Department of Cell Biology and Anatomy, Louisiana State University Medical Center, New Orleans, Louisiana 70112
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ABSTRACT |
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Weyand, Theodore G., Michael Boudreaux, and William Guido. Burst and Tonic Response Modes in Thalamic Neurons During Sleep and Wakefulness. J. Neurophysiol. 85: 1107-1118, 2001. Thalamic neurons can exhibit two distinct firing modes: tonic and burst. In the lateral geniculate nucleus (LGN), the tonic mode appears as a relatively faithful relay of visual information from retina to cortex. The function of the burst mode is less understood. Its prevalence during slow-wave sleep (SWS) and linkage to synchronous cortical electroencephalogram (EEG) suggest that it has an important role during this form of sleep. Although not nearly as common, bursting can also occur during wakefulness. The goal of this study was to identify conditions that affect burst probability, and to compare burst incidence during sleeping and waking. LGN neurons are extraordinarily heterogenous in the degree to which they burst, during both sleeping and waking. Some LGN neurons never burst under any conditions during wakefulness, and several never burst during slow-wave sleep. During wakefulness, <1% of action potentials were associated with bursting, whereas during sleep this fraction jumps to 18%. Although bursting was most common during slow-wave sleep, more than 50% of the bursting originated from 14% of the LGN cells. Bursting during sleep was largely restricted to episodes lasting 1-5 s, with ~47% of these episodes being rhythmic and in the delta frequency range (0.5-4 Hz). In wakefulness, although visual stimulation accounted for the greatest number of bursts, it was still a small fraction of the total response (4%, 742 bursts/17,744 cycles in 93 cells). We identified two variables that appeared to influence burst probability: size of the visual stimuli used to elicit responses and behavioral state. Increased stimulus size increased burst probability. We attribute this to the increased influence large stimuli have on a cell's inhibitory mechanisms. As with sleep, a large fraction of bursting originated from a small number of cells. During visual stimulation, 50% of bursting was generated by 9% of neurons. Increased vigilance was negatively correlated with burst probability. Visual stimuli presented during active fixation (i.e., when the animal must fixate on an overt fixation point) were less likely to produce bursting, than when the same visual stimuli were presented but no fixation point present ("passive" fixation). Such observations suggest that even brief departures from attentive states can hyperpolarize neurons sufficiently to de-inactivate the burst mechanism. Our results provide a new view of the temporal structure of bursting during slow-wave sleep; one that supports episodic rhythmic activity in the intact animal. In addition, because bursting could be tied to specific conditions within wakefulness, we suggest that bursting has a specific function within that state.
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INTRODUCTION |
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Owing to similarities in
receptive field structure, the lateral geniculate nucleus (LGN) appears
to largely function as a relay of visual information between the retina
and visual cortex. However, it is not a simple relay. Anatomical
studies indicate that <20% of afferent synapses are of retinal origin
(see Sherman and Guillery 1996, for recent review).
These other inputs likely provide a gain mechanism for controlling the
fidelity by which retinal inputs are transferred to cortex
(Livingstone and Hubel 1981
; Swadlow and Weyand
1985
; see reviews by McCormick and Bal 1997
;
Sherman 1996
; Singer 1977
) or synchronize
activity to boost the salience of spatially contiguous edges
(Sillito et al. 1994
). Bolstered by observations from
intracellular records, several groups (e.g., McCormick and
Feeser 1990
) have additionally promoted the idea that
the LGN operates as a switch. In the tonic or "on" mode, retinal
inputs are available for transfer to cortex, with the exact gain being
a function of the current potency and sign of nonretinal afferents. In
the "burst" or "off" mode, the membrane potential is
predominantly hyperpolarized, but occasional (or even periodic)
depolarization triggers a powerful low-threshold calcium conductance
(IT) that results in bursts of action
potentials. This burst mode is correlated with slow-wave sleep (SWS).
Whereas the function of the tonic mode is transparent, the function of the burst mode is obscure. Steriade et al. (1993)
recently speculated that because the burst mode is associated with
initiating and maintaining slow-wave sleep, its ability to drive the
cortex into oscillations may function to correct some ionic imbalances
caused by wakefulness. Although not nearly as frequent, bursting can also occur within wakefulness (Guido and Weyand 1995
;
McCarley et al. 1983
; Ramcharan et al.
2000
). Such observations are important, as they raise the
possibility that bursting is used as a distinct signal in sensory
processing (cf., Guido and Weyand 1995
; Guido et
al. 1995
; Sherman 1996
). The conditions under
which bursting occurs during wakefulness are not well-delineated.
Identifying these conditions was a major goal of the current study. A
second goal was to better delineate the incidence of bursting during slow-wave sleep. Several recent and influential reviews have portrayed thalamic neurons as "disconnected" from their sensory inputs during slow-wave sleep, whereupon such neurons become oscillatory as a result
of an interplay of their intrinsic conductances (McCormick and
Bal 1997
; Steriade et al. 1993
). Such portrayals
are a caricature of the one quantitative study of the temporal
structure of LGN activity during sleep and waking (McCarley et
al. 1983
). A reassessment of activity during slow-wave sleep
might help to clarify this important issue.
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METHODS |
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Initial surgery
All procedures were approved by the Institutional Care and Use
Committees at Louisiana State University Medical Center, and the
general methods have been described previously (Malpeli et al.
1992; Weyand and Gafka 1998
). Briefly, cats
underwent at least two sterile surgical procedures. In the first
surgery, we cemented an aluminum crown to the skull (to fix the head
during subsequent behavioral and recording sessions) and attached an
insulated wire loop to the sclera of one eye (to allow us to determine
gaze using the magnetic search coil technique) (Robinson
1962
). The anesthetized cat was placed in a stereotaxic frame,
and the fascia and muscle retracted to expose the skull. An aluminum
crown was custom-fitted to the contours of the skull and affixed to the
skull using stainless steel rod and bone cement. A Teflon-insulated
coil was then attached to the sclera of one eye similar to the methods
described by Judge et al. (1980)
. The leads from this
coil were fed under the skin and crown, and terminated by soldering
them to subminiature connectors. A fiberglass cover was attached to the
crown to protect the connectors and microelectrode drive when the
animal was not in the testing apparatus. The cat was removed from the
stereotaxic frame and returned to its home cage. Following training
(described in Training), the cat was subjected to a second
sterile surgery in which we implanted stimulating electrodes in visual
cortex and a swiveling base for holding a microelectrode drive
(Malpeli et al. 1992
). For this surgery, the
anesthetized cat was again placed in a stereotaxic frame, and holes
were drilled through the cement and bone to expose visual cortex and
the cortex overlying the LGN. Six to eight stimulating electrodes were
placed individually into the lateral gyrus (~1 mm apart and at depths
of 3-5 mm) through an intact dura. The electrodes were cemented into
place and the hole sealed with dental acrylic. A microelectrode base
with a 9-mm-long cannula was inserted through the hole over the LGN. A
protective stylus filled the cannula until we were ready to begin
recording sessions. The hole over the LGN was then sealed with dental
cement, and the cat removed from the stereotaxic frame and returned to
its home cage. To obtain electroencephalographic (EEG) records, we
either used a pair of unused stimulating electrodes, or, in one case,
we inserted a staggered pair (2-mm vertical separation) of
platinum-insulated wires into the contralateral cortex with the upper
tip flush with the cortical surface.
Training
Following at least 1 wk to recover from initial surgery, food deprivation was begun and 24 h later behavioral testing initiated. The cat was placed in a loose-fitting canvas bag and the head fixed to a Plexiglas frame. The cat faced a dimly lit (0.5 cd/m2) screen on which we could project images such as bars, square-wave gratings (contrast 0.4) and a 0.2° spot produced by a low-power laser that was dimmed with a 3.0 N.D. filter. The cat was trained to look at this spot, and if it jumped to a new location, make a saccade to reacquire fixation on the spot. The bars and gratings were used to probe the excitability of the cell under study and were never behaviorally relevant. The laser spot was always behaviorally relevant.
Testing/recording
Following at least 1 wk to recover from surgery to implant
stimulating electrodes, testing was initiated by placing the cat in the
apparatus and replacing the protective stylus with a tungsten-in-glass microelectrode (~1.0 M at 1 kHz). Signals from the microelectrode were amplified (×10,000), filtered (0.3-8 KHz, 24 dB/octave), and fed
to an oscilloscope and audio monitor. EEG signals were also amplified
(×10,000), filtered (1-60 Hz, 24 dB/octave), and passed to an
oscilloscope and an A/D (A-D) converter that digitized the signals at
250 Hz. Horizontal and vertical eye position signals were amplified,
filtered, and passed to an A-D converter that digitized these signals
at 250 Hz. All data acquisition, control of behavioral testing, and
stimulus display were under computer control. Single neurons were
isolated on-line using a voltage discriminator, whose output (pulses)
was fed to the computer. These pulses were then time-stamped (0.1-ms
resolution) and put into a data file that also included a record of eye
position, EEG, and stimulus status. For more than 30 recording
sessions, the eye position, EEG signals, and unit activity were also
passed to a VCR that digitized data at 22.5 kHz. These records were
used for off-line analysis and for producing figures of analog traces.
For most sessions, we used electrical stimulation of visual cortex to
help determine the position of the electrode in the brain. Electrical
stimuli consisted of 0.1-ms monophasic pulses of varying intensity.
Electrical activation was attempted for nearly all isolated neurons.
Antidromic activation was inferred by invariant latency and verified by
the test of impulse collision (Bishop et al. 1962). Some
neurons (especially those dorsal to the LGN) were activated
synaptically rather than antidromically.
Definition of bursts
Because we recorded extracellularly and filtered out activity
<300 Hz, we could not directly observe changes in membrane potential associated with activation of the low-threshold calcium conductance (IT). Lu et al. (1992)
showed that when two or more action potentials separated by <4 ms (250 Hz) are preceded by at least 100 ms of quiescence, the probability of
an underlying IT is better than 0.99. These criteria correspond to what has been used in previous studies of
bursting in anesthetized, paralyzed (Guido et al. 1995
; Lu et al. 1993
), and awake, behaving cat (Guido
and Weyand 1995
). Although we feel these criteria to be
sufficient, our results from activity patterns during sleep indicate
that they are overly conservative (cf., Lu et al. 1993
;
Ramacharan et al. 2000
). Therefore in presenting our
results, we have sometimes adopted more liberal criteria of two spikes
in 6.66 ms (150 Hz) preceded by 50 ms of quiescence. Adapting one
criteria or the other obviously alters the quantitative aspects of
burst incidence, but does not alter the major conclusions of the study.
In each figure and analysis, we indicate the criteria used:
conservative (4 ms or less interspike interval preceded by
100 ms or more of quiescence) or liberal (6.66 ms or less
interspike interval preceded by 50 ms or more of quiescence).
Two-dimensional interspike interval plots (joint-interval histograms,
JIHs) (McCarley et al. 1983
) shown in Figs. 2 and 6 are
particularly useful for viewing differences in burst incidence using
liberal or conservative criteria. In Fig. 2, we have inscribed two
"boxes" in the bottom right corner showing the
differences between using conservative (smaller box) or liberal (larger
box) criteria. For this representative example, changing criteria had essentially no effect in wakefulness, but increased the number of
bursts during slow-wave sleep by ~25%.
Definition of sleep
Slow-wave sleep was defined by the presence of dominant cortical
EEG spectra below 8 Hz. Although delta waves (0.5-4 Hz) were the most
impressive EEG event during slow-wave sleep (e.g., Fig. 1), these waves were interspersed with
episodes of faster activity that we also included as slow-wave sleep
when accompanied by closed eyes and slow or absent eye movements. Rapid
eye movement (REM) sleep was not observed, probably because we never
deliberately sleep-deprived our animals, or perhaps because the testing
situation was not particularly conducive to sleeping. In addition,
sleep spindles (large-amplitude 10- to 14-Hz cortical oscillations) were not commonly observed probably because our EEG electrodes were
above occipital cortex, where splindling is less pronounced (cf.,
Steriade and Llinas 1988).
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Analysis of rhythmicity
Bursting during sleep, while common, was episodic. To facilitate evaluating underlying rhythmicity of bursting within these episodes, we created and analyzed "burst bouts." A burst bout was defined as four or more bursts occurring within 2 s and interburst intervals <0.1 s. The onset of the bout was the first spike in the first burst, and the end was defined as the last spike in the last burst. Rhythmicity of these bouts was evaluated by first determining the standard deviation of interburst intervals for each bout. Standard deviation is inversely related to rhythmicity; perfect rhythmicity would yield a standard deviation of 0. Confidence that a given burst bout was rhythmic was assigned by comparing the rank of the observed standard deviation relative to 999 simulations. The simulations were created by using the same number of observed bursts, but with interburst intervals randomly drawn. The constraint on these random numbers was that they must lie within a range that could have occurred in the observed burst bout. The rank of the value of the observed standard deviation is a direct measure of the reliability in a statistical sense that the observed bout was rhythmic.
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RESULTS |
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We recorded from 148 LGN neurons in three cats. Among these, 109 were identified in layer A, 21 in A1, and 9 in C. In addition, we also
encountered nine fibers above the LGN, four of which could be
antidromically activated from visual cortex. We assumed that the cell
bodies of these fibers were in the LGN, although we cannot be sure of
the layer. Because of eye movements in the awake animal, we made no
attempt to classify neurons as X, Y, or W. Twenty-two neurons were
identified using antidromic identification. Based on latency, we had a
clear recording bias for Y cells (mean latency, 0.91 ms; = 0.61 ms; range, 0.4-3.0 ms; 15/22 latencies <1.2 ms) (So and
Shapley 1979
).
Bursts during sleep and waking
Figure 1 presents analog traces of an LGN neuron and concomitant
cortical EEG during sleep (top) and wakefulness
(bottom), illustrating the burst and tonic modes commonly
associated with these two states. As the name implies, the burst mode
is characterized by periods of quiescence interspersed with
high-frequency "bursts" of action potentials. Other studies have
demonstrated that these bursts are sodium spikes (action potentials)
riding a slower depolarization attributed to the activation of the
low-threshold calcium current IT. The
top records show expanded views of three bursts
(conservative criteria). Bursting during sleep tends to be episodic or
rhythmic, and we describe some quantitative aspects below. During
"tonic" episodes as illustrated in the bottom traces,
activity can be remarkably regular, often varying with ambient
illumination level. For the traces shown, 95% of the activity was at
50 Hz or less. Figure 2
shows the temporal distribution of a different LGN
neuron over a prolonged period (10 min) during which the cat drifts in and out of sleep. Figure 2A shows the distribution in raster
format with the first spike in a burst indicated by a large dot,
whereas Fig. 2B shows the spike distribution as JIHs
(McCarley et al. 1983) during each epoch of sleep
(left) and wakefulness (right). The JIHs sharply
contrast the temporal structure of activity during sleep and
wakefulness. During wakefulness, activity is remarkably regular (tonic)
and a central distribution is obvious. In contrast, during sleep the
regularity is disrupted (burst mode) and two tails form. The tail in
the bottom right of each JIH is the cardinal spike of a burst, while
the well-defined tail to bottom left indicates the shortest interspike
interval (highest frequency) achieved within a burst. Figure
2C shows the sleep/wake cycles collapsed into two JIHs and
includes insets to illustrate the differences in burst
incidence using the two sets of criteria used in this study. Dots
within the smaller inset represent the cardinal spike of
bursts (and hence, the number of bursts) using the conservative criteria, whereas dots within the larger inset represent the
cardinal spikes of bursts using the liberal criteria. Although these
figures illustrate the general temporal structure of many LGN neurons during sleep and wakefulness, the data presented below serve to emphasize that Figs. 1 and 2 are not necessarily representative. In
fact, bursting during sleep is not ubiquitous, and tonic activity during wakefulness could be interrupted to include bursting.
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Prevalence of bursting during SWS: how common is bursting during sleep?
Although bursting was common during sleep (0.51 bursts/s vs. 0.06 burst/s during waking, liberal criteria), the heterogeneity among LGN neurons in the degree to which they burst was striking. Figure 3A presents the incidence of bursting among 57 LGN neurons during SWS. For this figure, bursting during each 5-s epoch of SWS is coded as a shade of gray, with black being zero bursts observed and white being five bursts or more. Figure 3B presents the same data as in Fig. 3A, but now plotted as frequency of bursting during SWS. One-half of the cells failed to have a burst rate exceeding 0.5 bursts/s, and eight never burst. Another way of appreciating the heterogeneity in bursting among LGN neurons is that if we assume that these 57 neurons are representative, 50% of the bursting at any time is generated by 14% of the neurons.
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As with burst frequency, the fraction of action potentials associated with bursting during sleep was appreciable, but variable. During some sleep epochs, >70% of spikes were associated with bursting, whereas in several cells, bursting never occurred. Overall, we estimate 18% of spikes during slow-wave sleep were burst related. However, because we could not directly observe the IT events, we are confident this is an underestimate (see DISCUSSION).
Rhythmic bursting during SWS: how rhythmic is bursting during sleep?
Rhythmic bursting is believed to play a prominent role in
promoting the low-frequency EEG observed during SWS (e.g.,
McCormick and Bal 1997; Steriade et al.
1993
). Although we showed that bursting is not ubiquitous among
LGN neurons during SWS (Fig. 3), we did note that the presence of one
burst increased the probability of observing another (P < 0.005; based on comparing the observed median burst interval with
999 simulations in each of 17 LGN neurons). Figure
4A shows
the episodic nature of bursting (liberal criteria) in one LGN neuron
over a 16-min period during which the cat was mostly in SWS. Figure
4B shows the distribution of interburst intervals for 17 LGN
neurons for which we collected continuous records (10-50 min) for
various intervals of SWS. It indicates that ~95% of the bursts
occurred within 5 s of one another. To analyze potential
underlying rhythmicity of such episodes, we identified "burst
bouts" (see METHODS), i.e., periods during which bursting
was common. If bursting was rhythmic, then the standard deviation of
interburst intervals within a bout should approach zero. Figure
4C shows two examples of autocorrellograms of two burst
bouts, one highly rhythmic (Fig. 4C, top) and one arrhythmic (Fig. 4C, bottom). Figure 4D shows the
distribution of probabilities (determined using simulations; see
METHODS) for 475 burst bouts collected from
continuous records in the 17 LGN neurons. Using a criterion of
P < 0.05, 47% of these bouts were rhythmic. Figure 4E shows the distribution of interburst intervals, showing
that the vast majority of intervals were concentrated within the delta (i.e., 0.5-4 Hz) range. Although interburst intervals were within a
frequency range appropriate for driving cortical delta rhythms, we
failed to observe any consistent phase relationships between occipital
EEG and concomitant thalamic bursting. This may, of course, be
attributable to local variations in the occipital EEG (cf.,
McCarley et al. 1983
).
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Bursting during visual stimulation: variability among neurons
The majority of LGN neurons responded reliably and vigorously to visual stimuli of the appropriate polarity presented to the receptive field center. For most of these, bursting was not associated with the visually driven response. Figure 5A shows the visually driven response of an LGN neuron in tonic mode to a flashing stimulus placed over the receptive field, whereas Fig. 5B shows the visually driven response of a different LGN neuron whose initial response was often a high-frequency burst of action potentials (indicated by asterisks). Figure 5C shows the burst probabilities for 93 LGN neurons evaluated under different conditions of visual stimulation. This figure emphasizes the heterogeneity among LGN neurons. For 33/93 neurons (35%), we were unable to elicit any bursting, and as indicated, there was significant variability in bursting among the remaining 60 neurons. As with bursting during sleep, a minority of the neurons contributed most of the bursts. We found that 50% of the visually driven bursts originated from 9% (8/93) of neurons. Finally, although it would not seem unreasonable that neurons that burst prolifically during visual stimulation would also burst extensively during sleep, such a relationship was not observed.
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Bursting during visual stimulation: stimulus size
The probability of observing a burst as part of the visual
response could be increased by increasing stimulus size. Figure 6A shows the interspike
interval histograms generated from single trials in which a 4°
stimulus (top) or a 40° stimulus (bottom) are
used to elicit the response. Clearly, bursting was evident with the
large stimulus and not with the small stimulus. Figure 6B
shows JIHs for the responses when we used the 4° stimulus
(left) or the 40° stimulus (right). Probability
of bursting for the small stimulus was 0.01 bursts/cycle and 0.37 bursts/cycle for the large stimulus. Figure 6C shows the
probability of bursting we observed for each of four different stimulus
sizes on this neuron. Table 1 shows that
the results obtained for the single cell shown in Fig. 6 extend to a
sample of 16 LGN neurons in which stimulus size was parametrically
manipulated. For this larger sample, burst probability increased with
stimulus size (2 = 229, P < 0.001, 2 d.f.).
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Bursting related to state
The probability of bursting during visual stimulation was greatest
for the initial stimulus cycle. Figure
7A shows a single trial in
which the initial, but not subsequent stimulus cycles elicited a burst.
Figure 7B shows the ordinal distribution of 319 bursts from
67 cells across the first 5 cycles of visual stimulation in each trial.
Overall, if a visual stimulus was going to elicit a burst, the burst
would most likely occur to the first stimulus presented in a trial
(2 = 17.01, P < 0.005, 4 d.f.). One interpretation for this observation would be that the
animal's attention increased as the visual stimuli were presented. The
task demanded considerable attention in the sense that the cat must
inhibit the prepotent reflex of looking at a newly presented stimulus
in the field. In some trials, it is conceivable that, although the cat
was "on target," the cat was less attentive and the cell relatively
hyperpolarized.
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Further evidence for a negative relationship between attention and
bursting emerged when we analyzed bursting probability under
"active" versus "passive" conditions. Figure
8 presents traces showing spike frequency
during active (i.e., fixation spot present, Fig. 8A) and
passive (i.e., no fixation spot present, Fig. 8B)
conditions. As indicated by asterisks, bursts were present during six
of the eight cycles shown under passive, but absent under active
conditions. This cell was extensively analyzed under both active and
passive conditions. During passive conditions, bursting occurred in
79/862 cycles (9.2%), whereas only one burst (1/118 cycles, 0.8%) was
observed under active conditions. This observation in the single cell
could be extended to our overall sample, bursting was more common under
active than passive conditions (Table 2,
2 = 109, P < 0.001, 1 d.f., 65 neurons passive only, 14 neurons active only, 15 neurons
active and passive).
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Finally, the least-likely period during which a neuron would exhibit
bursting appeared to be active fixation, with or without an overt
fixation point. We analyzed 28 LGN neurons during which the cat
maintained fixation on the dimmed laser point placed at the center of
an otherwise blank screen. In some trials, this fixation point was
extinguished for 1 s and then returned. Compared to an epoch of
"spontaneous" activity taken immediately prior to trial onset,
burst probability during fixation was nominally decreased (0.046/s vs.
0.038 s, n.s., conservative criteria). However, for each cell, the
probability of observing a burst during the spontaneous epoch was
greater than during fixation (2 = 9.51, P < 0.01, 2 d.f.), reinforcing the view that
membrane potential is depolarized during "attentive" states.
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DISCUSSION |
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This study makes several contributions to our understanding of the burst/tonic dichotomy of LGN activity during sleeping and waking. First, the heterogeneity among LGN neurons in the degree to which they burst during either sleep or wakefulness had not been fully appreciated. Second, we were able to identify specific conditions within wakefulness in which burst probability increased dramatically. These conditions include presenting stimuli more likely to influence inhibitory surround mechanisms, as well as manipulating behavioral contingencies. The former observations reinforce the potency of the inhibitory surround in shaping visual response. The latter indicates that "wakefulness" can include shifts in membrane potential sufficient to de-inactivate the IT burst mechanism. Finally, our method of analyzing potential rhythmic bursting indicates that most LGN neurons burst rhythmically during sleep, if only for a few seconds.
What is the structure of the thalamocortical network during sleep?
Several recent papers (e.g., see review by McCormick and
Bal 1997) have promoted the idea that rhythmic bursting
represents the dominant form of LGN activity during slow-wave sleep.
Rhythmic bursting was the exception, rather than the rule, in the
previous study of activity in the cat's LGN (McCarley et al.
1983
) (5 of 26 neurons). The recent study by Ramcharan
et al. (2000)
did not observe rhythmic bursting in the LGN of
the sleeping monkey. We found that LGN bursting to be episodic, and
rhythmic nearly half of the time. Although this seems to be a much
different conclusion than the other studies, we strongly suspect that
we were analyzing nearly identical spike trains. The difference is the
method of analysis. McCarley et al. (1983)
collapsed
data across long epochs. Such treatment will necessarily dilute
momentary rhythmicity that is interrupted by other arrhythmic epochs.
The question McCarley et al. (1983)
were addressing was
whether there were overall trends in rhythmic behavior. The analysis
Ramcharan et al. (2000)
employed is straightforward:
construct fast Fourier transforms (FFTs) from autocorrellograms and
determine the statistical reliability of the FFT signal. Again, the
problem is that they were analyzing long epochs that would dilute brief
epochs of rhythmic behavior. Using an analysis that tests for
rhythmicity over brief epochs, we found most LGN cells burst
rhythmically, if only for a few seconds. Thus our observations support
the idea that slow-wave sleep is a period of episodic disconnections
from sensory inputs, allowing intrinsic conductances to manifest brief
episodes of rhythmic activity (cf., McCormick and Bal
1997
; Steriade et al. 1993
). Finally, we know
our measurements are conservative. Because we depend on extracellular
spikes to imply an underlying IT
event, we have restricted our analysis to episodes of two spikes or
more to identify bursting. In reality, there are
IT events that generate one or no
spikes. Thus underlying rhythmic activity is almost certainly even more common.
We observed significant heterogeneity among LGN neurons in the degree
to which they burst, whether during sleeping or waking. Although one
way of interpreting our results is that during any given second in
slow-wave sleep the probability of observing a burst in an LGN neuron
is just under 28%, such descriptions undermine the heterogeneity of
the sample. Perhaps most telling is that 50% of the bursting appears
to be produced by 14% of the neurons. McCarley et al.
(1983) also noted heterogeneity in bursting among LGN neurons.
They found a rather large fraction (37%) that did not burst during
slow-wave sleep (vs. 17% for our large sample, 57 neurons). The
functional significance of this heterogeneity is unknown. However,
large populations of synchronous rhythmic bursting would not be
desirable as such activity could easily incite epileptiform activity
(cf., Steriade et al. 1993
).
Variability in bursting among LGN neurons during sleep and wakefulness
indicates that either LGN neurons vary in the degree to which the
IT channel is expressed, and/or, there
are significant differences in intrinsic circuitry. Calcium imaging and
whole cell in vitro studies suggest the
IT channel and its kinetics are
homogenous among relay cells (Coulter et al. 1989;
Hernandez-Cruz and Pape 1989
; Munsch et
al. 1997
). Qualitative and quantitative differences exist among
relay cell afferents. Besides obvious differences in X, Y, or W retinal
afferents, there is evidence suggesting that intrinsic, brain stem, and
corticothalamic afferents vary in their density and distribution
(Friedlander et al. 1981
; Murphy and Sillito
1996
; Weber et al. 1989
; Wilson et al.
1984
). These differences in circuits offer an obvious avenue
for heterogeneity. For example, while global variables such as sleep
and waking might shift membrane polarity across large populations of
neurons, local variations in cortical afferent activity could alter the
probability of the specific neuron recorded expressing a burst.
Significant differences also exist between thalamic nuclei in the
degree to which bursting is rhythmic during sleep. This point is
explicit in the recent paper by Ramcharan et al. (2000). Despite an inability to observe rhythmic bursting in the monkey's LGN,
rhythmic bursting was striking in the nearby ventrobasal complex. The
observations by Ramcharan et al. (2000)
in the
ventrobasal complex appear consistent with the analyses offered by
Steriade and his colleagues (Domich et al. 1986
;
Glenn and Steriade 1982
) of highly rhythmic bursting in
the cat's ventral lateral, ventral medial, and central lateral nuclei
during slow-wave sleep.
Sensory inputs, vigilance influence IT mechanism during wakefulness
Activation of the IT mechanism
requires that the membrane potential be hyperpolarized for a period of
50-100 ms. This period allows the channel to "de-inactivate," such
that subsequent depolarization will alter channel configuration
allowing calcium entry. In agreement with previous studies, bursting is
much less common during wakefulness than slow-wave sleep (Hubel
1960; Livingstone and Hubel 1981
; McCarley et al. 1983
; Ramcharan et al.
2000
). The observation that bursting during wakefulness occurs
at all may be somewhat surprising since the membrane potential is
largely depolarized during this period (promoting the tonic mode).
However, variations in state is only one contributor to altering
conductances to alter membrane potential. The receptive fields of LGN
neurons have an antagonistic center-surround organization in which
stimuli of one polarity excite one portion of the field and inhibit the
other. In the awake animal, this inhibition is capable of
de-inactivating the IT channel,
despite the prominence of a tonic mode. Coenen and Vendrik
(1972)
show analog traces in which an obvious burst is evident
following the removal of inhibition by extinguishing a light presented
to the center of an off-center LGN neuron. Bursting could be promoted
during wakefulness by providing large stimuli that activated both
center and surround mechanisms, by manipulating level of alertness, or,
as previously documented, encouraging eye movements (Guido and
Weyand 1995
; Lee and Malpeli 1998
). We interpret
our results to simply indicate that during wakefulness membrane
potential is dynamic, and each of the conditions that promoted bursting
are conditions that would predictably hyperpolarize the membrane
potential. Whereas membrane potential is generally more depolarized
during wakefulness than during sleep (Hirsch et al.
1983
), we were able to relatively easily produce bursts in many
LGN neurons, indicating that the membrane could be hyperpolarized for
extended periods. Each of the conditions under which we observed bursting are conditions when the membrane potential could be expected to be hyperpolarized.
Increasing the size of the visual stimulus increased burst probability.
Given the center-surround organization of LGN neurons, increasing
stimulus size should have a greater influence on the surround than
smaller stimuli. For some LGN neurons, whole field illumination
sufficiently hyperpolarizes the LGN neuron to de-inactivate the
IT channel and the occasional retinal
input elicits a burst. In Fig. 6 we showed a more quantitative analysis
of how stimulus size affects burst probability on a different LGN
neuron. It is not obvious why large visual stimuli were more
efficacious than small stimuli at eliciting bursts since our stimuli
were sufficiently large in nearly all cases to include both the center
and surround. One possibility is that the larger stimuli activated
(inhibited) additional circuitry that lies beyond the classical
receptive fields (e.g., McIlwain 1964), additionally
hyperpolarizing the LGN neuron to deinactivate the
IT channel. Whatever the mechanism, the potency of large stimuli (over smaller stimuli) in elicting bursting has been observed previously in the LGN of the awake, paralyzed cat (Coenen and Vendrik 1972
; their Fig. 2).
Hyperpolarization associated with the large stimulus, but not the
smaller stimulus, would be sufficient to de-inactivate the
IT channel that elicits bursting on
the next cycle. Had we better control of gaze, it would have been
interesting to determine how effective stimuli restricted to the
surround such as annuli would have been in promoting bursting. Finally,
although we could promote bursting by using large stimuli,
it is worth emphasizing that large stimuli provided no guarantee of
observing burst responses. One-third of neurons never burst during
visual stimulation, and 50% of visually driven bursting originated
from 9% of cells analyzed. This again serves to underscore the
variability in response characteristics among LGN neurons.
State affects burst incidence because state affects membrane potential.
This has been demonstrated directly by Hirsch et al. (1983), and indirectly by a number of investigators. Again, the records of Coenen and Vendrik (1972)
are instructive.
Following extinguishing a spot to the center of an ON LGN
neuron, bursts could be observed in the "drowsy" state, but not in
the "awake" state (Coenen and Vendrik 1972
; Fig. 5).
Such an observation indicates that the membrane potential can become
sufficiently hyperpolarized to de-inactivate the
IT channel. Our own results, whether
based on increased visually driven bursts associated with passive
versus active gaze, or decreased burst probability associated with
fixation, argues that state changes, as well as surround antagonism are capable of de-inactivating the IT
channel. Several investigators have commented on the increased activity
and/or transfer ratio associated with alertness or arousal
(Coenen and Vendrik 1972
; Livingstone and Hubel
1981
; Sakakura 1968
; Swadlow and
Weyand 1985
; also reviewed by Sherman and Guillery
1996
; Singer 1977
).
What is the significance of bursting during wakefulness?
Having shown that bursting can occur during wakefulness and that
such observations would be consistent with the known circuitry of the
LGN, the question arises as to the functional significance of bursting
in the awake animal. If bursting were restricted to slow-wave sleep, it
and the supporting IT channel could be
viewed as an adaptation for establishing slow-wave sleep in cortex. In wakefulness, bursting is such a departure from a linear response; it is
not clear what it means as a visual signal. One attractive idea is that
bursting has little to do with analyzing visual detail, but instead
serves as a "wake-up call" to cortex, switching the LGN from burst
to tonic mode (Guido and Weyand 1995; Guido et al. 1995
; Sherman 1996
). As the animal's
attention wanes, the neuron hyperpolarizes, de-inactivating the
IT channel. Subsequent visual stimuli
would then activate the channel, sending a high-frequency volley of
action potentials to visual cortex. This volley would be sufficiently
powerful to drive corticothalamic neurons, which would then depolarize
and switch LGN neurons to tonic mode via activation of metabotropic
glutamate receptors (Godwin et al. 1996
). In contrast to
the burst mode, activity in the tonic mode is more linearly related to
stimulus attributes (Guido et al. 1995
; McCormick
and Feeser 1990
). Although attractive, our present results
indicate that this idea is too limited. It does not explain, for
example, the functional significance of bursting associated with
saccades, nor with large visual stimuli. Further, as appealing and
intuitive as the idea that bursting is more potent than single spike
activity in activating cortical circuits (e.g., Lisman
1997
; Reinagel et al. 1999
), empirical
evidence in a thalamocortical system is lacking.
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ACKNOWLEDGMENTS |
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B. Renzi provided invaluable software support.
This research was supported by National Eye Institute Grant EY-R01-11144.
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FOOTNOTES |
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Address for reprint requests: T. G. Weyand, Dept. of Cell Biology and Anatomy, Louisiana State University Medical Center, 1901 Perdido St., New Orleans, LA 70112 (E-mail: tweyan{at}lsuhsc.edu).
Received 5 June 2000; accepted in final form 8 December 2000.
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REFERENCES |
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