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The Journal of Neuroscience, May 15, 1999, 19(10):3992-4010
Precisely Synchronized Oscillatory Firing Patterns Require
Electroencephalographic Activation
Suzana
Herculano-Houzel,
Matthias H. J.
Munk,
Sergio
Neuenschwander, and
Wolf
Singer
Max-Planck-Institut für Hirnforschung, 60528 Frankfurt/Main,
Germany
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ABSTRACT |
Neuronal response synchronization with millisecond precision has
been proposed to serve feature binding in vision and should therefore,
like visual experience, depend on central states. Here we test this
hypothesis by examining the occurrence and strength of response
synchronization in areas 17 and 18 of anesthetized cats as a function
of central states. These were assessed from the frequency content of
the electroencephalogram, low power in the  and high
power in the  frequency ranges (here 20-70 Hz) being considered as
a signature of activated states. We evaluated both spontaneous state
changes and transitions induced by electrical stimulation of the
mesencephalic reticular formation. During states of low central
activation, visual responses were robust but lacked signs of precise
synchronization. At intermediate levels of activation, responses became
synchronized and exhibited an oscillatory patterning in the range of
70-105 Hz. At higher levels of activation, a different pattern of
response synchronization and oscillatory modulation appeared,
oscillation frequency now being in the range of 20-65 Hz. The strength
of response synchronization and oscillatory modulation in the 20-65 Hz
range increased with further activation and was associated with a
decrease in oscillation frequency. We propose that the oscillatory
patterning in the 70-105 Hz range is attributable to oscillatory
retinothalamic input and that a minimal level of activation is
necessary for cortical neurons to follow this oscillatory pattern. In
contrast, the synchronization of responses at oscillation frequencies
in the 20-65 Hz range appears to result from intracortical synchronizing mechanisms, which become progressively more effective as
central activation increases. Surprisingly, enhanced synchronization and oscillatory modulation in the frequency range were not
associated with consistent increases in response amplitude, excluding a
simple relation between central activation and neuronal discharge rate. The fact that intracortical synchronizing mechanisms are particularly effective during states of central activation supports the hypothesis that precise synchronization of responses plays a role in sensory processing.
Key words:
synchronization; frequency oscillations; EEG; cortical activation; visual perception; thalamocortical interactions; sleep
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INTRODUCTION |
The synchronization of neuronal
responses has been proposed as a mechanism complementary to rate
modulation for the definition of relations among distributed responses.
According to this hypothesis, synchronization of discharges with a
precision in the millisecond range serves to jointly raise the saliency
of responses, thereby defining for subsequent processing stages which
responses are related (for review, see Singer and Gray, 1995 ).
Synchronization in the millisecond range has been reported in several
brain areas and species (Gray et al., 1989; Frien et al., 1994; Kreiter
and Singer, 1994; Laurent and Davidowitz, 1994; Prechtl, 1994;
Livingstone, 1996; Neuenschwander et al., 1996a,b; Freiwald et al.,
1998) and is frequently associated with an oscillatory modulation of
discharges in the frequency range. There is increasing evidence
that these precise temporal relations among distributed discharges
carry information that cannot be extracted from the firing rate of
individual neurons. In the auditory cortex, the precise timing of
spikes signals the familiarity of species-specific calls (Wang et al., 1995). In the insect olfactory system, composite odors appear to be
encoded in the specific constellation of synchronous oscillatory discharges (Laurent, 1996), and in the visual cortex, synchronization of visual responses has been shown to reflect perceptual grouping criteria such as vicinity, continuity, colinearity, and common fate
(Gray et al., 1989; Engel et al., 1991; Kreiter and Singer, 1996;
Livingstone, 1996). Moreover, response synchronization correlates well
with sensory disturbances such as strabismic amblyopia (Roelfsema et
al., 1994) and with changes in perceptual dominance during interocular
rivalry (Fries et al., 1997).
It is during waking and dreaming [rapid eye movement (REM) sleep],
the two major brain states characterized by a desynchronized electroencephalogram (EEG) (Steriade et al., 1996), that visual experiences are possible. The EEG in these states is dominated by
low-voltage activity in the frequency range and is referred to as
"activated," contrasting with the prominent high-voltage (1-4
Hz) activity that is observed during non-REM sleep (Steriade and
McCarley, 1990; Llinás and Ribary, 1993). This predicts that neuronal response properties essential for perception should be expressed in the behavior of neuronal populations during states of
central activation and should be absent when the brain is in a state
that is incompatible with perception. To test this prediction, we
investigated how visual responses in the cat primary visual cortex
change in relation to EEG activation, as assessed from the frequency
content of the EEG. EEG changes characteristic of behavioral arousal
and of the transition from non-REM to REM sleep are caused by enhanced
activity of modulatory projections originating in the mesencephalic
reticular formation (MRF) (Moruzzi and Magoun, 1949; Hobson, 1992) and
can be induced by electrical stimulation of the MRF (Moruzzi and
Magoun, 1949). Therefore, we studied both spontaneous fluctuations of
central states and those induced by electrical stimulation of the MRF.
We have shown previously that MRF stimulation enhances the
synchronization of neuronal responses in the visual cortex of
anesthetized cats (Munk et al., 1996b). Here we extended this analysis
to investigate the variation in strength, oscillatory modulation, and
synchronization of visual responses in relation to central state changes.
Some of the results from this study have appeared in abstract form
(Munk et al., 1996a)
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MATERIALS AND METHODS |
Animals. Eight adult cats were studied. Anesthesia
was induced with 10 mg/kg ketamine and 2 mg/kg rompun injected
intramuscularly and maintained under artificial ventilation with 70%
N2O and 30% O2 supplemented with 1% halothane
during all surgical procedures, as described previously (Engel et al.,
1990). Heart rate, temperature, EEG, and end-tidal CO2 were
monitored continuously. After surgery, halothane concentration was
adjusted between 0.6 and 0.8% to allow a level of anesthesia
characterized by a polymorphic EEG exhibiting spontaneous variations
from slow waves to activity in the frequency range. Subsequently,
animals were paralyzed with 0.1 mg · kg 1 · hr1
intravenous pancuronium bromide, and recordings were obtained during
the following 3-4 d.
Multiple-electrode recording. Multiunit activity was
recorded simultaneously with up to three tungsten electrodes from a
total of 53 sites in areas 17 and 18 (45 pairs of recording sites). Neurons at different sites recorded simultaneously had nonoverlapping receptive fields, with an average center-to-center distance of 7.6 ± 5.1° of visual angle. Multiunit responses to repeated
presentations of an invariant visual stimulus were recorded in 21 sessions spanning 3-10 consecutive hours. The level of anesthetic was
kept constant during each session. Multiunit activity was filtered
between 1 and 3 kHz, fed through a Schmitt trigger whose threshold was
set higher than twice the noise level, and sampled at 100 kHz with a
1401 CED interface controlled by Spike 2 software (Cambridge Electronic
Design, Cambridge, UK). The EEG was recorded at the same time between
two epidural silver ball electrodes that were placed lateral and
posterior to the multiunit recording sites, either across the
hemispheres (one animal) or with a spacing of 3-5 mm in the same
hemisphere (seven animals). The EEG signal was filtered between 0.1 and
1000 Hz and digitized at 1 kHz.
MRF stimulation. Bipolar stimulation electrodes were
positioned bilaterally into the optic chiasm (A, 14.5; H, 5; L, ±3)
(Horsley-Clarke) and the MRF (A, 2; H, 8; L, ±3). The position of MRF
stimulation electrodes was adjusted so that delivery of a 61-msec-long
train (100 µsec pulses at 75 Hz, 1-3 mA; Singer, 1973) caused
maximal facilitation of the cortical potential evoked by a single shock (50 µsec, 1-2 mA) applied to the optic chiasm. MRF-induced
facilitation of the evoked potential was always tested before muscle
relaxation. No signs of stress such as motor reactions, increased heart
rate, temperature, or end-tidal CO2 were present after MRF
stimulation, indicating that the level of anesthesia was adequate. The
position of the MRF stimulation electrodes was verified in histological sections from electrolytic lesions made along the electrode tracks at
the end of the experiments.
Visual stimulation and experimental design. The visual
stimulus consisted of one or several patches of moving gratings
presented on a computer screen (100 Hz refresh rate). When several
patches were presented, they were, as far as possible, of the same
contrast, size, velocity, orientation, and direction of movement, so
that there was a good chance that visual responses elicited by them would exhibit synchronization (Gray et al., 1989). The experimental design is schematized in Figure 1. In
each 10 sec trial, the visual stimulus appeared 3 sec after trial start
and remained on for 4.5 sec. Data were acquired in blocks of 10 consecutive trials repeated at 2 sec intervals. The interval between
blocks was ~10 sec. Periods of 20-30 consecutive blocks, lasting
altogether ~1 hr, were alternated with periods of 10-30 consecutive
blocks in which the 75 Hz stimulation train was delivered to the MRF
150 msec before each presentation of the visual stimulus
(Fig. 1, top; see stimulus traces above raster
plots). The EEG and multiunit responses were continuously evaluated
on-line with a raster display.
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Figure 1.
Experimental design and data analysis. Spike
activity at up to four sites (top right, receptive field
for only one site depicted here) was recorded along with the EEG over
several hours, under constant levels of anesthesia, in blocks of 10 trials of consecutive presentations of an invariant visual stimulus
(top right). The structure of each trial is seen in more
detail in the raster display (multi-unit activity); each
trial lasted 10 sec, during which the visual stimulus appeared at 3 sec
and remained on for 4.5 sec. In blocks with MRF stimulation
(top, gray blocks), a 75 Hz stimulation train was
delivered to the MRF 150 msec before each presentation of the visual
stimulus (see MRF stimulus bars above rasters). The
artifact of MRF stimulation is clearly visible in both multiunit and
EEG raster displays. Inset to the
left, Digitized trace of the spikes during the
visual response and the threshold used for spike discrimination. A 500 msec window of the multiunit responses and the EEG is expanded to show
the oscillatory patterning of the spike activity and activity in
the EEG during individual visual responses. Power spectra and
auto-correlation functions were calculated for the EEG and spike
activity, respectively, for each trial, and then averaged across the 10 trials in each block.
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Data analysis. All data analysis was performed using
software programmed by one of us (S.N.) in LabVIEW (National
Instruments) running on a Macintosh platform.
Oscillatory modulation and synchronization of multiunit neuronal
responses to patches of moving gratings were analyzed by computing
auto- and cross-correlation functions averaged over 10 consecutive
trials, with a resolution of 1 or 2 msec and over a 160 msec interval
(Fig. 1, multi-unit activity). A 4 sec analysis window was
used to cover most of the response epoch, beginning 100 msec after
visual stimulus onset to avoid the initial transient, stimulus-locked
component of the responses. For quantitative analysis, a damped cosine
(Gabor) function was fitted to the correlograms (König, 1994).
Shift predictor averaged correlograms were calculated, and once
established that these were flat, quantification ensued based on
non-shift predictor-subtracted correlation functions. This allowed the
strength of oscillatory modulation to be assessed from the ratio
between the amplitude of the first satellite peak and the offset of the
function fitted to auto-correlograms [modulation amplitude of the
first satellite peak (MAS)]. Similarly, synchronization was assessed
from the modulation amplitude (MA) of the central peak in the
correlograms, using the ratio between the peak amplitude and offset of
the fitted function for quantification. A correlogram was considered to
reveal oscillatory (or synchronized) activity if the first satellite
(or central) peak had a Z score >2 and an MAS (or MA)
 0.1. Because all of our recordings consist of multiunit activity, the
fitted central peak of the auto-correlograms, which excludes the 0 msec
bin, indicates the degree to which the activity of neighboring neurons
is synchronized. The average firing rate for each block of 10 responses
was expressed as the average number of spikes within the analysis
window per trial per second.
A total of 7281 auto-correlograms averaged over 10 consecutive
presentations of the visual stimulus were analyzed, which amounts to an
average of 137 auto-correlograms per recording site (range, 30-320
auto-correlograms per site). Of the 7281 averaged visual responses
analyzed, 2409 (33%) had been obtained during MRF stimulation. From
the visual responses at the 45 pairs of sites recorded simultaneously, 3331 averaged cross-correlograms were computed; of these, 30% corresponded to visual responses obtained during MRF stimulation.
At four recording sites, averaged auto-correlation functions calculated
over 4 sec suggested a superposition of oscillatory modulation in two
different frequency ranges, around 30 and 90 Hz. To determine whether
the two oscillation frequencies occurred simultaneously or successively
during visual responses, we analyzed all responses in the recording
session using a sliding window correlation algorithm. Averaged sliding
auto- and cross-correlation functions were calculated for a 150 msec
analysis window, which was placed at time 0 of each trial and moved in
steps of 75 msec until the end of the visual response. The analysis
windows were then normalized to the total number of spikes and averaged
across corresponding windows in ten trials.
To assess the central state at the time of the visual responses, the
power spectrum of the EEG was calculated over the same analysis window
as the correlation functions (Fig. 1, EEG). Power spectra
were averaged over the 10 consecutive trials in each block. The
relative power in different frequency bands was obtained by normalizing
the integral power in each band to the total power of the spectrum
calculated between 1 and 120 Hz. Discrete peaks in EEG power were
observed in four frequency bands, of 1-4, 4-8, 8-18, and 21-71 Hz;
for the sake of simplicity, these bands will be referred to as ,
 ,  , and , respectively.
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RESULTS |
Oscillations in visual responses
Thirty-nine (74%) of the 53 sites recorded in areas 17 and 18 exhibited oscillatory visual responses in at least one of the averaged
auto-correlograms obtained during the 3- to 10-hr-long recording
sessions. In no case was oscillatory modulation phase-locked to the
visual stimulus or to the screen refresh rate (see Materials and
Methods). Overall, oscillation frequency ranged from 20 to 105 Hz, but
at each oscillatory site it was either restricted to one of two
frequency ranges, between 20 and 65 Hz at 30 sites and between 70 and
105 Hz at 3 sites, or alternated with no gradation between them at 6 sites (Fig. 2). Oscillatory modulation in
the two frequency ranges differed markedly with respect to its onset latency and duration. Oscillatory modulation in the range between 70 and 105 Hz occurred at the beginning of the responses and lasted maximally 1 sec (Fig. 2, top right), similar to retinal
oscillations (Neuenschwander et al., 1996b), whereas the modulation in
the 20-65 Hz range tended to increase over several hundred
milliseconds after response onset and lasted until the visual stimulus
was turned off (Fig. 2, top left). Henceforth, we shall
refer to oscillatory modulation in the 20-65 and 70-105 Hz frequency
ranges as frequency and retinal-like
oscillations, respectively. At four recording sites, retinal-like
oscillations occurred at the beginning of single visual responses and
were followed by frequency oscillatory modulation that persisted
until the offset of the visual stimulus. In such cases, averaged
correlation functions were calculated for two nonoverlapping windows
that contained oscillatory modulation in either of the two frequency
ranges, and for statistical analysis, the respective data were treated
as if recorded independently. Altogether, frequency oscillations
were analyzed at 36 recording sites, and retinal-like oscillations were
recorded at nine sites.
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Figure 2.
Center graph, Relative occurrence
of oscillation frequencies (y-axis) from 15 to
110 Hz (x-axis) at each of 39 recording sites
(z-axis). Bottom, Averaged distribution
of oscillation frequencies across all recording sites. The relative
occurrence of oscillation frequencies in the range of 15-110 Hz was
first calculated in 5 Hz bins for each recording site (center
graph); each bin was then summed and averaged across all 39 recording sites (bottom). The distribution is clearly
bimodal, clustering around 30-35 and 85-90 Hz. Top
insets, Sliding window (150 msec windows, 75 msec steps)
averaged auto-correlation functions at two different times for the
recording site indicated in red in the center
graph, showing oscillatory activity at 35 Hz
(left) and 89 Hz (right). Notice the
difference in response onset and duration between the two oscillation
frequencies.
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When visual responses exhibited oscillatory modulation, of either frequency or the retinal-like type, the EEG displayed on average
88 ± 49% higher and 24 ± 21% lower power than
when responses were nonoscillatory (p < 0.0001, Wilcoxon signed rank test; Fig. 3,
bottom and top graphs). No consistent relations existed between the occurrence of frequency oscillatory response modulations and or EEG power (Fig. 3, middle
graphs).
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Figure 3.
Scatterplots comparing the average power in
different EEG bands during epochs associated with oscillatory
(ordinate) and nonoscillatory (abscissa)
responses. When visual responses exhibited an oscillatory modulation,
the EEG contained lower relative and higher relative activity
than when responses were not oscillatory. Each point
represents one recording site (n = 39).
Symbols, Recording sites exhibiting either frequency
(circles) or retinal-like (triangles)
oscillations. Each block of 10 trials was classified as oscillatory or
not, and the concurrent averaged relative EEG power in each band was
pooled and averaged accordingly. Filled symbols,
Averaged EEG power significantly different between oscillatory and
nonoscillatory responses; p < 0.05, Mann-Whitney
U test (indicated in each graph); open
symbols, no significant difference. Notice that although frequency oscillations had no consistent relationship with EEG
activity, retinal-like oscillations occurred at five of nine sites with
significantly stronger and at none with weaker EEG activity than
nonoscillatory responses.
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frequency oscillations: effect of MRF stimulation on incidence
and strength
The probabilities of oscillatory patterning differed markedly
between sites and depended on the state of activation. The proportion of averaged auto-correlograms exhibiting significant oscillatory modulation varied from 9 to 97% at the different recording sites exhibiting oscillatory responses. In the absence of MRF stimulation, 28% of the 2918 averaged auto-correlograms recorded from 36 sites exhibited frequency oscillations. With MRF stimulation, the incidence of frequency oscillations increased to 56%
(p < 0.0001, Wilcoxon signed rank test),
reaching 100% at several sites (Fig. 4A). At five sites, frequency oscillations occurred only with MRF stimulation.
Additionally, MRF stimulation increased the strength of frequency
oscillatory modulation by an average of 55 ± 98% across all
sites (p < 0.01, Wilcoxon signed rank test;
Table 1). Oscillation strength at this
frequency range was increased significantly (p < 0.01) at 15 of the 31 sites showing oscillatory modulation in the
absence of MRF stimulation (Fig. 4B) and decreased at
none of the sites (Table 1; range, 30-482% increase with MRF).
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Figure 4.
Effect of MRF stimulation on oscillatory
modulation of visual responses. Scatterplots comparing percentile of
averaged auto-correlation functions indicative of oscillatory
modulation (A) and average oscillation strength
(B) at each recording site, obtained with
(ordinate) and without (abscissa) MRF
stimulation. Each point represents one recording site.
Circles, Sites exhibiting frequency oscillations
only; crosses, sites exhibiting retinal-like
oscillations only; triangles, sites exhibiting both
types of oscillations.
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The increased incidence and strength of frequency oscillatory
modulation with MRF stimulation could be attributed to the stable
enhancement of EEG activation produced by this treatment. During the
30-60 min period when MRF stimulation preceded each presentation of
the visual stimulus, the EEG became characterized by strong and stable
frequency activity (average increase, 67 ± 56%;
p < 0.001, Wilcoxon signed rank test), accompanied by
decreased relative content (average decrease, 27 ± 27%;
p < 0.01, Wilcoxon signed rank test; Fig.
5, shaded columns). In the
absence of MRF stimulation, the EEG power content exhibited marked
variation over time in each frequency band. Epochs of strong EEG
activity also occurred spontaneously but then never lasted longer than 10 consecutive minutes. During these epochs neuronal responses often
exhibited frequency oscillations (Fig. 5, nonshaded
columns).
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Figure 5.
Synchronous oscillatory modulation of visual
responses appears and disappears simultaneously in areas 17 and 18 concurrently with changes in the level of cortical activation occurring
spontaneously or in response to MRF stimulation. A,
Comparison of the time course of changes in EEG power (top
box) with response variables (bottom boxes) at
three recording sites in left area 18 (LA18), right area
18 (RA18), and right area 17 (RA17): firing rates, oscillation strength,
oscillation frequency, and synchronization between RA18 and RA17. As
the EEG becomes dominated by activity in the frequency range,
firing rates increase at recording site LA18 and decrease at recording
sites RA18 and RA17; responses at these two sites then begin to exhibit oscillatory
modulation in the frequency range, and their synchronization
becomes stronger. Shaded bars, Periods when MRF
stimulation precedes each presentation of the visual stimulus.
B, Receptive fields of the recording sites and visual
stimulus. C, From top to
bottom, averaged power spectra and auto- and
cross-correlation functions for sites RA18 and RA17 measured at the
times indicated by the arrows in A
(duration of analysis window, 4 sec).
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Correlation with EEG activation
frequency oscillatory modulation of visual responses occurred
reliably once EEG power was below and power above a critical level (Fig. 6). A critical limit could be defined, for 22 of 36 sites, as the relative
power of the EEG above which visual responses had a 90%
probability of exhibiting oscillatory modulation. At different sites,
the critical limit for the occurrence of oscillatory modulation
ranged from 4 to 29%, averaging 20 ± 8% relative power of
the EEG. A similar critical limit, below which oscillatory modulation had a 90% probability of occurring in
visual responses, could also be defined, albeit for fewer sites (n = 15). The average critical limit ranged between
10 and 32%, averaging 25 ± 11% relative EEG activity.
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Figure 6.
Relationship between strength of oscillatory
modulation and EEG activation. A, Three recording sites
are shown: two exhibiting frequency oscillations (the site depicted
on the left corresponds to RA17 in Fig.
5) and one exhibiting retinal-like oscillations. Each
point represents the strength of oscillatory modulation
of visual responses (MAS, ordinate) and concurrent
relative power of the EEG in the and frequency bands
(abscissa) averaged over 10 consecutive trials.
Insets, Spearman correlation coefficients;
*p < 0.01. Circles, Without MRF
stimulation; crosses, with MRF stimulation.
B, Distribution of Spearman correlation coefficients
obtained for each recording site from all data points (with and without
MRF stimulation). Filled bars, p < 0.01. C, Correlation between Spearman coefficients
calculated from all data points combined (with and without MRF
stimulation, ordinate) and Spearman coefficients
obtained exclusively from trials without MRF stimulation
(abscissa). Five recording sites exhibited oscillatory
modulation only during MRF stimulation and are therefore not included.
Insets, Linear correlation coefficients;
p < 0.01.
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Once above the or below the critical limit of EEG activation,
frequency oscillations varied in strength as a function of and
EEG activity (average correlation coefficients, 0.361 ± 0.339 and  0.261 ± 0.392, respectively; p < 0.001;
Table 2). According to a
p < 0.01 significance criterion, frequency
oscillations increased at 19 of 36 sites and in no case decreased with
increasing EEG power content. Likewise, frequency oscillations
increased in strength at 18 of 36 sites with decreasing and at only
one site with increasing (Table 2, Fig. 6). At a few sites the strength of frequency oscillations correlated also with changes in
or EEG power, but there was no consistent trend across recording sites (Table 2).
Oscillation frequency versus oscillation strength
So far, other studies have not found an explanation for the
variability in oscillation frequency across visual responses at a given
site (Engel et al., 1990; Livingstone, 1996). We found that oscillation
frequency in the frequency range decreased significantly
(p < 0.01) with increasing oscillation strength at 20 of 36 sites (Fig. 7A;
average Spearman correlation coefficient for the 20 sites, 0.539 ± 0.175). In no case was stronger oscillatory modulation significantly
associated with increased oscillation frequency (Fig.
7B).
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Figure 7.
Relationship between oscillation strength and
frequency for frequency oscillations. A, Scatterplot
for one recording site comparing the strength of oscillatory modulation
(MAS, ordinate) of visual responses with oscillation
frequency (hertz, abscissa). Each point
represents averages over 10 consecutive trials.
Inset, Spearman correlation coefficient;
p < 0.01. Circles, Without MRF
stimulation; crosses, with MRF stimulation.
B, Distribution of Spearman correlation coefficients
between oscillation strength and frequency across all sites exhibiting
frequency oscillations. Shaded bars, correlation
significant at p < 0.01.
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Effect of MRF stimulation on visual response
oscillation frequency
Because oscillation frequency was related to oscillation strength,
and the latter depended on EEG activation, we next examined whether
oscillation frequency was modulated together with EEG activation by MRF
stimulation. The overall effect of MRF stimulation was a significant
decrease in the average oscillation frequency in the range (Table
1). Eleven of 31 sites that exhibited frequency oscillations in the
absence of MRF stimulation underwent a significant
(p < 0.01) reduction in oscillation frequency
with MRF stimulation, which on average decreased from 43 ± 5 to
36 ± 5 Hz (Fig.
8A); oscillation
frequency remained unchanged at the other 20 sites (Table 1).
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Figure 8.
Effect of MRF stimulation on visual response
oscillation frequency in the frequency range. Scatterplots
comparing averaged oscillation frequency (A) and
its SD (B) at different recording sites, when
measured with (ordinate) and without
(abscissa) MRF stimulation. Each point
represents one recording site.
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The variability in oscillation frequency was consistently reduced with
MRF stimulation (Fig. 8B), the sample variance of
oscillation frequency decreasing by a factor of 3 at individual
recording sites (p < 0.0001, Wilcoxon signed
rank test). The stabilizing effect of MRF stimulation on EEG activation
and on oscillation frequency of the multiunit responses may reflect the
stabilization of the dynamics of underlying cortical circuits.
Relation between oscillation frequency and EEG activation
Interestingly, there was no consistent correlation across the
entire sample between oscillation frequency and EEG power, the
average correlation coefficient being 0.140 ± 0.475 across the 36 sites exhibiting frequency oscillations (p = 0.6177, one-sample rank test). This was, however, not attributable to independence between oscillation frequency and EEG power; rather, these two parameters were positively or negatively correlated at
different sites (Fig. 9A).
With increasing power of the EEG, oscillation frequency in the
20-65 Hz range increased significantly (p < 0.01) at 10 and decreased at 9 of 36 sites (Fig. 9B).
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Figure 9.
Relationship between oscillation frequency and
relative EEG power. A, Scatterplots for visual
responses obtained at three different recording sites (1 per
box). The oscillation frequency of visual responses
(hertz, ordinate) is plotted against the concurrent
relative power of the EEG (percent, abscissa). Each
point represents the averaged values from 10 consecutive
trials. Inset, Spearman correlation coefficients;
p < 0.01; ns, nonsignificant.
Circles, Without MRF stimulation;
crosses, with MRF stimulation. B,
Distribution of Spearman correlation coefficients between oscillation
frequency and EEG power across all sites. Shaded
bars, Correlation significant at p < 0.01.
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Retinal-like oscillations
Effect of MRF stimulation on incidence and strength
Retinal-like 70-105 Hz oscillations were affected by MRF
stimulation in a strikingly different manner. With MRF stimulation, retinal-like oscillations were suppressed completely at seven of nine
sites and decreased in strength at one (Fig. 4B,
crosses). At the six sites that showed both frequency and
retinal-like oscillations during spontaneous variations in EEG
activation, only the former persisted during MRF stimulation.
Correlation with EEG activation
EEG activation was stronger during responses exhibiting
retinal-like oscillations than during nonoscillatory responses (Fig. 3,
triangles), suggesting that the occurrence of retinal-like oscillations in cortical visual responses also required EEG activation. However, the strength of these retinal-like oscillations was only weakly correlated with EEG power in the different frequency bands (Fig.
6). The lack of a consistent covariation between EEG activation and
strength of retinal-like oscillations was probably attributable to the
suppression of these oscillations once EEG activation exceeded a
certain value.
Oscillation frequency
The oscillation frequency of retinal-like oscillations was at four
of nine sites correlated at the p < 0.01 level with
oscillation strength, stronger oscillations exhibiting lower
oscillation frequency (average Spearman correlation coefficient,
0.618 ± 0.184). Oscillation frequency in the 70-105 Hz range,
however, was never correlated with EEG activity (see example in
Fig. 9).
Relationship between retinal-like and frequency oscillations
At the six sites that exhibited oscillatory modulation in both
frequency ranges, retinal-like oscillations in general disappeared when
frequency oscillations appeared in the responses (Fig. 10). As cortical activation increased
spontaneously, the temporal patterning of cortical responses changed in
the course of successive visual responses first from nonoscillatory to
retinal-like and then to frequency oscillatory modulation (Fig. 10,
first four columns). With MRF stimulation, which induced
maximal EEG activation and strongest oscillatory modulation in the frequency range, retinal-like oscillations were suppressed. As EEG
activation declined, either spontaneously or at the end of the MRF
stimulation period, retinal-like oscillations reappeared and persisted
at levels of activation at which frequency oscillations had already
disappeared (Fig. 10, last column to the right).
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Figure 10.
Transition from nonoscillatory to retinal-like
and then to frequency oscillatory responses with increasing EEG
activation. Top row, Averaged EEG power spectra at
consecutive time points without MRF stimulation. Center
rows, Averaged sliding window auto-correlograms for responses
from sites recorded simultaneously from area 17 in the two hemispheres
[right area 17 (RA17), left area 17 (LA17)]. Bottom row, Averaged
sliding window cross-correlograms for the two recording sites. Sliding
window size, 150 msec; step size, 75 msec; bin width, 2 msec. All
sliding window correlation functions are normalized to the total number
of spikes in the period. The time course of visual stimulation is
indicated at the bottom. Oscillatory modulation was in
general absent when the EEG was dominated by activity (left
column). When the EEG content increased, the initial phase
of the light responses exhibited retinal-like oscillations at ~95 Hz
that appeared simultaneously at the recording sites in both hemispheres
(compare first three columns). As EEG power
increased further, retinal-like oscillatory modulation decreased again
and gave way to a sustained oscillatory modulation in the frequency
range (30-40 Hz, fourth column). Retinal-like
oscillations at this time had disappeared from the responses at site
RA17. At site LA17 there is a smooth transition between retinal-like
and frequency oscillations that is readily seen in single
responses. As EEG activation decreased again (right
column), frequency oscillations disappeared, whereas the
transient retinal-like oscillations are again well expressed in the
visual responses at both sites.
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At three of the six sites showing both frequency and retinal-like
oscillations, the latter occurred at levels of and EEG activity
intermediate to those observed when oscillatory modulation was absent
or occurred in the frequency range (Mann-Whitney U
test, p < 0.01; Fig. 10). Together with the fact that
in the transition from weak to strong EEG activation retinal-like
oscillations preceded frequency oscillations, this finding suggests
that the occurrence of both types of oscillations require a certain level of EEG activation, but that the level required for the occurrence of retinal-like oscillations is lower than for the occurrence of frequency oscillations.
Synchronization of visual responses across sites
If response synchronization plays a role in visual information
processing, it should, like visual experience, be modulated with
central state. We examined this hypothesis by looking at the variation
in response synchronization during different levels of EEG activation.
Twenty-four of the 45 pairs of sites recorded simultaneously exhibited
at least one averaged cross-correlogram indicative of response
synchronization in the millisecond range during the 3- to 10-hr-long
recording sessions. This synchronization was not attributable to
stimulus-locked rate covariations, as indicated by flat shift
predictors. In all 24 pairs, the cross-correlograms exhibited
significant satellite peaks, indicating that the underlying responses
were oscillatory. This agrees with the observation that responses from
at least one of the recording sites in each pair showed
oscillatory patterning in the auto-correlogram.
When visual responses were synchronized across sites, the EEG contained
on average 18 ± 22% lower and 66 ± 52% higher than when no synchronization occurred (p < 0.01, Wilcoxon signed rank test; Fig.
11, top and bottom
graphs; n = 20; four pairs had to be excluded
because all or almost all cross-correlograms exhibited synchronization). Two pairs of recording sites exhibited synchronous oscillations at both 20-65 and 70-105 Hz; at both frequency ranges, the EEG contained lower and higher activity when responses were
synchronized than when no synchronization occurred (Fig. 11,
triangles).
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Figure 11.
Scatterplots comparing the power in different EEG
bands (1 band per box) in epochs associated with
synchronized (ordinate) and nonsynchronized
(abscissa) responses. When visual responses exhibited
synchronization, the EEG contained lower relative and higher
relative activity than when the responses were not synchronized.
Each point represents one recording site
(n = 20). Symbols, Pairs of sites
exhibiting frequency (circles) or retinal-like
(triangles) oscillations. Responses averaged over each
block of 10 trials were classified as synchronized or not, and the
concurrent averaged relative EEG power in each band was pooled and
averaged accordingly. Filled symbols, Averaged EEG power
significantly different between synchronized and nonsynchronized
responses; p < 0.05, Mann-Whitney
U test (indicated in each graph); open
symbols, no significant difference.
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Effect of MRF on incidence and strength
Of the total sample of 2025 averaged cross-correlograms analyzed
for the 24 pairs, 1293 (68%) showed synchronization (range, 7-100%
for the different pairs). In 18 of 21 pairs, synchronization of visual
responses occurred more frequently during MRF stimulation (84%) than
during spontaneous EEG fluctuations (60%, p < 0.01, Wilcoxon signed rank test; two pairs not tested under MRF stimulation and another exhibiting synchronization at all times were excluded; Fig.
12). For one pair of sites,
synchronization occurred only when EEG activation was enhanced by MRF
stimulation.
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Figure 12.
Effect of MRF stimulation on incidence of
response synchronization. Scatterplot comparing percentile of
cross-correlation functions indicative of synchronization at each pair
of recording sites for responses obtained with
(ordinate) and without (abscissa) MRF
stimulation. Each point represents one recording site.
Circles, Synchronization across sites exhibiting only
frequency oscillations; triangles, synchronization
across sites exhibiting either frequency or retinal-like
oscillations.
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Additionally, the strength of synchronization across recording sites
increased with MRF stimulation in 8 of 17 pairs exhibiting a sufficient
number of synchronized correlograms to permit comparison (Table 2;
average increase, 55 ± 74% over all 17 sites; p < 0.01, Mann-Whitney U
test). In none of the pairs did MRF stimulation reduce synchronization strength.
Correlation between strength of synchronization across sites and
EEG activation
In addition to the dependence of the occurrence of response
synchronization on EEG activation, the strength of response
synchronization was correlated with and EEG power (average
correlation coefficients, 0.364 ± 0.274 and 0.256 ± 0.302, respectively; Table 2). According to a significance criterion of
p < 0.01 (Spearman correlation test), the strength of
synchronization across sites increased with EEG activity in 13 of
22 pairs (Fig. 13A-C, Table
2; one pair showing synchronization only during MRF stimulation was
excluded). In no case was increased EEG power associated with
reduced synchronization (Table 2). Synchronization became weaker with
increasing EEG activity in nine pairs and stayed unchanged in the
other pairs. The strength of synchronization across sites was
occasionally correlated with changes in and EEG power but with
no consistent sign (Table 2).
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Figure 13.
Relationship between EEG activation and strength
of synchronization across (A) or within
(B) recording sites. A, Data from
three different recording site pairs recorded in different sessions
(columns). Each point in the scatterplots
represents the strength of synchronization in visual responses (MA,
ordinate) and concurrent relative power of the EEG in
the various frequency bands (abscissa) averaged over 10 consecutive trials. Insets, Spearman correlation
coefficients; *p < 0.01. B,
Distribution of Spearman correlation coefficients obtained for each
recording pair from all data points (with and without MRF stimulation).
Filled bars, p < 0.05. C, Correlation between Spearman coefficients calculated
from all data points combined (with and without MRF stimulation,
ordinate) and Spearman coefficients obtained exclusively
from trials without MRF stimulation (abscissa). One
recording pair exhibited synchronization only during MRF stimulation
and is therefore not included. Insets, Linear
correlation coefficients; p < 0.01 and 0.02, respectively. D-F, Local synchronization of visual
responses. Conventions as in A-C. Two of the three
sites in A exhibited oscillatory modulation
(left and center columns), one did not
(right column). F, insets, Linear
correlation coefficients; p < 0.0001.
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Local synchronization of visual responses
Local synchronization of the multiunit responses recorded
simultaneously from one electrode was assessed by calculating the relative modulation amplitude of the center peak of the
auto-correlograms (see Materials and Methods). The strength of local
synchronization increased with MRF stimulation by 68 ± 107%
(p < 0.001; Table 1). According to a
significance criterion of p < 0.01 (Mann-Whitney U test), local synchronization strength increased at 23 and
decreased at only 3 of 36 sites exhibiting frequency oscillations
(Table 1). This suggests that local synchronization of oscillatory
visual responses increased with EEG activation.
As expected from the coherent effects of MRF stimulation on
synchronization and on the EEG, there was a close correlation between
the increase in local synchronization strength and in power of the
EEG across visual responses (average correlation coefficient,
0.341 ± 0.350; p < 0.0001, Wilcoxon signed rank
test). At 18 of the 36 sites exhibiting frequency oscillations, the correlation between local synchronization strength and EEG power was positive and significant at the p < 0.01 level
(Fig. 13D-F). Local synchronization decreased with
increasing EEG power at only one site. In contrast, at 3 of the 14 sites that at no time showed oscillatory responses, local
synchronization decreased significantly with increasing EEG activity.
Synchronization versus oscillatory modulation
In the pairs exhibiting synchronized visual responses, the
cross-correlograms often showed an oscillatory modulation, and the
corresponding auto-correlograms were oscillatory at least at one of the
two sites. We wondered therefore whether the strength of
synchronization and of oscillatory modulation were related, as
suggested by previous studies (König et al., 1995; Volgushev et
al., 1998). Comparison of the two variables revealed that the amplitude
of frequency oscillatory modulation at one or both sites was
positively correlated at the p < 0.01 level with the strength of synchronization across sites in 14 of 18 pairs (0.699 ± 0.122, average Spearman rank correlation; Fig.
14, top). Six of the 24 pairs were excluded because the number of correlograms indicative of
synchronous responses was too small for statistical evaluation
(n < 15). Likewise, the strength of oscillatory
modulation was also positively correlated at the p < 0.01 level with local synchronization at 30 of the 39 sites exhibiting
oscillations (average Spearman correlation coefficients: frequency
oscillations, 0.699 ± 0.148; n = 26; retinal-like
oscillations, 0.652 ± 0.229; n = 4; Fig. 14,
bottom). In no case was there a significant
(p < 0.01) negative correlation between the
strength of synchronization and the strength of oscillatory modulation
of the visual responses (Fig. 14, right).
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Figure 14.
Relationship between synchronization and
oscillation strength of visual responses. Left, Examples
of scatterplots of synchronization strength (MA,
ordinates) and oscillation strength (MAS,
abscissa), where each point represents
the average over 10 consecutive trials. Top,
Synchronization across two recording sites; bottom,
local synchronization at a different site. Insets,
Spearman correlation coefficient; p < 0.01. Circles, Without MRF stimulation;
crosses, with MRF stimulation. Right,
Distribution of Spearman correlation coefficients between strength of
oscillation and of synchronization across sites (top) or
locally (bottom). Shaded bars,
Correlation significant at p < 0.01.
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Interdependence of oscillation frequency, oscillatory modulation,
synchronization, and EEG activation
An increase in synchronization strength often went along with a
decrease but never with an increase in oscillation frequency of the
visual responses. For local synchronization, this relation reached
significance (p < 0.01) at 25 of 39 oscillatory
sites and for synchronization across sites in 15 of 18 pairs (average Spearman correlation coefficients, 0.644 ± 0.204 and
0.614 ± 0.174, respectively; Fig.
15, right column).
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Figure 15.
Relationship between local synchronization
(top row) or synchronization across sites (bottom
row) and power of the EEG (Sync × EEG , left column), oscillatory
modulation (Sync × Osc mod,
center column), and oscillation frequency
(Sync × Osc frq, right
column). Two sites were recorded from A17 of the same
hemisphere; the strength of local synchronization is depicted for only
one of the two sites. Ordinates, Strength of
synchronization (MA). Abscissas, from
left to right, Relative power of the
EEG, strength of oscillatory modulation (MAS), and oscillation
frequency (hertz) at the site depicted in the top row.
Examples of correlograms from the two sites are displayed in Figure 16.
Circles, No MRF; crosses, during MRF
stimulation. Insets, Spearman correlation coefficients;
p < 0.01.
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At 21 of 36 recording sites, increasing local synchronization was
correlated at the p < 0.01 level with both
increasing oscillatory modulation and decreasing oscillation frequency
and at 12 of these sites additionally with increasing EEG power
(Fig. 15, top row). Likewise, in 12 of 18 pairs stronger
intersite synchronization was correlated with both lower
oscillation frequency and stronger oscillatory modulation at least at
one of the sites and additionally with higher EEG power in 7 of
these pairs (Fig. 15, bottom row).
For the nine sites where the oscillation frequency of visual
responses decreased with increasing power of the EEG,
the strength of the oscillations and of the synchronization increased consistently with decreasing oscillation frequency and increasing EEG power (Fig. 16, compare
auto-correlograms from left to right). As the power of the EEG increased, the modulation of the frequency oscillations became stronger, and their oscillation frequency decreased. Local synchronization also increased significantly with both
increasing oscillatory modulation and decreasing oscillation frequency
at seven of the nine recording sites and with increasing EEG power
at six of these sites. Likewise, the strength of synchronization across
sites increased for 12 pairs with increasing oscillatory modulation and
decreasing oscillation frequency in at least at one of the sites and
with increasing EEG power in seven of these pairs (Figs. 15, 16,
compare correlograms from left to right).
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Figure 16.
Covariation of EEG activity with oscillation
frequency, oscillatory modulation, local synchronization, and
intrahemispheric synchronization of visual responses recorded from two
sites in A17 (e3, e4, same as in Fig.
15). Top row, Averaged EEG power spectra at four
nonconsecutive time points, the second one having been obtained during
MRF stimulation. Numbers refer to relative power in
the respective epochs. Second row, Averaged
auto-correlation functions of visual responses recorded during
corresponding epochs from site e3; third row, averaged
auto-correlation functions of responses from e4; bottom
row, averaged cross-correlation functions of visual responses
across sites e3 and e4. For each correlogram, oscillation frequency
(hertz), oscillation strength (MAS), and synchronization strength (MA)
and phase ( , milliseconds) are indicated. With increasing relative
content in the EEG (left to right
columns), the oscillation frequency of the visual responses
decreases from ~60 to <30 Hz at both sites. At the same time, the
strength of oscillatory modulation of local synchronization and of
synchronization across the sites increases.
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Taken together, our results demonstrate that oscillatory modulation,
oscillation frequency, and synchronization are interrelated. Moreover,
all three variables depend on EEG activation, whereby this dependence
is most consistent for the strength of oscillation and of
synchronization. Because visual responses have been reported to change
in strength with central states, we next examined how oscillatory
modulation and synchronization related to response firing rates.
Firing rates
Correlation with EEG activation
Overall, variations in response firing rate were not significantly
correlated with EEG power (Table 2). This was, however, not
attributable to independence between firing rates and EEG power;
rather, these two parameters were often significantly correlated but
with opposite signs at different sites (Fig.
17). With spontaneously increasing power of the EEG, response firing rates increased significantly
(p < 0.01) at 27 and decreased at 7 of 53 sites (Table 2). At 21 of these 34 sites, changes in firing rates were also
significantly (p < 0.01) correlated with
changes in EEG power, and as expected, this relation was inverse to
that found for the dependence on EEG power. At five other sites,
firing rates correlated only with and not with power,
positively at three sites and negatively at two. At another two sites,
response firing rates correlated exclusively with changes in EEG
power, negatively at one site and positively at the other. At three
sites, response firing rates correlated exclusively with EEG power, in all three cases positively, and at one site the firing rate was
positively correlated with changes in both and EEG power. On
several occasions, discharge rates at different, simultaneously recorded sites changed in opposite directions as a function of EEG
activation (see Fig. 5).
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Figure 17.
Relationship between firing rates and EEG
activation. A, Three different recording sites are
illustrated (a-c). Each point in the
scatterplots represents the response firing rate at a recording site
and the concurrent relative power of the EEG in the (top
row) and (bottom row) frequency bands,
averaged over 10 consecutive trials. Circles, No MRF;
crosses, during MRF stimulation. Insets,
Spearman correlation coefficient; *p < 0.01. B, Distribution of Spearman correlation coefficients
obtained for each recording site from all data points (with and without
MRF stimulation). Filled bars, p < 0.01. C, Correlation between Spearman coefficients
calculated from all data points combined (with and without MRF
stimulation, ordinate) and Spearman coefficients
obtained exclusively from trials without MRF stimulation
(abscissa). Insets, Linear correlation
coefficients; p < 0.0001.
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Effect of MRF stimulation
The general effect of MRF stimulation was a decrease in
response firing rates by 18 ± 48%; however, firing rates could
either increase or decrease at different sites during this treatment (Table 1). At the p < 0.01 significance level
(Mann-Whitney U test), MRF stimulation had significant
effects on average response firing rate at 33 of the 53 sites, causing
an increase at 10 sites (average, 42 ± 26%) and a decrease at 23 sites (average, 27 ± 21%; Fig.
18A). Because MRF
stimulation caused in most cases an increase in EEG power, this
implies that MRF stimulation affected firing rates and frequency
oscillations and synchronization differently. This is in line with the
fact that changes in firing rate with MRF stimulation were not always
in the same direction as changes associated with spontaneous EEG
activation. Of 27 recording sites at which response rates
increased with spontaneously increasing EEG activity, 8 showed an increase and another 8 showed a decrease in firing
rate with MRF stimulation (Fig. 17c), indicating that there
is no simple correlation between firing rates and EEG activation. Of
the seven sites at which response rates decreased with increasing EEG
activity, six also showed a decrease with MRF stimulation. The
remaining site was unaffected by MRF stimulation. However, irrespective
of whether MRF stimulation enhanced or reduced firing rates, it
stabilized the responses, reducing sample variance by 56 ± 40%
(p < 0.0001, Wilcoxon signed rank test; Fig.
18B).
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Figure 18.
Effect of MRF stimulation on visual response
firing rates. Scatterplots comparing averaged firing rate
(A) and its SD (B) at each
recording site, obtained with (ordinate) and without
(abscissa) MRF stimulation. Each point
represents one recording site.
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Relation among firing rate, oscillations,
and synchronization
Variations in firing rate alone could not account for the
variations in oscillatory modulation or synchronization. When frequency oscillation strength increased with increasing EEG power
(n = 19 sites), firing rates increased at six sites,
decreased at five sites, and remained unchanged at eight sites
(significance criterion, p < 0.01, Spearman
correlation test). Likewise, of the 13 recording pairs for which
synchronization across sites increased coordinately with EEG
activation, response rates changed jointly at both sites in 4 pairs (as
in Fig. 5), changed in opposite directions in 7& |
TOP
ABSTRACT
INTRODUCTION
