| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Department of Integrative Physiology, University of Colorado, Boulder, Colorado
Submitted 29 October 2004; accepted in final form 16 December 2004
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
; Macefield et al. 1996; Seyffarth 1940; Thomas et al. 1991). The relative contribution of these two mechanisms to an increase in muscle force varies across the working range of the muscle. Recruitment dominates at low forces, whereas rate coding is more significant at high forces (De Luca et al. 1982; Kernell and Sjoholm 1975; Milner-Brown et al. 1973a; Monster and Chan 1977; Person and Kudina 1972).
Despite a general consensus on the control scheme by which variation in motor unit activity grades the force exerted by a muscle, some details are uncertain. For example, there is no agreement on the relative distributions of minimal and maximal discharge rates across the motor unit population. Some reports indicate that the minimal rate is constant (Monster and Chan 1977) or decreases with recruitment threshold (Tanji and Kato 1973), whereas other findings suggest that the minimal rate increases with recruitment threshold (Bigland and Lippold 1954; Gydikov and Kosarov 1974). Similarly, maximal discharge rates have been observed to be greater (De Luca et al. 1982; Duchateau and Hainaut 1990; Hoffer et al. 1987; Mellah et al. 1990; Tanji and Kato 1973) and lesser (Bigland and Lippold 1954; Gydikov and Kosarov 1974; Kanosue et al. 1979; Kosarov and Gydikov 1976) in low-threshold units compared with high-threshold units. Nonetheless, maximal discharge rates seem to be insufficient to elicit peak tetanic forces in single motor units during brief isometric contractions (Fuglevand et al. 1999; Macefield et al. 1996; Thomas et al. 1991). Furthermore, the slope of the relation between discharge rate and force has been reported to vary as a function of recruitment threshold in some studies (Freund et al. 1975; Monster and Chan 1977), but not others (Milner-Brown et al. 1973a,b).
Much less is known about the distribution of two other factors that can also influence muscle force: discharge rate variability and correlated discharges. The coefficient of variation (CV) for discharge rate, which has a significant effect on the force fluctuations during steady contractions (Enoka et al. 2003; Laidlaw et al. 2000), is typically reported as a constant value of about 10–20% as muscle force increases (Nordstrom et al. 1992; Semmler and Nordstrom 1998). However, the CV for discharge rate, which is a measure of relative discharge rate variability, declines with an increase in mean discharge rate (Mori 1973; Person and Kudina 1972), and most of the measurements have been limited to low-threshold motor units. Similarly, the proportion of correlated discharges, one measure of which is motor unit synchronization, varies between pairs of motor units and depends on the task that is performed (Datta et al. 1991; Huesler et al. 1998; Milner-Brown et al. 1975; Schmied et al. 2000; Semmler et al. 2002). Some evidence suggests that the coupling is greatest between motor units with similar recruitment thresholds (Schmied et al. 1994), but relatively little is known about high-threshold motor units.
Given the uncertainty over the discharge characteristics across the motor unit pool during voluntary contractions, a reasonable strategy is to determine the extent to which simulations can replicate experimental results. Accordingly, Fuglevand et al. (1993) developed an elegant model of motor unit recruitment and rate coding that was able to match experimentally observed EMG-force relations. However, Taylor et al. (2003) were unable to reproduce the exact variation in force fluctuations as a function of muscle force with the Fuglevand model, even when the model was modified to include various forms of correlated discharges. The goal of this study was to measure the variation in the activity of a population of motor units across the operating range of a hand muscle and to use the findings to improve the match between experimentally measured and simulated force variability.
|
|
METHODS |
|---|
|
), and reported no known neurological disorders. The Human Subjects Committee at the University of Colorado approved the protocol, and informed consent was obtained before participation from each subject. Force measurement
Subjects were seated with the left shoulder abducted by 
0.79 rad (45°), and the forearm was restrained in a neutral position and resting on a platform. The elbow joint and forearm were immobilized with a vacuum pillow (Tumble Forms, Trenton, Ontario, Canada) and Velcro straps. The left hand was supported with the palm vertical, the third through fifth digits flexed slightly at the metacarpophalangeal joints and restrained in a brace, and the thumb extended vertically and held with a separate brace in the same plane as the palm of the hand at an angle of 1.1 rad (60°) to the index finger. The index finger was secured to a hinged splint to maintain both interphalangeal joints extended and to constrain finger excursion to the abduction-adduction plane. To maximize the contribution of the first dorsal interosseus muscle to the abduction torque, the index finger was flexed at the metacarophalangeal joint by 0.1 rad (5°). The abduction force exerted by the index finger was measured with a force transducer that was aligned with the proximal interphalangeal joint. To measure forces from <2% maximal voluntary contraction (MVC) to maximal levels with a sufficiently high signal-to-noise ratio, both low (0.049 V/N) and high (0.472 V/N) sensitivity transducers were used (model 13, Sensotec). Force was digitized with a Power 1401 (CED, Cambridge, UK) at 200 samples/s during the motor unit experiment and at 1,000 samples/s during the force steadiness task, and stored on a computer. To provide visual feedback of the abduction force exerted by the index finger, a 17-in computer monitor was located at eye level in front of the subjects at a distance of 1.6 m.
EMG measurement
Single motor unit potentials were recorded from the first dorsal interosseus muscle using Formvar-insulated, stainless-steel wires (diameter: 50 or 25 µm, California Fine Wire, Grover Beach, CA) that were fastened together at the recording tip with nontoxic glue and inserted into the muscle belly using a 27-gauge hypodermic needle. The needle was removed after the wires were inserted. The insulation was only absent from the recording tip of each wire, and three or four wires were included in each recording electrode to permit alternative bipolar configurations. The quality of the single motor unit recordings was optimized by using different pairs of recording wires and by making slight adjustments in the location of the electrode. The EMG signal was amplified x5,000 and band-pass filtered between 300 Hz and 8.5 kHz (S-series, Coulbourn Instruments, Allentown, PA). The motor unit signal was sampled at 20,000 samples/s with a Power 1401 (CED) and stored on a computer.
Single motor unit potentials were identified on-line using both a dual-window discriminator (S-series, Coulbourn Instruments) and Spike2 software (CED). A reference surface electrode for the single motor unit recordings was placed over the styloid process of the ulna (silver-silver chloride, 4 mm diam).
Experimental protocol
At the beginning of both experimental protocols, subjects performed several trials of the MVC task to determine the maximal force capacity of the first dorsal interosseus muscle. Participants were provided with verbal encouragement as they increased force from zero to maximum over a 3-s period and then held this maximal level for a further 1–2 s. Visual feedback of abduction force exerted by the index finger was provided on the computer monitor, and the subject's hand was closely observed by one of the experimenters to ensure that the task was performed correctly. The peak value from three or four trials was taken as the MVC force, provided it was within 5% of the peak value for another trial.
MOTOR UNIT EXPERIMENT. The goal of the motor unit experiment was to identify the potentials of a single motor unit that could be discriminated during brief contractions at multiple forces. Subjects were asked to perform three tasks during this experiment: 1) a graded minimal rate task with audio feedback of discharge rate, 2) a ramp contraction in which the force increased gradually and continuously, and 3) a discrete isometric force task with many target forces.
The experiment began by the subject gradually increasing contraction intensity as the investigators observed the intramuscular EMG signal for the appearance of a candidate motor unit. The subject was provided with visual feedback of index finger force and audio feedback of the discharge for the motor unit being tracked. Once a unit was identified, the subject was instructed to increase the force gradually until the motor unit became active and to reduce the force slowly to identify the minimal rate at which the unit could discharge action potentials repetitively. This task, which is referred to as the graded minimal rate task, was repeated three times, with each trial lasting between 5 and 20 s.
The recruitment threshold of the motor unit was characterized as the force at which the unit began to discharge action potentials repetitively during a ramp increase in index finger force. The target force level for the ramp contractions was set at twice the force associated with the graded minimal rate task. The ramp contractions were performed two to three times.
Subsequently, an additional ramp contraction was performed to determine the peak force at which the discharge of the motor unit could be discriminated. Once the upper limit was identified, a series of about 10 target forces was determined across the range that the unit could be discriminated. The actual number of target forces varied, however, due to both the variable number of trials needed to identify the minimal discharge rate and changes that occurred in the recording conditions during the experiment. Successive target forces were presented on the visual display, and participants were instructed to exert an index finger force to match the target and to do so without either exceeding the target force or generating a rapid contraction. Subjects were required to maintain the target force for 2–10 s, with briefer durations for the high forces. There was a rest period of 
30 s between trials. The force targets were presented in an ascending order, except that, around the recruitment threshold, the target forces were adjusted up and down in small increments (<1% MVC force) to identify the force associated with the minimal discharge rate. The minimal rate obtained with this protocol is referred to as the discrete minimal discharge rate. After the series of contractions to the target forces, MVCs were performed to verify that the observed discharge rates were not influenced by fatigue.
FORCE STEADINESS TASK. Subjects performed isometric contractions with the first dorsal interosseus muscle so that the index finger exerted an abduction force to match a series of eight target forces. Subjects practiced the task a few times at a moderate intensity before beginning the series. The target forces were presented in random order with two attempts at each of 2, 5, 15, 30, 50, 70, 85, and 95% of MVC force. Subjects were instructed to increase the abduction force to match the target indicated on the visual display and to hold that force as steadily as possible for 6 s or as close to 6 s as possible. Visual feedback of target force and the index finger force was provided for the first 3 s of each contraction, but was removed for the final 3 s. The entire force trace was shown to the participant after the completion of each trial. The gain of the force display was adjusted so that the target-force line was always at the same position on the monitor relative to zero force. Although the variation in gain likely influenced the subjects' perception of variability, only nonvisual feedback data from each trial were included in the analysis. A minimum rest interval of 30 s was provided between each trial, with considerably longer rest periods after high-force contractions.
Motor unit model
A model of motor unit recruitment and rate coding, originally developed by Fuglevand et al. (1993), was used to simulate the isometric force produced by a pool of motor units with characteristics resembling the first dorsal interosseus muscle (Taylor et al. 2002, 2003; Yao et al. 2000). The model was implemented in Matlab version 6.1 (Mathworks, Natwick, MA). A detailed description of the simulation has been published previously (Fuglevand et al. 1993; Taylor et al. 2002, 2003).
In brief, the model comprised a pool of motor units with systematic variation in recruitment threshold, minimal and maximal discharge rates, and twitch force. Relative discharge variability remained constant, typically at a nominal value of 20%. In this study, the number of motor units included in the pool was increased from 120 to 180, as suggested by motorneuron labeling data obtained from monkeys (Jenny and Inukai 1983). Motor unit recruitment and discharge rate were determined by an excitation function that acted on all the units in the pool. Motor unit 1 was the first recruited, had the smallest twitch force [1 arbitrary unit (au)], and had the longest twitch time (90 ms). In contrast, motor unit 180 was the last recruited, had the largest twitch force (100 au), and had the briefest contraction time (30 ms). Each motor unit generated a twitch force in response to a single discharge and a tetanic force when the activation involved multiple discharges. The amplitude of each tetanus was defined by a gain function that depended on discharge rate. The simulated muscle force was calculated as the sum of all the motor unit forces. Muscle force at each level of excitation was normalized to the force produced when all motor units were recruited and discharging at their maximal rates (analogous to the MVC).
Several simulation parameters were modified to reflect the experimental findings from this study. First, the recruitment range of the motor unit pool was expanded from 40% (Taylor et al. 2003) to an upper limit of 60% MVC. Second, the minimal discharge rate was increased linearly with recruitment threshold from 7.6 to 17.9 pps (see Fig. 4). The maximal discharge rate was also increased linearly, with recruitment threshold from 17.6 to 34.8 pps. In previous versions of the model, the minimal discharge rate was 8 pps for all motor units, and the maximal discharge rate decreased from 35 pps for the first recruited unit to 25 pps for the last recruited unit. Third, the CV for the interspike interval (ISI) observed in the experiments was implemented in two ways: 1) the mean CV for the ISI observed in the experiments (19.8%) was used for all motor units, and 2) the CV for the ISI decreased for each motor unit as the force increased. The force-dependent reduction in the CV for the ISI (CVISI) was modeled as an exponential function to reproduce the decline observed experimentally (30–10%) as force increased above recruitment threshold (
Force)
 | (1) |
|
Discrimination of single motor unit potentials was verified off-line by visual inspection of each potential and by using the template-matching features of the Spike2 software (Version 5.02, CED). ISIs >250 ms (<4 pps; n = 120, 0.37% of discharges) or <20 ms (>50 pps; n = 223, 0.67% of discharges) were excluded from the calculations of discharge rate. Long ISIs (<4 pps) were likely due to brief cessation of motor unit discharge, whereas very short intervals (>50 pps) exceed the rates normally observed during these types of contractions for human motor units (Bigland and Lippold 1954; De Luca et al. 1982; Kanosue et al. 1979; Tanji and Kato 1973) and were likely due to discrimination error or double discharges. To determine the region over which to calculate the mean and CV for discharge rate during each contraction, the force plateau was identified as beginning when the force rose to within 90% of the target force and ended 1 s before the force dropped below 90% of the target force. The force and discharge rate measurements were made over the intervening interval.
The recruitment threshold of each motor unit was determined by moving a 0.5-s window forward in time in 1-ms steps for the data from the ramp task until the CV for discharge rate for the potentials within the window was <50%. The force corresponding to the first discharge within this window was taken as the recruitment threshold of the motor unit.
The minimal discharge rate of each motor unit was measured in two tasks: 1) a graded decrease in force until a minimal rate was achieved and 2) discrete increments in target force around the recruitment threshold of the motor unit. The minimal discharge rate during the graded test corresponded to the lowest rate measured during a 2-s interval when the CV was <50%. The minimal rate during the discrete test was identified by moving a 2-s window forward in 1-ms steps and noting the rate when both the CV for discharge rate was <50% and 1 discharge occurred both before and after the 2-s window. Data for the graded decrease in force with audio feedback were only obtained for 29 of the 38 total motor units because the graded decrease was added to the protocol after 9 units had been recorded.
The average slope of the relation between discharge rate and force was approximated for each motor unit by fitting a linear regression to the data for three or more target forces occurring before the first plateau in discharge rate.
The abduction force exerted by the index finger at each target level during the steadiness contractions was quantified for a 1-s period commencing 500 ms after visual feedback of the force was removed. To minimize the contribution of slight errors in target-matching performance, the 1-s epoch was linearly detrended prior to the assessment of the CV for force. The mean force was calculated over this 1-s region for the two trials at each target level. The CV for force for the trial with the mean force closer to the desired target force was used in the analysis. Similarly, the CV for the simulated force was calculated after it had been detrended.
Statistical analysis
Repeated-measures ANOVAs were used to compare the forces and discharge rates obtained during the ramp, graded, and discrete tasks. The relations between recruitment threshold and the minimal and peak discharge rates observed during the brief contractions at different target forces were characterized with linear regression analyses. Similarly, regression analyses were used to determine the association between recruitment threshold and the slope of the relation between discharge rate and force.
A two-factor, repeated-measures ANOVA with Bonferroni posthoc tests was used to compare the simulated and experimental measures of the CV for force (between-subject factor) at each of the eight target levels (repeated-measures factor). Regressions were also performed on the average CV for force at each target level to quantify the improvement in the model's ability to match the experimental data, with the r2 value used as an index of improvement. All statistical procedures were performed with SPSS version 11.5 and SigmaPlot version 8.0 (SYSTAT Software). 
was set at 0.05, and all reported values are means and 95% CIs.
|
|
RESULTS |
|---|
|
|
The discrete target force at which motor units began to discharge action potentials repetitively was similar to the recruitment threshold of the units. Figure 1A shows that the recruitment thresholds obtained during ramp increases in force did not differ from the forces measured during the graded minimal rate task (P = 0.202) and the discrete minimal rate task (P = 0.784). In contrast, motor units discharged more slowly (7.5 ± 1.6 pps; a decrease of 2.7 ± 1.0 pps; P < 0.001) during the graded minimal rate task when subjects were given audio feedback (Fig. 1B). There was no difference (P = 0.514) in minimal discharge rate during the ramp task (9.7 ± 1.7 pps) and the discrete minimal rate task (10.2 ± 1.2 pps).
|
Motor unit discharge rate increased with index finger force for all units (Fig. 2). Twelve of the 38 motor units exhibited a clear plateau in discharge rate. The slope of the relation between discharge rate and force was not related to recruitment threshold (r2 = 0.08; P = 0.124) and had an average value of 0.99 ± 0.24 pps/%MVC.
|
|
Relative discharge rate variability (CV) decreased exponentially as force increased above recruitment threshold for each motor unit (Fig. 5, A and B). The CV for the ISIs for each motor unit declined as an exponential function of index finger force (r2 = 0.72 ± 0.07). The CV for ISIs ranged from peak values of 30.0 ± 3.5% to minimal values of 13.4 ± 1.1% for all motor units. The exponential decrease in the CV was most evident for the 12 motor units that exhibited clear plateaus in discharge rate (Fig. 5B), although the CV often reached minimal values before the plateau in discharge rate. The mean CV for ISIs for all trials was 19.8 ± 2.5%. Absolute discharge rate variability (SD) also decreased exponentially with an increase in force (Fig. 5C).
|
The CV for force decreased rapidly and then plateaued as force increased when 20 subjects produced steady contractions ranging from 2 to 95% MVC (Fig. 6A). The CV for force was significantly greater at 2 and 5% MVC than for the remaining force levels (P = 0.001). There was also a trend for an increase in the CV for force between 15 and 50% MVC (P = 0.140).
|
|
|
DISCUSSION |
|---|
|
Force variability
The CV for finger force observed in the subjects declined rapidly from 2 to 15% MVC and then remained relatively constant as the abduction force increased during a series of brief isometric contractions. This trend was similar to that observed in other studies on the first dorsal interosseus (Burnett et al. 2000; Galganski et al. 1993; Laidlaw et al. 1999, 2000) and quadriceps femoris (Christou et al. 2002; Tracy and Enoka 2002) muscles.
Discharge rate variability
The exponential decrease in the CV for ISI with an increase in index finger force for individual motor units (Fig. 5) seems to be a novel finding. One previous study reported that the CV for ISI initially decreased as force rose, but later increased at high forces (Tanji and Kato 1973). Other studies have observed a relation between absolute discharge rate variability (SD) and mean discharge rate when the data for all motor units were combined (Mori 1973; Person and Kudina 1972). When discharge rate variability from these studies was expressed as the CV for ISI, there was an approximate linear decrease in this CV with an increase in mean discharge rate. This trend is similar to that observed in the current study for the combined motor unit data, which contrasts with the exponential decrease in the CV for ISI with an increase in index finger force for individual motor units.
The addition of an exponential decrease in the CV for ISI to the motor unit model was the critical change that matched the force fluctuations observed experimentally during steady contractions with those simulated with a model of motor unit recruitment and rate coding. This observation is consistent with prior experimental (Kornatz et al. 2002; Laidlaw et al. 2000) and simulation (Enoka et al. 2003) results that have identified discharge rate variability as a key contributor to force fluctuations, especially at low forces. In the model, the exponential function (Eq. 1) caused the CV for ISI to decrease from 30 to 10% as force increased by 10% MVC above the recruitment threshold for each motor unit. The force-dependent decline in relative discharge rate variability contrasts with prior modeling work in which the CV for discharge rate was assumed to remain constant (Fuglevand et al. 1993; Hamilton et al. 2004; Taylor et al. 2003; Yao et al. 2000).
The result of adding this relation to the model was that most motor units exhibited high relative discharge rate variability at low force levels (i.e., 2–5% MVC), which enhanced the force fluctuations. Because the number of motor units recruited at each force level also decreased exponentially with an increase in muscle force (Fuglevand et al. 1993), there were fewer motor units recruited at intermediate forces (i.e., 15–50% MVC), and hence most of the active motor units had low relative discharge rate variability and the force fluctuations were depressed. At high forces (i.e., 70–95% MVC), all motor units were recruited and thus both discharge rate variability and force fluctuations were relatively low.
Minimal and peak discharge rates
The results from this study indicated that the minimal and peak discharge rates were significantly greater for high-threshold motor units compared with low-threshold units. This observation is similar to that reported in several experimental studies on hand and elbow muscles (Bigland and Lippold 1954; Gydikov and Kosarov 1974; Kanosue et al. 1979; Kosarov and Gydikov 1976) and the results obtained in some simulation studies. For example, Heckman and Binder (1991) showed that high-threshold motor units require greater discharge rates to produce a fused tetanus for muscle fibers with fast contraction speeds, similar to a previous theoretical argument (Hatze and Buys 1977; Kernell 1992). Furthermore, Taylor and Enoka (2004) found that high-threshold motor neurons reach greater discharge rates (23 pps) compared with low-threshold neurons (17 pps), presumably due to differences in axon diameter and membrane resistance.
This finding, that peak discharge rate is greater for high-threshold motor units, however, contradicts the results obtained in other studies on human hand muscles. Some studies have found a negative correlation between peak discharge rate and recruitment threshold during ramp isometric contractions (De Luca et al. 1982; Duchateau and Hainaut 1990). One explanation for the difference might be that a single ramp contraction in which the intensity increased gradually may have evoked history-dependent effects that are graded across the motor unit pool, such as those that involve persistent inward currents (Gorassini et al. 2002; Heckman et al. 2003). However, Tanji and Kato (1973) also performed a similar protocol with the abductor digiti minimi muscle to that used in this study and observed that two low-threshold motor units achieved greater peak discharge rates compared with the three high-threshold units. Thus the distribution of maximal discharge rates across the motor unit population remains unresolved. Nonetheless, maximal discharge rate seems to have only a minor influence on the variation in force fluctuations across the operating range of a muscle.
Other discharge characteristics
The upper limit of motor unit recruitment observed in this study for first dorsal interosseus was 60% MVC force, which is intermediate between the upper limit of 50% MVC force (De Luca et al. 1982; Kukulka and Clamann 1981) and 75% MVC force (Christou et al. 2004; Thomas et al. 1986) observed by others. Accordingly, the upper limit of motor unit recruitment was set at 60% MVC in the model, compared with the values of 41 and 50% used in previous studies (Moritz et al. 2004; Taylor et al. 2003; Yao et al. 2000). Nonetheless, this adjustment of the recruitment range had a negligible effect on the ability of the model to reproduce the experimental force-variability data, which is similar to the findings of Hamilton et al. (2004).
There was no interaction between the slope of the relation for discharge rate and force and recruitment threshold. This result is similar to the findings of Milner-Brown et al. (1973a) for the first dorsal interosseus muscle during ramp increases in force. Several figures in the literature, however, suggest that discharge rate rises more rapidly with increases in force for low-threshold units compared with high-threshold units (Freund et al. 1975; Monster and Chan 1977). The data in this study include a mixture of motor units, equally present at all levels of recruitment threshold, which either increased discharge rate rapidly and reached clear plateaus, or increased discharge rate more gradually over the force range in which they could be discriminated. Thus both the tonic and phasic motor units (Gydikov and Kosarov 1974) were present, although the distinction between these two types of units was less clear in the current data. In addition, some units exhibited the three-phase relation between discharge rate and force that has been observed in tibialis anterior, with an initial steep rise followed by a plateau and ending with another steep rise (Erim et al. 1996).
Notably, the model did not include any form of correlated activity between the motor unit discharges; there was no motor unit synchronization and no common modulation of discharge rate. Consequently, the model was able to replicate the variation in force fluctuations as a function of muscle force without the need for correlated discharges by the active motor units. This result is consistent with the experimental observation that the magnitude of the fluctuations during steady contractions can differ between individuals despite similar levels of motor unit synchronization (Kornatz et al. 2004).
Summary
Experimental measurements indicated that relative discharge rate variability decreased exponentially with an increase in finger force for each motor unit. When this function was implemented in the motor unit model, the ability of the model to predict the experimentally measured force fluctuations was improved significantly. These results underscore the assertion that discharge rate variability is a key determinant of the variation in the force fluctuations during steady contractions across the working range of a muscle.
|
|
GRANTS |
|---|
|
|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: C. T. Moritz, Dept. of Physiology and Biophysics, Box 357290, Univ. of Washington School of Medicine, Seattle, WA 98195-7290 (E-mail: ctmoritz{at}u.washington.edu)
|
|
REFERENCES |
|---|
|
Bigland B and Lippold OC. Motor unit activity in the voluntary contraction of human muscle. J Physiol 125: 322–335, 1954.
Burnett RA, Laidlaw DH, and Enoka RM. Coactivation of the antagonist muscle does not covary with steadiness in old adults. J Appl Physiol 89: 61–71, 2000.
Christou EA, Grossman M, and Carlton LG. Modeling variability of force during isometric contractions of the quadriceps femoris. J Mot Behav 34: 67–81, 2002.[Web of Science][Medline]
Christou EA, Rudroff T, Moritz CT, and Enoka RM. The variability in motor unit discharge is determined by low-frequency oscillations in discharge rate. Soc Neurosci Abstr 188.9, 2004.
Datta AK, Farmer SF, and Stephens JA. Central nervous pathways underlying synchronization of human motor unit firing studied during voluntary contractions. J Physiol 432: 401–425, 1991.
De Luca CJ, LeFever RS, McCue MP, and Xenakis AP. Behaviour of human motor units in different muscles during linearly varying contractions. J Physiol 329: 113–128, 1982.
Duchateau J and Hainaut K. Effects of immobilization on contractile properties, recruitment and firing rates of human motor units. J Physiol 422: 55–65, 1990.
Enoka RM, Christou EA, Hunter SK, Kornatz KW, Semmler JG, Taylor AM, and Tracy BL. Mechanisms that contribute to differences in motor performance between young and old adults. J Electromyogr Kinesiol 13: 1–12, 2003.[CrossRef][Web of Science][Medline]
Erim Z, De Luca CJ, Mineo K, and Aoki T. Rank-ordered regulation of motor units. Muscle Nerve 19: 563–573, 1996.[CrossRef][Web of Science][Medline]
Freund HJ, Budingen HJ, and Dietz V. Activity of single motor units from human forearm muscles during voluntary isometric contractions. J Neurophysiol 38: 933–946, 1975.
Fuglevand AJ, Macefield VG, and Bigland-Ritchie B. Force-frequency and fatigue properties of motor units in muscles that control digits of the human hand. J Neurophysiol 81: 1718–1729, 1999.
Fuglevand AJ, Winter DA, and Patla AE. Models of recruitment and rate coding organization in motor unit pools. J Neurophysiol 70: 2470–2488, 1993.
Galganski ME, Fuglevand AJ, and Enoka RM. Reduced control of motor output in a human hand muscle of elderly subjects during submaximal contractions. J Neurophysiol 69: 2108–2115, 1993.
Gorassini M, Yang JF, Siu M, and Bennett DJ. Intrinsic activation of human motoneurons: reduction of motor unit recruitment thresholds by repeated contractions. J Neurophysiol 87: 1859–1866, 2002.
Gydikov A and Kosarov D. Some features of different motor units in human biceps brachii. Pfluegers 347: 75–88, 1974.
Hamilton AF, Jones KE, and Wolpert DM. The scaling of motor noise with muscle strength and motor unit number in humans. Exp Brain Res 157: 417–430, 2004.[CrossRef][Web of Science][Medline]
Hatze H and Buys JD. Energy-optimal controls in the mammalian neuromuscular system. Biol Cybern 27: 9–20, 1977.[CrossRef][Web of Science][Medline]
Heckman CJ and Binder MD. Computer simulation of the steady-state input-output function of the cat medial gastrocnemius motoneuron pool. J Neurophysiol 65: 952–967, 1991.
Heckman CJ, Lee RH, and Brownstone RM. Hyperexcitable dendrites in motoneurons and their neuromodulatory control during motor behavior. Trends Neurosci 26: 688–695, 2003.[CrossRef][Web of Science][Medline]
Hoffer JA, Sugano N, Loeb GE, Marks WB, O'Donovan MJ, and Pratt CA. Cat hindlimb motoneurons during locomotion. II. Normal activity patterns. J Neurophysiol 57: 530–553, 1987.
Huesler EJ, Hepp-Reymond MC, and Dietz V. Task dependence of muscle synchronization in human hand muscles. Neuroreport 9: 2167–2170, 1998.[Web of Science][Medline]
Jenny AB and Inukai J. Principles of motor organization of the monkey cervical spinal cord. J Neurosci 3: 567–575, 1983.[Abstract]
Kanosue K, Yoshida M, Akazawa K, and Fujii K. The number of active motor units and their firing rates in voluntary contraction of human brachialis muscle. Jpn J Physiol 29: 427–443, 1979.[Web of Science][Medline]
Kernell D. Organized variability in the neuromuscular system: a survey of task-related adaptations. Arch Ital Biol 130: 19–66, 1992.[Web of Science][Medline]
Kernell D and Sjoholm H. Recruitment and firing rate modulation of motor unit tension in a small muscle of the cat's foot. Brain Res 98: 57–72, 1975.[CrossRef][Web of Science][Medline]
Kornatz KW, Christou EA, and Enoka RM. Steadiness training reduces the variability of motor unit discharge rate in isometric and anisometric contractions performed by old adults. Soc Neurosci Abstr 28: 665.610, 2002.
Kornatz KW, Semmler JG, Meyer FG, Pascoe MA, and Enoka RM. Correlated motor unit activity has only a minor influence on the fluctuations in acceleration during anisometric contractions. Soc Neurosci Abstr 188.12, 2004.
Kosarov D and Gydikov A. Dependence of the discharge frequency of motor units in different human muscles upon the level of the isometric muscle tension. Electromyogr Clin Neurophysiol 16: 293–306, 1976.[Medline]
Kukulka CG and Clamann HP. Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions. Brain Res 219: 45–55, 1981.[CrossRef][Web of Science][Medline]
Laidlaw DH, Bilodeau M, and Enoka RM. Steadiness is reduced and motor unit discharge is more variable in old adults. Muscle Nerve 23: 600–612, 2000.[CrossRef][Web of Science][Medline]
Laidlaw DH, Kornatz KW, Keen DA, Suzuki S, and Enoka RM. Strength training improves the steadiness of slow lengthening contractions performed by old adults. J Appl Physiol 87: 1786–1795, 1999.
Macefield VG, Fuglevand AJ, and Bigland-Ritchie B. Contractile properties of single motor units in human toe extensors assessed by intraneural motor axon stimulation. J Neurophysiol 75: 2509–2519, 1996.
Mellah S, Rispal-Padel L, and Riviere G. Changes in excitability of motor units during preparation for movement. Exp Brain Res 82: 178–186, 1990.[Web of Science][Medline]
Milner-Brown HS, Stein RB, and Lee RG. Synchronization of human motor units: possible roles of exercise and supraspinal reflexes. Electroencephalogr Clin Neurophysiol 38: 245–254, 1975.[CrossRef][Web of Science][Medline]
Milner-Brown HS, Stein RB, and Yemm R. Changes in firing rate of human motor units during linearly changing voluntary contractions. J Physiol 230: 371–390, 1973a.
Milner-Brown HS, Stein RB, and Yemm R. The orderly recruitment of human motor units during voluntary isometric contractions. J Physiol 230: 359–370, 1973b.
Monster AW and Chan H. Isometric force production by motor units of extensor digitorum communis muscle in man. J Neurophysiol 40: 1432–1443, 1977.
Mori S. Discharge patterns of soleus motor units with associated changes in force exerted by foot during quiet stance in man. J Neurophysiol 36: 458–471, 1973.
Moritz CT, Christou EA, Meyer FG, and Enoka RM. Distinguishing between time- and frequency-domain measures of motor unit synchronization. Soc Neurosci Abstr 188.10, 2004.
Nordstrom MA, Fuglevand AJ, and Enoka RM. Estimating the strength of common input to human motoneurons from the cross-correlogram. J Physiol 453: 547–574, 1992.
Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9: 97–113, 1971.[CrossRef][Web of Science][Medline]
Person RS and Kudina LP. Discharge frequency and discharge pattern of human motor units during voluntary contraction of muscle. Electroencephalogr Clin Neurophysiol 32: 471–483, 1972.[CrossRef][Web of Science][Medline]
Schmied A, Pagni S, Sturm H, and Vedel J. Selective enhancement of motoneurone short-term synchrony during an attention demanding task. Exp Brain Res 133: 377–390, 2000.[CrossRef][Web of Science][Medline]
Schmied A, Vedel JP, and Pagni S. Human spinal lateralization assessed from motoneurone synchronization: dependence on handedness and motor unit type. J Physiol 480: 369–387, 1994.
Semmler JG, Kornatz KW, Dinenno DV, Zhou S, and Enoka RM. Motor unit synchronisation is enhanced during slow lengthening contractions of a hand muscle. J Physiol 545: 681–695, 2002.
Semmler JG and Nordstrom MA. Motor unit discharge and force tremor in skill- and strength-trained individuals. Exp Brain Res 119: 27–38, 1998.[CrossRef][Web of Science][Medline]
Seyffarth H. The behaviour of motor units in voluntary contraction. Avhandlinger utgitt av det norske videnskap-akademi i Oslo I Matematisk-Naturvidenskapelig Klasse 4: 1–63, 1940.
Tanji J and Kato M. Firing rate of individual motor units in voluntary contraction of abductor digiti minimi muscle in man. Exp Neurol 40: 771–783, 1973.[CrossRef][Web of Science][Medline]
Taylor AM, Christou EA, and Enoka RM. Multiple features of motor unit activity influence force fluctuations during isometric contractions. J Neurophysiol 90: 1350–1361, 2003.
Taylor AM and Enoka RM. Quantification of the factors that influence discharge correlation in model motor neurons. J Neurophysiol 91: 796–814, 2004.
Taylor AM, Steege JW, and Enoka RM. Motor unit synchronization alters spike-triggered average force in simulated contractions. J Neurophysiol 88: 265–276, 2002.
Thomas CK, Bigland-Richie B, and Johansson RS. Force-frequency relationships of human thenar motor units. J Neurophysiol 65: 1509–1516, 1991.
Thomas CK, Ross BH, and Stein RB. Motor unit recruitment in human first dorsal interosseous muscle for static contractions in three different directions. J Neurophysiol 55: 1017–1029, 1986.
Tracy BL and Enoka RM. Older adults are less steady during submaximal isometric contractions with the knee extensor muscles. J Appl Physiol 92: 1004–1012, 2002.
Yao W, Fuglevand AJ, and Enoka RM. Motor unit synchronization increases EMG amplitude and decreases force steadiness of simulated contractions. J Neurophysiol 83: 441–452, 2000.
This article has been cited by other articles:
|
|
 |
|
  L. P. J. Selen, D. W. Franklin, and D. M. Wolpert Impedance Control Reduces Instability That Arises from Motor Noise J. Neurosci., October 7, 2009; 29(40): 12606 - 12616. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Oya, S. Riek, and A. G. Cresswell Recruitment and rate coding organisation for soleus motor units across entire range of voluntary isometric plantar flexions J. Physiol., October 1, 2009; 587(19): 4737 - 4748. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. A. Minetto, A. Holobar, A. Botter, and D. Farina Discharge Properties of Motor Units of the Abductor Hallucis Muscle During Cramp Contractions J Neurophysiol, September 1, 2009; 102(3): 1890 - 1901. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Contessa, A. Adam, and C. J. De Luca Motor unit control and force fluctuation during fatigue J Appl Physiol, July 1, 2009; 107(1): 235 - 243. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. R. Williams and S. N. Baker Renshaw Cell Recurrent Inhibition Improves Physiological Tremor by Reducing Corticomuscular Coupling at 10 Hz J. Neurosci., May 20, 2009; 29(20): 6616 - 6624. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I.-S. Hwang, Z.-R. Yang, C.-T. Huang, and M.-C. Guo Reorganization of multidigit physiological tremors after repetitive contractions of a single finger J Appl Physiol, March 1, 2009; 106(3): 966 - 974. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Schmitt, C. DelloRusso, and R. F. Fregosi Force-EMG Changes During Sustained Contractions of a Human Upper Airway Muscle J Neurophysiol, February 1, 2009; 101(2): 558 - 568. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. A. Riley, S. Baudry, and R. M. Enoka Reflex Inhibition in Human Biceps Brachii Decreases With Practice of a Fatiguing Contraction J Neurophysiol, November 1, 2008; 100(5): 2843 - 2851. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. J. Kutch, A. D. Kuo, A. M. Bloch, and W. Z. Rymer Endpoint Force Fluctuations Reveal Flexible Rather Than Synergistic Patterns of Muscle Cooperation J Neurophysiol, November 1, 2008; 100(5): 2455 - 2471. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Oya, B. W. Hoffman, and A. G. Cresswell Corticospinal-evoked responses in lower limb muscles during voluntary contractions at varying strengths J Appl Physiol, November 1, 2008; 105(5): 1527 - 1532. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. K. Hunter, T. Yoon, J. Farinella, E. E. Griffith, and A. V. Ng Time to task failure and muscle activation vary with load type for a submaximal fatiguing contraction with the lower leg J Appl Physiol, August 1, 2008; 105(2): 463 - 472. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Farina, C. Cescon, F. Negro, and R. M. Enoka Amplitude Cancellation of Motor-Unit Action Potentials in the Surface Electromyogram Can Be Estimated With Spike-Triggered Averaging J Neurophysiol, July 1, 2008; 100(1): 431 - 440. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. A. Riley, A. H. Maerz, J. C. Litsey, and R. M. Enoka Motor unit recruitment in human biceps brachii during sustained voluntary contractions J. Physiol., April 15, 2008; 586(8): 2183 - 2193. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Klass, S. Baudry, and J. Duchateau Age-related decline in rate of torque development is accompanied by lower maximal motor unit discharge frequency during fast contractions J Appl Physiol, March 1, 2008; 104(3): 739 - 746. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Ueda, L. Odhner, and H. H. Asada Broadcast Feedback of Stochastic Cellular Actuators Inspired by Biological Muscle Control The International Journal of Robotics Research, November 1, 2007; 26(11-12): 1251 - 1265. [Abstract] [PDF]
|
|
|
|
|
|
K. G. Keenan and F. J. Valero-Cuevas Experimentally Valid Predictions of Muscle Force and EMG in Models of Motor-Unit Function Are Most Sensitive to Neural Properties J Neurophysiol, September 1, 2007; 98(3): 1581 - 1590. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. G. Semmler, K. J. Tucker, T. J. Allen, and U. Proske Eccentric exercise increases EMG amplitude and force fluctuations during submaximal contractions of elbow flexor muscles J Appl Physiol, September 1, 2007; 103(3): 979 - 989. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. K. Barry, M. A. Pascoe, M. Jesunathadas, and R. M. Enoka Rate Coding Is Compressed But Variability Is Unaltered for Motor Units in a Hand Muscle of Old Adults J Neurophysiol, May 1, 2007; 97(5): 3206 - 3218. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. G. Keenan, D. Farina, F. G. Meyer, R. Merletti, and R. M. Enoka Sensitivity of the cross-correlaton between simulated surface EMGs for two muscles to detect motor unit synchronization J Appl Physiol, March 1, 2007; 102(3): 1193 - 1201. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. A. Knight and G. Kamen Modulation of motor unit firing rates during a complex sinusoidal force task in young and older adults J Appl Physiol, January 1, 2007; 102(1): 122 - 129. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Duchateau, J. G. Semmler, and R. M. Enoka Training adaptations in the behavior of human motor units J Appl Physiol, December 1, 2006; 101(6): 1766 - 1775. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. J. De Luca, A. Adam, R. Wotiz, L. D. Gilmore, and S. H. Nawab Decomposition of Surface EMG Signals J Neurophysiol, September 1, 2006; 96(3): 1646 - 1657. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. G. Martin, S. C. Gandevia, and J. L. Taylor Output of Human Motoneuron Pools to Corticospinal Inputs During Voluntary Contractions J Neurophysiol, June 1, 2006; 95(6): 3512 - 3518. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. G. Keenan, D. Farina, R. Merletti, and R. M. Enoka Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG J Appl Physiol, June 1, 2006; 100(6): 1928 - 1937. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Shinohara, C. T. Moritz, M. A. Pascoe, and R. M. Enoka Prolonged muscle vibration increases stretch reflex amplitude, motor unit discharge rate, and force fluctuations in a hand muscle J Appl Physiol, November 1, 2005; 99(5): 1835 - 1842. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. J. Mottram, E. A. Christou, F. G. Meyer, and R. M. Enoka Frequency Modulation of Motor Unit Discharge Has Task-Dependent Effects on Fluctuations in Motor Output J Neurophysiol, October 1, 2005; 94(4): 2878 - 2887. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. E. Jones, C. T. Moritz, B. K. Barry, M. A. Pascoe, and R. M. Enoka Motor unit firing statistics and the Fuglevand model J Neurophysiol, September 1, 2005; 94(3): 2255 - 2257. [Full Text] [PDF]
|
|
|
|
|
|
S. K. Hunter, A. Critchlow, and R. M. Enoka Muscle endurance is greater for old men compared with strength-matched young men J Appl Physiol, September 1, 2005; 99(3): 890 - 897. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
