Muscle Activation Patterns During Two Types of Voluntary Single-Joint Movement

Gerald L. Gottlieb

Boston University, Neuromuscular Research Center, Boston, Massachusetts 02215

    ABSTRACT
Abstract
Introduction
Methods
Results
Discussion
References

Gottlieb, Gerald L. Muscle activation patterns during two types of voluntary single-joint movement. J. Neurophysiol. 80: 1860-1867, 1998. We examined the systematic variations in the EMG patterns during two types of single joint elbow movements. These patterns may be interpreted as exhibiting rules by which the CNS controls movement parameters. Normal human subjects performed two series of fast elbow flexion movements of 20-100° in a horizontal plane manipulandum. The first series consisted of pointing movements (PMs) from an initial position to a target; the second series consisted of reversal movements (RMs) to the same targets with an immediate return to the starting position. Both series showed kinematic and electromyographic (EMG) patterns that followed our previously described speed-insensitive strategy for controlling movement distance. Kinematic patterns of PMs and RMs were identical to about the time of peak PM deceleration. Agonist EMG bursts were also initially the same, but RM bursts ended abruptly in a silent period, whereas PM bursts declined more gradually. Antagonist EMG bursts of RMs were later than those of PMs but were not larger, contrary to our prior expectation and despite the larger net extension torque during RMs. The increase in net RM extension-directed torque that takes the limb back to its initial position appears to be a consequence of reduced flexor muscle torque rather than increased extensor muscle torque. We propose that rules for movement control may be similar for different kinds of movements as long as they are functionally sufficient for the task. However, even in a single-joint movement paradigm, physics alone, that is, the knowledge of net muscle torque and limb kinematics, is not adequate to fully predict those rules or the muscle activation patterns they produce. These must be discovered by experiment. The simplest expression of such rules may not be in terms of torque or kinematic variables but rather explicitly in terms of muscle activation patterns.

    INTRODUCTION
Abstract
Introduction
Methods
Results
Discussion
References

Rotation of a single joint is a common paradigm for understanding how the brain controls voluntary movement. The most frequently studied movement is simple flexion of the elbow from one stationary position to another, an action we will refer to as a "pointing" movement (PM). PMs can be described in terms of their mechanical features and the associated patterns of muscle activation or, more specifically, in terms of the modulation of the timing and amplitude of sequential bursts of electromyographic (EMG) activity in agonist and antagonist muscles, the so-called triphasic pattern (Hallett et al. 1975; Hannaford and Stark 1985).

The EMG patterns reflect the convergence on the interneuron and motoneuron pools of the spinal cord of central commands with kinematically dependent afferent signals. Despite this potential complexity, many distinctive features of these patterns can be characterized rather simply in terms of the movements' kinematics and joint torques. We summarized much of this extensive literature and provided a small set of simple rules (Gottlieb et al. 1989b) that describe how the observable variables change as a function of features of the movement task. Movement is smooth with a velocity profile that has a single peak. Joint torque is a biphasic pulse. The acceleration impulse (integral of torque from onset to peak velocity) increases with distance, load, and speed. Changes in distance or loading do not alter the initial rate of rise of the torque, despite the fact that both affect movement speed. There is a proportional change in the initial rate with deliberate changes in speed. The agonist muscle initiates a fast movement with an EMG burst, the area of which is proportional to the accelerating impulse. The rate of rise of the burst is independent of distance or load and scales with intentional changes in speed in the same way as does the initial rise in joint torque. The duration of the agonist EMG burst increases with distance and load but is not directly affected by speed, although burst duration is a difficult parameter to quantify because the end of the burst is often poorly defined. The antagonist muscle first becomes active at a relatively low level shortly after the agonist and then produces a later, more or less distinct burst of EMG activity to slow and arrest the movement at its endpoint. The onset of this burst is delayed for movements over longer distances or with greater loads and is earlier if the movement is intentionally faster. The area of the antagonist burst increases with inertial load or intended speed and decreases with viscous and elastic loads but has only a weak dependence on distance.

Taken together, these provide logically consistent rules for the dependence of EMG, torque, and kinematic patterns on parameters of the movement task and also reasonable relationships among these three patterns (Corcos et al. 1989; Gottlieb 1993, 1996; Gottlieb et al. 1989a, 1992, 1995, 1996; Pfann et al. 1998). The rules do not however answer the question of whether the CNS plans such movements in terms of torque patterns or kinematic patterns or, for that matter, in terms of muscle activation patterns themselves. Plausible arguments can be made for all three. The rules are also not complete because there are variations and features of the EMG patterns that are neglected by this very simple pulse-step description. There is more EMG activity than just bursts, and the shapes of the bursts are more complex than the filtered rectangular pulses that are proposed by these rules. These omissions were usually ignored or treated as "second-order" effects that are not essential to the understanding how the CNS controls simple movements.

Our first aim in this experiment was to see whether the rules described previously are specific to PMs or whether they are used for other kinds of single-joint movements. Many investigators studied PMs (Brown and Cooke 1981; Gottlieb et al. 1989b; Hallett et al. 1975; LeBozec and Cnockaert 1980; Maton and Cnockaert 1980; Wadman et al. 1979, 1980), but few looked at other movements. Schmidt et al. (1988) and Sherwood et al. (1988) examined "reversal" movements (RM) that traveled to a target and immediately returned to the starting point. They showed EMG patterns similar to those of PMs but with two clearly separated bursts of activity in the agonist. They made no explicit comparison with PMs. We show here that the PM rules described previously account for many features of and are not violated by RMs but they do not predict all of our findings. The EMG and torque patterns for these different movements require us to pay more attention to the second-order features. These observations have implications for how the CNS plans its movements.

    METHODS
Abstract
Introduction
Methods
Results
Discussion
References

Nine subjects performed sets of PMs and RMs over four distances. We recorded elbow angle from a capacitative transducer mounted on the axis of rotation of the manipulandum and tangential acceleration from a piezoresistive transducer mounted near the end of the manipulandum and computed velocity.1 DELSYS model DE2.2L surface EMG electrodes with total gain of 2,000 and band pass of 20-450 Hz recorded bicep and lateral tricep EMGs. All data were digitized at 1,000/s with a 12-bit, ±10 V A/D converter.

Subjects sat in a chair with their upper arm nearly horizontal and aligned with the two shoulders. In all series, our subjects were instructed to make movements in the horizontal plane over a specified distance with their arms strapped to a light manipulandum as described previously (Gottlieb et al. 1989a). The visual targets were two vertical poles positioned just beyond the end of the manipulandum. The initial position aligned the forearm with one pole at 50°. The origin (0°) was defined with the forearm perpendicular to the upper arm and extension was positive. For PMs, subjects were instructed to flex quickly and align the manipulandum with the second target. Speed was stressed and accuracy was mentioned. The subjects grasped a vertical handle and were easily able to align the target pole within the width of the fist within a few practice trials. After they felt ready, they made 11 fast and accurate flexion PMs on an audio cue. They were then asked to move to the same target and return to the starting position as quickly as possible. They were allowed to practice and then 11 RMs were recorded. In about one-half of the series, RMs were performed before PMs. Both tasks were easy for the subjects to perform consistently and accurately. The target poles were 20, 40, 60, and 100° into flexion from the starting position. All subjects read and signed Boston University's Human Investigation Consent Form before participating.

Data analysis

We aligned individual records to the onset of the agonist EMG burst. Data for each distance were averaged for plotting the time series shown in the next section. Seven parameters were calculated from the individual records. Accelerating impulse (Ia) was the time integral of the joint torque from movement onset to peak velocity, and decelerating impulse (Id) was the time integral of the torque from peak velocity for the same duration as for acceleration or until the torque reversed sign, whichever was first.

Agonist EMG parameters were the integral of the burst over its first 30 ms (Q30) and the integral during acceleration to peak velocity (Qaga). We also computed the integral of the EMG over the next 75-ms interval of deceleration (Qagd). The antagonist EMG was integrated from agonist onset to when velocity toward the target had fallen to 5% of its peak (Qant). We verified by visual inspection that this interval encompassed virtually all of what could be termed an antagonist burst for both types of movements. The latency of the antagonist burst (Cant) was measured by computing the location of the centroid of the antagonist EMG activity that exceeded 75% of its peak (Gottlieb 1996).

These seven measures were computed for each subject at each distance and task and analyzed by two-way (distance by task), repeated measures analyses of variance (ANOVAs). In Fig. 2 we normalized each subject's values by the mean of their four distances and two tasks before averaging over subjects.


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FIG. 2. Dependence of different EMG parameters on movement distance. Data for each subject are normalized to unity for the 8 data points. Open symbols and dashed lines are for PMs. Solid symbols and lines are for reversal movements. A: initial agonist EMG Q30. B: area of the agonist burst during acceleration to peak velocity (Qaga). C: area of the agonist EMG over 75 ms of deceleration after peak velocity (Qagd). D: area of the antagonist burst to 5% of peak velocity (Qant). E: latency of the centroid of antagonist activity (Cant). F: impulse during acceleration (Ia) and deceleration (Id).

Forming conservative hypotheses

We assumed a conservative philosophy that PM muscle activation rules will be used to perform RMs, except when we reason from physics that they must be modified to perform the different task. This suggests specific predictions about how EMG and torque patterns will be preserved or modified across movement types.

The first prediction is that parametric measures that are independent of movement distance for PMs will remain so for RMs and measures that change with distance for PMs will similarly change for RMs. These correlations are shown in column 2 of Table 1. Zero indicates that the parametric measure in column 1 does not vary with distance and a plus sign indicates a positive correlation. Impulse in both acceleration and deceleration will increase with distance, but the initial slope of the acceleration record will not change. The rising phase of the agonist EMG (Q30) is not sensitive to movement distance, whereas the area of the burst (Qaga) and the latency of the antagonist burst (Cant) are positively correlated with distance. The area of the antagonist burst (Qant) does not show a consistent correlation with distance. We have no prior data to describe how Qagd might vary with distance.

 
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TABLE 1. Measures to characterize electromyograph, kinematic, and torque patterns

Column 3 in Table 1 showed our expectations of RM relative to PM measures over equal distances. Conservative reasoning suggests that the mechanical measures that take the subject to the target will not change. Physical reasoning suggests that RMs need more net extension-directed torque to return the limb from its flexed position to the original position. The mechanical features are consequences of the patterns of muscle activation. This leads to the preliminary suggestion that the agonist flexor EMG burst will not change across movement tasks, whereas the antagonist extensor EMG burst will increase to provide the desired changes in extension-directed torque.

Reciprocal activation patterns of antagonist muscles suggest that the "tail" of the agonist EMG burst that normally is coactive with the antagonist burst (contemporaneous with our Qagd) will be depressed by the increased antagonist activity. Thus the prediction that RMs have larger Qant measures than PMs leads to the prediction that they will have smaller Qagd values. This is also suggested by visual inspection of figures in (Schmidt et al. 1988; Sherwood et al. 1988) although those studies provided no direct comparisons between movement types. Furthermore, on the basis of our expectations of the changes in movement kinematics and joint torque, we also expect that the reversal movement antagonist burst will be delayed2 when it increases in area. Our results confirmed all of the predictions, summarized in Table 1, except the one in italics for Qant.

    RESULTS
Abstract
Introduction
Methods
Results
Discussion
References

Figure 1, A and B, shows average PMs and RMs over the four distances. Both movements demonstrate the same distinguishing characteristics in their kinematic and EMG patterns: the initially distance-independent rate of rise of the acceleration, peak velocities, accelerations, and decelerations that scale with distance. Because the muscles must move the inertia of the limb and manipulandum, we can infer from the acceleration curves that the impulse will increase with distance over both the accelerating and decelerating intervals. The early distance independence of the acceleration/torque is matched by a similar distance independence in the rate of rise of the flexor EMG burst, the muscle that is the agonist for this movement. The area of the agonist burst increases with distance but that of the antagonist burst does not show a clear correlation. Although visual inspection of Fig. 1 suggests that the flexor EMG peaks may be more strongly modulated by distance for PMs than for RMs, this was not a consistent difference. Across the nine subjects, both kinds of patterns were seen for both kinds of movements. Both the rising phase of the acceleration and the duration of the agonist burst are prolonged for longer distance movements while the latency of the antagonist burst increases.


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FIG. 1. Comparisons of average (of 10 movements) angle, velocity (computed from the measured acceleration), acceleration records, and biceps/triceps (inverted) electromyographic (EMG) records for pointing (PM) and reversal movements (RMs) over 4 distances. A: PM. B: RM. C: PM and RM with the same 40° target.

In Fig. 1C we plotted just the two 40° movements to show more clearly that the kinematics are essentially the same to near the peak of PM deceleration around t = 0.4 s. RM deceleration continues to increase and reverse the velocity, returning the limb to its original position. Consequently, RMs have larger peak decelerations and decelerating torques. The RM agonist EMG burst shows a more distinct termination as expected (Schmidt et al. 1988). The RM antagonist burst is delayed, but, in spite of the increase in decelerating torque that muscle would be expected to generate, its area is not larger than that of the PM burst.

The variations with distance and task of the five EMG measures listed in Table 1 are illustrated in Fig. 2. The rise of the agonist burst (Q30, Fig. 2A) was insensitive to distance. Its area during acceleration increased (Qaga, Fig. 2B) but was not significantly different between tasks. The late EMG during deceleration (Qagd, Fig. 2C) significantly differentiated the two tasks. The latency of the antagonist burst (Fig. 2E) increased with distance and was significantly longer for RMs. The area of the antagonist burst (Fig. 2D) demonstrated little or no dependence on movement distance as has been shown before (Gottlieb et al. 1989a; Wadman et al. 1979). There was a strong linear variation with distance between acceleration and deceleration impulse for both movement types (Fig. 2F). Consequently, there was a statistically significant relationship between the agonist burst (Qaga) and accelerating impulse (Ia) but no statistically significant relationship between antagonist burst (Qant) and decelerating impulse (Id). These data show that column 2 of Table 1 describes RMs and PMs equally well.

A two-way, repeated measures ANOVA was performed on seven of the parameters listed in Table 1. The first three rows of Table 2 show that Table 1's null hypotheses (no significant differences between PM and RM) were accepted. Visual inspection of Fig. 1C and equivalents for other distances and other subjects was used to confirm that the initial slopes of the accelerations showed no systematic differences across distances or tasks. The columns labeled RM and PM in the Table 2 give mean parameter values for all subjects over all distances.

 
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TABLE 2. Statistical comparison of RM and PM electromyographic and torque patterns

The last four rows of Table 2 show variables that either differed between PMs and RMs or, in the case of Qant, was predicted to differ but did not. The decelerating impulse was 70% greater for RMs, and the RM antagonist burst was 18% later. In the 75-ms deceleration interval, the RM agonist muscle was much quieter. In contrast to these differences, the expectation that larger RM decelerating impulse would be associated with stronger activation of the triceps muscles was not confirmed.

    DISCUSSION
Abstract
Introduction
Methods
Results
Discussion
References

These experiments compare reversal and PMs to test whether generalization of the standard rules is possible. The data demonstrate that for both tasks the process by which the CNS specifies the distance over which a limb segment will move preserves the initial rate of increase of the agonist EMG while the duration of that activation is scaled with distance. In comparison with PMs, the tail of the RM agonist burst is reduced. Rapid reduction of agonist activation is mechanically important because flexor muscle torque outlasts the EMG signal. Fully shutting off the agonist muscle after it accelerated the limb not only allows its flexing torque to decay more rapidly but it presumably increases the muscle's compliance, thereby allowing the antagonist muscle to more easily restretch it.

Note that the amount of antagonist EMG activity does not scale strongly with the torque. This weak dependence between EMG activity and decelerating torque was explained as a consequence of the influence of muscle viscous (i.e., force-velocity) properties on muscle force (Gottlieb et al. 1992; Lestienne 1979). We should also note however that the reduction in agonist muscle activity during RM deceleration (i.e., the difference between the 2 lines in Fig. 2C) does scale with movement distance. We might expect from this that the force in the flexor muscle during deceleration declines more for larger than for smaller movements.

The delay of the antagonist burst requires a more complex explanation than originally proposed. Because the RM control strategy does not modify the net flexing torque, it cannot increase the extending torque before the arm reaches its maximal flexion or the RM will reverse its direction short of the target. The delay of the antagonist burst therefore prevents the torque from changing too rapidly after the peak of acceleration. The rapid drop in the agonist EMG (flexion torque) is balanced by the delayed rise of the antagonist EMG (extension torque) to preserve the net torque up to the peak displacement.

Our failure to find an increase in the antagonist burst for RMs is surprising but not difficult to interpret. Although we cannot conclude that there is an increase in triceps (extension) torque to produce movement reversal, we can infer a reduction in biceps (flexion) torque from the changes in Qagd. This will increase net extension torque. The torque changes produced by flexor reductions and extensor increases are mechanically equivalent and neither physical reasoning nor neurophysiological principles would appear to favor using one muscle over the other. For example, reciprocal inhibition cannot explain both a reduction in the tail of the agonist EMG burst and a delay in the antagonist burst. This appears to be an example of the nervous system taking advantage of its surplus degrees of freedom to choose a more efficient strategy over a less efficient one. We conclude that the patterns of muscle control that are displayed over different movements show both a strong conservative trend, the ability to adapt to the physical requirements of the task, and the existence of features not predictable from either kind of reasoning.

One way to characterize the problem the CNS faces in planning a movement is that it must decide how to generate appropriate patterns of muscle activation. These activation patterns vary in a systematic way with the force requirements of the kinematic task, and this systematization applies to different types of movements (e.g., RM and PM). Nevertheless, we cannot infer the muscle activation patterns of one type of movement from those of another, even for movements involving only a single joint. This was not apparent from the study of PMs alone. All the relationships between PM torques and EMGs, while not unique solutions to the problem that the CNS must address, are simple and logical. Rules such as prolonging the agonist burst and delaying the antagonist burst for movements that take more time and increasing the area of the agonist burst for movements that require greater accelerating impulse seem almost self-evident. The prediction that for a RM the antagonist would be activated more strongly seemed equally simple, logical, and self-evident, but wrong.3 There is more extension directed torque during RMs than would appear to be explained by extensor muscle EMG activity, yet, up to the point where the net torques diverge (at t = 0.4 s in Fig. 1C), the kinematic histories of the muscles are identical for both movements. Therefore the differences in EMG patterns cannot be explained by differences in feedback. Figure 1C, for example, shows that the two movements are essentially indistinguishable for 200 ms, whereas the muscle activation patterns diverge after 100 ms.

Several investigators have suggested that our more complex movements consist of a superposition of simpler, elemental, kinematically defined components (e.g., Adamovitch and Feldman 1984; Feldman 1980; Morasso and Mussa Ivaldi 1982; Viviani and Flash 1995). Inspection of Fig. 1 suggests that an RM might be well described in such a manner; as the superposition of two, equal, and opposite PMs, one delayed with respect to the other. We performed such an analysis by subtracting the two waveforms shown in Fig. 1C. Figure 3 shows the PM from Fig. 1C and a "synthetic" PM (sPM), the difference between Fig. 1C's two movements, aligned with the PM. These two PMs differ near the end of the movement, but most of the time the sPM is just a slower version of the PM. Thus, from a kinematic point of view, the RM in Fig. 1C can be constructed from the two parts of Fig. 3 according to the following equation
RM(<IT>t</IT>) = PM(<IT>t</IT>) − sPM(<IT>t</IT> − 0.135)
The process by which we synthesize a complex movement by the superposition of the kinematic trajectories of two simpler movements that are similar to each other cannot be extended to the EMG patterns. The bottom panel of Fig. 3 shows the results of subtracting the PM EMGs from the RM EMGs. It is quite clear that, although the kinematic features of the sPM are similar to those of a real PM, the EMG features are very different. In fact, both synthetic EMGs in Fig. 3 are mostly negative.


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FIG. 3. A synthetic PM (sPM, solid line) is computed by subtracting the 2 movements of Fig. 1C. When inverted, the angle velocity and acceleration records of this figure are similar to the 40° PM of Fig. 1 (dashed line). When the sPM is delayed by 135 ms and subtracted from the PM, the resulting wave from reproduces the kinematics of the 40° RM of Fig. 1. However, the EMG patterns produced by this manipulation (bottom panel) are unlike those of the Fig. 1 PM EMGs.

We conclude that RMs are not generated by the sequential superposition of two PM plans, at least if we consider the EMG pattern as a measure of that plan. Of course it is also argued that EMG patterns are more reflective of kinematic feedback signals than of central plans (Feldman and Levin 1995). The general validity of this contention cannot be argued on the basis of these data, but the data do show that the earliest differences between the EMG patterns of PMs and RMs precede differences in kinematics, as of course they logically must. This EMG divergence requires different central commands for PMs and RMs that cannot be created by sequential superposition of two similar PM commands. Furthermore, whatever may be the reflex contribution to the EMG patterns, it is quite difficult to explain the difference between PM and RM EMG patterns as consequences of kinematically driven reflexes.

Because neither kinematics nor joint torques are sufficient to uniquely predict EMG patterns, this could imply that the CNS plans movements directly in terms of muscle activation patterns. These patterns can be codified according to rules to match some demands of the task, for example, distance, speed, and load. The muscle activation features that distinguish the patterns of different tasks and that cannot be inferred from theoretical principles must be discovered by experiment, much the same perhaps as they may have been discovered by the CNS early in life through trial and error.

These are of course highly speculative conclusions because CNS plans are not observable variables. The process of inferring such plans from the indirect evidence provided by kinematic and EMG data can lead investigators to very different hypotheses. This perhaps is an argument in favor of the continued study of single degree of freedom movements. Models of how the CNS controls many degrees of freedom are likely to be so difficult to verify (or refute) that they will be suspect if they cannot also describe these relatively simpler kinematic and EMG data sets over a variety of movement tasks.

    ACKNOWLEDGEMENTS

  We thank Dr. D. M. Corcos for suggestions on this manuscript.

  This work was supported in part by National Institute of Arthritis and Muscoloskeletal and Skin Diseases Grants RO1-AR-33189 and RO1-AR-44388.

    FOOTNOTES

1   The range of our angle transducer is ±50°. As a result, in some movements there was distortion (clipping) of the angle record if the limb position exceeded these limits which distorted the velocity near zero when computed by differentiation. None of our quantitative analyses were affected by these errors. Velocity in Figs. 1 and 3 was computed by integrating the measured acceleration and do not have this distortion.

2   It is necessary to delay the antagonist burst if it is increased. If this is not done, the resulting stronger extension-directed torque will produce zero velocity and then start the limb's return before it reaches the target.

3   Although our review of the literature shows that most single joint EMG studies recorded from only a single agonist and antagonist pair, those that recorded more than that (e.g., Corcos et al. 1989; Gottlieb et al. 1989a; Wadman et al. 1980) found only subtle differences among synergists. It is of course possible that the anticipated EMG increase might have been found in other extensor muscles had we had the foresight to record from them. Although the available data do not suggest dramatic differences among synergists for the two movements we studied, we cannot exclude this possibility and would not be able to do so with only surface recording.

  Address for reprint requests: Boston University, Neuromuscular Research Center, 44 Cummington St., Boston, MA 02215.

  Received 12 March 1998; accepted in final form 15 June 1998.

    REFERENCES
Abstract
Introduction
Methods
Results
Discussion
References

0022-3077/98 $5.00 Copyright ©1998 The American Physiological Society