesia and during natural states of vigilance in chronic experiments, revealed the presence of fast oscillations (80-200 Hz), termed ripples. During anesthesia and slow-wave sleep, these oscillations were selectively related to the depth-negative (depolarizing) component of the field slow oscillation (0.5-1 Hz) and could be synchronized over ~10 mm. The dependence of ripples on neuronal depolarization was also shown by their increased amplitude in field potentials in parallel with progressively more depolarized values of the membrane potential of neurons. The origin of ripples was intracortical as they were also detected in small isolated slabs from the suprasylvian gyrus. Of all types of electrophysiologically identified neocortical neurons, fast-rhythmic-bursting and fast-spiking cells displayed the highest firing rates during ripples. Although linked with neuronal excitation, ripples also comprised an important inhibitory component. Indeed, when regular-spiking neurons were recorded with chloride-filled pipettes, their firing rates increased and their phase relation with ripples was modified. Thus besides excitatory connections, inhibitory processes probably play a major role in the generation of ripples. During natural states of vigilance, ripples were generally more prominent during the depolarizing component of the slow oscillation in slow-wave sleep than during the states of waking and rapid-eye movement (REM) sleep. The mechanisms of generation and synchronization, and the possible functions of neocortical ripples in plasticity processes are discussed.
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INTRODUCTION |
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Fast oscillations
(>100 Hz), termed ripples, were described in CA1 hippocampal area and
perirhinal cortex, where they were associated with bursts of sharp
potentials during anesthesia, behavioral immobility, and natural sleep
(Chrobak and Buzsáki 1996; Collins et al.
1999
; Csicsvari et al. 1998
, 1999
; Ylinen et al. 1995
). In the neocortex, fast oscillations (>200 Hz)
have been found in sensory-evoked potentials in rat barrel cortex
(Jones and Barth 1999
), during high-voltage
spike-and-wave patterns in rat (Kandel and Buzsáki
1997
), and around epileptic foci in humans (Allen et al.
1992
; Fisher et al. 1992
). Studies in epileptic patients have revealed the presence of high-frequency oscillations in
the hippocampus and entorhinal cortex (Bragin et al.
1999a
,b
).
Different roles have been hypothesized for ripples in physiological and
pathological phenomena, such as sensory information processing
(Jones and Barth 1999), replay of neuronal activation sequences for memory consolidation (Nádasdy et al.
1999
), and involvement in seizures (Grenier et al.
2000
; Traub et al. 2001
).
In light of their strong presence in, and possibly initiation of,
seizures, we have undertaken a study of neocortical ripples (80-200
Hz) during the cortical slow oscillation (0.5-1 Hz), which is a major
rhythm of slow-wave sleep (Steriade et al. 1993a,b
) and
which may develop, often without discontinuity, into seizure episodes
(Steriade et al. 1998a
). In the present report, we
address the following questions: how are field potential ripples
correlated between different cortical sites, what is their relation to
neuronal activity, and how do they vary during various natural states
of vigilance compared with anesthetized preparations.
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METHODS |
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Acute and chronic experiments were conducted on 49 adult cats.
Preparation and recordings in acute experiments
Thirty-five, intact-brain cats were acutely prepared under
ketamine-xylazine anesthesia (10-15 and 2-3 mg/kg im, respectively). The animals were paralyzed with gallamine triethiodide after the electroencephalogram (EEG) showed typical signs of deep general anesthesia, essentially consisting of a slow oscillation (0.5-1 Hz),
which is similar under this type of anesthesia (Contreras and
Steriade 1995) to that occurring during natural slow-wave sleep
in chronically implanted animals (Steriade et al. 1996
). The cats were ventilated artificially with the control of end-tidal CO2 at 3.5-3.7%. In some experiments, the
effect of halothane was tested by administration through the artificial
ventilation at a concentration of 0.5-2%. The body temperature was
maintained at 37-38°C and the heart rate was ~90-100 beats/min.
Single and dual intracellular recordings were performed from
suprasylvian association areas 5 and 7 using glass micropipettes filled
with a solution of 3 M potassium-acetate (KAc) or potassium-chloride (KCl) (d.c. resistances, 30-60 M). A high-impedance amplifier with
active bridge circuitry was used to record the membrane potential (Vm) and inject current into the
neurons. Field potentials were recorded in the vicinity of impaled
neurons, using bipolar coaxial electrodes, with the ring (pial surface)
and the tip (cortical depth) separated by 0.8-1 mm. An array of seven
or eight electrodes, ~1.5 mm apart, was inserted along the
suprasylvian gyrus (Fig. 1) or across
the marginal, suprasylvian and ectosylvian gyri (Fig. 2). Intracellular signals were
recorded, together with field potential activity, on an eight-channel
tape with a band-pass of 0-9 kHz. At the end of experiments, the cats
were given a lethal dose of pentobarbital.
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In 10 acutely prepared cats under ketamine-xylazine anesthesia, small
isolated cortical slabs were prepared from areas 5 and 7 using a
crescent knife. The method is fully described elsewhere (Timofeev et al. 2000). Field potential and
intracellular recordings within the slab were done with similar types
of macroelectrodes and micropipettes as in intact-cortex animals.
Chronic experiments
Chronic experiments were performed on four cats. For details on
surgical procedures used for implantation of electrodes and chambers
used for field potential and intracellular recordings, see
Steriade et al. (2001) and Timofeev et al.
(2001)
. The method used to keep the head rigid without pain or
pressure during the recording sessions was similar to that described
previously (Steriade and Glenn 1982
). The experimental
protocol was approved by the committee for animal care in our
university and also conforms to the policy of the American
Physiological Society. At the end of experiments, cats were given a
lethal dose of pentobarbital.
Analyses
NORMALIZED AMPLITUDE OF OSCILLATIONS. The average of the rectified value of the filtered EEG trace (80-200 Hz for ripples, 30-80 for gamma activity) was calculated for successive periods of 50 ms. The period displaying the highest average was taken as standard, and all averages were divided by this maximal value. This yielded a series of values ranging between 0 and 1, each corresponding to the normalized oscillation amplitude of one 50-ms epoch (see Fig. 5).
WAVE-TRIGGERED-AVERAGES (WTAS) FROM ARRAYS OF EIGHT ELECTRODES. All EEG traces were filtered between 80 and 200 Hz. One of the eight traces was taken as the time-trigger source. From this trace, single cycles of ripples in which the depth-negative peak exceeded four times the value of the standard deviation (SD) of the trace were selected as triggers for the averages. WTAs were then computed by averaging 30 ms of data from each channel centered at the negative peak of the elected trigger. They are referred to as a combination of 2 numbers A-B, in which A is the number of the site from which triggers were selected, and B is the number of the site from which the WTA was computed (see Figs. 2 and 3).
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RELATIVE AMPLITUDE VERSUS TIME LAG PLOTS. Depth-negative peaks of ripples used as trigger for WTAs (see preceding section) were used for these plots. The filtered (80-200 Hz) trace from the third site of recording (see Figs. 2 and 3) was used in combination with each of the eight filtered channels. For each combination, a plot 3-A was produced, in which A is the number of the other trace. Each depth-negative peak of ripples from trace 3 used as triggers for the WTAs was compared with the closest negative ripple peak in the corresponding A trace. The time difference between the two peaks constituted the time lag. The amplitude ratio between the peaks (A/3) gave the relative amplitude of the ripple peak. A graphic representation of these calculations is given in Fig. 3 (middle left). These two values were represented as a point (time lag, relative amplitude) in the plot 3-A.
FIRING PROBABILITY VERSUS RIPPLE INTENSITY. In the filtered (80-200 Hz) EEG trace, ripple cycles in which the depth-negative peak had an amplitude greater than that of integer multiples of the SD of the filtered trace were identified. We then calculated the probability of finding an action potential within a 10-ms window centered on those peaks. This was determined by dividing the number of action potentials detected in these windows by the number of analyzed windows. These probabilities are presented as percentages (Fig. 7 and 14) or firing frequencies (Table 1).
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RESULTS |
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First, we describe ripples (80-200 Hz) in the neocortex of cats under anesthesia, and the extent of their correlation between different sites within the same gyrus and across different gyri. Second, we present the neuronal correlates of ripples. We show that ripples in field potentials are linked with neuronal depolarization and firing, especially from fast-rhythmic-bursting neurons, and that inhibition plays an important role in their patterning. Finally, we show that ripples are also present during natural states of vigilance: slow-wave sleep, REM sleep, and wakefulness.
Ripples in cortical field potentials
Ripples were present in EEG recordings under ketamine-xylazine
anesthesia during the depth-negative phase of the slow oscillation at
0.5-1 Hz (Fig. 1), which corresponds in intracellular recordings to
neuronal depolarization (Steriade et al. 1993a,b
). Field
recordings from an array of seven electrodes inserted in the
suprasylvian gyrus along the anteroposterior axis are presented in Fig.
1. The depth-negative phase of the slow oscillation grouped ripples in
spindle-shaped sequences (see EEG filtered traces in Figs. 1 and 2).
Since the slow oscillation shows coherence across different areas of
the cortex (Amzica and Steriade 1995
), ripples had also a tendency to appear at about the same time in different cortical sites
(Figs. 1 and 2). However, their intensity could vary from cycle to
cycle of the slow oscillation within the same site (Figs. 2 and 3; see
also Fig. 6) and also between different sites during the same cycle
(see Fig. 9).
Cross-correlations of filtered EEG traces revealed that ripples are correlated with time lags of 0-2 ms between different sites separated by 1.5 mm over the same gyrus, up to a total distance of 9 mm (Fig. 1). Similar results were found in 7 of 10 of our experiments. In the remaining three experiments, ripples appeared more independently in each site. The 0 time lag between activities that were relatively distant (lead 4-1 in Fig. 1), compared with those recorded from neighboring foci that displayed a time lag of ~1.25 ms (lead 4-3 in the same figure), is probably due to the averaging procedure.
Correlations between ripples from distant sites were restricted to the same gyrus. This was shown by recording field potentials first along the suprasylvian gyrus with an array of eight electrodes and then, in the course of the same experiment, placing the array in the mediolateral direction (Fig. 2). In the latter case, two electrodes were in the marginal gyrus, three in the suprasylvian, and the other three in the ectosylvian gyrus. With the parasagittal placement of electrodes, WTAs revealed a strong phase correlation between recording sites, with small time lags (<1.5 ms), and the amplitude of the correlations decreasing as distance increased (Fig. 2). When the array was disposed mediolaterally over the three gyri, there were strong correlations between sites within the same gyri, but correlations fell off quickly when the WTAs were computed from sites in different gyri (Fig. 2, compare WTAs from same gyrus and different gyri from sites 2-4, 4-6, and 6-5). We also routinely recorded field potentials using an electrode over the contralateral suprasylvian cortex. Ripples generally occurred almost simultaneously between the homotopic sides, but there was no strict phase-correlation between the ripples from the two sides (data not shown).
The cross-correlations (Fig. 1) and WTAs (Fig. 2) data indicate very small time lags between ripples recorded from different sites along the same gyrus. Analysis of individual cycles of ripples revealed that time lags could vary for different cycles. We selected a recording site (site 3) from the same experiment as in Fig. 2, with all recording sites in the suprasylvian gyrus, and plotted time lag versus relative amplitude for individual ripple cycles involved in the calculation of WTAs (Fig. 3). Averages showing no time lags (such as WTAs 3-5 to 3-8) were the result of averaging between sites in which there was no overall preferential time lag. The time lag between ripples at different sites could vary from cycle to cycle, ranging mostly from 0 to 2 ms (Fig. 3). Sites that lead others may represent sites where ripples are preferentially generated.
To summarize, ripples may appear almost simultaneously in different cortical sites, but phase correlation (±2 ms) was usually achieved only between ripples from sites within the same gyrus. There was rarely a correlation on a cycle-to-cycle basis between ripples in different gyri, including contralateral foci.
Halothane strongly reduces ripples
Fast oscillations have been reported to be abolished by halothane,
a blocker of gap junctions, in vivo (Ylinen et al. 1995) and in vitro (Draguhn et al. 1998
) in the hippocampus,
and in vivo in the neocortex (Jones et al. 2000
). In our
experiments, halothane strongly reduced neocortical ripples within a
few minutes of its administration (data not shown). After the
termination of halothane administration, the ripples progressively
recovered in field activities.
Intracellular correlates of ripples
Intracellular and field recordings in the suprasylvian gyrus (the micropipette was <1.0 mm away from the macroelectrode) in anesthetized cats were used to reveal the relations between field potentials and neuronal activities.
Intracellular recordings (n = 238) identified four
classes of neurons: regular spiking (RS), fast spiking (FS),
intrinsically bursting (IB), and fast rhythmic bursting (FRB). The
criteria for the first three classes were the same as shown in previous in vitro and in vivo studies (Connors and Gutnick 1990;
McCormick et al. 1985
; Nuñez et al.
1993
; Thomson et al. 1996
). As for FRB neurons,
they discharged high-frequency (300-600 Hz) spike bursts, recurring at
fast rates (30-50 Hz), on depolarizing current pulses (Gray and
McCormick 1996
; Steriade et al. 1998b
). The
proportions of various neuronal classes were as follows: 58% RS, 8%
FS, 12% IB, and 22% FRB. These values are similar to those found in
our previous in vivo experiments on acutely prepared animals. The incidence of two cellular classes, investigated intracellularly, is
significantly different during natural wakefulness of chronically implanted animals, in which FS neurons reach 24%, whereas the proportion of IB neurons is <5% (Steriade et al.
2001
).
Intracellular recordings revealed a strong relation between ripples in filtered field potentials (80-200 Hz) and neuronal depolarization and firing. The neuron in Fig. 4 was an FRB cell, as identified by its firing pattern in response to depolarizing current pulses (bottom right). The neuron was more depolarized and fired more frequently when ripples were ampler in the filtered EEG (middle). This phenomenon was observed in a great majority of our recordings for all neuronal classes. It is demonstrated in a plot (Fig. 5; same neuron as in Fig. 4) showing the relation between the membrane potential (Vm) and the normalized amplitude of ripple oscillation. When a period was associated with high-amplitude of ripples, the mean Vm had a stronger tendency toward depolarized levels than when the ripple amplitude was closer to zero (Fig. 5, right). Even though gamma oscillations (generally 30-80 Hz) are usually associated with tonic activation of cortical networks, the tendency to depolarization during gamma oscillations was never as strong as with the faster (80-200 Hz) ripples (compare Fig. 5, right and left). The difference in mean neuronal membrane potential between 50-ms periods of strong (>6 standard deviation, SD) and weak (<1 SD) oscillations was significantly higher for ripples than gamma oscillations (13.2 ± 2.0 and 8.8 ± 1.2 mV respectively, n = 20, all neuronal types pooled, paired Student's t-test, P < 0.01).
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The relation between ripple amplitude and neuronal firing was
determined for all neuronal types recorded with KAc-filled pipettes, as
well as for RS cells recorded with KCl-filled pipettes. All neuronal
types displayed a progressive increase in firing probability around
ripples of increasing amplitude, in agreement with their increased
depolarization during ripples. Data for neurons of different cellular
types are presented in Table 1. Graphic examples of these calculations
are presented in Figs. 7 and 14. FRB and FS neurons fired at higher
frequency around strong ripples than IB and RS neurons. RS cells
recorded with KCl-filled pipettes fired at higher frequencies around
strong ripples than RS cells recorded with KAc-filled pipettes,
suggesting the presence of Cl-mediated
potentials during ripples (Table 1).
The strongest relation between neuronal firing and the amplitude of ripples occurred between neurons and close field potentials. This was revealed with dual intracellular recordings combined with recordings from an array of eight EEG electrodes along the suprasylvian gyrus (n = 4). Even though ripples were generally correlated among different sites, their relative amplitudes in different foci varied from cycle to cycle (Fig. 6, A and B: right center). The ripples of highest amplitude were selected from each cortical channel, and the numbers of action potentials fired in relation to those ripples were calculated for each neuron. This number was higher when the neuron and the EEG lead were close (Fig. 6, bottom right). Thus the relation between neuronal firing and high ripple amplitude is local, and it decreases with the distance between the neuron and the site of occurrence of ripples.
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The presence of inhibition during ripples, even though they are
associated with neuronal depolarization and firing, was revealed with
dual intracellular recordings from pairs of very close neurons (<0.5
mm), in which one recording pipette was filled with KAc (as control)
and the other one with KCl to reverse
Cl-mediated GABAA
inhibitory postsynaptic potentials (IPSPs, n = 11). One
typical case in which the two recorded cells were RS is shown in Fig.
7. Firing probability increased with
increasing ripple amplitude for both cells but more so for the cell
recorded with a KCl-filled pipette than for the control cell recorded
with a KAc-filled pipette (see also Table 1). The firing probability with strong ripples was also higher than the overall firing frequency for the neurons (represented by bars between 0 and 1 in Fig. 7, bottom right). Spike-triggered-averages (STAs) of filtered
EEG traces (between 80 and 200 Hz) revealed that action potentials in
the RS cell recorded with KAc occurred ~0.5 ms before the peak negativity of the field potential (Fig. 7, bottom left).
This was the case for all RS cells analyzed (mean time lag =
0.3 ± 0.5 ms, n = 5, Table 1). In contrast, the
relation between the firing of cell 2 (KCl-filled pipette)
and the ripple cycle was shifted by ~4 ms. In the latter case, firing
preferentially occurred around 3 ms after the peak negativity (Fig. 7,
bottom left). This was similar to other RS cells recorded
with Cl
-filled pipettes (mean time lag = 3.1 ± 0.5 ms, n = 5, Table 1). The difference in
timing of action potentials in relation to ripples for RS cells
recorded with and without KCl was statistically significant (unpaired
Student's t-test, P < 0.001). This
suggests that GABAA inhibition is involved in the
precise timing of firing during ripple oscillations.
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The possible involvement of inhibition in the phase-locking of neurons
during ripples was corroborated by the activity of FS neurons recorded
during the slow oscillation. Not only was the firing of FS neurons
increased in relation with ripples (Table 1), but there was also a
strong phase relation between the firing of FS cells and ripples (Table
1 and Fig. 8). FS neurons fired preferentially ~2.5 ms before the depth-negative peak of a ripple cycle (mean time lag = 2.4 ± 0.2 ms, n = 5). The timing of action potentials with ripples was statistically
different between FS cells and RS, FRB, and IB cells (unpaired
Student's t-test, P < 0.01).
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FRB neurons, which showed the strongest firing in relation to ripples,
also displayed a strong phase relation between firing and ripple
cycles. The area 7 FRB neuron presented in Fig.
9 fired preferentially ~1 ms before the
depth-negative peak of ripples in the same area, but the relation with
the other, more distant field potential recordings (areas 5 and 21)
were much weaker. This is consistent with the fact that the correlation
of ripples decreases with distance (see also Fig. 6). Similar phase
relations between FRB neurons and ripple cycles were found for all FRB
neurons analyzed (mean time lag = 0.7 ± 0.4 ms,
n = 5).
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Ripple generation in isolated cortical slabs
We verified if cortical networks by themselves could generate
ripples by recording from isolated cortical slabs, which are small
islands of cortex in which all connections with the rest of the brain
have been severed. Such slabs of neocortex display long periods of
silence interrupted by brief epochs of activity (Timofeev et al.
2000). Ripples were present during these periods of activity at
the field potential level. Neurons displayed phase relations between
their action potentials and ripples in field potentials that were
similar to those seen in intact brain experiments (n = 5; Fig. 10).
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Ripples during natural states of vigilance
Recordings in chronically implanted cats revealed that ripples are also present during all natural states of vigilance but mainly during the EEG depth-negative phase of the slow sleep oscillation, whereas they are reduced almost to noise-level during the EEG depth-positive phase (Fig. 11). During the two EEG-activated states of REM sleep and waking, ripples appeared more continuously and at values intermediate between the two (opposite) levels of the slow oscillation in slow-wave sleep. Quantification of these observations is illustrated in Fig. 12. Similar results were obtained in different cats (n = 4) and for different days in the same cat (n = 5) for the suprasylvian gyrus. Three of five recordings analyzed from other areas also showed the same relation, while in two others ripples reached higher values during REM. The comparison between the state of natural slow-wave sleep and ketamine-xylazine anesthesia administered in the same animal (n = 2) revealed that ripples during the EEG depth-negative phase of the slow oscillation in ketamine-xylazine anesthesia have a higher amplitude than those during the same component of the slow oscillation in slow-wave sleep (Fig. 13). These results are particularly interesting in light of the fact that, as a general observation from our experiments, seizures were obtained much more often in animals under ketamine-xylazine anesthesia than during natural slow-wave sleep.
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Finally, we studied the behavior of neocortical neurons in relation to ripples in chronic animals. As the coherence between ripples and neuronal activities decreases with distance (see Fig. 6), a limited number of intracellular recordings were close enough to the site of field potential recording to study the relations between neuronal firing and ripples. The firing probability of one such neuron, a RS cell, was calculated as a function of ripple amplitude (Fig. 14, bottom plots). Again, ripples of the highest amplitude were found during slow-wave sleep. The firing probability of the neuron increased in all three states with increasing amplitude of ripples.
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DISCUSSION |
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There are four major results from our experiments. 1) Ripples are present in neocortex during the depolarizing phase of the slow oscillation under ketamine-xylazine anesthesia as well as during the same phase in natural slow-wave sleep, and they are also present, but generally less pronounced, during the activated states of waking and REM sleep. 2) Ripples are linked with neuronal depolarization and firing in all electrophysiologically characterized cell classes. 3) FRB and FS neurons fire at highest frequencies during ripples. And 4) inhibition plays an important role in the patterning of this fast oscillation.
Neocortical ripples in acute and chronic experimental conditions
The intracellular recordings in the present study were made from the suprasylvian gyrus, but field potentials were obtained from other areas as well, such as the ectosylvian and marginal gyri in which ripples were also seen (Fig. 2). In chronic experiments, recordings were also made from cortical sensorimotor and association areas 3, 4, 5, 7, 17, 18, and 21, and in all these sites, ripples were present.
In CA1 hippocampal area, the frequency of ripples is lower (~100 Hz)
under ketamine anesthesia than in the awake rat (~200 Hz)
(Ylinen et al. 1995) and very fast oscillations in
barrel cortex (
200 Hz) are also attenuated or extinguished by many
anesthetics (Jones and Barth 1999
). In our experiments,
the frequencies of ripples were similar under ketamine-xylazine
anesthesia and in behaving animals. Although the general features of
the slow oscillation recorded under ketamine-xylazine anesthesia are
basically similar to those during natural sleep in animals
(Steriade et al. 1996
) and humans (Achermann and
Borbély 1997
; Amzica and Steriade 1997
), the amplitudes of ripples during the slow oscillation were higher and
the rhythmical occurrence of their sequences much more evident under
ketamine-xylazine anesthesia than during natural sleep (Fig. 13). The
stereotyped slow oscillation under ketamine-xylazine anesthesia indicates a high degree of synchronization of neocortical neurons; this
may explain the stronger presence of ripples under ketamine-xylazine anesthesia than during slow-wave sleep.
Possible mechanisms of ripples generation
In the hippocampus, it was reported that the phase relation
between intracellular recordings of pyramidal cells and field ripples
was modified when recordings were made with KCl-filled pipettes
(Ylinen et al. 1995). The generation of fast rhythms has
also been ascribed to axo-axonal gap junctions between principal cells
in hippocampal slices (Draguhn et al. 1998
; Traub
et al. 1999
). Sensory-evoked very fast oscillations (400-600
Hz) in the rat barrel cortex are reported to be due to synchronous
firing of FS neurons (Jones et al. 2000
).
As ripples can be generated within small isolated slabs of cortex (Fig.
10), neocortical networks are sufficient to produce them. FRB neurons
were the neuronal type showing the strongest firing associated with
ripples. In addition to their fast spike-bursts, FRB neurons can
discharge in tonic firing without adaptation, like FS neurons
(Steriade et al. 1998b). This makes them better suited
to follow the high frequency of ripples on a cycle-to-cycle basis
compared with RS cells. We have also shown that in intracellular recordings of FRB neurons, compound EPSPs had a structure consistent with their being produced by the signature bursts from another FRB
neuron (Steriade et al. 1998b
). A network of
interconnected FRB neurons could mutually reinforce each other's
excitation and so help sustain the excitation level concurrent with
field ripples.
Ripples involve an increased amount of both excitatory and inhibitory
activity. The overall balance of this increase is probably tilted
toward excitation since most neurons depolarize when they occur. As the
firing of all neuronal types, including conventional FS (presumably
local GABAergic) neurons, displays a strong phase relation with ripple
cycles, ripples involve temporally structured excitation and
inhibition. We suppose that all neuronal targets of FS neurons receive
fairly synchronous GABAA-mediated IPSPs ~2.5 ms
before the depth-negative peak of ripples. In single-axon IPSPs
studies, the rise time of IPSPs elicited in pyramidal neurons by FS
interneurons is ~2.7 ms (Tamás et al. 1997;
Thomson et al. 1996
). As these IPSPs should reach their
peak amplitude just after the depth-negative peak of the ripple cycle,
the positive phase of the cycle is probably produced by the effect of
these synchronous IPSPs. This would explain the phase-shift in the
firing of cells when they are recorded with KCl-filled pipettes
(Fig. 7) because in these conditions,
GABAA-mediated IPSPs become reversed. Consistent
with this mechanism, we have recently proposed that short-lasting IPSPs
can precisely control the timing of action potentials in the neocortex
(Timofeev et al. 2001
). Moreover, single inhibitory
interneurons contact many pyramidal cells in their vicinity
(Kisvárday et al. 1993
). As all FS neurons that we
recorded fired with about the same phase preference to ripples, this
can rapidly lead to a sizable inhibitory effect occurring fairly
synchronously in many neighboring pyramidal-shaped neurons. As has been
proposed for other fast oscillations (Jones et al. 2000
;
Ylinen et al. 1995
), the presence of these fast
oscillations in field recordings is probably due to the currents
generated in pyramidal cells by synchronous IPSPs resulting from the
activity of FS neurons. However, although the phase-locking between
action potentials is not perfect for every spike and every neuron,
ripples still reflect periods in which firing is increased and
phase-locked in pyramidal-shaped neurons (RS, IB, and some FRB). Field
reflection of synchronous action potential has been proposed in other
phenomena; for example, the first component of thalamically
evoked neocortical field potentials is the reflection of
synchronous activity of thalamocortical fibers (Morin and
Steriade 1981
) and the fast ripples (200-500 Hz) around
epileptogenic lesions in hippocampus, entorhinal cortex, and dentate
gyrus have been proposed to reflect the pathological synchronous
bursting of a group of pyramidal neurons (Bragin et al.
1999b
). We suggest that action potential synchrony may be
strong enough during ripples for action potentials to be reflected in
field ripples.
There may also be more than chemical synapses involved in ripples. A
number of substances that block gap junctions strongly reduce the
occurrence of ripples (Draguhn et al. 1998; Jones
et al. 2000
; Ylinen et al. 1995
). As well, in
our experiments, halothane strongly reduced the occurrence of ripples
within a few minutes after administration. This suggests that gap
junctions play a role in ripple generation. It was recently shown that
electrical synapses can lead to a tight synchronization (<2 ms) of
action potential firing in electrically coupled neocortical FS cells when they are already close to firing threshold (Galarreta and Hestrin 1999
; Gibson et al. 1999
). However, this
coupling is also present between pyramidal (Traub et al.
2001
) and glial (Dermiezel and Spray 1993
) cells.
Cortical synchronizing mechanisms
Our results demonstrate that ripples can be correlated with <2-ms
time lag from up to 9 mm along the same gyrus (Figs. 1-3). The
distribution of time lags reached higher values as the distance between
sites increased. These delays are consistent with a transmission of
ripples through intra-gyral excitatory connections. Based on these
results, we propose that ripples arise locally in neocortical networks
through chemical (and possibly electrical) synaptic interactions between different neuronal types under conditions of strong activity. In particular, local inhibitory interneurons play an important role in
their patterning. Their correlation between different sites for
distances of 9 mm may be ascribed to excitatory connections. This
excitatory input could either excite the efferent site to an activity
level at which ripples may be generated or entrain ripples that were
already ongoing. Since FRB neurons are the excitatory neurons showing
the strongest firing in relation to ripples, we propose that they play
a major role in sustaining the local excitation necessary for their generation.
Possible functional role of ripples
Ripples indicate the presence of a strong and, at the same time,
highly structured activity in the neocortex. Precise temporal patterning of neuronal firing on a scale of a few milliseconds might
have an important functional impact. It is becoming clearer that the
precise timing of firing is important in plasticity processes between
neurons (Paulsen and Sejnowski 2000). It has also been postulated that hippocampal ripples constitute a replay at a faster time scale of firing sequences coding for important events so that they
can be encoded in a more permanent manner (Nádasdy et al.
1999
). The possible involvement of ripples in plasticity processes is consistent with a role of slow-wave sleep in the consolidation of memory traces (Steriade 2001
) as
ripples reach their strongest amplitudes during slow-wave sleep.
The fact that ripples of higher amplitude are generally found
during slow-wave sleep instead of the activated states of waking and
REM sleep may seem paradoxical since the latter behavioral states are
linked to strong neuronal activity as are ripples. However, our recent
data from intracellular recordings during the natural states of
vigilance indicate that the Vm during
the depolarizing phase of the slow oscillation in slow-wave sleep has a
similar value to that reached during waking and REM sleep and that some
neurons even display periods of stronger activity during slow-wave
sleep than during the brain-activated states (Steriade et al.
2001). These periods of higher activities and ripples in a
state during which the neocortex is disconnected from the outside world
might be relevant for synaptic reorganizations involved in memory processes.
Concluding remarks
We propose that neocortical ripples arise through chemical (and
possibly electrical) interactions, that they are dependent on neuronal
depolarization, and that inhibition plays a role in their patterning.
Their correlation for distances of 9 mm may be ascribed to excitatory
connections. Their preferential presence during slow-wave sleep, a
state during which the brain is disconnected from the outside world,
and their link to strong neuronal activity make them good candidates to
be involved in plasticity processes.
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ACKNOWLEDGMENTS |
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We thank P. Giguère and D. Drolet for technical assistance. I. Timofeev is a Scholar of the Fonds de la Recherche en Santé du Québec.
This work was supported by grants from the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Human Frontier Science Program, and National Institutes of Health. F. Grenier is a PhD student, partially supported by the Savoy Foundation.
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FOOTNOTES |
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Address for reprint requests: M. Steriade (E-mail: mircea.steriade{at}phs.ulaval.ca).
Received 7 February 2001; accepted in final form 11 June 2001.
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REFERENCES |
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