Department of Molecular and Integrative Physiology and Smith Mental Retardation and Human Development Research Center, University of Kansas Medical Center, Kansas City, Kansas 66160
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ABSTRACT |
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McKiernan, Brian J., Joanne K. Marcario, Jennifer Hill Karrer, and Paul D. Cheney. Correlations Between Corticomotoneuronal (CM) Cell Postspike Effects and Cell-Target Muscle Covariation. J. Neurophysiol. 83: 99-115, 2000. The presence of postspike facilitation (PSpF) in spike-triggered averages of electromyographic (EMG) activity provides a useful means of identifying cortical neurons with excitatory synaptic linkages to motoneurons. Similarly the presence of postspike suppression (PSpS) suggests the presence of underlying inhibitory synaptic linkages. The question we have addressed in this study concerns the extent to which the presence and strength of PSpF and PSpS from corticomotoneuronal (CM) cells correlates with the magnitude of covariation in activity of the CM cell and its target muscles. For this purpose, we have isolated cells during a reach and prehension task during which the activity of 24 individual proximal and distal forelimb muscles was recorded. These muscles show broad coactivation but with a highly fractionated and muscle specific fine structure of peaks and valleys. Covariation was assessed by computing long-term (2 s) cross-correlations between CM cells and forelimb muscles. The magnitude of cross-correlations was greater for muscles with facilitation effects than muscles lacking effects in spike-triggered averages. The results also demonstrate a significant relationship between the sign of the postspike effect (facilitation or suppression) and the presence of a peak or trough in the cross-correlation. Of all the target muscles with facilitation effects in spike-triggered averages (PSpF, PSpF with synchrony, or synchrony facilitation alone), 89.5% were associated with significant cross-correlation peaks, indicating positively covarying muscle and CM cell activity. Seven percent of facilitation effects were not associated with a significant effect in the cross-correlation, whereas only 3.4% of effects were associated with correlation troughs. In contrast, of all the muscles with suppression effects in spike-triggered averages, 38.9% were associated with significant troughs in the cross-correlation, indicating an inverse relation between CM cell and muscle activity consistent with the presence of suppression. Fifty-five percent of suppression effects was associated with correlation peaks, whereas 5.6% was not associated with a significant effect in the cross-correlation. Limiting the analysis to moderate and strong facilitation effects, the magnitude of PSpF was correlated weakly with the magnitude of the cell-muscle cross-correlation peak. Nevertheless, the results show that although many CM cell-target muscle pairs covary during the reach and prehension task in a way consistent with the sign and strength of the CM cell's synaptic effects on target motoneurons, many exceptions exist. The results are compatible with a model in which control of particular motoneuron pools reflects not only the summation of signals from many CM cells but also signals from additional descending, sensory afferent, and intrinsic spinal cord neurons. Any one neuron will make only a small contribution to the overall activity of the motoneuron pool. In view of this, it is not surprising that relationships between postspike effects and CM cell-target muscle covariation are relatively weak with many apparent incongruities.
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INTRODUCTION |
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Spike-triggered averaging (SpTA) of rectified electromyographic
(EMG) activity has proven to be a useful means of identifying motor
cortex cells with synaptic linkages to motoneurons. The presence of
postspike facilitation (PSpF) or postspike suppression (PSpS) in SpTAs
is interpreted as evidence of an underlying synaptic linkage between
the cortical cell and motoneurons of its target muscles (Fetz
and Cheney 1980; Kasser and Cheney 1985
;
Lemon et al. 1986
). By compiling SpTAs for multiple
muscles in a limb, it is possible to determine the "muscle field"
of a corticomotoneuronal (CM) cell. Muscle field is defined as the
group of agonist and/or antagonist muscles that are facilitated or
suppressed by the CM cell during active movement (Buys et al.
1986
; Fetz and Cheney 1979
, 1980
; Kasser
and Cheney 1985
). Besides identifying cells possessing a
synaptic linkage to motoneurons, the magnitude of PSpF or PSpS can be
used as a measure of the strength of the cell's facilitation or
suppression of target motoneurons (Buys et al. 1986
;
Fetz and Cheney 1980
; Kasser and Cheney
1985
).
In contrast to SpTA, which reveals the synaptic linkages between
premotor cells and motoneurons, the extent of functional covariation in
activity between premotor neurons and muscles can be quantified by
computing long-term cross-correlation functions. Houk et al.
(1987) and Miller et al. (1992
, 1993
) have
described such a cross-correlation function for estimating the strength and relative timing of covariation between premotor cells and target muscles.
Houk et al. (1987) correlated cells of the cat red
nucleus and forelimb muscles over a 4-s period during a functional
task. Although they found correlational evidence for strong linkages between red nucleus cells and forelimb muscles, they cautioned that
other neurophysiological methods need to be combined with cross-correlation data to define specific neuroanatomic relationships between cells and muscles. Miller et al. (1992
, 1993
)
used the cross-correlation technique to examine the relationship
between the discharge rate of red nucleus cells in the monkey and the magnitude of rectified, multiunit EMG from forelimb muscles during a
reach and prehension task. In addition to quantifying the strength, timing, and dynamics of cross-correlation functions for multiple forelimb muscles, they also calculated SpTAs for the same cell-muscle pairs (Miller et al. 1992
). They proposed that large,
centrally located peaks in the cross-correlation functions suggest the
presence of "functional linkages" between a cell and covarying
muscle(s). They concluded that there was a tendency for
rubromotoneuronal (RM) neurons with strong synaptic linkages
(identified by the presence of PSpF in the SpTAs) to also show
relatively strong functional linkages.
Using a different methodology, Bennett and Lemon (1994)
studied the intrinsic muscles of the hand and found that there was only
a weak correlation between the amplitude of a PSpF and the strength of
covariation between CM cells and target muscles in the hand. Further,
there was no consistent relation between the firing frequency of a CM
cell and the amplitude of EMG activity in its target muscles. Although
20 of 48 cell-muscle pairs showed a significant correlation between
cell discharge rate and EMG activity, others pairs that were tested
showed no correlation or even a negative correlation. However, in a
later study, they found that 9 of 13 CM cells discharged more intensely
when the target muscle receiving the strongest PSpF was most active
(Bennett and Lemon 1996
). Fetz and Finnochio
(1975)
had earlier concluded that coactivation of a cell and
muscle is neither necessary nor sufficient evidence for establishing
anatomic connections between the two.
We previously described a population of CM cells that produced multiple
postspike effects (PSEs) in both proximal and distal muscles of the
forelimb of a monkey performing a reach and prehension task
(McKiernan et al. 1998). Inspection of the patterns of
EMG activity revealed broad coactivation of many muscles at different joints throughout task performance but with a high degree of
individuality in the fine structure of EMG peaks and troughs. Although
the broad patterns of agonist muscle coactivation that occur in
relation to simple, single joint, alternating movement tasks we have
used in previous work are unlikely to form a useful substrate for
testing relations with PSEs, the highly specific fine structure of EMG activity in different muscles during the reach and prehension task was
ideal for the purposes of the present study. The goal of this study was
to investigate the extent to which the sign and strength of PSEs from
CM cells correlates with the sign and magnitude of the covariation
pattern between CM cells and their target muscles during a reach and
prehension task. We attempted to determine if CM cell-target muscle
pairs showing PSpF covary positively and if the strength of PSpF
correlates with the magnitude of the cell-target muscle covariation.
Similarly, we wanted to determine if CM cells covary inversely with
target muscles showing PSpS.
The results of this study show that, during a complex movement task, the presence of strong covariation between a CM cell and its target muscles did not consistently predict the presence of PSpF. On the other hand, PSpFs were consistently associated with significant covariation. Target muscles with PSpF generally covaried positively with the CM cell, whereas many muscles with PSpS covaried negatively. For strong PSpFs, there was weak but positive correlation between the strength of PSpF and the magnitude of cell-target muscle covariation.
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METHODS |
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Training procedures
The data for this project were collected from two male rhesus monkeys (Macaca mulatta) trained for ~9 mo on three different behavioral tasks. Only one of the tasks (reach and prehension) was used to collect the data reported in this paper. Each monkey weighed ~6 kg when data collection began. During each data collection session, the monkey being tested was seated in a standard primate chair that was placed in a sound-attenuating chamber. The monkey's left forelimb was restrained during task performance in a foam-padded tube that was fitted to the forearm and elbow, while the right forelimb was unrestrained. The monkey was guided in performance of the task by audio and video cues provided by an IBM-compatible computer.
Behavioral task
The task chosen for this project activated multiple proximal and
distal forelimb muscles in natural, functional synergies as the monkey
actively reached for and retrieved a food reward. The task was
self-paced and controlled by a personal computer. The monkey initiated
the task by placing its right hand on a pressure plate at waist height
directly in front of him. Pressing on this plate for a preprogrammed
length of time triggered the release of a food reward and a
GO signal. The monkey then reached out to a small well
located at shoulder level a little less than an arm's length away. The
monkey used one or two digits to dig the food reward from the well,
then grasped it and brought it to his mouth. More details regarding the
design of this task and its implementation can be found in
McKiernan et al. (1998).
Surgical procedures
After training, a 22-mm diam stainless steel chamber was centered over the hand area of the motor cortex of the left hemisphere of each monkey and anchored to the skull with 25-30 vitallium screws and dental acrylic. Threaded nylon nuts also were anchored in dental acrylic over the occipital aspect of the skull to allow for attachment of a flexible head-restraint system during recording sessions. For all implant surgeries, the monkeys were tranquilized with ketamine (10 mg/kg) and anesthetized with isoflurane gas. Surgeries were performed in an AAALAC (Association for Assessment and Accreditation of Laboratory Animal Care)-accredited facility using full sterile procedures. Postoperatively, monkeys received prophylactic antibiotic and analgesic medication. All work involving these monkeys conformed with the procedures outlined in the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health.
EMG records from 22-24 different forelimb muscles were recorded with
pairs of multistranded stainless steel wires inserted into the target
muscles (Table 1). With the monkey under
isoflurane anesthesia, pairs of insulated, multistranded stainless
steel wires (Cooner AS632) were inserted transcutaneously into each of
the target muscles under sterile surgical conditions. Approximately 2 mm of insulation was removed from the end of each wire before insertion. The bared end of each lead wire was inserted "backward" into the cannula of a 21-gauge needle for transcutaneous insertion into
the muscle belly. This procedure formed a hook at the end of each wire
that tended to anchor the wire in the muscle after the needle was
withdrawn. Once inserted, each wire could withstand mild tugging
without dislodging. The insertion points for each muscle were
identified based on palpation and dissection studies in which optimal
insertion points were mapped with reference to external bony landmarks.
The ends of each pair of wires were separated by ~5 mm (Loeb
and Gans 1986). The placement of each electrode pair
was tested for accuracy by electrical stimulation through the
electrodes while observing the nature of the resulting movement. In
some cases, this also was done midway through the life of the implant
to confirm location. Once all electrodes were positioned, the wires
were anchored to the monkey's arm with medical adhesive tape (Johnson
& Johnson 5174). This tape is elasticized and highly adhesive. In
general, the tape remained firmly anchored to the skin throughout the
life of implant. The EMG implants were installed in three independent
sections: one for the forearm that included muscles of the wrist and
digits and intrinsic muscles of the hand; one for the upper arm that
included muscles of the elbow; and one for the shoulder. With this
modular approach, specific sections could be replaced, if necessary,
without disturbing the entire implant. Each monkey wore a canvas jacket
with a full sleeve on the right forelimb while in its home cage to
protect the implanted wires. The implants generally remained functional
for 5-8 wk.
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Recording procedures
The electrical activity from single motor cortex cells was
recorded using a glass insulated platinum-iridium electrode with a
typical recording impedance between 0.7 and 1.5 M. The electrode was
positioned over the recording area using an X-Y positioner and was
advanced into the motor cortex with a manual hydraulic microdrive.
Cortical cell and EMG activity were simultaneously recorded on analogue
tape along with position signals from the task.
Spike-triggered averaging procedures
During each recording session, cortical cell activity and EMG
activity were monitored continuously on oscilloscopes. The action potentials of single cells in the motor cortex served as the triggers for computing SpTAs. Single-unit spikes from the cell of interest were
isolated from other cortical cell spikes with a pair of time/amplitude window discriminators connected in series. Two PDP-11/73 computers rectified and digitized the analogue EMG signals before compiling simultaneous SpTAs for all recorded muscles. Some data collection and
analysis also was performed with CED (Cambridge Electronics Design)
hardware and custom SpTA software for Windows (Neural Averager, Larry
Shupe, University of Washington, Seattle). The sampling rate for SpTA
was 4 kHz., and the analysis period was 60 ms: 20 ms preceding the unit
spike and 40 ms following it. To ensure that sweeps of EMG were only
added to the SpTA if there was significant EMG activity present, we
implemented a sweep-filtering protocol as previously described for
identification and analysis of CM cells (McKiernan et al.
1998).
Quantification of postspike effects
On the basis of criteria established by Flament et al.
(1992), we assigned the SpTA for each cell-muscle pair into one
of seven possible groups: pure PSpF; postspike facilitation on an underlying synchrony facilitation (PSpF+S); pure synchronous
facilitation (SyncF); pure PSpS; postspike suppression on an underlying
synchrony suppression (PSpS+S); pure synchronous suppression (SyncS);
and no postspike or synchrony effect (Flament et al.
1992
; McKiernan et al. 1998
).
All identified postspike and synchrony effects also were assigned
a qualitative ranking of weak, moderate, or strong based on visual
assessment of the magnitude of the facilitation or suppression effect
relative to baseline activity. All effects that were classified as PSpF
or PSpS were quantified further in terms of their latency, duration,
and magnitude. First, we subtracted any nonstationary, ramping
baselines (e.g., see Fig. 6 in Lemon et al. 1986). To do this, a ramp
function was calculated using a linear least-squares fit to a selected
data range. The ramp function then was subtracted while retaining the
record's baseline mean. EMG values from a range of bins in the
pretrigger period then were averaged to derive a baseline mean and SD.
The baseline typically was determined by averaging the first 10 ms of
each record (
20 to
10 ms pretrigger). Onset and offset latencies of
PSEs then were identified as the points where the envelope of the SpTA
crossed a level equivalent to 2 SD above or below the mean of the
baseline EMG.
The peak of each effect was defined as the highest point in the PSpF or
the lowest point in the PSpS. The magnitude of PSpF was quantified in
terms of its peak percent increase (PPI) as follows: PPI = 100 *
(Maximum bin value baseline mean)/baseline mean. A similar
measure [peak percent decrease (PPD)] was calculated for PSpS.
Quantification of cell-muscle covariation
Cell-muscle covariation was quantified by computing analogue
cross-correlation functions between cortical cells and recorded muscles
using the algorithm described by Miller et al. (1992, 1993
). Cross-correlations were compiled off-line using the
Neural Averager software package (Larry Shupe, University of
Washington, Seattle) from data saved on a 28-channel TEAC
instrumentation tape recorder. We collected 90 s of continuous
data (~20-30 complete trials of the reach and prehension task) for
each cross-correlation. Single-unit spikes were discriminated and the
resulting pulses were sent to a frequency meter, the output of which
was proportional to the inverse of interspike interval. The output of
the frequency meter was sent to a 2-pole Butterworth low-pass filter
(12 dB/octive attenuation >20 Hz) creating an analogue signal that was
a smoothed representation of the cell's firing frequency over time
(Cheney et al. 1998
). EMG signals from all recorded
muscles were amplified, full-wave rectified, and also low-pass filtered
in the same way as unit firing rate. Amplifier gains were adjusted to
normalize EMG signal amplitude across all channels. Both the CM cell
firing rate and the EMG signals were sampled at 200 Hz before
calculating the cross-correlation. The analysis window for each
correlation was 2 s. EMG records were shifted in 5-ms increments
relative to the CM cell activity record to yield a cross-correlation
with an analysis window of ±1 s.
Analysis of cross-correlations
We assigned each cross-correlation to one of eight qualitative groups based on visual inspection of the shape of the cross-correlation plot. The categories are illustrated in Fig. 1 and included: single peak; double peak; single trough; biphasic (initial trough followed by a peak); biphasic (initial peak followed by a trough); phasic/tonic (with identifiable peak); ramp (either rising or falling); and complex. The last category consisted of nonzero correlations with patterns that did not fit clearly in any of the other eight categories.
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Three quantitative measures then were calculated for each
cross-correlation (Fig. 2). The peak
value of each correlation (max) was defined as
the single point with the largest absolute difference from zero
(positive or negative) that was straddled on each side by at least one
point of lower absolute value. As with the Pearson's correlation
coefficient,
max could potentially range from
1.0 to +1.0. The peak lag was defined as the time from
max to the center of the analysis window
(time 0). Positive values indicate that peaks in EMG
activity followed peaks in CM cell activity; negative values indicate
that EMG peaks preceded peaks in CM cell activity. We also calculated
(when possible) the width (duration) of the correlation envelope at a
magnitude halfway between the maximum and minimum values in the record.
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At what magnitude can a cross-correlation peak be considered
statistically significant? As pointed out by Miller et al.
(1993), statistical analysis of cross-correlation effects is
complicated by a number of factors. To circumvent this problem, they
performed a Monte Carlo simulation on their cross-correlation data and
concluded that a reasonable estimate of the 5% level of significance
for analogue cross-correlations of this type is
max
±0.15. Because our data were obtained
using the same cross-correlation method (Houk et al.
1987
; Miller et al. 1993
), we have adopted
max
±0.15 as the level of significance and
max
±0.25 as the criterion for identifying
the clearest cross-correlations worthy of further detailed study. With
these criteria in mind, we constructed the following five-point ordinal
scale for characterizing the significance of cross-correlations: strong
peak,
max
+0.25; moderate peak, 0.25 >
max
+0.15; not significant, 0.15 >
max >
0.15; moderate trough,
0.25 <
max
0.15; strong trough,
max
0.25.
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RESULTS |
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We tested 174 cells for the presence of PSEs. One hundred twelve
cells showed PSEs in at least one of the tested muscles (facilitation or suppression). We chose 23 of those 112 cells for analysis and calculated cross-correlations for 499 cell-muscle pairs. The 23 cells
were selected based on the fact that each contained at least two PSpFs
that were rated as moderate or strong, although many had additional
weak facilitation effects and one or more suppression effects. We
targeted cells with two or more moderate or strong facilitation effects
based on the fact that Miller et al. (1992) found a
tendency for rubromotoneuronal (RM) neurons with strong synaptic
linkages (identified by the presence of PSpF in the SpTAs) to also show
relatively strong covariation (i.e., cross-correlation peaks).
A small number of cross-correlations (34) had peaks that occurred at the positive or negative limit of the analysis period. These correlations [including 19 shoulder (SHL), 19 elbow (ELB), and 1 wrist (WRS)] were judged to be of little functional interest and were excluded from further analysis. The clear majority (281, 60%) of the remaining 465 cross-correlations had a single peak as their primary qualitative feature and were classified in category 1 (Fig. 1). Fifty two (11%) of the correlations were classified as complex because they could not easily be placed into any of the other seven categories. The remaining 124 correlations (28%) were distributed across the other six categories with the double-peak category being most common after the single-peak category.
One hundred twenty two (26%) of the 465 cross-correlations were associated with either a postspike or synchrony effect in SpTAs. Eighty six of these effects were facilitation (57 PSpF, 19 PSpF+S, 10 SyncF), whereas 36 were suppression effects (11 PSpS, 10 PSpS+S, 15 SyncS). Three hundred forty-three cross-correlations were not associated with any type of facilitation or suppression effect in the SpTA (pure or synchronous).
Figure 3A shows the peak lag
times of all 465 cross-correlations plotted against their respective
peak magnitudes (max). The overwhelming
majority of
max values were positive (438 of 465, 94%). This was especially evident in cross-correlations
associated with facilitation (Fig. 3B) but was also true for
cross-correlations associated with suppression (Fig. 3C) and
those not associated with any postspike or synchrony effect (Fig.
3D). There was a weak but statistically significant tendency
for cross-correlations with higher
max to have
lower peak lag times (r =
0.28 and P < 0.001 when plotting the absolute value each
max against the absolute value of its
corresponding lag time). This is especially evident in Fig.
3B where we have plotted only the correlations for the 86 cell-muscle pairs with facilitation in the SpTA (PSpF, PSpF+S, and/or
SyncF). Interestingly, the 10 cross-correlations that were associated
with SyncF in the SpTA had the smallest aggregate lag times. Lag times
for this subgroup ranged from
95 to +55 ms, and
max ranged from 0.196 to 0.662. The widest
range of lag times occurred for those correlations with the lowest peak
magnitudes.
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Cross-correlations that were associated with suppression in the SpTA
had generally higher positive and negative lag times and lower peak
magnitudes (Fig. 3, C vs. B). The clear majority of cross-correlations with negative max were
in the group that was not associated with any kind of postspike or
synchrony effect (Fig. 3D).
Magnitude and latency of cross-correlations associated with PSpF
Before quantitatively examining the relationship between
max and PPI, we compared the magnitude and
timing of the group of cross-correlations associated with pure PSpF
(n = 57) with those that were not associated with any
postspike or synchrony effect (n = 343). Figure
4A contains histograms
comparing the range of
max for these two
groups. The mean
max for the group of
correlations associated with PSpF (0.36 ± 0.15) was significantly
greater than the mean
max for those
cross-correlations not associated with a postspike or synchrony effect
(0.29 ± 0.16, P < 0.001). The overall
distribution of
max values was somewhat
broader for the group not associated with postspike or synchrony
effects, and the histogram for that group also shows a small secondary
peak for negative
max values not found in the
histogram of correlations associated with PSpF.
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The distribution of lag times was also different between the PSpF and
no-effects groups (Fig. 4B). Although the distributions appear very similar, closer examination reveals that the
cross-correlations associated with PSpF had lag times that were closer
to zero (shorter) and significantly different from the lag times for
correlations not associated with a postspike or synchrony effect (1.6 ±329.2 vs. 119.8 ± 328.3 ms, P = 0.013). This
also is reflected in the fact that the median lag time for the group
associated with PSpF was 5 ms, whereas the median lag time for the
group without PSEs was +75 ms. The shorter lag times of the
facilitation group (Fig. 3B) compared with the no-effect
group (Fig. 3D) can be attributed in part to the fact that
the facilitation group had a larger fraction of distal muscles (77.9 vs. 46.1%) and distal muscles tend to have shorter lag times than
proximal muscles. For example, in the facilitation group, the median
lag for distal muscles was
10 ms compared with 125 ms for proximal
muscles. The corresponding lags for the group not associated with any
facilitation or suppression effect were 30 ms for distal muscles and
110 ms for proximal muscles.
Does the magnitude of PSpF correlate with max?
Figure 5A plots the
magnitude of pure PSpF, measured as PPI, against
max for all cell-muscle pairs that showed PSpF
and had >1,000 sweeps in the SpTA (n = 53).
Spike-triggered averages containing PSpF+S and SyncF as well as those
containing <1,000 sweeps were excluded from this analysis because we
could not confidently calculate PPI (McKiernan et al.
1998
). PPI and
max for this group of
cell-muscle pairs were not correlated (r = 0.08, P = 0.6). However, when the analysis was limited to the
30 cell-muscle pairs in which the magnitude of PSpF had been rated as
moderate or strong, a weak, positive correlation emerged between PPI
and
max (r = 0.33, P = 0.08, Fig. 5B) although the correlation
failed to reach statistical significance.
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Although there were several cell-muscle pairs that had both a high PPI
and a high max, there were also many instances
where the PPI was large but
max small and vice
versa. Figure 6 shows examples of four
different cell-muscle pairs to illustrate this point. In Fig.
6A, both PPI and
max are high,
indicating that the cell had both a strong synaptic linkage with
motoneurons of this muscle and also strongly covarying activity.
Although PPI is almost as large for the PSpF in Fig. 6B,
max is very weak. However, the
cross-correlation peak lag time is near time 0, suggesting that there were points during the task when both the cell and muscle
activity covaried synchronously. The low
max
suggests an overall weak covariation. This could arise from a
consistent but weak covariation at one or more points throughout the
movement cycle. Alternatively, a strong correlation at one point during the movement cycle could be degraded by the absence of covariation or
even an inverse relationship between cell firing and muscle EMG at
another point during the movement cycle. In Fig. 6C, PSpF magnitude was weak despite a strong
max,
indicating strong covariation between the cell and this muscle during
the task but only a weak synaptic linkage. Finally, Fig. 6D
shows the cross-correlation with the single highest
max we found (0.79). However, in this case the
muscle showed no identifiable PSpF and therefore demonstrated a very
strong covariation between cell firing and muscle EMG in the absence of
any detectable synaptic linkage.
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We also calculated the correlation coefficients between PPI and the peak lag of the corresponding cross-correlation peaks as well as between PPI and the durations of the cross-correlation envelope at 50% peak magnitude. Neither of these relationships was statistically significant.
Because the relationship between PPI and max
was either weak (when considering moderate and strong pure PSpF) or
nonexistent (all pure PSpF), we wondered if a clearer pattern would
emerge for PSpFs of different magnitudes within a single CM cell's
muscle field. Table 2 lists the PSpF
magnitudes for target muscles belonging to several different CM cells.
Listed for each muscle is
max and its rank
order within the cell's muscle field based on
max. For some cells (e.g., 21n7,
125k1, and 148n5), the rank orders for PPI and
max were identical. However, for several
additional cells there was little or no agreement between the
respective rank orders of PPI and
max within a
muscle field.
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Postspike suppression and inverse cell-muscle covariation
For 17 cross-correlations of CM cell and muscle activity, the
predominate or only qualitative feature was a trough near time 0 (Fig. 1, Category 3). For 13 of these correlations,
max was negative and occurred at the bottom of
the trough. However, in the other four cases,
max was positive because the trough was superimposed on an otherwise broad positive correlation. The existence of a centrally located trough demonstrates the presence of an inverse
pattern of cell/muscle covariation during the task. Despite the
positive values for
max in some of these
cases, the correlation trough appeared to be the most significant
feature, especially in those cases where the low point of the trough
had a much smaller positive or negative lag than
max.
An additional 36 of 465 cross-correlations had a different type of
trough. These troughs came from categories 2, 4, and 5. In each case,
the cross-correlation contained a positive peak on one or both sides of
a dip in the cross-correlation envelope yielding a positive
max, which was used in other analyses in this
paper (e.g., Fig. 3). However, on the basis of the premise that the
trough might be the most significant feature in the cross-correlation, we identified the lowest point in the cross-correlation occurring within a 500-ms window (±250 ms) around time 0 and measured
its lag and magnitude (Fig. 7). Starting
with that point, we sequentially evaluated preceding points in the
cross-correlation until we found the point where the value of the
cross-correlation envelope no longer continued to rise. We labeled this
point the "pretrough peak." Subtracting the low point from the
pretrough peak yielded a "fall magnitude." Similarly, subtracting
the lag of the low point from that of the pretrough peak yielded a
"fall time." Any cross-correlation with a fall magnitude
0.25,
the low point of which had a smaller lag time than
max, was categorized as a "strong trough."
Likewise, any cross-correlation with a fall magnitude
0.15 but
<0.25, the low point of which had a smaller lag time than
max, was categorized as a "moderate
trough."
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Based on these criteria, 24 of the 36 cross-correlations were
categorized as containing significant troughs despite the presence of a
positive max. The lag of the low point in the
trough was between
50 and +50 ms for 17 of the 24 recategorized
correlations. The average fall magnitude for these 24 troughs was 0.29, and the average fall time was 229 ms.
A number of the cross-correlations containing troughs were associated
with postspike and/or synchrony suppression. However, as with
facilitation effects, there was no clear quantitative relationship
between the magnitude of suppression and the magnitude of the troughs.
Figure 8 shows examples of troughs that
were associated with suppression effects in SpTAs. In Fig.
8A, max for PDE is weakly to
moderately positive throughout except for a visible trough the low
point of which has a lag of +2 ms. The small lag time for the bottom of
the trough in this correlation demonstrates that muscle activity fell
at the same time cell activity was rising. This inverse relation
between cell and muscle activity is functionally consistent with the
presence of clear PSpS in the SpTA. In Fig. 8B, the
cross-correlation between cell firing and FCR muscle activity rose
early in the correlation to a value significantly greater than zero.
Beginning at a lag of
155 ms, the correlation magnitude dropped to
near zero, forming a clear trough. Unlike the correlation in
A, the magnitude of this correlation never dropped below
zero, although it was close. Fall magnitude was 0.16 and the trough reached its low point at a lag of +30 ms. This inverse covariation between cell and muscle activity is again consistent with the presence
of a strong PSpS+S in the SpTA.
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The cross-correlation in Fig. 8C is similar in shape to
those in A and B. The correlation rose to its
largest positive value with a lag of 120 ms, at which point it began
falling to a low of 0.05 at a lag of +5 ms. The corresponding SpTA
showed synchronous suppression. Although the onset of suppression
appears sharp in the SpTA, it begins too early to be attributed solely
to a synaptic output linkage between the cell and muscle. Moreover no
discontinuity could be identified between the onset and the negative
peak of the effect that might indicate the presence of a true PSpS
together with the synchrony suppression (McKiernan et al.
1998
).
Not every suppression effect in the SpTA was associated with a trough
in the cross-correlation. Figure 8D illustrates a case where
a clear PSpS appeared in the SpTA despite a strong positive correlation
between cell and muscle. Similarly not every trough in the
cross-correlation was associated with a suppression effect in the SpTA.
Three (12%) of the cross-correlations with significant troughs were
associated with PSpF. In each of these cases,
max was positive and had a negative lag, but a
clear trough was present and reached a low point closer to time
0 than
max.
The majority of facilitation effects in SpTAs (89.5%) was associated with significant peaks in the cross-correlation (Table 3, Fig. 9). However, 55.6% of the cross-correlations associated with suppression effects and 84.3% of the cross-correlations not associated with any facilitation or suppression effects also had significant correlation peaks. Therefore the presence of a significant peak in the cross-correlation is clearly not a good predictor of a facilitation effect in the SpTA. It also should be emphasized that this relatively high incidence of significant cross-correlation peaks occurred in nontarget muscles of identified CM cells. The incidence of significant correlations might be much lower for a random sample of cells in forelimb motor cortex.
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There was a greater percentage of strong correlation peaks in cell-muscle pairs with PSpF+S than in cell-muscle pairs with either PSpF or SyncF alone. Almost 95% (18/19) of the SpTAs with PSpF+S were associated with a strong peak in the cross-correlation compared with 75.4% (43/57) of the SpTAs containing only PSpF and 80% (8/10) of the SpTAs with only SyncF.
A large number of muscles with suppression effects (38.9%) were associated with cross-correlations troughs (Table 3, Fig. 9). In comparison, only 3.5% of muscles with facilitation effects were associated with cross-correlation troughs. We visually identified troughs in several additional cross-correlations associated with suppression effects, but these troughs were too small to meet our significance criteria. However, about half of the suppression effects (55.6%) were associated with cross-correlation peaks in which there was no evidence of a trough. Therefore the presence of postspike or synchrony suppression appears to be a relatively poor predictor of the presence of a trough in the cross-correlation. However, if a trough appears in the cross-correlation, there is almost a fivefold greater probability of suppression in the SpTA than facilitation (Table 3). As was true for cross-correlations associated with facilitation effects, a greater percentage of strong troughs was associated with PSpS+S than pure PSpS or SyncS. Overall, it is noteworthy that for all CM cells tested, at least one muscle of the target muscle field exhibited a functionally consistent relationship between the postspike effect and cell-muscle covariation (PSpF associated with a significant correlation peak or PSpS associated with a significant correlation trough).
SpTAs containing no postspike or synchrony effects were much more likely to be associated with cross-correlations containing significant peaks than correlations without significant features. Nearly two-thirds of the SpTAs in this group were associated with cross-correlations containing a strong peak (64.5%, 221/343).
Analysis of cross-correlations for muscles of different joints
In a previous study, we showed that postspike effects from CM
cells are more common and generally stronger in distal forelimb muscles
compared with proximal muscles during performance of the reach and
prehension task (McKiernan et al. 1998). As a final analysis in this study, we examined our data to determine if the characteristics of CM cell-muscle cross-correlations also might show
differences for muscles at different joints. First, we compared the
strength of cross-correlations across each of the five joints studied.
Figure 10, A and
B, were constructed from all correlations lacking postspike
or synchrony effects (facilitation or suppression) in the SpTAs. In
Fig. 10A, the number of cross-correlations with strong peaks
(
max
0.25) is expressed as a percentage of
all cross-correlations calculated for each of the forelimb joints studied. There was a higher percentage of strong cross-correlation peaks in muscles of the wrist and digits than in muscles at other joints. For example, 88% of the correlations for forearm digit muscles
had a peak with
max > 0.25, whereas only 53%
of the correlations for muscles of the elbow were equally strong.
However, Fig. 10B shows that there was no difference across
joints in average
max for these
cross-correlations.
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The results were somewhat different for cross-correlations associated with facilitation effects (PSpF, PSpF+S, and SyncF). Overall, the percentage of cell-muscle pairs with strong correlation peaks in this group was greater than for cell-muscle pairs that did not show a facilitation or suppression effect. This was particularly true of proximal muscles. Most noteworthy is the fact that the percentage of proximal muscles showing strong correlation peaks was equally as great as the percentage of distal muscles when the analysis was limited to muscles associated with facilitation effects in spike-triggered averages (Fig. 10C). The same was true for the average magnitude of correlation peaks (Fig. 10D).
The timing of cross-correlation peaks also showed differences for
muscles at different joints. Lag times for cross-correlations of
shoulder muscles ranged widely around zero (both positive and negative)
compared with those of wrist and digit muscles, which were more tightly
clustered around time 0. The mean cross-correlation lag
times for shoulder, elbow, wrist, digit, and intrinsic hand muscles
were 97.7, 210.7, 6.3, 28.2, and 55.6 ms, respectively (all
cell-muscle pairs).
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DISCUSSION |
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The results of this study demonstrate that cortical cells possessing a synaptic linkage with motoneurons tend to show stronger covariation with their target muscles than with nontarget muscles. The results also demonstrate a significant relationship between the sign of the postspike effect (facilitation or suppression) and the presence of a peak or trough in the cross-correlation. Of all the target muscles with facilitation effects in spike-triggered averages (PSpF, PSpF with synchrony, or synchrony facilitation alone), 89.5% were associated with significant cross-correlation peaks, indicating positively covarying muscle and CM cell activity. Seven percent of facilitation effects was not associated with a significant effect in the cross-correlation, whereas only 3.4% of effects was associated with correlation troughs. In contrast, of all the muscles with suppression effects in spike-triggered averages, 38.9% was associated with significant troughs in the cross-correlation indicating an inverse relation between CM cell and muscle activity consistent with the presence of suppression. Fifty-five percent of suppression effects was associated with correlation peaks, whereas 5.6% was not associated with a significant effect in the cross-correlation. Finally, the results also suggest that the magnitude of a PSpF (excluding weak effects) is related, albeit weakly, to the magnitude of the corresponding cell-muscle functional covariation.
One limitation of this approach is the use of PSpF and PSpS as a
measure of the strength of the underlying synaptic linkage. A recent
computer simulation of the CM system by Baker and Lemon (1998) is highly relevant to this issue. Examination of
simulated postspike effects in their model revealed that the magnitude
of PSpF can be influenced heavily by the number of CM cells with which
the tested cell is synchronized. Synchronization with 10 other CM cells
had little effect on magnitude but synchronization with 30 CM cells
doubled PSpF magnitude. Of course, little is known about how many CM
cells actually may show synchronized discharge during real movements.
Baker and Lemon also emphasize that the levels of synchrony they used
were probably at the upper limit of what is likely to exist in the CM
system. Nevertheless this problem should be noted as a factor that
could degrade the relationship between PSpF magnitude and cell-muscle covariation.
Although many CM cell-target muscle pairs covaried during the reach and
prehension task in a way consistent with the sign and strength of the
CM cell's synaptic effects on target motoneurons, many exceptions were
found. It also should be noted that nearly 50% (231 of 465) of the CM
cell-muscle cross-correlations had a max
±0.25 but were not associated with any postspike or synchrony effect
in the corresponding SpTA. The existence of many strong correlations
between cell and muscle activity in cases lacking any demonstrable
synaptic linkages simply means that the presence of a strong
cross-correlation is of little or no predictive value for the presence
of postspike effects in spike-triggered averages. The presence of such
correlations in the absence of synaptic linkages could be related, in
part, to the fact that the task involves periods in which different
muscles consistently are coactivated as a natural requirement of task
execution but without the presence of underlying common synaptic input.
For example, a wrist movement task requiring alternation between
flexion and extension position zones will produce broad and uniform
coactivation of all extensors in one direction and all flexors in the
other direction. The highly uniform and stereotyped pattern of EMG
activity in this task makes it unsuitable for investigating
relationships between effects in spike-triggered averages and
cell-muscle covariation. In contrast, the reach and prehension task
produced highly fractionated patterns of EMG activity suitable for more
rigorous correlational analysis of cell-muscle covariation. However,
stereotyped coactivation of some muscles did occur in this task and
would have contributed to cases of strong cell-muscle covariation in
the absence of demonstrable synaptic connections. Nevertheless it is
important to emphasize that the majority of muscles with facilitation
effects in spike-triggered averages (89.5%) also had significant
cell-muscle cross-correlation peaks. Even more striking is the fact
that when we limited the analysis to only the correlations with strong
peaks (
max
±0.25), there was a greater
difference between the correlations associated with facilitation
effects and the correlations that were not associated with any PSE.
Sixty nine of the 86 correlations associated with facilitation effects
(80.2%) contained strong peaks, whereas only 220 of the 343 correlations not associated with any facilitation or suppression effect
(64.1%) contained strong peaks. Perhaps our most important observation
is that none of the correlations associated with pure PSpF had a
negative
max {see explanation of cell
21N7 [extensor digitorum communis (EDC)] in the legend for Fig.
3}.
It is interesting to note that the synchrony group of effects showed a higher level of agreement with the cross-correlation effects than the PSpF group. Although not a large difference, we would attribute this to the fact that the presence of synchrony reflects an underlying linkage between the discharge of a group of CM cells. The group, then, should have a greater influence on motoneuron discharge than any single neuron (reflected by the pure PSpF group). Of course, this assumes the individual CM cells within the group actually show covarying discharge.
A large number of muscles with suppression effects in spike-triggered averages had significant troughs in the cell-muscle cross-correlation (38.9%), although a greater number (55%) had significant peaks. This simply may reflect errors in the selection of appropriate CM cells by the central motor program or limitations in the number of appropriate cells for specific parts of the task. Alternatively, the presence of suppression in a muscle with increasing activity should not necessarily be interpreted as dysfunctional. Suppression could serve an important role in shaping or braking muscle activity despite the presence of a correlation peak. Finally, it is important to recognize that the level of activation of all motoneurons, just as with other CNS neurons, is the net result of a balance between on-going excitatory and inhibitory processes and the behavior of the muscle at any given point in time may not reflect the influence of a single input neuron.
The characteristics of the cross-correlations in this study make an
interesting comparison with those reported by Miller et al.
(1992, 1993
) for red nucleus neurons. The median
max was greater in our data, but the most
frequent lag times were very similar for our data on CM cells and
Miller et al.'s data on red nucleus cells. The most common
max fell between 0.30 and 0.35 for
cross-correlations associated with PSpF as well as those that were not
associated with a postspike or synchrony effect. In Miller et
al.'s study (1992)
, the most common peak magnitude fell
between 0.15 and 0.20 for both groups. The larger
max values for CM cells in this study may be
related to stronger synaptic coupling between CM cells and target
muscles (Cheney et al. 1988
) and/or the fact that
cortical input to motoneuron pools in the primate are likely to be a
much more dominant than red nucleus input (Mewes and Cheney 1994
). Our results may have been influenced by the fact that we only included CM cells that produced two or more moderate or strong PSpFs in target muscles, although this seems unlikely given the lack of
a relation between the magnitude of PSpF (all effects) and the
magnitude of
max. Even if they did not produce
PSpF in all target muscles, the cells in this study may have been part of larger functional clusters of cortical cells the summed output of
which would tend to facilitate synergist muscles during various phases
of the task. This, in turn, would increase the probability of a
significant correlation. On the other hand, Miller et al. (1993)
did not limit their analysis to RM cells. The fact that they included cross-correlations from at least some cells with no
demonstrated synaptic linkage to target muscles may be a further explanation for the difference in cross-correlation peak magnitudes between our study and theirs.
For cross-correlations with max
±0.25,
Miller et al. (1993)
reported that the most frequent lag
times occurred between 0 and +25 ms (~14% of the sample). When we
examined our data with similar 25-ms bins, the most frequent lag times
also fell between 0 and +25 ms both for the cross-correlations
associated with PSpF and for those not associated with any postspike or
synchrony effects (11.3 and 7.8%, respectively). However, we had a
much broader range of lag times than Miller et al., even for those
cross-correlations with
max
0.25.
Functional implications of cross-correlations and spike-triggered averages
Miller et al. (1992) point out that it is
particularly difficult to characterize the relationship between
activity of premotor cells and target muscles during complex free-form
movements because of the phasic nature of both cell and muscle
activity. They maintain that the long-term cross-correlation method is
well suited to studying the relationship between randomly varying
signals and argue that the identification of "functional linkages"
by the cross-correlation method has a number of advantages over other methods.
We would agree with this assessment. However, it is also important to
emphasize a point made by Fetz and Finocchio (1975) that
temporal correlations are neither necessary nor sufficient evidence to
establish a synaptic connection between a premotor cell and motoneurons
of a target muscle. Nevertheless, as they point out, there is an
intuitive inclination to expect the activity of connected elements to
be correlated. The vast majority of the cell-muscle pairs with PSpF in
our study, in fact, did have strong cross-correlation peaks. However,
there was only a weak relationship between the magnitude of PSpF and
the magnitude of cell-target muscle covariation measured as
max. As pointed out in the preceding text,
this most likely reflects the fact that an individual CM cell
represents only a very small fraction of the total synaptic input to a
particular motoneuron or motoneuron pool. The contribution of any given
CM cell to target muscle activation may be modified or completely
overshadowed by inputs from other premotor neurons. Of course, as noted
earlier, the limitations of PSpF as a measure of the strength of the
underlying synaptic linkage also may have contributed. Because of the
nature of the reach and prehension task, neither CM cells nor target
muscles were tonically active throughout the movement cycle. Rather
both cells and muscles tended to burst one or more times during the
task, usually in relation to specific phases of reaching and retrieving
the food reward. In almost none of the cell-muscle pairs studied was
every burst of cell activity consistently accompanied by a similar
burst of EMG activity in all the cells target muscles.
Figure 11 contains a conceptual model
of the combinations of synaptic (spike-triggered averages) and
functional (task covariation) linkages we observed in this study. This
model provides a conceptual construct for explaining the various
relations between postspike effects and task-related CM cell-muscle
covariation that we observed in this study. Four CM cells
(A-D) with different muscle fields are represented in Fig.
11. Based on previous findings (Cheney and Fetz
1985), individual CM cells probably are organized as functional groups or cell clusters in which each cell is linked by
sharing a common (or similar) muscle field. Therefore although we have
represented only single cells this figure, it should be recognized that
each cell can be viewed as a cluster of cells with similar muscle
fields. Spike-triggered averages compiled from each CM cell show
different patterns of facilitation based on their unique connections
with spinal motoneuron pools.
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S1 and S2 are cortical interneurons modeled after those suggested by
Huntley and Jones (1991). These neurons send extensive, horizontally oriented, intrinsic axon collaterals to many different forelimb movement representations and "may be recruited during complex movements to coordinate the activity of motor cortical zones
whose predominant output is to forelimb muscle groups acting synchronously." Once again, S1 and S2 can best be viewed as
functional clusters of neurons, although we have only represented one
neuron of each type. The activity of cells S1 and S2 is out of phase (Fig. 11D). However, both S1 and S2 will tend to
synchronize the discharge of their respective target CM cells.
Consider the case in which movement execution involves powerful input to CM cells A and B from S1. INT 1 and DIG 1 show strong PSpF from CM cell A and strong task-related covariation with A (like cell-muscle pair 65N6-FDP in Fig. 7). The strong PSpF in these muscles and WRS 1 reflects the correspondingly strong synaptic connections from CM cell A to INT 1, DIG 1 and WRS 1 motoneurons. On the other hand, task-related covariation will be determined by the actions of cell clusters S1 and S2. S1 is the dominant input to CM cells A and B during this movement task. Accordingly, S1 will activate CM cells A and B, which in turn, will coactivate INT 1, DIG 1 and WRS 1. Because INT 1 and DIG 1 do not receive input from S2, input from S1 will drive the task-related functional activity of CM cell A and muscles. However, unlike INT 1 and DIG 1, WRS 1 shows little or no covariation with CM cell A despite the presence of strong PSpF. This occurs because input from S2 is out of phase with respect to the activity of S1. Because both S1 and S2 have roughly equivalent synaptic actions on WRS 1 motoneurons (represented by number of synaptic contacts in Fig. 11A), excitation from increasing activity in S1, on average, is opposed by disfacilitation from decreasing activity in S2. As a result, the activity of WRS 1 does not covary with CM cell A. In fact, it is conceivable that the activity of WRS 1 might be related inversely to the activity of CM cell A depending on the relative strengths of synaptic inputs from cell A compared with S2-related CM cells and the degree and phase of modulation of inputs from S1 and S2.
ELB 1 represents the reverse condition in which there is an absence of PSpF but relatively strong covariation (like cell-muscle pair 148N5-BIL in Fig. 6). The lack of synaptic connections between CM cell A and ELB 1 motoneurons results in an absence of PSpF. However, S1 provides common input not only to CM cell A but also to other CM cells, some of which do make synaptic connections with ELB 1 motoneurons. Because ELB 1 does not receive input from S2-related CM cells, S1 is the dominant input for this task. Consequently, ELB 1 will covary with CM cell A and other muscles for which S1 is the dominant or only input.
In conclusion, this example emphasizes the fact that effects in spike-triggered averages will not necessarily correlate with the degree of covariation between the cell and muscles involved in the task. The strength and presence of postspike effects will reflect underlying synaptic connections, whereas the presence of task-related functional covariation, revealed by long-term cross-correlation methods, will reflect the summation of input signals to a particular CM cell relative to the summation of input signals to motoneuron pools of muscles to which the cell activity is being compared. Because a particular CM cell represents only one of potentially many parallel CM and non-CM inputs to a motoneuron pool, the activity of a CM cell and its target muscles can easily be divergent. In view of this, perhaps one of more remarkable findings of this study is the extent to which CM cells and their target muscles do closely covary.
Conclusions
The primary question addressed by this study concerns the extent to which the presence and strength of PSpF and PSpS from CM cells correlates with the magnitude of covariation in activity of CM cells and their target muscles revealed by computing long-term cross-correlations. We found that the magnitude of cross-correlations is greater for muscles with facilitation effects in spike-triggered averages than for muscles lacking effects in spike-triggered averages. Our results also demonstrate a significant relationship between the sign of the postspike effect (facilitation or suppression) and the presence of a peak or trough in the cross-correlation. Finally, the magnitude of PSpF (moderate and strong effects only) was correlated weakly with the magnitude of the cell-muscle cross-correlation peak. Nevertheless, although many CM cell-target muscle pairs covary during the reach and prehension task in a way consistent with the sign and strength of the CM cell's synaptic effects on target motoneurons, many exceptions exist. Muscles lacking demonstrable evidence of a significant effect in spike-triggered averages also could show strong covariation with CM cells; however, these cases cannot be viewed as providing a test of the extent to which synaptic effects of a CM cell are consistent with cell-target muscle covariation. The results are compatible with a model in which control of particular motoneuron pools reflects not only the summation of signals from many CM cells, but also signals from other premotor neurons. Any one neuron will make only a small contribution to the overall activity of the motoneuron pool. In view of this, it is not surprising that relationships between postspike effects and CM cell-target muscle covariation are relatively weak with many apparent incongruities.
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ACKNOWLEDGMENTS |
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The authors thank R. Lininger, T. Gleason, J. Kenton, and R. Thompson for technical assistance and L. Shupe for programming assistance.
This work was supported by National Institutes of Health Grants NS-25646 and HD-02528.
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FOOTNOTES |
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Address for reprint requests: P. D. Cheney, Mental Retardation and Human Development Research Center, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160.
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 4 November 1998; accepted in final form 30 August 1999.
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REFERENCES |
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