1Department of Kinesiology and Applied Physiology, University of Colorado, Boulder, Colorado 80309-0354; and 2Department of Physiology, University of Arizona, Tucson, Arizona 85721
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ABSTRACT |
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Yao, Wanxiang, Rew J. Fuglevand, and Roger M. Enoka. Motor-Unit Synchronization Increases EMG Amplitude and Decreases Force Steadiness of Simulated Contractions. J. Neurophysiol. 83: 441-452, 2000. The purpose of the study was to determine the effect of motor-unit synchronization on the surface electromyogram (EMG) and isometric force using a computer model of muscle contraction. The EMG and force were simulated by generating muscle fiber action potentials, defining motor-unit mechanical characteristics and territories, estimating motor-unit action potentials, specifying motor-unit discharge times, and imposing various levels of motor-unit synchronization. The output (EMG and force) was simulated at 11 levels of excitation, ranging from 5 to 100% of maximum. To synchronize motor-unit activity, selected motor-unit discharge times were adjusted; however, the number of motor units recruited and the average discharge rate of each unit was constant across synchronization conditions for a given level of excitation. Two levels of synchronization were imposed on the discharge times: a moderate and a high level, which approximated the experimentally observed range of motor-unit synchronization. The moderate level of synchrony caused the average EMG to increase by ~65%, whereas the high level caused a 130% increase in the EMG with respect to the no-synchrony condition. Neither synchrony condition influenced the magnitude of the average force. However, motor-unit synchronization did increase the amplitude of the fluctuations in the simulated force, especially at intermediate levels of excitation. In conclusion, motor-unit synchronization increased the amplitude of the average rectified EMG and decreased the steadiness of the force exerted by the muscle in simulated contractions.
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INTRODUCTION |
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Descending, spinal, and peripheral excitatory
inputs to spinal motor neurons diverge extensively to make synaptic
connections across a large segment of a motor neuron population
(Buys et al. 1986; Fetz and Cheney 1980
;
Kirkwood and Sears 1978
; Mendell and Henneman
1971
). Such branching of presynaptic fibers will provoke near
simultaneous excitatory potentials in motor neurons and thereby slightly increase the probability that some motor neurons will be
brought to action-potential threshold at nearly the same time (Sears and Stagg 1976
). Consequently, when the timing of
action potentials discharged by motor neurons is compared, there is
often a significant coincidence in the relative timing of these events between many pairs of motor units (Datta and Stephens
1990
; Nordstrom et al. 1992
; Sears and
Stagg 1976
). The magnitude of this synchronized discharge among
motor units is variable and is influenced by such factors as the task
that is examined, the motor units and muscles involved in the task, and
the type of habitual physical activity performed by the individual
(Bremner et al. 1991
; Huesler et al. 1998
; Schmied et al. 1994
, 1998
;
Semmler and Nordstrom 1995
, 1998
; Sturm et al. 1997
).
Attempts to identify the functional significance of motor-unit
synchronization have focused on comparing levels in different groups of
subjects and on examining its effects on the output of the motor-unit
pool. The level of synchronization appears to be reduced between motor
units in the hand muscles of individuals who require greater
independent control of the fingers. This includes musicians
(Semmler and Nordstrom 1998) and the dominant hand of control subjects (Semmler and Nordstrom 1995
), but not
muscles in the dominant arm of control subjects (Schmied et al.
1994
; Semmler and Nordstrom 1998
). In contrast,
population and pair-wise assessments of motor-unit synchronization
suggest that it is greater among motor units in the hand muscles of
individuals who consistently perform such gross motor activities as
strength training (Milner-Brown et al. 1975
;
Semmler and Nordstrom 1998
). One commonly cited
interpretation of these findings is that an enhancement of motor-unit
synchronization contributes to training-induced increases in muscle strength.
Several observations also suggest that synchronization increases the
variance in the electromyogram (EMG) and force signals during
steady-state contractions. These findings range from a comparison of
the force evoked by distributed and synchronous activation of ventral
root filaments in experimental animals (Rack and Westbury
1969) to an increase in the amplitude of the power density
spectrum derived from simulated EMG signals (Weytjens and van
Steenberghe 1984
). Furthermore, motor-unit synchronization may
contribute to the fluctuations observed in the EMG, acoustic myogram,
and acceleration signals (tremor) recorded from the hand during
sustained submaximal contractions (Halliday et al. 1999
; McAuley et al. 1997
). Others, however, have found no
significant associations between tremor amplitude and the level of
motor-unit synchronization (Semmler and Nordstrom 1998
).
On the basis of these reports, it is apparent that some uncertainty
remains concerning the functional significance of motor-unit synchronization. The purpose of the study was to determine the effect
of motor-unit synchronization on the simulated EMG and force for a
human hand muscle, such as the first dorsal interosseus, during
isometric contractions. We found that moderate to high levels of
synchronization increased the amplitude of the average EMG, but not the
average force, and increased the fluctuations in the force signal at
submaximal intensities. Preliminary results have been presented in
abstract form (Yao et al. 1997, 1998
).
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METHODS |
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The study involved computer simulations that were based on
models previously described in detail by Fuglevand and colleagues (Fuglevand et al. 1992, 1993
), with the
addition of a model that manipulated the relative timing of action
potentials (synchrony) across the pool of motor neurons. In general,
these models represent the activation of a motor neuron pool that
produces motor-unit action potentials and forces consistent with the
known physiological properties of a human hand muscle. The simulations
involved three main steps: 1) determination of the
recruitment and discharge times of a population of 120 motor neurons in
response to different levels of excitatory drive, 2)
prediction of the isometric muscle force based on the nonlinear
relationship between discharge rate and force for single motor units;
and 3) simulation of the surface EMG derived from estimates
of the number and location of muscle fibers for each motor unit and the
conduction velocity of the muscle fiber action potentials. The output
of the simulation was a surface-detected EMG and the associated net
force exerted during steady-state isometric contractions that ranged
from minimal to maximal levels of muscle activation. The magnitudes of
the EMG and force were compared across different synchrony conditions.
Activation of the motor neuron pool
The distributions of properties across the motor-unit pool were
based on associations that have been established in testing the Size
Principle (Binder and Mendell 1990; Henneman
1979
). These included the distributions of net synaptic input,
recruitment thresholds, the time course and magnitude of twitch forces,
innervation ratios, and the sizes of motor-unit territories (the
cross-sectional area of muscle containing the fibers of a unit), and
conduction velocities of muscle fiber action potentials. In general,
the motor neuron with the lowest recruitment threshold was associated with the longest duration and weakest twitch force, the lowest innervation ratio, the smallest motor-unit territory, and the slowest
conduction velocity of muscle fiber action potentials.
RECRUITMENT.
Input to the model was an excitatory drive function
[E(t)] that represented the net synaptic input
to motor neurons during a voluntary muscle contraction (Heckman
and Binder 1991). For simplicity, it was assumed that the input
was uniformly distributed across the pool such that all neurons
received the same level of excitatory drive. The recruitment thresholds
(RT) of the 120 motor neurons in the pool were determined
from an exponential function that assigned many neurons to have
relatively low thresholds and progressively fewer neurons to have
higher thresholds (Fuglevand et al. 1993
). The range of
recruitment thresholds across the pool was set at 12-fold
(Gustafsson and Pinter 1984
; Powers and Binder 1985
). The units of measurement for recruitment threshold and excitatory drive were arbitrary excitation units. A motor neuron was
recruited when the excitatory drive
RT for the motor neuron.
MOTOR-UNIT DISCHARGE TIMES. Once a motor neuron was recruited, the discharge behavior of the neuron was defined by four parameters: its minimum and maximum discharge rates, the relationship between excitatory drive and discharge rate, and the variability of discharge rate.
The minimum rate at which motor neurons discharged action potentials repetitively was assumed to be 8 impulses/s (imp/s) for all motor neurons (Monster and Chan 1977
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(1) |
Motor-unit force
TWITCH.
The time course of the twitch response of a motor unit was modeled as
the impulse response of a critically damped second-order system
(Milner-Brown et al. 1973a). The distribution of twitch force magnitudes across the pool of motor units was modeled as an
increasing exponential function with the lowest threshold motor unit
assigned to have the smallest force and the highest threshold unit to
have the greatest force. The range of twitch forces was set at 100 (Milner-Brown et al. 1973c
; Monster and Chan
1977
; Stephens and Usherwood 1977
).
FORCE-FREQUENCY RELATION.
As outlined in Fuglevand et al. (1993), the nonlinear
relationship between discharge rate and force was simulated by varying the impulse response magnitude as a function of the instantaneous discharge rate and contraction time of the motor unit. The general form
of the relation between steady-state force and activation frequency was
sigmoidal, but the specific shape varied depending on the contraction
time of the unit. Motor units with longer duration contraction times
required lower activation rates to attain half-maximal or maximal force
compared with motor units with briefer contraction times. Muscle force
was calculated as the sum of the forces exerted by the active motor units.
EMG simulation
A dipole representation of the transmembrane current was used to
simulate individual muscle fiber action potentials in a
three-dimensional muscle volume based on previously derived
formulations (Andreassen and Rosenfalck 1981;
Fuglevand et al. 1992
). The model included the effects
of anisotropic muscle conductance, innervation zone location, fiber
length, electrode size, and electrode configuration. In these
simulations, we used square electrodes (4 mm2) located over
the surface of the muscle a few millimeters distal to the innervation zone.
MOTOR-UNIT TERRITORIES.
The simulated muscle had a circular cross-section with a diameter of 15 mm. All muscle fibers were assumed to have the same diameter (56 µm),
and the total number of fibers in the muscle was set at 71,747, compared with values of 40,500 for first dorsal interosseus and 10,200 for the first lumbrical (Feinstein et al. 1955). The
number of fibers comprising a single motor unit was assumed to be
directly proportional to the twitch force of the unit. The smallest
motor unit innervated 28 fibers and the largest unit (motor unit
120) innervated 2,728 fibers. The fibers of a motor unit were
assumed to be scattered over a broad region of the muscle cross-section
and to intermingle with fibers belonging to many other units. The
density of unit fibers within the territory of the motor unit was
assumed to be 20 fibers/mm2. For simplicity,
motor-unit territories were assumed to be circular and to be
distributed randomly within the muscle (Fuglevand et al.
1992
, 1993
; Milner-Brown and Stein
1975
).
MOTOR-UNIT ACTION POTENTIALS.
The surface-recorded action potential for a motor unit was determined
as the sum of the action potentials generated by the muscle fibers
innervated by the motor neuron. The amplitude and duration of the
muscle fiber action potentials varied as a function of the distance
from the recording electrode (Fuglevand et al. 1992;
Roeleveld et al. 1997
). Additionally, the conduction
velocity of muscle fiber action potentials was assumed to be inversely correlated with contraction time (Andreassen and Arendt-Nielsen 1987
), with values ranging from 2.5 m/s for the unit with the longest contraction time (motor unit 1) to 5.5 m/s for the
unit with the briefest contraction time (motor unit 120).
Motor-unit synchronization
To determine the effect of motor-unit synchronization on the
simulated EMG-force relationship, the timing of the independently generated motor neuron action potentials was adjusted to impose a
temporal association between some of the action potentials discharged by different motor neurons. The magnitude of the imposed
synchronization was based on values reported in the literature;
however, these values were derived from experiments that involved
low-force contractions (<20% of maximum) and were assumed to remain
valid for high-force contractions. The magnitude of motor-unit
synchronization was assumed not to change as a function of excitation
(Nordstrom et al. 1992) and to be distributed randomly
over time contrary to the apparent clustering of synchrony that can
occur between pairs of motor units (Dengler et al.
1984
).
At each level of excitation, the action potentials chosen for a timing adjustment were selected randomly. For this purpose, each action potential was represented as an impulse with a duration of 1 ms (Fig. 1). The adjustments were performed such that every motor unit served as a reference unit to which other motor units had selected action potentials synchronized. A schematic diagram illustrating the process for motor unit 1 is shown in Fig. 1. The selection of the action potentials to be synchronized involved two steps: 1) the random selection of a proportion (e.g., 10%) of the action potentials discharged by the reference unit during the 10-s simulation, and 2) for each selected action potential of the reference motor unit, a proportion of the other active motor units (e.g., 10%) were randomly selected to have the timing of the nearest action potential adjusted so that it was coincident with the reference action potential. Figure 1 shows two action potentials (bolded impulses) discharged by motor unit 1 that were selected to be synchronized. For the first reference impulse, motor units 3, 4, 7, and 9 were then selected to have the timing of their closest impulse adjusted so that it was coincident with the bolded impulse; the arrows indicate the adjustments. For the second reference impulse, motor units 5, 6, 8, and 9 were selected for this adjustment.
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On the basis of the criteria derived from experimental measures of synchrony (described below), we used two levels of adjustments in the timing of the action potentials to represent moderate and high levels of synchrony. To effect a moderate level of synchrony, 10% of the impulses of each unit served as the reference impulse to which 10% of the other active units had impulses aligned. To produce a high level of synchrony, we used 15% of the impulses for the reference unit and 15% of the impulses discharged by other active units.
Two conditions were used in the alignment of the selected impulses:
perfect synchrony and variable synchrony. The perfect-synchrony condition was achieved by aligning the selected impulses so that they
were exactly coincident. The variable-synchrony condition was more
physiological and involved the introduction of some variability into
the adjustments so that the time separation between the synchronized impulses had a Gaussian distribution with a mean of 0 ms and a standard
deviation () of 1 or 2 ms.
Once the adjustments had been made for motor unit 1, the same process was repeated for motor unit 2, then motor unit 3, and so on, until all the active motor units were processed. When the adjustments were made for a specific motor unit, all other active motor units were candidates for the randomly selected adjustments; those previously adjusted were not excluded from subsequent reselection. Because of this recursive process, some impulses that were originally synchronized to one unit could subsequently be synchronized to a different unit.
Statistics
The outcome variables of the study were the mean and variability of the simulated EMG and force as a function of excitation. Because there was little variation for repeated simulations at each steady-state level in the calculated statistics for the relatively long-duration simulation, we report the results for one simulation trial only for each of the conditions. Average values for the rectified EMG were determined over the interval from 0.01 to 9.9 s at each level of excitation. The average values and the variability (standard deviation and coefficient of variation) of the force were determined over the interval from 1.0 to 9.9 s at each level of excitation. Power density spectra (Hamming window) were determined for the simulated EMG and force signals with a program written in MATLAB (Mathworks, Natick, MA). The block size for the fast Fourier transformation (FFT) was 2,048 points for the EMG and 1,024 points for the force, which yielded resolutions of 2.44 and 0.49 Hz, respectively. The blocks overlapped by one-half of block size, and the spectra were averaged across blocks for each trial. The mean values were subtracted from the simulated force records before performing the FFT so that the spectra would not be dominated by low-frequency signals.
The conventional procedure used to evaluate the degree of synchrony
involves construction of cross-correlation histograms from the
discharge times of pairs of motor units (Sears and Stagg 1976). The number of synchronized impulses in excess of that
expected due to chance is computed from the peak in the
cross-correlation histogram. This value is then normalized to some
measure of baseline discharge rate, total number of impulses, or trial
duration. For example, the common input strength (CIS) index
of Nordstrom et al. (1992)
normalizes the number of
synchronous impulses to the trial duration. The CIS value
therefore indicates the frequency of excess synchronous impulses for a
pair of motor units. It is calculated as the number of counts in the
peak (P) of the cross-correlation histogram above that
expected due to chance divided by the duration (D) of the
trial in seconds (Nordstrom et al. 1992
);
CIS = P/D. The expected number of
synchronous impulses due to chance is estimated from the mean level of
the histogram in the off-peak region.
Because synchrony was imposed randomly on selected motor units in the
simulation, the CIS values for a few pairs of motor units
may not be an accurate index of the overall synchrony within the
population. However, it would not be practical to construct cross-correlation histograms and to calculate the CIS index
for all combinations of motor units. Consequently, we devised a more economical method to appraise the level of synchrony across the population of active motor units. This involved dividing the entire simulation period into 1-ms bins and enumerating the number of impulses
discharged by the active motor units in each bin. This distribution was
used to generate a histogram that indicated the number of times one
impulse, two impulses, three impulses, and so on, occurred within a
1-ms bin. The total number of coincident impulses (NCI) was
then calculated from the histogram as
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RESULTS |
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The output of the simulations comprised the surface-detected EMG
and the whole-muscle force of the motor-unit pool in response to 11 steady-state levels of excitation: at 5% of maximum and then at
10-100% of maximum in 10% increments. The EMG was simulated at an
effective rate of 5,000 samples/s and the force at 500 samples/s. The
predictor variable was motor-unit synchronization. At each level of
excitatory drive, the number of motor units recruited and the average
discharge rate of each motor unit remained the same across the various
synchronization conditions; these conditions ranged from zero
synchronization to a high physiological level. Simply manipulating the
timing of the action potentials discharged by the active motor units
had a substantial influence on the output of the motor-unit pool. The
principal effects are evident in Fig. 2,
which shows the interference EMG, and the force during activation of
the motor-unit pool for the zero and high-synchrony conditions ( = 2 ms) at the 50% level of excitation. The slight asymmetry of the EMG signal about the isopotential level was a consequence of the
proximity to the innervation zone (Fuglevand et al.
1992
). Comparable EMG signals are often observed experimentally
(Fig. 1 in Enoka et al. 1989
; Fuglevand et al.
1993
; Inbar et al. 1986
). Motor-unit
synchronization had a substantial effect on the amplitude of the EMG,
but not the average force, although it did increase the amplitude of
the force fluctuations.
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Comparison of synchronization indices
Figure 3 compares the population
synchrony index (PSI) with the common input strength
(CIS) (Nordstrom et al. 1992) estimate of
synchrony for two randomly selected pairs of motor units across three
levels of synchronization (none, moderate, and high). These indexes
were derived from a simulation in which 84 motor units were activated
for a duration of 30 s. The trial was divided into 30,000 bins,
each with a duration of 1 ms, and the number of impulses within each
bin was counted. In the no-synchrony condition (Fig. 3A, left
column), the total number of bins contained one, two, three, etc.,
impulses (bars) essentially identical to that predicted to occur due to
chance from the Poisson distribution (
). Approximately 11,000 of the
30,000 bins contained a single impulse, ~5,000 bins had 2 coincident
impulses, and rarely were there cases in which >5 motor units
discharged impulses synchronously. In the moderate-synchrony condition
(Fig. 3A, middle column), the number of cases in which there
was one or two impulses in each bin decreased with respect to that
expected due to chance. Furthermore, there was an increase in the
number of cases in which four or more motor units discharged synchronously. This shift in the distribution is reflected in a greater
PSI, from near zero for the no-synchrony condition to 0.94 for the moderate-synchrony condition. In the high-synchrony condition
(Fig. 3A, right column), there was a marked reduction in the
number of cases with one or two impulses in each bin and a substantial
increase in the number of cases in which five or more motor units
discharged at the same time (see inset). There was about a
twofold increase in the PSI from the moderate- to the high-synchrony
condition.
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There was a good correspondence between the PSI values and
common input strength (CIS) values calculated from the
cross-correlation histograms for selected pairs of motor units. For
example, the cross-correlation histograms for two pairs of units are
shown in Fig. 3, B and C, for each of the
synchrony conditions. Plotted above each histogram is the corresponding
cumulative sum (cusum) (Ellaway 1978), which was
used to identify the location of the peak in the cross-correlation
histogram. For the moderate-synchrony condition, the CIS
values were 1.02 and 0.94 extra synchronous impulses/second for the two
pairs of units. The CIS values for these two pairs
doubled (2.01 and 1.83 extra synchronous impulses/second, respectively)
under the condition that imposed high synchrony. These
CIS values were within the range of experimental values reported for motor units in the first dorsal interosseus muscle of
humans (0-3.0 extra synchronous impulses/second) (Semmler et al. 1997
). The widths of the synchronous peaks shown in Fig. 3 with
= 2 ms ranged from 8 to 14 ms, which is similar to that reported by others (Schmied et al. 1994
; Semmler
et al. 1997
). Therefore the amount of synchrony imposed
in the current simulations was similar to that found experimentally.
Synchronization increased EMG amplitude
The effect of imposing synchronization on the trains of motor-unit
action potentials was quantified from the average rectified EMG and the
average force during the steady-state period of each simulation for the
different levels of synchrony. The effects of two levels of
synchronization are compared with the no-synchrony condition in Fig.
4. Synchronization had a negligible
effect on the magnitude of the steady-state force, but it had a
substantial effect on the amplitude of the average EMG. The average
force achieved during all levels of excitation was not influenced by the presence of motor-unit synchronization or the variability () in
the imposed synchronization (Table 1). In
contrast, the average EMG detected during maximum excitation was
profoundly influenced by variation in the timing of the action
potentials (Table 1). The presence of variability (
= 1 and 2 ms) in the alignment of the synchronized impulses reduced the EMG
amplitude compared with the no-variability condition (
= 0 ms).
For the most physiological simulation (
= 2 ms), moderate
synchrony increased EMG amplitude by ~65%, whereas the
high-synchrony condition caused an increase of ~130% relative to the
no-synchrony condition (Table 1).
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Differences in the timing of the impulses had a marked influence on both the total power and the distribution of power in the EMG power spectra (Fig. 5). The total power in the spectra increased with the level of excitation and the amount of synchronization (Table 2). For example, the increase in total power at 100% excitation compared with 50% excitation for the two synchrony conditions averaged ~80%. Similarly, the total power at 100% excitation for the high-synchrony condition was 94% greater than that for the moderate-synchrony condition. The shift in the distribution of power was evident by a decrease in the median frequency and the location of the peak frequency for the two synchrony conditions (Table 2). This shift in power distribution to lower frequencies for the synchrony conditions presumably was caused by a decrease in the number of independent impulses and an increase in the superposition of constituent action potentials.
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Synchronization decreased force steadiness
Although the presence of motor-unit synchronization did not
influence the magnitude of the average force (Fig. 4), it did decrease
the steadiness of the force signal (Fig. 2). This effect was quantified
as the absolute (standard deviation) and normalized (coefficient of
variation) fluctuations in the simulated force during steady-state
excitation. As found experimentally with submaximal forces (Enoka et
al. 2000; Galganski et al. 1993
; Keen et al. 1994
), the standard deviation increased and the
coefficient of variation decreased as a function of excitation level
for all conditions (Fig. 6). Furthermore,
the magnitude of the force fluctuations, both the standard deviation
and the coefficient of variation, increased with the level of
synchrony. The effect of motor-unit synchronization on the coefficient
of variation of the force fluctuations was greatest at the lower levels
of excitation (Fig. 6B).
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The total power in the power density spectrum of the force signals increased with both the level of excitation and the level of synchronization (Figs. 2 and 5 and Table 2). For the high-synchrony condition, for example, the total power (0-250 Hz, with the average removed) increased from 633 to 979 au2 · Hz (a 55% increase) when excitation increased from 50 to 100% excitation (Fig. 5, F vs. E). The total power at 100% excitation for the high-synchrony condition was 424% greater than that for the no-synchrony condition (Fig. 5, F vs. D). Similarly, the magnitude of the peak power increased with both synchronization and excitation (Table 2), which indicates that the simulated force became more tremulous when the timing of the action potentials was adjusted and when there was an increase in the number of action potentials. For the moderate level of synchronization, peak power was greatest at intermediate levels of excitation (e.g., 50% excitation in Table 2). Despite these effects, the median frequency for the power spectra derived from the simulated forces was similar across all conditions (range: 4.9-9.8 Hz).
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DISCUSSION |
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The purpose of the study was to determine the effect of motor-unit
synchronization on the surface EMG and the isometric force. Because of
the difficulty associated with experimentally manipulating the timing
of action potentials discharged by concurrently active motor neurons
(however, cf. Guimaraes et al. 1994), we used a computer-simulation approach as a tractable means to evaluate the
influence of synchronization on motor-unit output. With this approach,
it was possible to assess the influence of specified amounts of
synchrony on EMG and force without changing either the average
discharge rate or the number of active motor units. An increase in the
amount of motor-unit synchronization had three main effects:
1) an increase in the average rectified EMG; 2) a
pronounced increase in the force fluctuations, but not the average force; and 3) an increase in the power in the low-frequency
domains of the EMG power spectra.
The amount of synchronization used in the simulations was comparable to
that observed experimentally. This was accomplished by randomly
selecting 10 or 15% of the action potentials discharged by each motor
unit and adjusting the timing of these action potentials to coincide
with those discharged by 10 or 15% of the other active motor units.
The timing adjustments were made so that the action potentials were
either exactly coincident or had some degree of variability ( = 1 or 2 ms). In initial simulations, we also used another alignment
scheme in which motor units with similar recruitment thresholds were
induced to have greater synchrony than motor units with disparate
thresholds (Datta and Stephens 1990
; Schmied et al. 1993
, 1994
). However, this scheme had
similar effects on the simulated EMG and force to the random scheme
described in this report (Yao et al. 1997
,
1998
).
The appropriateness of these imposed levels of synchronization was
verified by computing measures of synchronization for the population
and pairs of motor units. From calculations of the CIS
index, it was apparent that the high level of synchronization was
comparable to that observed experimentally, which meant that the
simulated range included the physiologically relevant limits. The
amount of synchronization typically measured experimentally between
pairs of motor units falls around the moderate level of synchronization
imposed in the simulation. Furthermore, the simulations indicated that
a standard deviation () of 2 ms when aligning synchronous impulses
produced cross-correlation histograms that were similar to those
observed experimentally (Nordstrom et al. 1992
).
Increase in average EMG
The primary findings of the study were that motor-unit
synchronization increased the magnitude of the average rectified EMG and the fluctuations (variability) in the simulated force, but not the
average force. Although the number of action potentials discharged by
the active motor units was similar across synchronization conditions at
each level of excitation, the average EMG was substantially greater for
the moderate- and high-synchrony conditions compared with the
no-synchrony condition. At the maximum level of excitation, for
example, the average EMG was 229% greater ( = 0 ms) and 133% greater (
= 2 ms) for the high-synchrony conditions. This
effect can be explained by a reduction in the cancellation of the EMG signal that occurs when positive and negative phases of action potentials overlap (shaded regions in Fig.
7). With perfect synchrony (bottom
panel in Fig. 7), there is little cancellation, and both the
interference pattern and the average rectified EMG are larger. There
were many more coincident impulses for the high-synchrony conditions
(Fig. 3), which meant that for a given number of action-potential trains (a function of excitation level) there was less cancellation of
the EMG signal.
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This rationale suggests that some of the increase in EMG was due to a reduction in the cancellation due to overlapping positive and negative phases of action potentials. One outcome of this interpretation is that the intensity of multiunit neurophysiological signals (e.g., electromyogram, electroencephalogram, electroneurogram) depends not only on the number of units that are active and their discharge rates but also on the relative timing of the activity across the population. Consequently, some of the experimentally observed increases in EMG, such as those that occur during fatiguing contractions or as a consequence of physical activity interventions, may be partially explained by variation in the amount of cancellation associated with changes in the level of motor-unit synchronization.
As has been observed experimentally for isometric contractions
(Matthews and Muir 1980), there was no peak around the
tremor frequency (~10 Hz) in the power spectra derived from the
simulated EMG. Rather, the peak power in the EMG spectra across the 11 levels of synchronization for the moderate- and high-synchrony
conditions occurred in the range of 12-41 Hz. For the no-synchrony
condition, the peak power in the EMG spectrum occurred in the range of
19-63 Hz. The median frequency across the 11 levels of excitation for the moderate- and high-synchrony conditions averaged 48 Hz (range: 44-51 Hz) compared with 76 Hz (61-85 Hz) for the no-synchrony condition. One prominent feature of the EMG spectra was the emergence of a peak at ~30 Hz for high levels of excitation (80-100%). For example, compare the distinct peak in the spectrum of Fig.
5C with that in Fig. 5B. The distinct peak was
evident in the spectra for the two synchrony conditions at excitation
levels
80%. A broader peak was present in the spectra for the
no-synchrony condition at the two highest levels of excitation. This
means that at high levels of excitation, the superimposition of the
motor-unit action potentials produced a net shape that was dominated by
a ~30-Hz modulation in the interference EMG.
Decrease in force steadiness
When a previously untrained individual performs a
strength-training program, the short-term increase in strength appears
to be mediated by neural mechanisms rather than by muscle hypertrophy (Semmler and Enoka 2000). One mechanism commonly identified as responsible for this adaptation is a training-induced increase in
motor-unit synchronization. The basis for this suggestion can be traced
to studies performed by Milner-Brown et al.
(1973b, 1975
) in which they found that
the levels of synchronization among populations of motor units in a
hand muscle increased after participation in a strength-training
program. Similarly, Semmler and Nordstrom (1998)
reported that the level of synchronization between pairs of motor units
in the first dorsal interosseus muscle was greatest among
weightlifters, less in untrained subjects, and least in musicians.
Although these studies suggest that motor-unit synchronization may
change with strength training, neither study provides evidence that an
increase in synchronization by itself causes an increase in muscle force.
Indeed, when a large proportion of motor units comprising a muscle are
stimulated synchronously at subtetanic rates, not only is the force
profile less smooth but the average force may be less
than that exerted when the motor units are activated asynchronously (Clamann and Schelhorn 1988; Rack and Westbury
1969
). Although the mechanisms underlying this nonlinear
attenuation in force have not been fully identified, it appears that
such factors as series compliance and frictional interactions among
muscle fibers must play a role (Clamann and Schelhorn
1988
; Powers and Binder 1991
; Troiani et
al. 1999
). Because nonlinear summation of force across motor
units was not a feature of the present model, diminished muscle force
with more synchronized activity was not produced in the simulations
(e.g., Fig. 2). Nonetheless, the deficit in average force that is
observed experimentally only occurs when at least 25% of the entire
muscle fiber population is activated synchronously (Clamann and
Schelhorn 1988
). Otherwise, in less-synchronized and more
natural conditions, such as those simulated in the present study,
synchronous activity has little influence on average force.
The results of the simulations demonstrate that when the amount of
motor-unit activity remains constant, the average force is not
enhanced, at any level of excitation, by the inclusion of motor-unit
synchronization in the activation scheme. The absence of an effect on
the average force begs the question of why motor-unit synchronization
might increase in muscles subjected to a strength-training stimulus.
One possibility is that increased levels of synchronization may promote
higher initial discharge rates by motor neurons during rapid
contractions, which appear to enhance the rate of increase in force
(van Cutsem et al. 1998).
When motor units discharge action potentials within the range commonly
observed during voluntary contractions, the net force is not completely
steady but exhibits some degree of variability (McAuley et al.
1997). Experimental studies have found that the amplitude of
these force fluctuations (standard deviation) increases as a function
of force (Enoka et al. 2000
; Galganski et al.
1993
; Keen et al. 1994
). The simulations
produced a similar effect (Fig. 6). Furthermore, the standard deviation
of the force fluctuations was greater at most excitation levels
(20-100%) for the high-synchrony condition. When the fluctuations
were normalized to the average force (coefficient of variation), the
fluctuations were greatest at low levels of excitation, as has been
found experimentally, and were greater for the high-synchrony
condition. The coefficient of variation had values of ~8% at the
lowest level of excitation (Fig. 6), which is similar to the values
measured for young (7%) and old (11%) adults when they exerted a
force that was 5% of the maximum voluntary contraction force with the
first dorsal interosseus muscle (Galganski et al. 1993
).
Similarly, the coefficient of variation had values that ranged from 2 to 5% at the highest level of excitation (Fig. 6), which was
comparable to the values measured for young (3%) and old (4%) adults
when they exerted a force that was 75% of the maximum voluntary
contraction force with the first dorsal interosseus muscle
(Enoka et al. 2000
). Despite these similarities, the
decline in the coefficient of variation as a function of excitation
level for the simulated forces was not as monotonic as that observed
experimentally. Nonetheless, these results suggest that motor-unit
synchronization could contribute to a decline in steadiness, such as
occurs in older adults.
The total power in the spectra derived from the force signals was
influenced by the quantity of motor-unit activity (% excitation) and
by the timing of the action potentials (level of synchronization). Of
these two effects, the level of synchronization had the greater influence on the total power in a spectrum (Table 2), which means that
increases in motor-unit synchronization accounted for more of the
variance of the force signal. A prominent low-frequency (~3 Hz) peak
in the power density spectrum derived from the simulated force
exemplified this effect. These spectra were similar to those observed
experimentally (Homberg et al. 1986; Matthews and
Muir 1980
). The one exception was the emergence of a peak in
the force spectra at ~30 Hz at high levels of excitation (Fig.
5F). The appearance of this peak coincided with a
similar peak in the EMG spectra and was more pronounced for the two
synchrony conditions. However, there were no other peaks in the spectra
for any level of excitation or synchronization, which suggests that the
peaks observed at 10, 20, and 40 Hz in the EMG, acoustic myogram, and acceleration records of sustained contractions (McAuley et al. 1997
) cannot be explained by short-term motor-unit synchronization.
In contrast, Halliday et al. (1999) estimated that
motor-unit synchronization accounts for ~20% of the fluctuations in
finger acceleration in the frequency ranges of 1-12 and 15-30 Hz
during a postural trial. This conclusion was derived from a coherence analysis that was based on comparing the timing of motor-unit action
potentials with the frequency spectra of surface EMG and acceleration.
They observed peaks in the EMG spectrum at ~12 and 25 Hz, which were
attributed to motor-unit synchronization. These results have been
interpreted to indicate that rhythmic modulation of motor-unit
synchronization in the frequency range of 15-30 Hz is related to
rhythmic cortical activity (Farmer et al. 1993
; Halliday et al. 1999
). In the current simulations,
however, the randomly distributed synchronization had a substantial
effect on the power in the EMG spectrum in the 15- to 30-Hz frequency band but did not produce a peak in the 1- to 12-Hz range. These simulations suggest that low-frequency modulation of action potential timing may be a more significant contributor to the distribution of
power in the EMG spectrum than modulation in the higher band.
In summary, the presence of synchronized activity among motor units during simulated isometric contractions substantially increased the amplitude of the average EMG, but not the average force, and caused pronounced fluctuations in the force of submaximal contractions.
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ACKNOWLEDGMENTS |
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We thank Dr. Sophie J. De Serres for assistance with data analysis and Dr. John G. Semmler for commenting on a draft of the manuscript.
This study was supported by National Institutes of Health Grants NS-20544 to R. M. Enoka and AR-42893 to A. J. Fuglevand.
Present address of W. Yao: Dept. of Health, Physical Education, Recreation and Coaching, University of Wisconsin, Whitewater, WI 53190.
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FOOTNOTES |
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Address reprint requests to R. M. Enoka.
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 21 January 1999; accepted in final form 8 September 1999.
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REFERENCES |
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