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Perception and Motor Systems Laboratory, School of Human Movement Studies, The University of Queensland, Brisbane, Australia
Submitted 2 July 2004; accepted in final form 2 June 2005
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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; Lackner and Dizio 1994; Lay et al. 2002). Because motor tasks, by definition, are achieved by activating skeletal muscles, changes in overall performance measures such as error rate or movement time must be associated with alterations in the pattern of muscle activation. A fundamental question in skill acquisition therefore concerns how muscle activation patterns change with practice.
During the performance of many motor tasks it has been found that groups of muscles are recruited together as a single unit rather than as individual elements; such functional linkages or units are referred to as muscle synergies (Bernstein 1967; Gelfand et al. 1966). The term muscle synergy has been used by many authors to describe the concurrent activation of multiple muscles during voluntary movement, although the application of this concept has often been confused by differences of opinion as to the level of the neuromuscular system at which synergies should be defined. We use the definition of muscle synergies proposed by Buchanan et al. (1986) in this paper because it does not assume the existence of a specific neural mechanism and allows us to examine changes in the organization of efferent commands based on measures of motor output. According to this definition, two muscles are considered complete synergists if they are linearly coactivated across all observed actions (i.e., the amount of activation recorded from each muscle across a range of actions is linearly related). Partial synergists are defined as muscles that are coactivated linearly in a subset of the observed actions, but whose activation is not linearly related in the remaining actions. Antagonists are defined as muscles that share a reciprocal activation relationship in all observed actions.
The roles of individual muscles within the context of task-related muscle synergies are governed in part by the directions in which they produce joint torque. As a simple example, consider the task of producing maximum isometric elbow flexion torque with the forearm in a neutral posture without simultaneously producing any net torque about the pronation–supination axis. Biceps brachii is a primary agonist for elbow flexion torque production but will also have a supination moment with the forearm neutral (Ettema et al. 1998). Thus if biceps is used in the task under consideration, it will be necessary to cancel out its supination moment with an equal pronation moment using a suitable muscle such as pronator teres. In this context, biceps brachii and pronator teres act as two elements of the same muscle synergy. Adaptation during practice may occur through a refinement of the relative phasing between such muscles that act as functional synergists (Macpherson 1991). There may also be a modulation in the amount of activity observed in muscles that have a compensatory function (van Zuylen et al. 1988). We hypothesize that the time course of activation in synergistic muscles will become more similar after practice, allowing the electromyographic traces of a large number of synergistic muscles to be described by a small number of waveforms. This would provide evidence that the CNS increasingly simplifies the problem of control as skill is acquired by reducing the number of separate commands it sends to activate muscles. This hypothesis is based on previous observations that agonist muscles (including partial agonists) are recruited synchronously before the onset of practiced movements and antagonist muscles are recruited synchronously during movement deceleration (Sergio and Ostry 1995). These observations provide evidence of similarities in the timing of activity in muscles with functionally similar roles.
Significant changes in the magnitude of muscle activity are most likely to be observed in monofunctional muscles in circumstances in which the activation of bifunctional muscles represents an impediment, rather than an aid, to the solution of task goals. Indeed, it was proposed previously (MacConaill and Basmajian 1977) that, at least in the context of single degree-of-freedom (df) movements, the development of expertise is accompanied by an attenuated level of engagement of multifunctional muscles. Based on this idea and evidence of the adaptive nature of muscle synergies demonstrated in previous studies (Barry et al. 2005a; Jamison and Caldwell 1993; Macpherson 1991; Sergio and Ostry 1995), we predict that the amount of activity in bifunctional muscles will be reduced in actions for which their line of action is incongruent with the intended combination of joint torques.
The ability to generalize learned patterns of muscle activation is also important in the context of daily activities because it provides us with the opportunity to improve performances on a wide range of actions through practice on a subset of those tasks. In the current experiment, we are interested in determining whether the modification of muscle activation patterns through practice at one level of torque can enhance performance in similar tasks performed with higher or lower torque demands than those practiced. We predict that skill acquired by practice will be readily transferred to tasks in which the required magnitude of torque production is modified.
In the current study, we therefore seek to determine the nature of changes in muscle synergy organization that mediate the decreases in target-acquisition time that result from practice of a task requiring the isometric generation of torque in two degrees of freedom: flexion/extension about the elbow and pronation/supination of the forearm. Specifically, we will test the hypothesis that the role of bifunctional muscles is reduced after practice in tasks for which their activation produces torque that is incongruent with task goals. Additionally we will determine whether the number of underlying waveforms that can describe the electromyographic (EMG) patterns of synergist muscles acting about the elbow and forearm are reduced after practice, thus increasing the simplicity of CNS control.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Eight right-hand–dominant (Oldfield 1971) participants between the ages of 20 and 45 yr (seven males and one female) were involved in this experiment. Participants gave their informed consent before involvement in the study. All experimental procedures were approved by the Medical Ethics Committee of The University of Queensland and conformed with the Declaration of Helsinki.
Apparatus
Participants were seated in a height-adjustable chair 65 cm from a computer display positioned at eye level. The dominant arm was placed in the pendant position with the elbow flexed at 90° and the forearm in a neutral position. The elbow joint was held stationary in a padded brace by a Velcro strap and the hand grasped a manipulandum. Padded clamps were located above and below the hand to minimize the force required to grip the manipulandum (Fig. 1). A molded thermoplastic restraint was attached around the wrist to prevent motion at this joint. The positions of all adjustable elements of the apparatus were recorded for all participants during their first experimental session and remained constant for all subsequent sessions.
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The manipulandum was instrumented with a multiple df force/torque transducer (Delta ATI, Industrial Automation, Perth, Australia). Force (flexion/extension) and torque (pronation/supination) were sampled at 2,000 Hz at an analog-to-digital interface (AT-mio-16E-10, National Instruments, Austin, TX) and stored on a personal computer. Labview (v. 5.0, National Instruments) was used to write the custom experimental control and data-acquisition routines.
Electromyographic recordings
The EMG activity of eight muscles was recorded during the pre- and posttests as well as during the first, third, and fifth days of training. Bipolar (AgCl) surface electrodes (diameter 5 mm) were used to obtain recordings from 1) brachioradialis [BRAD], 2) triceps brachii (lateral head) [TRI], 3) pronator teres [PT], 4) flexor carpi radialis [FCR], and 5) extensor carpi radialis [ECR]. The electrodes were placed approximately 2 cm apart on the muscle belly, nearly parallel to muscle fibers, and recordings were tested by asking participants to perform test contractions according to procedures outlined by Delagi (1980). The EMG activity of the 6) long [BB(L)] and 7) short head [BB(S)] of biceps brachii, and 8) brachialis [BRA] was recorded using fine-wire (75 micron) bipolar hook electrodes, inserted into the muscle 2 cm apart by 27-gauge needles. The needles were removed before recording. The EMG signals were amplified (500–10,000 times; P511 amplifiers, Grass Instruments, Berkshire, UK), band-pass filtered (30–1,000 Hz), sampled at 2,000 Hz, and stored in the manner described previously.
To provide a means of normalizing the amplitude of the EMG signals across the experimental sessions, maximal M-waves were evoked in all muscles by electrical stimulation (Digitimer DS7A: pulse width 0.5 ms) of the brachial plexus at Erb's point, before the commencement of each session. The stimulation intensity was increased in steps from an imperceptible level until the magnitude of the EMG responses no longer increased. The intensity of stimulation was then increased by a further 20%, and the responses to eight supramaximal stimuli were recorded at intervals varying randomly between 6 and 8 s.
Torque feedback
The torques produced in pronation/supination and flexion/extension were presented to the participants in real time as the position of a cursor on a computer display. Flexion of the elbow resulted in a cursor movement vertically upward; elbow extension caused the cursor to move vertically downward; pronation of the forearm moved the cursor to the left and supination to the right. The amount of cursor movement generated for each unit of applied torque differed for each participant and was proportional to the maximal voluntary torque (MVT: the amount of torque produced during a maximal voluntary contraction) for each direction. Pronation and supination torques were measured directly from the torque transducer. Torques in flexion and extension were obtained by multiplying the force measured in the vertical plane by the distance between the center of rotation of the elbow and the transducer.
Familiarization
Before the commencement of training, all participants completed a familiarization session. To determine the mapping between torque and movement of the cursor on the visual display, MVTs were recorded in flexion (FLX), extension (EXT), pronation (PRO), and supination (SUP). The participants were asked to produce and hold a maximal contraction for 3 s. During this procedure, the participants were given visual feedback of the torque produced in the other df, and were instructed to keep this torque as close to zero as possible. Trials in which this level became >0.5 Nm were repeated after a period of rest.
The participants subsequently performed eight familiarization trials in each of the eight torque combinations that were to be used during the practice sessions. Unlike the experimental task (described below), the torque produced in each df was presented as a bar graph. The target torque level for each df was indicated by line superimposed on the graph. When the applied levels of torque fell within the target area (target torque ±5% MVT) there occurred a change in the color of the bar.
Target-acquisition task
Targets representing combinations of torques in 2-df movements (FLX/PRO, FLX/SUP, EXT/PRO, EXT/SUP) were positioned such that a constant visual distance was maintained between the center of the screen and each target. The vertical (TF/E) and horizontal (TP/S) torques required to reach the targets were calculated as
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is the angle between the target and the positive horizontal visual axis (Fig. 2).
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The practice period consisted of five sessions on consecutive days during which the participants acquired each of the eight targets (defined at 30% of MVT) 16 times (128 trials per day). During the practice sessions, the targets were presented in a block-randomized order such that all target positions were presented before any were repeated. During the pre- and postpractice sessions the participants completed 16 trials to each target at two force levels (defined at 20% MVT and 40% MVT, 256 trials per session; Fig. 3). The presentation of targets during these sessions was counterbalanced such that the order of presentation within each of 16 blocks of 16 targets (eight target positions at two levels of torque) was unique and adjacent blocks did not result in the consecutive presentation of the same target. The retention test was conducted 4 wk after the posttest and involved a series of target acquisitions identical to those of the practice sessions. During the nonpractice period no further interaction with the experimental device was permitted. Periods of rest were granted at any time during a session if required by a participant, although these were requested infrequently because the nature of the task was such that it did not result in significant fatigue.
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All torque data were digitally low-pass filtered by dual-pass through a 15-Hz second-order, Butterworth filter. Torque onset was determined for each trial as the point at which the torque produced in either df first exceeded 5% of the associated MVT after presentation of the target. All trials were visually inspected to ensure that this criterion eliminated false onset identification. A target was acquired when the torque produced by the participant fell within 5% of the associated MVT and was held within that limit for 100 ms. For the data analysis, target acquisition was defined as the beginning of the 100-ms hold period. All kinetic variables were calculated between movement onset and target acquisition.
Because the participants were instructed to acquire the targets as rapidly as possible, target-acquisition time (the time in seconds from torque onset to target acquisition) was the primary measure of performance. The peak rate of torque development was obtained as the first temporal derivative of the normalized (with respect to the MVT in the corresponding direction) resultant torque vector. A time-normalized measure of the extent to which trajectories deviated from straight paths to each target was determined by calculating the root mean squared (RMS) error between the normalized torque trajectory and the vector defined between zero torque (the initial condition) and the target. The values were expressed as a proportion of the torque range in the dimension orthogonal (in torque space) to that of the target. For example, the directional error for PRO and SUP targets were normalized to the torque range of the FLX/EXT axis. Normalizing ranges for diagonal targets were determined by calculating the angle of the target in torque–space and normalizing to the torque range of the orthogonal torque–space vector. A measure of target overshoot was obtained as the difference between the peak of the resultant torque trace and the target value.
The EMG recordings obtained from each muscle were full-wave rectified and enveloped (low-pass Butterworth dual-pass) at 40 Hz. An overall measure of EMG amplitude was obtained as the RMS for a time window defined from the presentation of the target-to-target acquisition. The values obtained for each muscle were subsequently normalized with respect to the amplitude of the corresponding maximal M-wave recorded before the session. The onset of muscle activity was defined as the time at which the rectified, enveloped EMG data either first exceeded a value equal to a baseline mean (calculated from a window of data before the presentation of the target) plus 2.5SDs of the baseline over the same time window, or exceeded a value equal to 20% of the maximum recorded for that trial. The onset times were expressed relative to the time of target presentation. The offset of muscle activity was defined as the time at which the EMG signal fell below the value that defined the onset of activity and remained below that level for 50 ms.
The peak rate of muscle activation was determined for the first burst of activity in each muscle from the first derivative of (rectified) EMG data low-pass filtered at 6 Hz. The mean burst onset rate was defined as the mean slope of the EMG, in the region about the peak rate of rise of the EMG, for which the magnitude of the signal was increasing (i.e., the average of the segment between zero crossings of the differentiated time series that contained the largest positive value). Principal component (PC) analyses were also performed for each subject on EMG data filtered at 6 Hz. Mean EMG traces from each of the eight recorded muscles and for each target direction were entered into the PC analysis, with the data truncated between 200 ms before movement onset and 400 ms after movement onset. The resulting matrix contained 1,200 rows (600 ms recorded at 2,000 Hz) and 64 columns (eight muscles x eight targets). The number of PCs retained for analysis was determined using the rule that components associated with eigenvalues of less than unity represent noise (Kaiser 1974). All components with associated eigenvalues of greater than unity were therefore included in subsequent analyses. Obtaining the PCs involved calculating the correlation matrix for the entered data, the extraction of the principal components, the attrition of components with eigenvalues below the criterion, the application of a varimax rotation (described in Davis and Vaughan 1993), and the calculation of the component scores (also referred to as eigencurves; Thomas et al. 2005).
Statistical analysis
The median values of the 16 trials performed in each condition were used as the basis of all statistical analyses. Each dependent measure was analyzed separately using a repeated-measures time (pre- and postpractice) by target position ANOVA. Planned comparisons were used to assess whether the outcome measures obtained after practice differed from those recorded before practice. In assessing the changes that occurred during the practice period, the outcome measures obtained during sessions 2–5 were compared individually to the values obtained during the first practice session. The measures obtained during the retention session were compared with those recorded during the final (fifth) practice session. To assist in the interpretation of the tests of significance, the effect size index for ANOVA (f) was calculated following Cohen (1969). A small effect size is considered by convention to be indicated by an f value of <0.25, a medium effect size by an f value of between 0.25 and 0.4, and a large effect size by an f value of >0.4. In the text, all data are presented as means ± SD.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Participants modified the manner in which they developed torque during the acquisition of each target after practice (representative data are shown in Fig. 2). The nature of such changes, however, differed across the eight target directions. The dependent variables obtained from these data further demonstrate the nature of changes in the torques produced before and after practice.
Before practice, acquisition times were longest for targets that required combinations of extension torque with either pronation or supination torque (Fig. 4). The acquisition times for pronation (20%: 0.30 ± 0.1 s; 40%: 0.65 ± 0.56 s) and supination (20%: 0.30 ± 0.07 s; 40%: 0.53 ± 0.53 s) targets were significantly lower than the average acquisition time across all target directions (P < 0.05). No reliable practice-related changes in performance were evident when participants produced torque solely in extension or pronation at 20% of their MVTs, or when extension and supination torques were combined at 40% of their MVTs. In all other conditions however, acquisition times were substantially lower after practice (P < 0.05; f > 0.4). During the postpractice session, targets requiring supination torque were acquired most rapidly, whereas those requiring extension torque in combination with either supination or pronation torque were acquired most slowly. Targets that required extension torque in isolation were also among those acquired most slowly at 20% MVT (0.33 ± 0.1 s).
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The greatest levels of activity in each muscle were generally observed when the required torques corresponded to the principal muscle moments (Fig. 6). The brachialis muscle for example, was most active during pure flexion actions and those combining flexion and pronation. Consistent with previous observations (Jamison and Caldwell 1993; Sergio and Ostry 1995), however, the same muscle displayed lower levels of activation when flexion and supination torques were combined, despite having a line of action that would allow it to contribute positive work in this context.
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The rate of onset of muscle activity was always greatest when each muscle was engaged as an agonist (Fig. 7). Both heads of the biceps brachii, for example, were recruited most rapidly during combinations of flexion and supination torque. Likewise, the monofunctional elbow flexors, brachialis and brachioradialis, were recruited most rapidly during pure flexion actions and during combinations of flexion and pronation, when biceps brachii activity was reduced. After practice, the rate of activation of all muscles was greater than that observed initially, during actions in which they acted as agonists (all P < 0.05; f > 0.4). The extent of these changes was similar for each muscle, with the rates of agonist muscle recruitment after practice being around 100% greater than those observed before practice. In contrast, during actions for which each muscle acted as an antagonist, the rates of initial activation generally showed no change after practice. This presumably reflects the relatively minor role played by antagonist muscles during rapid isometric contractions.
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The first PC at 40% MVT was similar to the first PC at 20% MVT extracted before practice in that it rose rapidly before movement onset and remained close to its peak level for the duration of the trial. The shape of this PC remained comparable in shape after practice, although a slight increase in the initial rate of rise was observed (Fig. 9B). The second PC at 40% MVT was very similar in shape to the second PC extracted at the lower target torque level and remained consistent in shape after practice. The third prepractice PC reflected a pulse in muscle activity that began 150 ms before movement onset and peaked at movement onset, whereas the fourth contained one peak at around 80 ms before movement onset and a second peak that rose from above baseline levels to a maximum at 190 ms after movement onset. A third of these PCs was replaced by a single significant waveform (PC3) after practice, which contained one peak 80 ms before movement onset and another around 100 ms after movement onset.
The extent to which an individual muscle is activated in the manner described by the first PC in each target direction can be determined by examining the loading of each EMG trace onto the first PC (Fig. 10). The same method can be used to determine the extent to which EMG traces are described by each additional PC. To reduce the complexity of the Fig. 10, only the loadings for the first PC are presented at each target torque level. Loadings of note on subsequent PCs are noted in the text.
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Kinematic adaptations exhibited during practice and in retention
Target-acquisition times were significantly lower in six of the eight target directions (Flx/Sup, Ext/Sup, Ext, Ext/Pro, Pro, and Flx/Pro) during the final practice session than during the initial practice session (Fig. 11A). These performance improvements were associated with reliable increases in peak movement speed in each target direction (Fig. 11B). Participants also decreased the extent to which cursor trajectories deviated from a straight path in Flx/Sup (Fig. 11C). The decrease in trajectory deviation in Flx/Sup was associated with significant changes in the timing of agonist muscle activity ([BRA] Day 1: –10 ± 24 ms, Day 5: –52 ± 19 ms; [FCR] Day 1: –81 ± 43 ms, Day 5: –13 ± 25 ms). The amount of target overshoot remained unchanged in most target directions, increasing only when pure elbow flexion was required (Fig. 11D).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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A major issue addressed by this paper is that of the extent to which the CNS is able to simplify the problem of controlling multiple muscles by driving groups of muscles together as synergists. By performing a principal component analysis on the EMG data obtained from each recorded muscle in each target direction, we obtained an indication of the extent to which such a simplification may occur. The reduction of activation patterns from 64 (eight muscles in each of eight target directions) to fewer than five in many cases is striking evidence that a simplification of neuromuscular control is occurring (Fig. 8). Additionally, the fact that the number of significant principal components was reduced after practice for every participant and at both target torque levels strongly suggests that further simplification of the control problem is an important aspect of skill acquisition. These results are supported by previous studies that have identified a small number of underlying waveforms that describe the activation of a large number of muscles during the human gait cycle (Davis et al. 1991; Ivanenko et al. 2004).
A reduction in the number of waveforms that describe the activation profiles of many muscles is analogous to the original idea proposed by Bernstein (1967) that the number of degrees of freedom that must be controlled by the CNS is reduced during even the performance of novel tasks. As skill is acquired, it seems that the CNS becomes more selective with respect to the particular muscles that are driven by each of the underlying waveforms (Fig. 10). We found that it was not the case that muscles with similar mechanical actions loaded equally onto each PC, suggesting that synergistic groupings are based on task-specific functions (such as steady force production and stabilization) of each muscle rather than their lines of action about the joint complex. When performing an elbow flexion task at 40% MVT, for example, most of the eight muscles loaded most heavily onto the first PC, which showed a rapid increase in activity followed by a plateau beginning at movement onset. The short head of the biceps brachii, however, loaded almost exclusively onto the second PC, which provided a more pulsatile contraction that was reduced after movement onset. The separation of even two heads of the same muscle is a clear indication that synergistic groups are not formed based purely on mechanical criteria. In this case it is conceivable that the additional pulse of activity in the short head of the biceps brachii was produced to provide stabilization to the cursor during movement onset, although it is difficult to intimate the precise functions of each activation waveform.
The generalization of acquired skill to unpracticed areas of the workspace was successful in all target directions, regardless of whether targets represented larger or smaller joint torques than those practiced. The fact that performance improvements were observed in both generalization conditions (i.e., generalization to torque targets of higher and lower magnitude than those practiced) demonstrates that patterns of muscular activity and joint torque development acquired with practice can be scaled in magnitude to improve performance on tasks that require dissimilar levels of joint torque. The magnitude of the performance improvements was larger when the targets were presented in unfamiliar areas of the workspace (i.e., at 40% MVT) than when they were presented in familiar workspace areas (i.e., at 20% MVT). The percentage decrease in acquisition times for smaller torque targets than those practiced (20% MVT) was 16.14 ± 7.6%, compared with 26.58 ± 6.4% for higher torque targets (40% MVT). This observation appears to be contrary to the common observation that familiarity with a task workspace facilitates motor learning (Shadmehr and Mussa-Ivaldi 1994). It is possible that the success of skill generalization to actions requiring greater levels of torque than were experienced during practice was attributable, at least in part, to an enhanced facility for torque production. A number of studies have demonstrated evidence of increases in muscle strength after the repetition of tasks that require moderate levels of torque production (around 40% of maximum levels) (Barry and Carson 2004; Faigenbaum et al. 1999; Ohta et al. 2003).
We have observed previously that increases in the rate of agonist recruitment transfer effectively from programs of resistance training to a target-acquisition task similar to that used in the current experiment (Barry et al. 2005b). In a series of experiments carried out by Barry et al. (2004, 2005a,b), increases in the rate of biceps brachii recruitment were observed after training in which the participants were required to produce coupled torques in elbow flexion and forearm supination that represented a moderate (40%) to high (100%) proportion of their maximum capacity. The increases in agonist recruitment rate observed in this experiment are consistent with changes induced by resistance training in similar tasks and it may be hypothesized therefore that the neural mechanisms through which these changes take place are also similar.
Carroll et al. (2002) showed that the functional motor changes induced by a program of resistance training are mediated by adaptations in the firing properties of spinal motoneurons. Specifically, the gain of corticospinal pathway was altered such that a greater level of muscular activation was produced with the same amount of cortical input after training. The effects of changing the firing properties of spinal motoneurons are likely to be expressed on each occasion that the adapted motoneurons are used. The generalized nature of such adaptations is therefore quite distinct from the highly context dependent effects that result from motor learning. An adaptive mechanism that modifies the operational characteristics of spinal motoneurons may therefore be responsible for the success of skill generalization and retention observed during the current study.
Interestingly, the single action for which performance improvement was mediated by both increases in the rate of torque development and the decrease of directional deviation (Flx/Sup) demonstrated significant increases in target-acquisition times after the nonpractice period. The regression of performance in this action was associated with the return of directional deviation to levels observed during the initial practice session. It appears therefore that such performance adaptations were less stable than those that were brought about purely by increases in the rate of torque development. The distinction between these two practice-related performance adaptations is consistent with previous evidence that has suggested the existence of separate neural mechanisms that produce modifications in the speed and accuracy of movements (Hikosaka et al. 2002). In this study, we used cursor path deviation as a measure of the extent to which the production of joint torques is appropriately coordinated for the task. This assumption follows from work demonstrating that straight effector trajectories are a central feature of human limb movements (Flanagan and Rao 1995; Flash and Hogan 1985; Lackner and Dizio 1994; Shadmehr and Mussa-Ivaldi 1994) and is supported by data from the current study that demonstrate an association between a decrease in path deviation during coupled flexion and supination, and changes in the relative timing of agonist muscle activation. We consider it likely that improvements in the coordination of joint torques or muscle forces represent a relatively complex method of adaptation (in that many variables must change) compared with increases in the rate of agonist muscle recruitment. Serrien et al. (2002) demonstrated that interference during the consolidation period in a bimanual coordination task occurred as a function of coordinative complexity. It is unknown at this stage whether the complexity of adaptive mechanisms governs their stability in the same manner on discrete, unimanual tasks, although the current results would support this supposition.
In summary, the performance improvements observed during the current experiment were primarily associated with increases in the rate of agonist muscle recruitment and, consequently, in the rate of torque development in each target direction. Task-related increases in the contribution of bifunctional muscles were observed in both single and dual df variants of the task, suggesting that changes in the organization of muscle synergies during practice were designed to facilitate the rate of increase in agonist recruitment. We demonstrated that the control of isometric tasks involving many muscles may be simplified by grouping muscles into synergies that are each driven with a distinct control command. It also appears likely that the CNS attempts to reduce the number of muscle synergies during skill acquisition, thus further simplifying the control problem. Increasing the rate of recruitment of all agonist muscles during practice appears to be a method of adaptation capable of improving performance on this type of isometric task set and offers benefits over other potential modes of adaptation (synergistic reorganization) in terms of the generalization and retention of acquired skill.
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FOOTNOTES |
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Address for reprint requests and other correspondence: J. Shemmell, Perception and Motor Systems Laboratory, School of Human Movement Studies, The University of Queensland, Brisbane QLD 4072, Australia (E-mail: jshemm{at}bu.edu)
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