1Department of Neurosurgery, SUNY Upstate Medical University, Syracuse, New York 13210; and 2Yerevan Physics Institute, Yerevan 375049, Republic of Armenia
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ABSTRACT |
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Apkarian, A. Vania, Ting Shi, Johannes Brüggemann, and Levon R. Airapetian. Segregation of Nociceptive and Non-Nociceptive Networks in the Squirrel Monkey Somatosensory Thalamus. J. Neurophysiol. 84: 484-494, 2000. The somatosensory thalamus (here we examine neurons in the caudal cutaneous portion of ventral posterior lateral nucleus, VPL) is composed of a somatotopic arrangement of anteroposteriorly oriented rods. Each rod is a collection of neurons with homogeneous properties that relay sensory information to specific cortical columns. We developed a multi-electrode recording technique, using fixed-geometry four-tip electrodes that allow simultaneous recordings from small populations of neurons (4-11), in a ~150 × 150 × 150 µm3 volume of brain tissue (i.e., the approximate diameter of rods) and study of their spatiotemporal interactions. Due to the fixed geometry of the four-tip electrodes, the relative locations of these neurons can be determined, and due to the simultaneity of the recordings, their spike-timing coordination can be calculated. With this method, we demonstrate the existence of two distinct functional networks: nociceptive and non-nociceptive networks. The population dynamics of these two types of networks are different: cross-correlations in each type of network were different in direction and strength, were a function of the distance between neurons, had an opponent organization for nociceptive networks and a non-opponent organization for non-nociceptive networks, and rapidly changed under different stimulus conditions independent of changes in firing rates. A simple neural network model mimicked these physiological findings, demonstrating the necessity of inhibitory interneurons and different amounts of afferent input synchronization. Based on these results, we conclude that the somatosensory thalamus is composed of two modules, nociceptive and non-nociceptive rods, and that the response dynamics differences between these modules are due to spatiotemporal differences of their afferent inputs.
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INTRODUCTION |
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From a single-unit recording point of view, the
thalamus is generally regarded as faithfully transmitting incoming
inputs to the cortex. A variety of electrophysiological studies have asserted this viewpoint at least for the three primary sensory nuclei
of the thalamus: somatosensory, visual, and auditory (Jones 1985). For example, the properties of neurons in the
somatosensory thalamus, such as receptive field size, position, and
modality specificity, reflect incoming medial lemniscal properties,
which were thought to be relayed with little transformation to the
cortex (Poggio and Mountcastle 1963
). However, it is
also clear that the thalamus plays a complex role in the processing of
sensory information with different states of sleep, vigilance, and
maybe even memory consolidation (Steriade 1999
).
Single-unit recording techniques are certainly
suitable to characterize the response properties of individual neurons,
and by correlating these physiological data with anatomic and
behavioral data, a vast amount of knowledge has been gained about the
organization of the nervous system. To date, nociceptive representation
in the primate thalamus has only been studied using single electrodes with single- or multi-unit recording techniques. Nociceptive cells have
been described in a number of thalamic nuclei (Apkarian and Shi
1994; Bushnell et al. 1993
; Casey and
Morrow 1983
; Dostrovsky and Craig 1996
;
Kenshalo et al. 1980
; Lenz et al. 1993
;
Willis 1985
). In this study, we concentrate on
nociceptive representation in the lateral somatosensory region
[cutaneous caudal portion of ventral posterior lateral nucleus
(VPL)]. In the monkey VPL, most nociceptive cells respond convergently
to innocuous and noxious stimuli and are described randomly
intermingled with non-nociceptive cells (e.g., Apkarian and Shi
1994
). These responses have been characterized based on changes
in the mean firing rate of individual cells. Recent studies, however,
indicate that spike-timing changes, i.e., changes in the coordination
of spikes across neurons, may be another mechanism for coding afferent
inputs. Populational studies in the cortex indicate that besides
relying on a mean rate of neuronal firings, coordination in relative
timing of action potentials across neurons may be a fundamental
principle of information coding (deCharms and Merzenich
1996
; Gray et al. 1989
; Singer and Gray
1995
). Single-unit recordings cannot detect such coordination changes. The spatial and temporal interactions between neighboring and
distant neurons (populational codes) can only be determined by
simultaneous recordings from groups of neurons (e.g., Nicolelis et al. 1993
). In this study, we re-examine the role of the
somatosensory thalamus in nociceptive information coding when spiking
activity of small groups of neighboring neurons are examined simultaneously.
Given the strong bidirectional connectivity between the cortex and the thalamus and given that most sensory inputs (except the olfactory) are relayed through the thalamus, what is the role of the thalamus in the spike-timing-based population codes revealed in the cortex? We examine this question using our new population recording technique. The lateral somatosensory thalamic populational codes are examined when the skin is stimulated with innocuous and noxious stimuli. Spike-timing coordination changes are determined as a function of stimulus and as a function of distance between neurons.
This work is in partial fulfillment of the PhD dissertation requirements for T. Shi.
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METHODS |
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Neuronal population activity was recorded using four-tip tungsten electrodes in the somatosensory thalamus of five squirrel monkeys. The monkeys were chronically implanted with a recording chamber, and once every 1-2 wk were anesthetized with 0.5-1.2% halothane, in 1/3 N2O and 2/3 O2, and used for recordings. In each monkey during the first recording session, single tungsten electrode recordings were made to establish the boundaries of VPL. Subsequent recordings used the four-tip electrodes, targeting neuronal groups in VPL. The housing, care, and surgical procedures followed the institutional guidelines established by the Committee for the Humane Use of Animals. At a given recording site, multi-unit activity collected from each of the tips was recorded and displayed on a personal computer. The receptive field of the units was determined on-line based on the multi-unit responses. Innocuous and noxious stimuli were then applied within this receptive field. The collected data were clustered off-line to individual neuronal activity, and their responses, coordination, and locations in space relative to the recording tips were calculated.
Initial surgery
The surgery for chamber implantation was done under sterile conditions. Each of the animals was pretreated with dexamethasone (0.25 mg/kg im) and antibiotics (Rocephin 75 mg/kg im) prior to surgery. Anesthesia was induced with ketamine (30 mg/kg im) and continued with a mixture of 0.5-1.2% halothane, 1/3 N2O and 2/3 O2 during surgery. The animals were intubated and given lactated Ringer solution intravenously. Expired CO2, oxygen saturation, and heart rate were monitored non-invasively and maintained within physiologic range. Body core temperature was maintained within 36.5-38°C by an electrical blanket. The skull was opened to access VPL, leaving the dura intact. A stainless steel recording chamber was implanted over the opening and attached to the occipital skull with screws and dental cement. The chamber was filled with saline and Neosporin and sealed with a screw. Following surgery, the animals were administered antibiotics for 3 days and checked for normal recovery. The chamber was cleaned every week. The first recording session was 2-3 wk after implantation.
Recording and stimulation
The same anesthesia used for the initial surgery was used during recording sessions. The anesthetic level was kept at a level where withdrawal responses to noxious stimuli were suppressed. Vital signs were monitored as in the preceding text. The animal was intubated and breathed spontaneously.
The animal was initially placed in a stereotactic frame. After the head was immobilized by attaching a holder to the skull, the stereotactic head frame was removed. Single tungsten microelectrodes were used to map and electrophysiologically identify VPL. Subsequent recordings were performed with four-tip electrodes. The neuronal activity was collected using a multi-channel amplifier (Model CDA-100, Micro Probe), and processed using commercial software running on a personal computer (DataWave Technologies, Longmont, CO). The data-sampling rate was 20 kHz for each channel. Spike discrimination windows were set at the same threshold for each of the channels, and the noise level was adjusted to the same by controlling the gain of the amplifiers. The time for acquiring a spike was set 0.5 ms prior to and 1.0 ms after the time when the rise phase of a spike exceeded the discrimination threshold. If a spike was detected on one channel, all other channels would be triggered and potentials on all channels collected simultaneously. The neuronal activity including spike shapes, on-line peristimulus-time histograms (PSTH), and the stimulus analog curves were displayed on a monitor and stored for off-line analyses. The analog signals were also stored on videotape using a digital system (Vetter, Rebersburg, PA).
Somatic mechanical response properties and receptive fields of the main units on each channel were determined on-line. Mechanical stimuli included brush (bristles 5-10 g, 2-3 Hz), touch (50-100 g, 2-3 Hz), tap (with a smooth probe, 100-200 g, 2-3 Hz), pressure (large forceps or weights, 100-200 g on a 5-10 mm diameter area), pinch (serrated clamp, 600-900 g on a 5-mm-diam area), and deep tissue squeeze. The pinch and squeeze stimuli were painful when applied to the experimenter. During data analysis, all stimuli were catalogued into four modalities: spontaneous activity, brush (including brush, touch, and tap), pressure, and pinch.
Four-tip electrode
The fixed-geometry four-tip electrodes (custom made by FHC,
Bowdoinham, ME) synchronously monitored activity across all tips. Since
each triggered event was recorded from all four tips, off-line analysis
based on the shapes of the action potentials on all four tips enabled
identifying single-unit activity, a procedure identical to the one
first introduced for tetrodes (Wilson and McNaughton 1993). We optimized the electrode tip geometry to enable
recording from as many distinct thalamic neurons as possible while
monitoring individual spikes by all four tips. Thus these electrodes
combine the advantages of single-cell recordings (large signal-to-noise ratio of tungsten electrodes) with that of tetrodes (a fourfold increase in the confidence that a unit is properly classified as coming
from the same neuron). The electrode tips make up a tetrahedron with
the electrodes being 115 µm apart horizontally and electrodes 1 and 3 being 100 µm shorter than the other two. These electrodes enable
examining the spatiotemporal properties of small groups of neighboring neurons.
Clustering individual neurons
The multiple units recorded on each channel were sorted and clustered with Autocut (DataWave). The amplitudes and time to peak on each channel were used as parameters to differentiate spike shapes. The joint scatter of these parameters, for each pair of channels, clusters the spikes into different neurons (spike generation points). Clusters were differentiated using 2.25 SD distance from the center of a given cluster as the minimum cutoff between clusters. The clusters cut automatically were slightly modified by the experimenter.
Classification of response properties
Response types were determined both subjectively (on-line) and
by measuring the mean change in the rate of unit responses (off-line).
A given unit was classified as responsive to a specific stimulus only
if its mean firing rate changed significantly (t-test, P < 0.05) and by 30%. Neurons were classified as
low threshold (LT) when the increase in activity was not different
between innocuous and noxious stimulation, as wide dynamic range (WDR)
when noxious stimulation elicited a higher neuronal activity as
compared with innocuous pressure, and as nociceptive specific when only
noxious stimulus intensities lead to increased neuronal activity (see Apkarian and Shi 1994
).
During on-line monitoring of multi-unit activity, if units on any electrode tip responded to a noxious stimulus, the recording site was defined as nocisite. Sites where on-line multi-unit activity did not show nociceptive responses were defined as non-nocisites. This classification was compared with the responses of the neurons after being separated off-line.
Locating spike generation sites
Distance calculations between neurons relative to the electrode
tips were based on the average spike amplitudes observed on each tip.
The decay rate of spike amplitude as a function of distance was
determined in single-electrode recordings (Fig.
1). The experimentally measured decay
rates were smoothed and the variance in fitting the data with various
power rules was calculated. The minimum variance resulted from a
Euclidean distance rule (P = 2 in Fig. 1C).
This dependence was determined in dorsoventral penetrations using
single electrodes (Fig. 1, A and B). The same
rule was assumed to apply to the action potential decay in the other
two spatial dimensions (mediolateral and anteroposterior) since most
VPL cells (projecting neurons and interneurons) are spherical
(Shi and Apkarian 1995; Turner et al.
1997
; Williams et al. 1996
). Therefore we assume
that an action potential gives rise to a three-dimensional bell shaped
potential curve, the peak of which we define as the spike generation
point. This rule is then applied to localize spike generation points in
the four-tip recordings.
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Because the impedances of the four tips are similar and also signal
levels of the four channels are normalized by adjusting background
noise levels, a neuron's (ni) average
spike amplitudes on the four channels (as shown in Fig.
2), d1,
d2,
d3, or
d4, can be used to calculate its
distance from each electrode, by solving the 4 × 4 matrix derived
directly from the Euclidean distance rule
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Figure 2 also shows the variability in the size of the action potential measured on each channel. This translates into a positional variability, which we estimate to be ~10 µm in all three dimensions. Therefore any given spike generation point has a 5-10% jitter relative to the dimensions of the four-tip electrode.
Cross-correlations
The cross-correlations were calculated with a window width of
200 ms, a bin size of 1 ms, and smoothed with a Gaussian function (Abeles 1982). Statistically significant
cross-correlation peaks or valleys were defined as three or more bins
exceeding 2 SD from the mean spike count and lasting for at least four
bins within the first 30 ms. Cross-correlation strength was calculated
by measuring the area of the peak or valley above the mean count, multiplied by bin size, and divided by the total number of spikes. The
latter expresses the cross-correlation strength in extra spikes observed above the mean number of spikes. The negative
cross-correlation strengths are underestimated with this procedure;
however, we did not manipulate these measures any further to present
the most conservative estimates of cross-correlation strengths.
Neural network model
A four-element neural network model was developed to help explain our physiological findings. The model consisted of two inhibitory interneurons (i1, i2) and two projecting neurons (p1, p2). There is no direct connection between the projecting neurons. The inhibitory interneurons are driven by the projecting neurons through excitatory synapses and in turn are negatively connected to the opposite projecting neuron. All afferent inputs are excitatory and impinge on all four neural elements. Synaptic connections are defined by time varying probabilities. Asynchronous inputs are defined as spikes arriving independently and with a random distribution on all four neural elements. Synchronous inputs are events that arrive in fixed time relationship to each other (with or without delays), with a random interval between events. The cross-correlations between p1 and p2 were calculated as a function of firing rate and quality of afferent inputs (nonsynchronized, or synchronized). Details of the simulations are posted on our web site (http://alpha.nmrlab.hscsyr.edu/pain/).
Histology
The animals were perfused with 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.2). Fifty-micrometer frontal sections of the brain were mounted, and alternating sections were Nissl stained and stained for cytochrome oxidase to identify recording tracts. We did not make electrolytic lesions in these animals because these were surviving animals and were used in multiple recording sessions over a time period of several months and because VPL was mapped and physiologically identified with a single electrode in each experiment before a four-tip electrode was used.
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RESULTS |
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Most recording sites were ascertained to be in VPL by histology (Fig. 4). However, because the recordings were done in a chronic preparation over a period of months, not all tracks could be histologically identified. Altogether 34 penetrations with four-tip electrodes were performed, and 15 tracts were recovered in the histological sections. The locations of the tracks inside VPL (13 tracks) matched the receptive field properties of the individual neurons. The majority of the recovered tracks (13/15) penetrated VPL (see Fig. 4, top). In the experiment where two tracks were outside (lateral) of VPL, the protocol report indicates one penetration with no receptive fields and another one where visual inputs were recorded. Therefore we assume that most of the recordings were done from the caudal cutaneous portion of VPL based on stereotaxic coordinates, neuronal responses, and the topographic arrangement of body representation.
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At 10 recording sites the distance between the studied neurons, averaged for 308 pairs of neurons was 54 ± 29 (mean ± SD) µm. One hundred and twenty four neurons were isolated from 17 recording sites. At each recording site clusters of 4-11 (7 ± 2, mean ± SD) neurons were identified. Distances between neurons in histological sections of the squirrel monkey VPL were found to range between 10 and 150 µm, and there were 20-40 neurons in a 115 × 115 × 100 µm3 volume of VPL tissue. Therefore we estimate that 1/4-1/2 of the neurons embedded between the four electrode tips were identified as active neurons and studied. Figure 4, bottom, is an example of the reconstruction of part of a recording site in a 50-µm-thick coronal section, showing the tracks of two of the four tips. Thirteen somata can be seen. Because our electrode tips are ~100 µm apart, this would result in an estimate of ~26 neurons at this recording site, which compared with our mean number of physiologically identified neurons again shows that ~1/4 of the local neurons were studied.
Single-unit properties
The receptive fields of the neurons recorded at 16 sites were cutaneous and deep at one recording site. At all 17 sites receptive fields of neurons were contralateral and near each other and limited to one extremity or to the tail.
Eighty-three neurons were clustered from 11 nocisites and 41 neurons were clustered from 6 non-nocisites. The pooled prevalence of nociceptive neurons was 0.16 across all recording sites. The variability of the prevalence of nociceptive neurons among the 17 sites, relative to an hypothesis of a common prevalence, produced a deviance (Go2, with 16 df) of 24.9, suggesting heterogeneity in the distribution of nociceptive cells. All recording sites defined on-line as nocisites indeed contained nociceptive neurons, while recording sites defined on-line as non-nocisites contained neurons responsive only to innocuous stimuli. Within nocisites, 25% (21 cells) were classified as LT type and 24% (20 cells) as nociceptive. At non-nocisites, 51% (21 cells) were classified as LT and none as nociceptive. The remaining neurons responded either inconsistently (~30%) or not at all (the remaining 18%) to repeated stimulation. The mean firing rates of the nociceptive neurons during spontaneous activity, brush, pressure, and pinch were 10.2 ± 8.9, 12.3 ± 10.8, 19.9 ± 14.4, and 26.5 ± 16.4 spikes/s, respectively.
Figure 3B shows mean firing rates at a nocisite composed of a 10-neuron cluster, when tap, pressure, and two intensities of pinch were applied to the same body part. Neurons 1, 3, 8, and 10 responded to tap; neurons 1, 2, 3, 5, and 8-10 responded to pressure; and neurons 1, 2, 3, and 10 responded to pinch stimuli. At this site three neurons were nociceptive, four non-nociceptive, and three did not respond to any of these stimuli. Therefore the responses, when based on mean firing rates, illustrate the intermingling of nociceptive neurons with other neurons.
Cross-correlations
To examine the spike-timing relationship between neurons, cross-correlations between neuron pairs identified in single recording sites were examined. Figure 5 shows cross-correlations at one site for four stimulus conditions. Cross-correlations are shown only for neurons 1 and 2 with the rest of the population for the cluster whose firing rates are shown in Fig. 3. Figure 5 illustrates that most cross-correlations with large positive peaks are unidirectional (span one side or the other of time 0). There are also a large number of negative valleys, most of which are of short-duration and close to time 0, and there are dramatic changes in the cross-correlations for different stimulus conditions. In this example, cross-correlations are significantly changed between neurons 1 and 3, 1 and 5, 1 and 9, 1 and 10 and 2 and 3, 2 and 5, 2 and 9 across the four stimulus conditions. Also, most cross-correlations between neuron 1 and the rest turn negative during pinch (Fig. 5D). Moreover there is no clear relationship between firing rates and cross-correlations. Generally stimulus related firing rate changes were not predictive of cross-correlation changes at noci- and at non-nocisites. Figure 6 illustrates the relationship between cross-correlations ("connectivity") and mean firing rates, as a function of stimulus, for the same 10 neuron cluster shown in Fig. 3A. There are no discernible connectivity relationships between the cells responsive or non-responsive to the stimuli. Also the changes in connectivity from one stimulus to the next (tap vs. pinch) reorganizes the network connectivity but again with no clear relationship to the neurons that respond to the stimuli.
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The properties of spike timings were distinct between noci- and
non-nocisites. The percentage of neuron pairs with significant cross-correlations was dependent on the type of recording site and the
polarity of the correlations. There were more positively correlated
pairs at non-nocisites (38.9 ± 20.6%, n = 6 sites) than at nocisites (27.6 ± 11.7%, n = 11)
and more negatively correlated pairs at nocisites (14.1 ± 10.1%)
than at non-nocisites (4.9 ± 6.7%; 2-way ANOVA, for recording
sites F = 33.1 P < 0.0001 and for
correlation polarities F = 55.5 P < 0.0001). The cross-correlation incidences were also stimulus modality
dependent: At non-nocisites, the number of positive cross-correlations
increased during brush (from 11 ± 6.3/site during spontaneous
activity to 17.7 ± 9.1/site; Kruskal-Wallis 1-way ANOVA,
P < 0.01). At nocisites, the number of negative
cross-correlations increased during pinch (from 4.2 ± 2.6/site
during spontaneous activity to 6.2 ± 4.3/site; Kruskal-Wallis 1-way ANOVA, P < 0.05). Positive cross-correlations
were much stronger (0.20 ± 0.29, n = 467) than
the negative ones (0.07 ± 0.05, n = 197).
Moreover at nocisites the time delay to reach the peak or valley of the
cross-correlations was a function of stimulus and of polarity. The time
delays to the peak of positive cross-correlations for spontaneous
activity, brush, pressure, and pinch stimuli, respectively, were
8.69 ± 7.76, 10.18 ± 8.67, 9.67 ± 8.3, and 7.22 ± 6.32 ms (n = 121, 147, 116, and 83), and the time
delays to the valley of negative cross-correlations were 3.85 ± 6.85, 4.07 ± 6.66, 2.62 ± 4.66, and 1.45 ± 1.23 ms
(n = 47, 44, 53, and 53; 2-way ANOVA, for polarity
F = 91.09, P < 0.0001 and for stimulus
modalities F = 3.41, P < 0.05). These
significant differences in correlation properties between sites
indicate that the non-noci- and nocisites are segregated far better by
spike-timing relationships between neurons than by the mean firing
rates of individual neurons, which indicated only borderline
differences in the prevalence of nociceptive neurons across recording sites.
Connectivity as a function of distance
The four-tip electrodes enable calculating distances between
neurons at a given recording site (Fig. 3A), and for the
same neurons, the presence of statistically significant
cross-correlations and their strengths also can be determined. We
combined these measures to generate spatial maps of spike-timing
relationships for the population, separated by groupings (noci- vs.
non-nocisites) and stimulus conditions. Both the strength (Fig. 7,
A and B) and the
incidence (Fig. 7, C and D) of cross-correlations
as a function of distance indicate the presence of an opponent
organization of spike timings at nocisites and a non-opponent
organization at non-nocisites. At nocisites, strong positive
correlations occur at short distances (65% of the positive
cross-correlations are for neuron pairs with <40 µm separations);
and negative cross-correlations rarely occur (<2%) between nearby
neurons (<30 µm) (2 = 191, P < 0.0001). Most negative cross-correlations are seen between neuron pairs separated by 30-120 µm. These spatial
properties of spike timings at noci- and at non-nocisites undergo
subtle complex changes with different stimuli. In one case, this
stimulus-dependent change seems readily interpretable. At the
non-nocisites, the incidence of positive cross-correlations for nearby
neurons (<40 µm) decreases during the pinch stimulus (in comparison
to the spontaneous activity and longer distances, Fisher's exact
P = 0.052), consistent with the independence assumption
between neuronal activity at these sites and noxious stimuli.
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Minimal neural network model
These experimental results show changes in spike-timing relationships between neurons for different sensory stimuli. What types of mechanisms are necessary to bring about such timing changes? Moreover why do we observe such a prominence of negatively correlated neuronal activity in the thalamus, when these are rarely seen in the cortex? Figure 8A shows a simulated neural network, which captures the major timing relationships that we observe and illustrates our conceptual model. The model indicates the presence of inhibitory interneurons (i1, i2) between projecting cells (p1, p2) that are appropriately distant from each other. The inhibitory interneurons are necessary for nocisites when the pair of neurons are distant from each other. The cross-correlations between p1 and p2, for the four-element network is shown for non-synchronized afferent inputs (unconnected arrows in model; result shown in Fig. 8B) and for synchronized inputs (connected arrows in model; results shown in Fig. 8, C-E). As the non-synchronized inputs increase, the mean firing rates of p1 and p2 increase and the negative symmetrical cross-correlation becomes stronger and observable due to the presence of i1 and i2 (Fig. 8B). Most negative cross-correlations that we observe physiologically are of this type, mainly seen at nocisites during pinch. For a fixed mean firing rate, increasing the strength of the synchronized input (relative to the non-synchronized input) increases the peak of the positive cross-correlation (Fig. 8C). For fixed-strength synchronized inputs, as the amount of non-synchronized input is increased the mean firing rate increases and the positive peak of the cross-correlation decreases (Fig. 8D). If the synchronized input arrives at p1 and p2 with the same delay, the positive cross-correlation peak is symmetrical around time 0. Different delays between the inputs to p1 and p2 result in shifted positive cross-correlation peaks (Fig. 8E). Therefore depending on the amount of synchronized inputs and their delay properties, one obtains positive cross-correlations that are either symmetrical or unilateral. The unilateral cross-correlation implies a unidirectional information flow between two units that are not directly connected with each other. The cross-correlations demonstrated in Fig. 8, B-E are all seen in our experimental data. This network therefore demonstrates spike-timing coordination changes that span the main changes we see physiologically. Importantly, this is achieved without changing the local synaptic connectivity of the network and only manipulating the amount of synchronous versus asynchronous afferent inputs.
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DISCUSSION |
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We recorded simultaneously from members of groups of thalamic
neurons located in small, contiguous neighborhoods. The properties of
these neurons were characterized in the traditional
single-electrode/single-cell recording method and by examining the
spatiotemporal interactions between neurons in any given neighborhood.
Mechanical innocuous and noxious stimuli segregate these neurons into
nociceptive and non-nociceptive cells and segregate the groups of cells
into noci- and non-nocisites. While the prevalence of nociceptive
neurons in VPL was in the same range as reported in single-cell
recording studies (Apkarian and Shi 1994), the changes
in firing rates during stimulation were not predictive of changes in
the cross-correlations of pairs of neurons. The results of the present
study demonstrate that the neuronal interactions at non-nocisites are
distinct from those found at nocisites with respect to several aspects:
non-nocisites had more positive cross-correlations, while nocisites had
more inhibitory cross-correlations (which are seldom seen in the
cortex); the incidence of cross-correlations was stimulus dependent;
and at nocisites indications for surround inhibition were observed, and
there was no evidence for such an organization at non-nocisites.
Single-unit properties
We estimate that ~18% of the neurons were unresponsive. We do
not think that this is due to missing receptive fields because at any
given recording site most of the body of the animal was investigated
with innocuous stimuli, and the multi-unit responses on each of the
four tips of the electrode were determined. However, we could have
missed some nociceptive neurons because noxious stimuli were not
applied as systematically as innocuous ones. This is not likely,
however, because in a previous study (Apkarian and Shi
1994), using single-electrode/single-unit recording techniques the incidence of nociceptive neurons was 10% in VPL, and in the present study, it was 25% at nocisites, indicating that with our four-tip electrode technique, nociceptive neurons can be detected quite
efficiently. In addition, we did not study neurons with very low
ongoing activity, and neurons with inconsistent responses were only
studied in the correlation analysis.
The properties of VPL neurons recorded in the present study are similar
to those reported in single-electrode recordings in firing rates,
receptive fields, somatotopy, and response properties (for references,
see Apkarian and Shi 1994). Although the receptive fields of individual neurons were not determined for every cell, on-line monitoring showed that these neurons had the characteristic receptive fields of VPL neurons. Almost all nociceptive cells had
responses to innocuous stimuli as well (i.e., they would be classified
as WDR type), except one, which only responded to pinch (nociceptive
specific type). In our single-electrode recording study of VPL cells,
all except two nociceptive cells were of WDR type (Apkarian and
Shi 1994
). The overall incidence of nociceptive cells in the
current study was 16% (20/124), which is slightly higher than in our
single-electrode recording study (9.4% or 19/203,
2 test, P > 0.1). Since all
nociceptive neurons were found at the nocisites, the percentage (24%)
is even higher at nocisites. These observations and the homogeneity
test imply that the nociceptive cells are grouped in bunches in VPL and
that recording sites containing such neurons are physiologically and
functionally distinct from those containing only non-nociceptive
neurons. In addition, nociceptive neurons have been repeatedly
described intermingled with neurons responsive selectively to innocuous
stimuli (Apkarian and Shi 1994
; Bushnell et al.
1993
; Casey and Morrow 1983
; Lenz et al. 1993
; Willis 1985
). The latter is consistent
with our observation that at nocisites, 25% of the cells respond to
innocuous stimuli only.
The segregation of nociceptive neurons is in agreement with the patchy
spinothalamic terminals (which is thought to be the most important
pathway to convey nociceptive information) found in anatomic studies
(Apkarian and Hodge 1989; Berkley 1980
;
Hodge and Apkarian 1990
; Rausell et al.
1992
; Shi et al. 1993
). It is also possible that
the nocisites correspond to the "matrix" domain in VPL since the
matrix domain is suggested to be more specific to spinothalamic inputs
(Rausell et al. 1992
). Our results do not support this
hypothesis since the matrix region is a cell-poor region, mostly devoid
of GABAergic cells (Rausell and Jones 1991
). Our data
show a very similar rate of incidence of cells per site between noci-
and non-nocisites (mean 7.5 and 6.8, respectively;
2 test P > 0.9), and the
neural network model implies the necessity of GABAergic inhibitory
interneurons at nocisites, although this conclusion is tempered by the
lack of histological evidence regarding the exact locations of our
recordings in VPL in relation to the matrix domain. In fact
Rausell et al. (1992)
state that the spinothalamic terminations in VPL only "tend to be concentrated" in the matrix region, and our physiological sampling does not show this tendency. A
more compelling demonstration of the segregation of spinothalamic and
medial lemniscal inputs in the macaque VPL was shown by Ralston and Ralston (1994)
. They demonstrated that the spinothalamic
inputs contact relay neurons forming primarily simple axodendritic
synapses, and only 15% of these terminations are on GABAergic
interneurons. In contrast, medial lemniscal inputs are mediated through
complex synaptic structures (triads and glomeruli, 85% of the
medial-lemniscal terminals), within these structures they make direct
contacts with relay neurons (54%) and indirect contacts through
GABAergic structures (46%). Therefore the medial lemniscal and
spinothalamic inputs have distinct synaptic signatures in VPL.
Populational properties
To our knowledge, this is the first description of the local connectivity properties of groups of neurons studied in the CNS. A long list of connectivity measures distinguishes between noci- and non-nocisites. Therefore noci- and non-nocisites must be regarded as distinct functional entities within VPL.
Both positive and negative cross-correlations were seen in the monkey
VPL, and these were related to the type of site and stimulus modality.
In VPL of rat and cat, cross-correlations between neurons recorded with
a single microelectrode are also reported to be positive, negative,
unilateral, and symmetrical (Alloway et al. 1995).
Alloway et al. report that 35-40% of pairs of nearby neurons showed
positive correlations during innocuous stimulation. This incidence is
very similar to our results for innocuous stimuli (positive
correlations to brush at non-nocisites were 32% and at nocisites,
48%).
In the current study, most cross-correlations with large positive peaks
were unidirectional (span one side or the other of time 0).
There are also a large number of negative valleys, most of which are of
short-duration and close to time 0. Both properties seem
unique to the thalamus because in the cortex the majority of
cross-correlations exhibit positive, bidirectional peaks, implying coordinated spike timings (deCharms and Merzenich 1996;
Gray et al. 1989
; Singer and Gray 1995
;
Vaadia et al. 1995
). Thus the occurrence of inhibitory
cross-correlations appears to be a specific feature of the thalamus and
specific for nocisites.
Both in the cortex and at non-nocisites in VPL, the density of
inhibitory GABAergic interneurons are similar (15-25%), yet in both
places negative cross-correlations are rarely observed. This, as
demonstrated in our model, must be a reflection of the strength of the
inhibitory synapses, the rate of ongoing activity and extent of
synchronicity of the afferent inputs. In contrast, at nocisites even
during spontaneous activity, we observe a large number of negative
correlations. Therefore at nocisites, the synaptic strengths of
inhibitory interneurons must be stronger than at non-nocisites. Two
types of GABAergic interneurons have been described in the ventrobasal
complex of the cat (Meng et al. 1996). We speculate that
these two types may correspond to the two classes of GABAergic neurons
implied by our results. If we generalize to assume that nocisites
reflect spinothalamic inputs and non-nocisites reflect medial lemniscal
inputs (Apkarian 1995
; Hodge and Apkarian
1990
; Willis 1985
), then the relay cells at
nocisites receive primarily direct excitatory spinothalamic inputs
while the cells at non-nocisites receive medial lemniscal inputs
that are damped presynaptically through interactions with
GABAergic structures. Since, as the neural network model illustrates,
the type of afferent input can mask the interneuronal connectivity in
the cross-correlation calculation, the extensive presynaptic modulation
that medial lemniscal inputs undergo may explain the correlation
properties of the non-nocisites. On the other hand, the more direct
excitatory spinothalamic inputs to both relay cells and interneurons
imply that the circuitry of the model presented may better correspond
to the connectivity of nocisites. It should be emphasized that the
details of the local circuitry among afferent inputs, relay cells,
interneurons and thalamic reticular inhibitory inputs, and cortical
back projections remain for the most part unknown. Also, at
non-nocisites the innocuous stimuli, we used to study the populational
properties were very simple. To reveal the full dynamics of
non-nocisites, we need to explore a larger array of cutaneous patterns.
Dependence of connectivity on interneuronal distances
The plots of connection strength and connection incidence as a
function of the distances between neurons imply a three-dimensional radial symmetry regarding local connectivity rules. To our knowledge, this is the first physiologic determination of local connectivity in
the CNS. The results in general confirm an idea first proposed by Sholl
(Sholl 1956; Sholl and Uttley
1953
) that connection strength from any given neuron
should decrease exponentially by distance. Sholl proposed the idea
based on the anatomy of dendritic branching patterns in the cortex,
where he actually calculates the distance decay rate for stellate
cells. We observe an exponential decay in VPL at non-nocisites during
spontaneous activity in accordance to Sholl's rule. Similar but more
complex dependences are seen for nocisites and for various stimulus
conditions. That these connectivity rules are dynamically modified by
the stimulus conditions reinforces the notion that spike-timing
coordination across neurons may be a fundamental principle of
information coding in the thalamus and more specifically in coding
innocuous and noxious somatic inputs, as already proposed for the
cortex (Abeles et al. 1993
, 1995
; deCharms and
Merzenich 1996
; Gray et al. 1989
; Singer
and Gray 1995
; Vaadia et al. 1995
).
At non-nocisites, the spatial connectivity dramatically reorganizes
between spontaneous activity and brush. Such reorganization of local
connectivity must be the mechanism by which cutaneous receptive fields
dynamically shift in time, forming a spatiotemporal distributed
representation of the whiskers in the thalamus (Faggin et al.
1997; Nicolelis et al. 1995
) (multi-electrode
recordings in the whisker region of the thalamus and cortex where
population dynamics is examined for cells separated from each other by
millimeters). At nocisites, we observe an opponent organization of
connectivity. To our knowledge, this is the first demonstration of a
center-surround organization at the level of a central neuronal network
and the first observation of a center surround organization in the
nociceptive system. If this organization also holds true for thermal
stimuli, then it can be the mechanism that gives rise to the large
cortical inhibitions we have recently described for painful stimuli
applied to a very small part of the body (Apkarian et al.
2000
). This may also be the mechanism by which the thermal
grill illusion comes about because we have recently proposed that this
illusion can be explained by lateral inhibition
(Brüggemann and Apkarian 1999
).
Implications of the model
The architecture of the neural network model we propose is
identical to the local connectivity proposed for lateral thalamic nuclei many years ago based on anatomic studies (see Fig. 20 in Szentagothai and Arbib 1975). According to the simple
neural model we developed, positive cross-correlation strengths depend
on the afferent synaptic strengths and the relative proportion of
synchronized and non-synchronized inputs. It needs to be emphasized
that these network properties are between pairs of model neurons that
do not have direct synaptic connectivity between them. Most likely differences in the local anatomic connectivity would partially explain
the opponent versus non-opponent organization of noci- and
non-nocisites, although this remains to be studied more carefully. It
is reasonable to assume that the afferent pathway to the nocisites is
the spinothalamic tract, while the primary afferent pathway for
non-nocisites is the dorsal column pathway (Apkarian
1995
; Hodge and Apkarian 1990
; Willis
1985
). Therefore the opponent versus non-opponent organization
of the noci- and non-noci-networks is attributed to differences in the
spatiotemporal properties of the two pathways, in terms of the amount
of synchronized and non-synchronized inputs relative to the synaptic
strengths between projecting cells and interneurons. This organization
of the somatosensory thalamus must also depend on the
thalamo-cortico-thalamic loops as previously demonstrated for pairs of
neurons in the visual thalamus (Sillitto et al. 1994
).
Our results indicate that non-nocisites and nocisites in the somatosensory thalamus are segregated, i.e., we are recording from different functional entities. The size of the volume of tissue we are recording from and the similarity of the incidence of neurons at both types of sites leads us to the conclusion that we are recording from thalamic rods. This then implies the existence of two types of rods with distinct inputs: nocirods with dominant input from the spinothalamic pathway supplying asynchronous inputs during noxious stimuli and non-nocirods with dominant input from the dorsal column system supplying relatively more synchronous inputs. The three-dimensional structure of these two different functional networks is not known yet, i.e., whether they are elongated, continuous aggregates, or spherical and intermingled, which remains to be determined.
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ACKNOWLEDGMENTS |
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We thank Drs. D. R. Chialvo, S. A. Khan, B. Motter, and D. Pelli for comments on the manuscript.
This work was supported by the Department of Neurosurgery, SUNY Health Science Center, and the National Institute of Mental Health. J. Brüggemann was funded by Fogarty and Alexander von Humboldt fellowships.
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FOOTNOTES |
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Address for reprint requests: A. V. Apkarian, Dept. of Neurosurgery, Research Laboratories, WKH 3118, SUNY Upstate Medical University, 766 Irving Ave., Syracuse, NY 13210 (E-mail: apkarian{at}mail.upstate.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 13 September 1999; accepted in final form 21 March 2000.
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REFERENCES |
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