Focal Synchronization of Ripples (80-200 Hz) in Neocortex and Their Neuronal Correlates

François Grenier, Igor Timofeev, and Mircea Steriade

Laboratoire de Neurophysiologie, Faculté de Médecine, Université Laval, Quebec G1K 7P4, Canada


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 MOmega ). 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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Fig. 1. Synchronization of ripples (80-200 Hz) during the slow oscillation. Ketamine-xylazine anesthesia. Field potential recordings from the depth (~1 mm) of areas 5 and 7. Seven electrodes, separated by 1.5 mm, were aligned along the antero-posterior axis (see brain figurine). Top left: the slow sleep oscillation (0.8-0.9 Hz), synchronous in the 7 different leads. One cycle of the slow oscillation is expanded at bottom left; ripples are superimposed on the late part of the depth-negativity. The trace of the same epoch was filtered between 80 and 200 Hz to illustrate only ripples (top right). Cross-correlations between point 4 and other recorded foci, as well as autocorrelation (4-4), are displayed at bottom right. Note that activity from various recorded foci is correlated, with time lags varying from ~0.7 to 1.2 ms down to virtually 0 time difference (4-1).



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Fig. 2. Ripples are more strongly correlated among sites along the same gyrus than among different gyri. Ketamine-xylazine anesthesia. Depth field potential recordings with an array of 8 electrodes, separated by 1.5 mm, and aligned along the antero-posterior axis over the suprasylvian gyrus or the medio-lateral axis, covering the medial, suprasylvian, and ectosylvian gyrus (see brain figurine). Left middle: an epoch of the slow oscillation. The traces of the same epoch were filtered between 80 and 200 Hz (bottom left). WTAs from sites 2, 4, and 6 were calculated for all leads (see METHODS) and for both electrode placement and are shown at right. Note that correlation is stronger for recordings from the same gyrus.

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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Fig. 3. Variability in the correlation between individual ripple cycles from different sites. Ketamine-xylazine anesthesia. Same experiment as last figure. Top left: a short epoch of field recordings filtered between 80 and 200 Hz. Bottom left: a WTA from site 3 was computed (see METHODS). Each trigger-time used to compute the WTAs was also used to generate plots of time lag vs. relative amplitude at right. Each point in those plots represent the time lag and relative amplitude between one ripple cycle depth-negative peak from site 3 and another one from one of the other sites (see METHODS). Middle left: a graphic illustration of how these were computed. The 2 cycles compared in this illustration led to one point in the plot 3-4 at right. In the plot 3-3, all points fall at coordinates corresponding to 0 time lag and an amplitude ratio of 1 since each ripple cycle is compared with itself. For each relative amplitude vs. time lag plot, the mean time lag and the mean relative amplitude were computed and are displayed in middle center plots. Note that depth-negative peak of ripples from site 3 had a tendency to occur before those from sites 2 and 4. This is made clear by the fact that more points are in the positive part of the plots 3-2 and 3-4 and by the mean time lags in the middle center plot. The relative amplitude of ripple cycles decreased with distance, which is shown in the middle center mean-relative-amplitude-of-ripple plot.

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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Table 1. Relation between field ripples and neuronal activity for different neuronal classes under ketamine-xylazine anesthesia

Values in the text and table are given as means ± SE.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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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Fig. 4. Progressively increased amplitude of ripples with increased depolarization of cortical neurons during the slow oscillation under ketamine-xylazine anesthesia. Intracellular and depth-EEG recordings from area 7. Top panel illustrates an epoch with the slow oscillation (0.6-0.7 Hz). Fast-rhythmic-bursting (FRB) neuron; see electrophysiological identification of this neuronal type at bottom right (see also text). Below the EEG trace, a filtered trace (80-200 Hz) is also shown. Part indicated by horizontal bar and arrow is expanded below and the cycle at left is further expanded at the bottom. Note, in the bottom trace, the progressively increased amplitude of ripples in EEG field potentials, in parallel with neuronal depolarization and increased frequency of action potentials.



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Fig. 5. Relations between membrane potential (Vm) and firing rate of FRB neurons in 2 types of fast oscillations, gamma (30-80 Hz) and ripples (80-200 Hz). Ketamine-xylazine anesthesia. The normalized amplitude of oscillation (abscissa, conventional units from 0 to 1) was calculated from filtered traces of the EEG, between 30-80 and 80-200 Hz. The Vm of the FRB neuron from Fig. 4 was plotted as a function of the oscillation amplitude for 50-ms time windows (see METHODS). The curve which was fitted to the points was an exponential of the form: y = A + B exp(-Cx). The coefficients ± SD for the 30- to 80- and 80- to 200-Hz plots were, respectively, A = -63 ± 1 and -64 ± 1; B = -26 ± 1 in both cases; and C = 5.6 ± 0.7 and 31 ± 3. The stronger value of the C coefficient in the case of ripples indicates that the increase in neuronal depolarization with increasing oscillation amplitude is steeper than for 30- to 80-Hz activity.

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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Fig. 6. Neuronal firing is related to local ripple amplitude. Dual intracellular recordings and depth EEG recordings from the suprasylvian gyrus. The 8 electrodes were aligned along the anteroposterior axis and separated by 1.5 mm. Cell 1 was recorded <1 mm from the 2nd-most anterior EEG electrode, while cell 2 had the same relation with the 5th most anterior EEG electrode. Two epochs are expanded at right with the filtered EEGs (between 80 and 200 Hz). Neuronal firing was related to ripple amplitude in the closest EEG recordings. The number of action potentials occurring within ±5 ms of a ripple of given amplitude was computed for the 2 cells in relation to each of the EEG electrodes. The closer the cell was to the site of EEG recording, the more chance a ripple of a given level had of being linked to an action potential in the cell. Ripples were strongly phase-locked between the different sites, but their amplitudes might vary between different leads.

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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Fig. 7. Chloride-mediated inhibition plays a role in ripples. Dual intracellular recordings of RS neurons from area 7 (1 pipette filled with KAc, the other filled with KCl), together with depth-EEG from the same suprasylvian area. Ketamine-xylazine anesthesia. Neurons were close to each other (<0.5 mm) and to the EEG electrode (<1 mm). Top: an epoch with depth-EEG recording and, below, filtered EEG trace (80-200 Hz, multiplied by 10), together with simultaneous intracellular recordings from two neurons. Bottom left: spike-triggered average of filtered EEG activities (top, neuron recorded with KAc-filled pipette; bottom, KCl-filled pipette). The action potentials (time 0) were selected when they occurred within ±5 ms of a ripple cycle whose negative peak had an amplitude of at least 4 times the standard deviation of the filtered trace. Note that the phase relation between the action potentials and the ripples in the field potential was shifted when the neuron was recorded with a KCl-filled pipette. Bottom right: the firing probability of neurons around ripple cycles of increasing amplitudes. The increased firing rate of the RS neuron recorded with the KCl-filled pipette is much more dramatic than that of the RS neuron recorded with KAc-filled pipette. A firing probability of 150% means that the neuron fired on average 1.5 action potentials per ripple cycle.

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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Fig. 8. FS neurons fire in relation with ripples. Intracellular and depth-field potential recordings from area 7. Top: an epoch with the slow oscillation. The neuron was a FS neuron. Below the field trace, a filtered trace (80-200 Hz) is also shown. Part indicated by horizontal bar and arrow is expanded below. A peri-event histogram of the neuron firing in relation to ripple depth-negative peak is shown at right, revealing that the neuron fired preferentially ~2.5 ms before the depth-negative peak of ripples.

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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Fig. 9. FRB neuron firing is correlated with the depth-negative peak of ripples. Intracellular recording from an area 7 FRB neuron, together with depth-EEG field potentials from areas 5, 21, and 7; below each EEG original trace, a filtered trace between 80 and 200 Hz (with amplitude multiplied by 10) is depicted. The top panel illustrates an epoch of the slow oscillation. The part marked by horizontal bar and arrow is expanded at bottom left (only filtered EEG traces are shown). Bottom right: spike-triggered-averages (STA) resulting from neuronal spikes were computed for the 3 filtered EEG traces. The action potentials clearly occurred close to the peak negativity of the depth-EEG recorded from the same cortical region (area 7). In this case, ripples had a frequency of ~110 Hz. Weaker correlations or their absence was observed with more distant cortical leads, even those from the same gyrus (areas 21 and 5).

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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Fig. 10. Ripples can be generated in isolated neocortical slabs. Intracellular and depth-EEG recordings from a slab in the suprasylvian gyrus. Top: an epoch with a spontaneous burst of activity in the field and the neuron. The filtered EEG trace is also shown. Part indicated by arrow is expanded at bottom left. An STA and peri-event histogram (bottom right) reveal that neuronal firing was correlated with the depth-negative peak of ripples.

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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Fig. 11. Ripples are present during the 3 natural states of vigilance. Chronically implanted cat. The 4 traces represent field potential recordings from cortical depth in area 7, filtered EEG trace (80-200 Hz, amplified 5 times), electromyograph (EMG), and electrooculogram (EOG), during slow-wave sleep, REM sleep and waking (recorded in this order). Parts indicated by horizontal bar are expanded (amplified ×2) below (up-arrow ).



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Fig. 12. Ripples are ampler during the depth-negative component of the slow-wave sleep (SWS) oscillation than during waking, REM sleep and the depth-positive phase of the SWS oscillation. Chronically implanted cat. Periods of 2 min from the recordings depicted in the previous Fig. 13 were used. The mean amplitude of ripples for 50-ms windows (from the absolute value of traces) was computed in each state of vigilance. For the depth-positive component of the slow oscillation, all depth-positive peaks above a certain threshold were used as centers for the windows. For the depth-negativity of the slow oscillation, all depth-negative peaks below a certain threshold were used, but the windows were centered between 50 and 200 ms after the peak because ripples do not appear at a fixed time during this component. For waking and REM sleep, where the distribution of ripples is much more uniform than in SWS, random points were selected as centers of windows. Fifty windows in each state (50 each also in the depth-positive and -negative components of the SWS oscillation) were used. The distribution of values is plotted in the left panel. Note much ampler ripples during the depth-negative phase of the slow oscillation in SWS than in waking and REM sleep, while the depth-positive phase of the slow oscillation in SWS displayed the least numerous and ample ripples. Right: the distribution of ripples across behavioral states of vigilance. All cycles of ripples that showed a peak between a given range of values were computed for a 2-min period of each state. For example, relative amplitudes of 2 (see abscissa) refers to ripple cycles which had a peak value between 2 and 3 times the standard deviation of the trace. For each relative amplitude level, the total number of ripples for all states were calculated and the number for each state divided by that number. These relative values are plotted in the right panel for each level of ripples. The amplest ripples, reaching more than 7 times the standard deviation of the signal, were only obtained in SWS. Waking was the state in which the amplest ripples reached the smallest values.



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Fig. 13. Ripples are more intense under ketamine-xylazine anesthesia than during natural slow-wave sleep (SWS). Field potential recording from area 5. Chronically implanted cat; a period of natural SWS was recorded and, thereafter, ketamine-xylazine anesthesia was administered. There was a 30-min period between the epoch analyzed during natural SWS and that under ketamine-xylazine to allow anesthesia to develop fully. Top traces: original depth-EEG recordings and filtered traces (80-200 Hz, amplitudes increased 5 times) are shown underneath. A cycle of slow oscillation for each state is marked by horizontal bar and expanded below (down-arrow ); the field trace is multiplied by 1.5. Bottom left: a histogram of the filtered trace values (sampling rate, 1 ms) for each state, computed for a 200-s period. The distribution curve for ketamine-xylazine anesthesia is wider, indicating that ampler ripples are obtained under ketamine-xylazine anesthesia than during SWS. In the bottom right panel, this fact is made more obvious by subtracting the SWS distribution from the ketamine-xylazine one (values are displayed in 0.01 windows; only the positive parts of the distribution were used since they are symmetrical).

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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Fig. 14. Field potential and intracellular recordings from area 7 during the 3 natural states of vigilance (slow-wave sleep, REM sleep, and waking, recorded in this order). Chronically implanted cat. Short periods from each state are expanded in the middle panels (arrows) showing action potentials and filtered EEG trace (80-200 Hz). The probability that the neuron fired increased with the amplitude of ripples. This was computed (see METHODS) for 30-s periods in each state and displayed in bottom panels. The 1st bar in each graph is the global firing probability (total number of action potentials during the 30-s period divided by 30 s).


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    ACKNOWLEDGMENTS

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.


    FOOTNOTES

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.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

0022-3077/01 $5.00 Copyright © 2001 The American Physiological Society