Department of Neuroscience and Anatomy, Milton S. Hershey Medical Center, Penn State University College of Medicine, Hershey, Pennsylvania 17033-2255
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ABSTRACT |
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Roy, Stephane and
Kevin D. Alloway.
Stimulus-induced increases in the synchronization of local neural
networks in the somatosensory cortex: a comparison of stationary and
moving stimuli. Spontaneous and stimulus-induced responses were
recorded from neighboring groups of neurons by an array of electrodes
in the primary (SI) somatosensory cortex of intact, halothane-anesthetized cats. Cross-correlation analysis was used to
characterize the coordination of spontaneous activity and the responses
to peripheral stimulation with moving or stationary air jets. Although
synchronization was detected in only 10% (88 of 880) of the pairs of
single neurons that were recorded, cross-correlation analysis of
multiunit responses revealed significant levels of synchronization in
64% of the 123 recorded electrode pairs. Compared with spontaneous
activity, both stationary and moving air jets caused substantial
increases in the rate, proportion, and temporal precision of
synchronized activity in local regions of SI cortex. Among populations
of neurons that were synchronized by both types of air-jet stimulation,
the mean rate of synchronized activity was significantly higher during
moving air-jet stimulation than during stationary air-jet stimulation.
Moving air jets also produced significantly higher correlation
coefficients than stationary air jets in the raw cross-correlograms
(CCGs) but not in the shift-corrected CCGs. The incidence and rate of
stimulus-induced synchronization varied with the distance separating
the recording sites. For sites separated by 300 µm, 80% of the
multiunit responses displayed significant levels of synchronization
during both types of air-jet stimulation. For sites separated by
500
µm, only 37% of the multiunit responses were synchronized by
discrete stimulation with a single air jet. Measurements of the
multiunit CCG peak half-widths showed that the correlated activity
produced by moving air jets had slightly less temporal variability than
that produced by stationary air jets. These results indicate that
moving stimuli produce greater levels of synchronization than
stationary stimuli among local groups of SI neurons and suggest that
neuronal synchronization may supplement the changes in firing rate
which code intensity and other attributes of a cutaneous stimulus.
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INTRODUCTION |
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Recent work in the visual system suggests that
perceptual objects are represented by synchronous activity among
populations of neurons that respond to the individual components of the
object (for reviews, see Singer and Gray 1995;
Singer et al. 1997
). Cross-correlation analysis of
neuronal activity in striate cortex, for example, has shown that
neurons representing adjacent parts of the visual field become
synchronized if they have similar orientation preferences and are
activated by a single bar of light that stretches across their
receptive fields (Gray et al. 1989
). This result has
prompted the hypothesis that cortical synchronization represents a
dynamic mechanism for increasing the salience of activity among those cortical neurons that respond to different segments of the same linear
stimulus. According to this view, synchronization in striate cortex is
mediated by intracortical connections that provide a substrate for
linking separate neural populations into functional assemblies for the
perception of contours and other stimulus features that have spatial
continuity (Singer and Gray 1995
; Ts'o et al. 1986
).
If neuronal synchronization is a universal principle of cortical
physiology that underlies aspects of perception in all sensory modalities, then stimulus-induced synchronization should occur in other
sensory regions, including the somatosensory cortex. In support of this
hypothesis, neurons in layer III of somatosensory cortex have extensive
intracortical projections that allow them to communicate with
neighboring neurons that have similar receptive fields (Bernardo
et al. 1990; Burton and Fabri 1995
; Jones
et al. 1978
; Lund et al. 1993
; Schwark
and Jones 1989
). Such connections allow SI neurons to receive
sensory information from outside their receptive field, but these
subthreshold inputs are not apparent unless the local inhibitory
circuits are antagonized (Alloway and Burton 1991
;
Alloway et al. 1989
; Dykes et al. 1984
;
Kyriazi et al. 1996
). Although intracortical connections
probably are involved in reorganizing SI cortex after digit amputation,
nerve transection, or other forms of sensory deprivation
(Diamond et al. 1994
; Fox 1994
;
Merzenich et al. 1984
; Pons et al. 1991
), their functional role during normal somatosensory processing remains unclear.
One possible function for intracortical connections within SI cortex is to synchronize the activity of adjacent populations of neurons during certain stimulus conditions. It is conceivable, for example, that intracortical connections might prime neighboring cortical populations to respond more effectively to a cutaneous stimulus that moves across the skin. To determine whether neuronal synchronization might have a role in coding somatosensory information, we compared the amount of synchronization present in spontaneous activity with that produced by cutaneous stimulation. Furthermore we also tested the possibility that a moving stimulus enhances synchronization in SI cortex more than a stationary stimulus.
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METHODS |
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Four adult cats were used in this study and were treated
according to National Institutes of Health guidelines for the use and
care of laboratory animals. Most experimental procedures were described
previously and are only briefly reported here (Johnson and
Alloway 1994).
Sterile operating techniques were used to expose SI cortex and to implant a stainless steel recording chamber onto the surrounding cranium. During this operation, a stainless steel bolt was attached to the occipital ridge to immobilize the animal's head during subsequent recording experiments. After implantation of the recording chamber, SI activity was recorded from each cat twice per week for 4-6 wk. During each recording session, the animal was intubated through the oral cavity and ventilated with a 2:1 gaseous mixture of nitrous oxide and oxygen containing 0.5-1.0% halothane. Heart rate and end-tidal CO2 were monitored continuously, and body temperature was maintained at 37°C by a thermostatically controlled heating pad.
Cortical electrophysiology
During each recording session, an array of 3-6 tungsten
electrodes (2-5 M; Frederick Haer, New Brunswick, ME) was used to record neuronal discharges in SI cortex. In virtually all experiments, the electrode arrays were configured to sample neurons separated by no
more than 600 µm to ensure that all of the recorded neurons had
overlapping receptive fields (Dykes and Gabor 1981
).
Only three or four electrodes, arranged in a linear configuration
(1 × 3 or 1 × 4; 300-µm separation), were used in the
initial experiments. In later experiments, a matrix of six electrodes
(2 × 3; 250-µm separation) was used to record a larger number
of neuron pairs simultaneously. The electrode array entered the forearm
representation of SI cortex located in the rostromedial bank of the
coronal sulcus (Felleman et al. 1983
). Electrodes
penetrated the cortex at a 25° angle to the parasagittal plane and
were advanced by a hydraulic microdrive until single neurons could be
isolated on at least two or more electrodes. Recordings were made only
from layers III or IV because the neurons in those layers are the most
responsive to cutaneous stimulation (Johnson and Alloway
1996
). Extracellular neuronal waveforms were displayed on an
oscilloscope and converted into digital signals for off-line data
analysis (DataWave Technologies, Broomfield, CO).
Cutaneous stimulation
Once neurons were isolated on multiple electrodes, their receptive fields (RFs) were mapped by manually stroking the hairy skin while listening to their neuronal discharges over an acoustic speaker. Most neurons recorded in this study were sensitive to hair movements and could be activated by jets of air that stimulated their RFs.
Computer-controlled air jets were presented in blocks of 100 or 200 trials. Each trial was subdivided into three periods: a prestimulus period for recording spontaneous activity, a stimulation period that contained a series of stationary and/or moving air jets, and a poststimulus period. Neuronal activity was recorded during all three periods but was not recorded during intertrial intervals, which lasted 2 s. Prestimulus and poststimulus periods lasted 3 and 2 s, respectively. The duration of the stimulation period ranged from 2 to 6 s and depended on the number of air jets that were delivered. In the initial experiments, the stimulus period contained only stationary air jets or a moving air jet; in later experiments both types of air jets were presented within each trial.
Stationary air jets were delivered by three or four hollow tubes (1 mm ID) that were aligned in a micromanipulator. The tubes were spaced at equal intervals, ranging from 5 to 20 mm, and were oriented orthogonal to the hairy skin surface. Each tube was connected to a four-channel manifold in which each channel's air flow was controlled by an electronic valve (Clippard ET-2 M). The electronic valve for each channel was controlled by a digital timer that was triggered by the data acquisition system (DataWave Technologies). Air pressure (20 psi) to the manifold was regulated by a needle valve in series with a pressure gauge.
Previous work has shown that moving air jets activate discrete regions
of the hairy skin without producing the lateral distortions caused by
dragging a probe across the skin (Ray et al. 1985). Therefore we modified a Grass polygraph pen module to deliver moving
air jets in a curvilinear trajectory. The ink pen of the polygraph
module was replaced by a tube identical to those used for the
stationary air jets except that the end of the tube was curved to
direct a jet of air orthogonal to the sweeping motion of the tube. Air
flow through the tube was controlled by an electronic valve as
described for the stationary air jets. A waveform generator in series
with a DC-coupled amplifier was used to produce constant velocity
sawtooth movements of the air-jet tube. The waveform generator cycled
at 0.5 or 1.0 Hz so that a moving jet of air traversed forward and
backward across the skin for 1 or 2 s. The amplitude of the
waveform generator was adjusted to produce a trajectory of movement
that corresponded to the length of the RFs combined from all recording
sites. Given the variability in RF sizes, the stimulus velocities for
moving air jets ranged between 4 and 14 cm/s and averaged nearly 10 cm/s. For each experiment, the moving air jet was positioned to pass
over the same sites stimulated by the stationary air jets.
In the initial experiments, stationary and moving air jets were presented in separate blocks of trials. In later experiments, a single block of trials was administered in which moving and stationary air jets were presented sequentially during each trial.
Analysis of neuronal responses
Neuronal discharges were sorted on the basis of several parameters including spike width, spike amplitude, and time of maximum spike peak. The time of each neuronal discharge was recorded to within 0.1 ms, and time stamps from each group of sorted waveforms were used to generate summed peristimulus histograms (PSTHs) and cross-correlograms (CCGs). Binwidths for the PSTHs and CCGs were 25 and 0.5 ms, respectively.
Cross-correlation analysis was used to characterize neuronal activity
at one electrode as a function of neuronal activity recorded at a
second electrode (Perkel et al. 1967). In a
stimulus-based paradigm, an increase in correlated neuronal activity
can be produced by stimulus coordination or may occur by chance due to
the increased rate of neuronal discharges during peripheral
stimulation. To estimate the magnitude of these effects, a linear shift
predictor was subtracted from the raw CCG to produce a shift-corrected
CCG (Alloway et al. 1993
; Gerstein and Perkel
1972
; Johnson and Alloway 1996
). For our
analyses, the shift predictor was the mean of three CCGs calculated
from pairing the first 97 of 100 reference responses (or 197 of 200 responses when 200 trials were administered) with subsequent target
responses shifted by one, two, or three stimulus trials. Using only 97 trial responses in a linear shift, rather than all 100 trial responses
in a circular shift, avoided the pairing of responses having large time
separations. Because stimulus-induced responses are not identical from
one trial to the next, subtraction of the shift predictor may not
remove all instances of stimulus coordination. Nonetheless, the shift
predictor can detect many instances of stimulus coordination,
especially at stimulus onset when neurons are most responsive and their
response latencies are similar across trials. The shift predictor also
was used because it represents a convenient tool for determining if
correlated events are statistically significant. Because the shift
predictor was based on independent spike trains (recorded in response
to separate stimuli), the counts in each bin of the shift predictor were assumed to reflect a Poisson process and were used to calculate a
Z score to evaluate the significance of values obtained in
the shift-corrected CCG. The square root of each value in the shift predictor was multiplied by 1.96 to yield a 95% confidence limit (Aertsen et al. 1989
). Peaks within the shift-corrected
CCG that exceeded the 95% confidence limits on two or more contiguous
bins were considered statistically significant (Aertsen et al.
1989
; Gochin et al. 1989
).
CORRELATION COEFFICIENT.
The correlation coefficient, (
0), was calculated to
indicate the proportion of discharges in the spike trains that were correlated (Abeles 1982
). The formula for calculating
the cross-correlation coefficient was adapted from Eggermont
(1992)
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SYNCHRONIZATION RATE.
Because the correlation coefficient is independent of firing rate and
does not indicate how often neuron pairs discharge simultaneously, we
also calculated the rate of synchronized discharges from the raw and
shift-corrected CCGs. For this parameter, the number of coincident
events in the highest 2-ms peak was divided by the total recording
time. Thus synchronization rate expresses the number of coincident
events occurring per second. An interval of 2 ms was chosen for
measuring synchronization rate because this duration encompasses most
sharply synchronized events in local regions of SI cortex
(Swadlow et al. 1998).
PEAK HALF-WIDTH. We measured the peak half-widths of the shift-corrected CCGs to determine the amount of temporal variability among correlated discharges. Peak half-width was obtained by measuring the width of the CCG peak at half the height of its tallest bin. For spike trains having low rates of synchronized activity, peak half-width was difficult to measure because the bin heights were highly variable. For this reason, we ignored single 0.5-ms bins that dipped into a broader CCG peak. We also measured peak half-widths from both smoothed and unsmoothed CCGs. Smoothed CCGs were generated by averaging each bin in the unsmoothed CCG with its two adjacent bins. Although smoothing removes much of the variability in CCG peak, it may cause an increase in the width of CCG peaks, which consist of only one or two tall bins. Thus smoothing was useful for measuring peak half-widths in CCGs based on low rates of spontaneous activity but was less accurate for measuring highly synchronized responses evoked by peripheral stimulation. Therefore we used smoothed CCGs to compare spontaneous and stimulus-induced synchronization but used unsmoothed CCGs to compare the degree of synchronization produced by stationary and moving air jets.
Autocorrelation analysis
Autocorrelograms (ACGs) were constructed from spontaneous and
stimulus-induced activity to detect oscillations among neurons showing
coordinated responses in the shift-corrected CCGs. For this analysis,
ACG peaks exceeding the expectancy value by at least 2 SDs (95%
confidence limits) were considered statistically significant, and the
ACG had to contain three or more significant peaks at regular temporal
intervals to be classified as oscillating (Eggermont
1992). Each ACG was displayed over a time frame of at least
±125 ms.
Histology
The final recording session for each animal was terminated by deeply anesthetizing the animal with an intravenous injection of pentobarbital sodium followed by an injection of 20 mg lidocaine and 1,000 USP units of heparin. The animal was transcardially perfused with 0.9% saline, neutral formalin, and 10% sucrose in formalin. The brain was removed and placed into 30% sucrose in formalin until it sank. The SI cortex was blocked, frozen, and cut into 50 µm coronal sections that were mounted onto chrom-alum-coated slides and stained with thionin.
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RESULTS |
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We recorded extracellular discharges from ~700 SI neurons but excluded from analysis those neurons that did not respond to air-jet stimulation or that failed to discharge at least five times per stimulus trial. On the basis of these criteria, we acquired stimulus-induced responses from 544 SI neurons. In this sample, 263 neurons were stimulated by moving air jets in one block of trials and by stationary air jets in a separate blocks of trials; the remaining 281 neurons were stimulated by both types of air jets in a single block of trials. For this latter group, Table 1 displays the mean discharge rates recorded during spontaneous activity and in response to moving and stationary air jet stimulation. As Table 1 indicates, the SI neurons in this study exhibited low rates of spontaneous activity and were activated by stationary and moving air jets. Statistical comparison of the responses evoked from the most effective stationary site and those evoked by the preferred direction of movement indicates that these neurons were more responsive to moving stimulation (paired t = 6.25, P < 0.0001).
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We assessed the coordination of stimulus-induced neuronal activity in 880 pairs of SI neurons. This group includes neuron pairs in which stationary and moving air jets were both administered within the same block of trials (n = 577) as well as those in which moving and stationary air jets were administered in separate blocks of trials (n = 303). Inspection of the shift-corrected CCGs indicated that 10% (n = 88) of these neuron pairs were synchronized during both spontaneous and stimulus-induced activity.
Modulation of SI synchronization by stationary air jets
Stationary air-jet stimulation produced synchronization of SI activity in 87 neuron pairs. These data were obtained from experiments in which stationary air jets were delivered alone (n = 27) or were interleaved with moving air jets on each trial (n = 60).
An example of SI synchronization during spontaneous activity and in responses to a series of stationary air jets is shown in Fig. 1. In this case, neuron CC52a was excited by each of the three air jets aimed at the ventral forelimb, while neuron CC52b preferred air-jet stimulation at the more distal sites. The smoothed shift-corrected CCG compiled from spontaneous activity revealed weak synchronization in which the peak half-width lasted 15 ms and the correlation coefficient was only 0.0236. Cortical synchronization was enhanced noticeably during air-jet stimulation as indicated by the fact that the peak half-widths were narrower (2-6.5 ms) and the correlation coefficients were larger (0.0259-0.0283) than those obtained during spontaneous activity.
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The effect of cutaneous stimulation on SI synchronization is illustrated by a pair of scatter plots, which show the distribution of correlation coefficients and peak half-widths for 87 neuron pairs tested with stationary air jets (Fig. 2). Because more than one RF site was stimulated for each neuron pair, each point in Fig. 2 represents the air-jet response having the largest correlation coefficient. Compared with spontaneous activity, air-jet stimulation caused a larger proportion of SI cortical activity to become synchronized. Thus the mean correlation coefficient increased from 0.01569 ± 0.0009 (mean ± SE) during spontaneous activity to 0.02379 ± 0.0010 during stationary air-jet stimulation (paired t = 7.99; P < 0.0001). Cutaneous stimulation also reduced the temporal variability of correlated discharges in the local network as indicated by the decline in peak half-widths from a mean of 8.17 ± 1.37 ms during spontaneous activity to a mean of 3.93 ± 0.41 ms during stationary air-jet stimulation (paired t = 3.06; P < 0.002).
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Modulation of SI synchronization by moving air jets
Cutaneous stimulation with a moving air jet caused synchronization in 88 neuron pairs. These data represent the combined results from experiments in which moving air jets were administered alone (n = 28) as well as experiments in which moving and stationary air jets were interleaved on each trial (n = 60).
Figure 3 illustrates the effects of a moving air jet on the same pair of neurons the responses of which to stationary air jets were shown in Fig. 1. The mean extracellular waveforms in Figs. 1 and 3 show that both neurons remained well isolated, although the waveform for neuron CC52a had increased in amplitude since the time the data in Fig. 1 were recorded. The spontaneous activity of neuron CC52a also had declined slightly, but both neurons continued to respond vigorously to air jets moving distally or proximally across their RFs. Although cross-correlation analysis indicated little change in the half-widths of the CCG peaks obtained during spontaneous activity (4 ms) or in response to a moving air jet (2 ms), the change in the proportion of correlated discharges was quite striking. The smoothed shift-corrected CCG for spontaneousactivity contained a small peak near time 0, which had a correlation coefficient of 0.0157. During stimulation with a moving air jet, the correlation coefficient increased to 0.029 during movement in the preferred (forward) direction but declined to 0.0151 when the air jet moved in the nonpreferred (reverse) direction even though both neurons showed similar rates of activity (8.85 vs. 5.57 spikes/s for CC52-a1 and 60.0 vs. 50.6 spikes/s for CC52-b1) in both directions. The lack of a relationship between firing rate and correlation coefficient was underscored by the fact that the correlation coefficient during movement in the nonpreferred direction (0.0151) was lower than during spontaneous activity (0.0157) even though the discharge rates of both neurons was several times higher during cutaneous stimulation. This was not an isolated case as 17% (n = 15) of the neuron pairs showed directional preferences in synchronization without showing corresponding changes in their underlying rate of activity. Furthermore smoothing the shift-corrected CCGs was not a factor in these cases because the unsmoothed CCGs displayed the same degree of directional preferences in their correlation coefficients.
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The effects of moving air jets on cortical synchronization are summarized for all 88 neuron pairs in Fig. 4. The scatter plots in this figure show the distribution of correlation coefficients and peak half-widths obtained during spontaneous activity and moving air-jet stimulation. Because the pattern of coordination often differed when the air jet moved in opposite directions, each data point represents the air-jet response having the largest correlation coefficient. Compared with spontaneous activity, moving air jets produced a substantial increase in cortical synchronization. Thus the correlation coefficients increased from a mean of 0.01692 ± 0.0012 during spontaneous activity to 0.02284 ± 0.0011 during air-jet movements in the preferred direction. A matched-sample t-test indicated that these differences were highly significant (paired t = 4.63; P < 0.0001). A similar comparison of mean peak half-widths obtained during spontaneous activity (7.03 ± 1.31 ms) and during air-jet movement (3.03 ± 0.30 ms) indicated that moving air jets caused a decrease in the temporal variability of correlated activity (paired t = 3.11; P < 0.002).
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Comparison of synchronization induced by stationary and moving air jets
We compared the effects of stationary and moving air-jet stimulation on 60 neuron pairs that showed significant levels of cortical synchronization during stationary and moving air-jet stimulation. This analysis was conducted only on neuron pairs in which stationary and moving air jets were both administered within the same block of trials. This restriction was necessary because many spike trains showed clear signs of nonstationarity when different blocks of trials were compared (see Figs. 1 and 3).
In addition to analyzing single neurons, we also analyzed multiunit responses recorded across pairs of electrodes. Multiunit activity was analyzed because many electrodes recorded two or three distinguishable waveforms, but cross-correlation analysis usually failed to detect coordination among any of the single neuron pairs even though their PSTHs were highly similar. Cross-correlation analysis of multiunit responses revealed significant levels of correlated activity across 79 pairs of electrodes.
A representative example comparing the effects of stationary and moving air-jet stimulation on SI synchronization is presented in Fig. 5. In this case, three distinct waveforms were recorded from one electrode (CC69a) and two neuronal waveforms were recorded simultaneously by an electrode located 250 µm away (CC69b) to yield a total of six neuron pairs. Cross-correlation analysis revealed substantial amounts of synchronization in the multiunit responses and in four of the six single neuron pairs. The shift-corrected CCGs obtained from the multiunit responses contained tall peaks in which correlation coefficients were largest during moving air-jet stimulation (0.120) and smallest during spontaneous activity (0.104). The half-widths of these peaks showed that the relative timing of correlated activity was less variable during moving air-jet stimulation (0.5 ms) than during spontaneous activity (1.5 ms) or during stationary air-jet stimulation (2.5 ms). Comparison of the multiunit CCGs with those obtained from single neurons revealed noticeable variability in the coordination of specific pairs of neurons. In one pair of neurons (a3 and b2), for example, each of the CCGs obtained from spontaneous and stimulus-induced activity contained a prominent peak centered around time 0. In another pair of neurons (a1 and b1), correlated activity was barely detected during the moving air-jet response, whereas the stationary air jets produced a broad peak of correlated activity that was located 1-2 ms to the right of time 0.
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Although the correlation coefficients for CC69 appeared similar during for both moving and stationary air-jet responses, mean firing rates were higher during air-jet movement. This difference was not apparent from CCGs generated from the complete stimulus period because the stationary air jets lasted 1,000 ms whereas the moving air jets lasted only 500 ms in each direction. Furthermore although each stationary air jet was aimed at overlapping regions of the neuron's RFs, the moving air jets traversed the entire length of each RF, and this meant that the beginning and end of each sweep of the moving air jet was ineffective for activating both neurons. Therefore to fully appreciate any differences in the rate of synchronized activity produced by moving and stationary air jets, it is necessary to examine the rate of correlated activity produced during equivalent time periods when both types of air jets stimulate overlapping portions of the RFs. Because moving air-jet responses were largest in the midst of the sweep, we conducted cross-correlation analysis on the activity occurring in the middle portion (100-400 ms) of an air jet moving in the preferred direction. Furthermore we also conducted cross-correlation analysis on equivalent 300-ms periods at the beginning, middle, and end of the best stationary air jets. Figure 6 illustrates the differences produced by moving and stationary air jets by presenting the raw and shift-corrected CCGs calculated from 300 ms periods for the same multiunit responses shown in Fig. 5. The differential effects of moving and stationary air jets on the rate of synchronized activity is clear from comparing the amplitude of the CCG peaks in Fig. 6. Whereas stationary air jets produced relatively short and broad CCG peaks for each 300-ms period, the moving air jet produced a much taller peak of correlated activity. In fact, most of the correlated activity produced by the moving air jet occurred precisely at time 0 in the raw and shift-corrected CCGs, and the rate of synchronization during the moving air jet (44.6 coincident events/s) was substantially higher than that produced by the stationary air jet (ranging from 26.4 to 16.8 coincident events/s). A comparison of the correlation coefficients showed that the proportion of synchronized activity was higher during the moving air jet (0.127) than during the initial period (0.117) or in any subsequent part of the stationary air jet (0.107-0.096).
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The effects of stationary and moving air jets on the mean rate of
synchronization are summarized for single- and multiunit responses in
Fig. 7. For stationary air jets, we
measured synchronization rate for the complete stimulation period
(1,000 ms) and from the best 300-ms period occurring at the beginning,
middle, or end of the air jet as shown in Fig. 6. In 90% of the cases,
synchronization rates were highest during the first 300-ms period and
declined progressively during the remaining periods. For moving air
jets, synchronization rates were calculated for the 300-ms period in the middle of the sweep in the preferred direction as shown in Fig. 6.
We did not calculate the rate of synchronization for the entire 500-ms
period because many neurons were not activated by the onset or end of
each sweep. As shown by Fig. 7, regardless of whether we examined the
raw or shift-corrected CCGs, moving air jets produced higher rates of
synchronized activity than stationary air jets for both the single- and
multiunit responses (paired t 3.56, P < 0.001 for all comparisons).
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To compare the proportion of activity synchronized by stationary and
moving air jets, we calculated the correlation coefficients for the
single- and multiunit responses that had the highest synchronization rates. As Fig. 8 indicates, mean
correlation coefficients for multiunit responses were substantially
larger than those obtained from pairs of single neurons. This result is
consistent with other evidence that correlation coefficients are larger
when multiunit responses are analyzed because there are more
opportunities for detecting correlated discharges that occur among
subsets of different neuron pairs (Bedenbaugh and Gerstein
1997).
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As shown by Fig. 8, the raw CCGs for both single- and multiunit activity had higher correlation coefficients during the moving air jets than during the stationary air jets (paired t = 4.65, P < 0.0001 for multiple neurons; paired t = 3.47, P < 0.001 for single neurons). Analysis of the shift-corrected CCGs, however, failed to reveal any significant difference in the proportion of correlated activity produced by moving or stationary air jets among pairs of single or multiple neurons. Thus subtraction of the shift-predictor caused considerable reduction in the correlation coefficients, but the resulting levels remained above those obtained during spontaneous activity.
A comparison of the mean peak half-widths obtained from the shift-corrected CCGs for single and multiple neurons is shown in Fig. 9. We did not measure peak half-widths for the raw CCGs because we frequently found that half the height of the tallest peak was within the background level of correlated activity and rendered this parameter meaningless. Consistent with the results shown previously in Figs. 2 and 4, both moving and stationary air jets produced substantial decreases in CCG peak half-widths when compared with spontaneous activity. Peak half-widths for the stationary air-jet responses were larger when the entire 1,000-ms period was analyzed, and this result parallels a trend seen in 90% of the single- and multiunit responses in which the rate and proportion of synchronized activity gradually declines during successive 300-ms periods of the stationary air jet (see Figs. 6-8). Analysis of the multiunit responses showed that moving air jets produced slightly less variability in the timing of correlated activity than stationary air jets (paired t = 2.04, P < 0.05 for comparison with the best 300-ms period). A similar comparison of single neuron pairs, however, failed to detect significant differences in the peak half-widths produced by moving and stationary air jets (paired t = 2.005, P < 0.052 for comparison with the entire 1,000-ms period).
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Effects of electrode separation on stimulus-induced cortical synchronization
There was considerable RF overlap when neurons were separated by
only 250-300 µm, but the amount of overlap declined with increasing
distance and was much less than 50% of the combined RFs for neurons
separated by 600 µm. This was consistent with other reports showing
that RF overlap varies systematically with cortical separation
(Alloway and Burton 1985; Dykes and Gabor 1981
; Sur et al. 1980
). Unless objective
criteria are used, however, RF boundaries are difficult to define and
may vary according to experimenter bias. We also found that obtaining
precise RFs for each neuron was problematic when two or more neurons
were recorded simultaneously from the same electrode. For these
reasons, we analyzed cortical synchronization as a function of the
distance between pairs of electrodes because this parameter was
measured easily and, with distances of
600 µm, appeared to be
correlated with RF overlap.
Table 2 indicates that the probability of
detecting cortical synchronization tended to decline with increasing
distance between recording sites. Because this trend was less evident
among single neuron pairs, we analyzed only the CCGs of multiunit
responses to determine how cortical synchronization varied with
electrode separation. For both the raw and shift-corrected CCGs,
electrode separation had a significant effect on synchronization rate
(F = 72.4, P < 0.0001 for raw CCGs;
F = 66.5, P < 0.0001 for
shift-corrected CCGs). Synchronization rates were higher for moving air
jets than for stationary air jets at each of the electrode separations, and the highest synchronization rates were recorded at separations of
300 µm (Fig. 10). Unexpectedly,
synchronization rates were higher for neurons separated by 600 µm
than for neurons separated by 250 or 354 µm. Electrode separation
also had a significant effect on correlation coefficients, but this
effect was more evident in the raw CCGs than in the shift-corrected
CCGs (F = 32.7, P < 0.0001 for raw
CCGs; F = 5.5, P < 0.01 for
shift-corrected CCGs). Analysis of correlation coefficients from the
raw CCGs also revealed a propensity for greater amounts of
synchronization at 300-µm increments (Fig.
11). When the shift-corrected CCGs were
analyzed, however, the proportion of correlated activity was highest at 300-µm intervals, but there was little difference in this parameter at separations of 250, 354, or 600 µm. Electrode separation also had
a significant effect on the temporal variability of synchronized discharges (F = 23.8, P < 0.001), but
this effect was more apparent for spontaneous than for stimulus-induced
synchronization (Fig. 12). Thus the
mean half-width of CCG peaks constructed from spontaneous activity
increased from 6 ms at the short intervals (250-354 µm) to nearly
15 ms at intervals of 600 µm. By contrast, cortical synchronization
produced by moving or stationary air jets had little temporal
variability and mean peak half-widths remained <2.40 ms at the shorter
intervals (250-354 µm). At separations of 600 µm, however, peak
half-widths increased to a mean value of 2.77 ± 1.13 ms for the
moving air jets and 4.33 ± 0.26 ms for the stationary air jets
(paired t = 2.35, P < 0.055).
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Lack of oscillations during stimulus-induced synchronization
Many reports indicate that neuronal oscillations may play an
essential role for synchronizing activity in segregated cortical areas
(Bressler et al. 1993; Eckhorn et al.
1988
; Gray and McCormick 1996
; Gray et
al. 1989
, 1992
; Konig et al. 1995
; Murthy
and Fetz 1996a
,b
; Steriade et al. 1994
).
Therefore we applied autocorrelation analysis to the multiunit
responses to determine if stimulus-induced synchronization was
dependent on oscillatory activity. In this analysis, oscillatory
activity was considered to be present if the ACG contained three or
more peaks which exceeded the expectancy level by 2 SDs at regular
temporal intervals (Eggermont 1992
). Only five recording
sites showed clear cases of oscillatory activity during air-jet
stimulation, but two of these responses were identical to oscillatory
patterns that appeared spontaneously. None of the remaining multiunit
responses contained any clear patterns of oscillation, and this is
consistent with other reports indicating that oscillations are not
necessary to synchronize cortical neurons separated by
2 mm
(Konig et al. 1995
; Swadlow et al. 1998
).
Figure 13 illustrates some examples of
multiunit responses that were synchronized during moving and stationary
air-jet stimulation yet failed to display any oscillations.
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DISCUSSION |
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The results of this study demonstrate that neuronal synchronization is an important part of the cortical response to tactile stimulation. Consistent with the view that neuronal synchronization is a potential mechanism for coding certain aspects of sensory stimuli, our results indicate that correlated activity in somatosensory cortex may supplement the changes in firing rate that code intensity and other attributes of a cutaneous stimulus. Although many studies have shown that neuronal synchronization might play a role in visual perception, this is one of the first studies to show the potential utility of stimulus-induced synchrony in somatosensory cortex.
Anatomic factors affecting synchrony in SI cortex
The probability of detecting synchronized responses to a discrete
air jet was highest for SI neurons located across an interval of 300 µm and was substantially lower for neurons separated by 354-600 µm
(see Table 2). One factor that appears to be related to this trend is
the degree of RF overlap. Neurons separated by 250-300 µm share the
majority of their RFs, whereas neurons separated by 354-600 µm share
only a small portion of their RFs. This observation confirms reports
showing that RF overlap in somatosensory cortex is related inversely to
the distance intervening between neurons (Alloway and Burton
1985; Dykes and Gabor 1981
; Sur et al.
1980
). Those studies also found that neurons separated by
600-800 µm had nonoverlapping RFs that represented adjacent skin
regions. Because we rarely recorded responses from sites separated by
>600 µm, we do not know if a discrete moving stimulus can
synchronize SI populations that are spaced more widely.
The incidence and strength of synchronization between neighboring parts
of SI cortex also appears to be related to anatomic factors. Like other
cortical areas, pyramidal neurons in SI cortex, especially those in
layer III, give rise to axonal collaterals that have extensive contacts
with other neurons in the vicinity of the soma as well as neurons
located more distantly (Bernardo et al. 1990;
Burton and Fabri 1995
; DeFelipe et al.
1986
; Juliano et al. 1990
; Lund
et al. 1993
; Schwark and Jones 1989
). Although the spatial distribution of intracortical connections probably is
related to RF overlap, any discontinuities in this distribution might
explain why the incidence and rate of synchronization were higher for
neurons separated by 600 µm than for neurons separated by 250, 354, 500, or 560 µm. Intracortical connections in striate cortex, for
example, cluster at regular intervals (Gilbert 1992
), and some evidence indicates that focal collateralizations also may
occur at regular intervals in SI cortex (DeFelipe and Jones 1986
).
Common inputs from thalamocortical projections are also likely to play
a major role in synchronizing adjacent groups of SI neurons.
Thalamocortical relay neurons have axon collaterals that terminate in
multiple patches of SI cortex and may span a distance of 600 µm
(Garraghty and Sur 1990
; Landry and Deschenes
1981
; Snow et al. 1988
). Consistent with this
finding, our CCG peaks usually straddled time 0, a
coordination pattern that suggests the presence of common inputs from a
third source (Fetz et al. 1991
; Perkel et
al. 1967
).
Synchronization in SI cortex and sensory coding
Compared with spontaneous activity, both stationary and moving air
jets caused substantial increases in the rate, proportion, and temporal
precision of synchronized activity in local regions of SI cortex.
Although both types of stimuli produced large increases in the rate of
synchronized activity, moving air jets were significantly more
effective than stationary air jets in boosting this parameter. Furthermore the increased rate of synchronized activity produced during
moving air-jet stimulation was not just the result of an increase in
neuronal firing rate but was accompanied by significant increases in
the proportion of correlated activity as measured by the correlation
coefficients for the raw CCGs. Finally, differences in the rate of
synchronization produced by stationary and moving air jets were most
prominent among neural populations that were separated by 600 µm and
thus had minimal RF overlap. This finding is important for sensory
coding because the stationary and moving air jets were identical with
respect to the skin area that was stimulated at any moment in time.
Whereas a previous study found that a discrete tactile stimulus can
synchronize SI cortical neurons separated by 500 µm
(Metherate and Dykes 1985
), our study extends those
results by suggesting that synchronization may occur over wider regions
of SI cortex if the stimulus sequentially activates groups of neurons
representing contiguous skin regions.
Some evidence suggests that sensory stimulation evokes recurrent
excitation among neighboring cortical populations that may, under
certain conditions, interact with incoming thalamocortical activity to
enhance cortical synchronization (Douglas et al. 1995). On the basis of the differences in firing rate and proportion of
correlated discharges produced by moving and stationary stimuli, we
believe that moving stimuli are more effective than stationary stimuli
in promoting cooperativity among related thalamocortical and
corticocortical networks. In our view, moving stimuli sequentially recruit neighboring populations of thalamocortical neurons that project
to the subliminal fringe of excitation surrounding the cortical area
activated in the preceding time frame. Compared with a stationary
stimulus, a moving stimulus evokes more cortical excitation and
continuously activates neighboring populations of thalamocortical and
corticocortical networks that are strongly interconnected. One
consequence of this appears to be a tremendous increase in the rate of
highly synchronized activity in neighboring regions of cortex and
suggests that local regions of cortex are wired to become synchronized
when the same stimulus activates neighboring parts of this network. We
speculate that the cortical area over which a single moving stimulus
may cause neuronal synchronization probably is related to the speed of
stimulus motion and the time period over which recurrent excitation
persists. In any case, our findings are consistent with the view that
synchronization is a plausible mechanism for linking adjacent cortical
populations and suggest that shifts in highly synchronized activity
from one cortical region to the next is an important neural correlate
of the sensation of movement produced by a single moving stimulus.
We also obtained preliminary evidence suggesting that neuronal
synchronization in SI cortex can signal more specific attributes of a
cutaneous stimulus. Thus 15 of our neuron pairs were strongly synchronized by air jets moving in one direction but not the other even
though the underlying rate of activity was similar for both directions
of movement. Although earlier studies indicate that some SI neurons are
directionally sensitive (Ruiz et al. 1995; Warren
et al. 1986
; Whitsel et al. 1972
), those studies
did not analyze whether groups of such neurons become synchronized or whether synchronization might code stimulus direction independent of
changes in firing rate. Although our results suggest that
synchronization might play a role in coding direction of movement, we
only tested stimuli that moved back and forth for one stimulus cycle.
Hence in these cases, we do not know whether synchronization might vary with the level of adaptation, the direction of the initial stimulus, or
other factors. To examine these effects, we are currently studying SI
responses to repetitive back-and-forth movements.
Parallels with other sensory systems
The presence of synchronized activity within distributed
populations of cortical neurons has been investigated in many sensory systems because of its theoretical importance as a potential coding mechanism (Konig and Engel 1995), and many of the
findings in those studies bear a resemblance to our results in SI
cortex. In auditory cortex, for example, synchronization among local
groups of neurons is substantially greater during sound stimulation
than during periods of spontaneous activity (de Charms and
Merzenich 1996
; Dickson and Gerstein 1974
;
Eggermont 1994
; Frostig et al. 1983
). In
addition, neighboring neurons in auditory cortex display stimulus-induced interactions that have narrower CCG peak widths and
larger correlation coefficients than neuron pairs that are more widely
separated (Eggermont 1997
). Finally, for a significant fraction of neurons in auditory cortex, synchronization is sensitive to
the direction of sound movement (Ahissar et al. 1992
).
Just as we have observed that synchronization in SI cortex is more
likely for neurons sharing similar RF properties, a large body of data
indicate that synchronization in visual cortex is governed by similar
principles (Singer and Gray 1995). Thus local populations of striate neurons are more likely to become synchronized when they have similar response properties and overlapping RFs (Gray and Singer 1989
; Toyama et al.
1981a
,b
). Consist with the Gestalt criteria for visual
perception, adjacent populations of neurons in striate cortex are more
likely to be synchronized if they have similar orientation and
directional preferences (Ts'o et al. 1986
) and
represent colinear portions of the visual field (Gray et al.
1989
). These findings indicate that the spatial continuity of a
visual stimulus is important for organizing striate cortical neurons
into functional assemblies (Singer and Gray 1995
).
Comparisons of multiple and single neuron responses
Many laboratories have analyzed multiunit activity to reveal
cortical synchronization during sensory stimulation. Cross-correlation analysis of multiunit activity has been used widely to demonstrate that
neuronal synchronization is a potential coding mechanism in visual and
auditory cortex (de Charms and Merzenich 1996;
Eckhorn et al. 1988
; Engel et al. 1990
,
1991
; Gray and Singer 1989
; Gray et al.
1992
). In addition, temporal analysis of local field potentials also has been used to determine whether segregated populations of
neurons become synchronized during different sensory or behavioral conditions (Bressler et al. 1993
; Engel et al.
1990
; Gray and Singer 1989
; MacKay and
Mendonca 1995
; Murthy and Fetz 1996b
; Sanes and Donoghue 1993
).
Some investigators have concluded that cross-correlation analysis
of multiunit activity is more sensitive for detecting cortical synchronization than an analysis of single neuron pairs
(Bedenbaugh and Gerstein 1997; de Charms and
Merzenich 1996
). We agree with this view because synchronized
activity was apparent in only 10% of our single neuron pairs, yet
appeared among 64% of our electrode pairs when multiunit responses
were analyzed. Although the proportion of synchronized neuron pairs
varied as a function of electrode separation, the ability to detect
cortical synchronization over longer distances was improved greatly
when multiunit responses were analyzed. Finally, small differences in
the effects of stationary and moving air jets on mean peak half-widths
were detected by our analysis of multiunit responses but not by a
similar analysis of single neuron pairs.
Although cross-correlation analysis of multiunit activity appears
to be more sensitive for detecting neuronal coordination, the data
acquired with this technique must be interpreted carefully (Bedenbaugh and Gerstein 1997). A potential problem with
comparing multiunit responses with particular stimuli concerns the
recruitment of different sets of neurons. If stationary and moving air
jets activate different groups of neurons, any change in correlated activity produced by these stimuli might reflect a sampling difference rather than a change in functional connectivity. Moving air jets evoked
higher rates of activity than stationary air jets (see Table 1), and it
could be argued that a moving stimulus recruits more SI neurons than a
stationary air jet. There are two reasons why this possibility does not
explain the differences that we observed. First, we noted the
characteristics of the extracellular waveforms recorded during each
stimulus and did not observe differences in the shape, amplitude, or
width of discharges evoked by moving and stationary stimuli air jets
administered in the same block of trials. Second, a comparison of the
responses to stationary and moving air jets showed that the CCG peak
half-widths were slightly narrower during stimulus movement (see Fig.
9). If moving air jets activated a larger population of cortical
neurons, then this should have produced an increase, not a decrease, in
the temporal variability of their coincident discharges.
Interpretation of raw and shift-corrected CCGs
In this study we presented data from both the raw and shift-corrected CCGs for a variety of reasons. First, raw CCGs represent the actual patterns of synchronized activity that are available to the organism for sensory perception and discrimination. Second, our stimuli are relatively long in duration (500-1000 ms), and responses to moving and stationary air jets can be highly variable from one trial to another. Thus it could be argued that subtraction of the shift predictor in our paradigm does not really remove enough stimulus-coordinated events to portray accurately the amount of correlated activity mediated by neural connections. Finally, we wished to determine whether the raw and shift-corrected CCGs revealed similar patterns in the relative amounts of synchronization produced by moving and stationary air jets. We found that moving air jets produced higher synchronization rates than stationary air jets and that this difference was present in both the raw and shift-corrected CCGs. By contrast, correlation coefficients were significantly higher for the moving air-jet responses when the raw CCGs were analyzed, but this difference disappeared when the shift predictor was subtracted.
It is not immediately obvious why the correlation coefficients produced by moving and stationary air jets should be different in the raw but not in the shift-corrected CCGs. Any plausible explanation must consider what the shift predictor represents in these experiments. Consistent with the flat appearance of the 95% confidence limits, which are derived from the shift predictor, we did not observe any prominent peaks in the shift predictor around time 0. Instead, the shift predictor was either completely flat or, in a few instances, contained relatively broad elevations that gradually tapered away from time 0. The lack of a prominent peak suggests that events in the shift predictor do not reflect temporal characteristics of the stimulus but could reflect correlations due to chance. In cases where neurons are not interconnected and do not share any common inputs, the shift predictor accurately indicates the probability of chance correlations, and this value is determined largely by the rate of activity in the recorded neurons. Because discharge rates were significantly greater during moving air jets than during stationary air jets, it could be argued that correlations due to chance are disproportionately greater for the responses to moving air jets. In cases where neurons are likely to be interconnected or to share common inputs, however, the coincident events subtracted from the raw CCG are likely to represent a combination of chance correlations and correlations produced by direct neuronal interactions or common inputs. In experiments such as ours, in which the neurons share RFs and are likely to share thalamocortical inputs, the differential effects of stationary and moving stimuli on synchronization rate may reflect true differences in the proportion of correlated activity produced by neuronal connections. Hence the larger correlation coefficients observed in the raw CCGs during moving air-jet stimulation suggest that this stimulus enhances the cooperativity of thalamocortical projections to common postsynaptic targets.
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ACKNOWLEDGMENTS |
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The authors thank Dr. Barry Dworkin for providing the polygraph pen module that was used for moving air-jet stimulus.
This work was supported by National Institute of Neurological Disorders and Stroke Grant NS-29363 to K. D. Alloway.
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FOOTNOTES |
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Address for reprint requests to K. D. Alloway.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 15 May 1998; accepted in final form 3 November 1998.
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REFERENCES |
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