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Laboratory of Motor Control, Department of Kinesiology, Faculteit Bewegings- en Revalidatiewetenschappen, Katholieke Universiteit Leuven, Leuven, Belgium
Submitted 24 March 2005; accepted in final form 27 May 2005
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ABSTRACT |
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INTRODUCTION |
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; Carson et al. 1997; Kelso 1984; Lee et al. 2002; Park et al. 2001; Semjen et al. 1995; Stucchi and Viviani 1993; Swinnen 2002; Swinnen et al. 1997, 1998). For motions toward and away from the body midline, inphase patterns are characterized by movements in different directions in extrinsic space and antiphase patterns by same-direction movements. As such, muscle grouping and direction appear confounded. Nevertheless, it has also been demonstrated that there are independent influences of muscle grouping and direction on coordination, with the former playing a more dominant role than the latter (Swinnen et al. 1997, 1998).
Much less attention has been devoted to the principles governing the coordination between joints within a limb. Studies on intersegmental (intralimb) coordination have revealed that simultaneous flexion or extension of the elbow and wrist joints (isodirectional) is associated with higher stability than coordination modes in which flexion in one joint is performed together with extension in the other joint, or vice versa (nonisodirectional) (Dounskaia et al. 1998; Kelso et al. 1991). Such interactions between joints within a limb may arise from dynamical as well as neural sources. For example, it has been recognized that mechanical interactions between adjacent body segments have an important influence on multijoint control during the execution of various single-limb tasks (Bernstein 1967; Dounskaia et al. 1998; Gribble and Ostry 1999; Hollerbach and Flash 1982; Levin et al. 2001).
So far, interlimb and intralimb coordination constraints have predominantly been explored in relative isolation from each other. This raises questions about the coalition of these constraints when interlimb and intralimb coordination patterns of the upper limbs are combined within a single task. Such tasks provide a unique opportunity to study the mutual interactions between the aforementioned constraints. In the present study, we investigated bilateral shoulder–elbow (expt I) and shoulder–wrist joint combinations (expt II). The interlimb coordination patterns referred to the inphase and antiphase coordination modes. The intralimb coordination patterns referred to the isodirectional and nonisodirectional modes.
Our aim was to determine whether the higher accuracy/stability of inphase versus antiphase and isodirectional versus nonisodirectional patterns was also evident in these complex multijoint tasks. More important, three additional objectives addressed the interactions between interlimb and intralimb coordination patterns. First, we assessed how the mode of coordination between the bilateral segments impacts on the quality of coordination between the joints within a limb, i.e., the effect of interlimb on intralimb coordination. Second, wealso determined whether the modes of intralimb coordination influenced the quality of interlimb coordination. More specifically, it was determined whether the coordination mode adopted within the dominant versus the nondominant limb had a differential effect on the quality of interlimb coordination. Finally, the convergence of these principles across both combinations that differed with respect to whether the moving joints were adjacent (expt I) or nonadjacent (expt II) was also a focus of the investigation.
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METHODS |
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Twenty-six young healthy adult volunteers without known neuromuscular disorders participated in this study. Twenty-four were right-handed and two left-handed (Oldfield 1971). Fourteen participants (all male; aged 19–20) were tested in expt I and 12 (five male, seven female; aged 19–25) in expt II. The experimental procedures were conducted in accordance with the Helsinki Declaration and were approved by the ethical Committee of Biomedical Research at K. U. Leuven. All participants signed an informed consent before the experiment.
Apparatus
Participants were seated in front of a height-adjustable table with fixation of the upper and lower torso to a chair and the upper limbs to a fixed frame that was positioned at the table, to restrict any unintended trunk movements and ensure stable postural control. Their right and left arms were positioned horizontally just above the table surface with the hands in a neutral position. In expt I a splint secured to the ventral surface of the forearm prevented wrist movement (Fig. 1A). In expt II braces were used to restrict unintended elbow movements (Fig. 1B). Single-tone auditory signals, providing pacing for the movements, were presented with a metronome (Korg digital tuner metronome DTM-12, Keio Electronic Laboratory).
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Participants were instructed to perform cyclical flexion and extension movements with their shoulders and elbows/wrists in the horizontal plane. They were required to produce one complete movement cycle for each metronome beat (duration = 752 ms, 1.33 Hz). Participants were instructed to move continuously and to maintain the pacing rhythm and coordination mode as accurately as possible. All participants were successfully able to follow the pacing of the movement.
The experimental conditions consisted of a combination of inphase (IN) or antiphase (AN) coordination modes between both shoulders and elbows/wrists (interlimb) with isodirectional or nonisodirectional coordination modes between the joints within each limb (intralimb). This resulted in the following eight conditions (e.g., shoulder–elbow combination): 1) shoulder and elbow inphase with either isodirectional (IN–IN Iso–Iso, i.e., inphase shoulder, inphase elbow, isodirectional nondominant limb, isodirectional dominant limb) or 2) nonisodirectional (IN–IN NonI–NonI) coordination modes within both limbs; 3) shoulder inphase and elbow antiphase with nonisodirectional movements at the nondominant limb and isodirectional movement at the dominant limb (IN–AN NonI–Iso), or 4) vice versa (IN–AN Iso–NonI); 5) shoulder antiphase and elbow inphase with nonisodirectional movements at the nondominant limb and isodirectional movements at the dominant limb (AN–IN NonI–Iso) or 6) vice versa (AN–IN Iso–NonI); 7) antiphase shoulder and elbow coordination with isodirectional (AN–AN Iso–Iso) or 8) nonisodirectional (AN–AN NonI–NonI) coordination patterns within each limb (Fig. 2).
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Motion recording
Angular displacements of both arms were obtained by using an opto-electronic motion-analysis system (Optotrak 3020). Eighteen markers (infrared-emitting diodes) were attached to both upper arms and forearms to measure the segmental motion. Custom software (Angle, Optrotrak Data Analysis Package) was used to calculate the joint angles in the horizontal plane. The marker displacements were recorded at 150 Hz. The motion data were low-pass filtered (second-order Butterworth with cutoff frequency at 8 Hz, with zero lag). Angular motion of shoulders and elbows/wrists were retained for further analysis.
Relative phase
The relative phasing between joint angle pairs was obtained from the instantaneous phase of each signal, derived from the Hilbert transform (Boashash 1992a,b; Carson et al. 2002). The relative phase analyses were conducted, using the equation adapted from Kelso et al. (1986)
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i refers to the phase of the movement in joint i (i = 1, 2) at each sample, Xi is the position of the joint after rescaling to the interval [–1, 1] for each cycle of oscillation, and dXi/dt is the normalized instantaneous velocity. The relative phase estimate with respect to interlimb coordination (e.g., shoulder–elbow combination) was
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; Mardia 1972) were used to calculate the mean continuous relative phase relationship between two displacement signals. The mean absolute error (AE) score, reflecting the degree of deviation from the target relative phase, was then calculated: 0° for inphase interlimb coordination and isodirectional intralimb coordination and 180° for antiphase and nonisodirectional coordination. The within-trial SD of the relative phase was used as a measure of relative phase variability or coordinative stability. The mean AE and SD scores of relative phase between both shoulders and between both elbows/wrists were calculated to determine the quality of interlimb coordination. The AE and SD scores of relative phase between the shoulder and elbow within the dominant and the nondominant arm were computed to determine the quality of intralimb coordination. Data were calculated for each trial and then averaged across trials within each performance condition, resulting in four mean AE and SD scores of relative phase for each condition.
Statistical analysis
The analysis is exemplified for the shoulder–elbow combination (expt I) and is comparable with the shoulder–wrist combination (expt II).
Interlimb coordination
The mean AE and SD scores of relative phase were computed between both shoulder and both elbow joints to assess the quality of interlimb coordination. Two 2 x 2 x 2 x 2 [Joint x Shoulder Coordination Mode (Shoulder–INAN) x Elbow Coordination Mode (Elbow–INAN) x Intralimb Coordination Mode (ISO–NONISO)] ANOVAs were applied with repeated measures on all factors (Statistica 5.5). The factors included: 1) joint consisting of the shoulder and elbow joint (Joint); 2) the coordination pattern at the shoulder, consisting of the inphase versus the antiphase mode (Shoulder–INAN); 3) the coordination pattern at the elbow, consisting of the inphase versus the antiphase mode (Elbow–INAN); 4) the coordination pattern between the joints within a limb consisting of the isodirectional and nonisodirectional mode (ISO–NONISO or shortly ISON). Because the analyses involving the factor intralimb coordination mode revealed very similar findings for the dominant and nondominant limbs, only the analysis focusing on the coordination mode within the dominant limb will be reported. Overall, this design allowed us to assess the effect of interlimb as well as intralimb coordination modes on the quality of interlimb coordination.
Intralimb coordination
To assess the quality of intralimb coordination, the mean absolute error and SD scores of relative phase were computed between the shoulder and elbow joints within each limb. A 2 x 2 x 2 x 2 [Limb x Intralimb Coordination Mode (ISON) x Shoulder Coordination Mode (Shoulder–INAN) x Elbow Coordination Mode (Elbow–INAN)] ANOVA with repeated measures allowed us to assess the impact of intralimb and interlimb coordination modes on the quality of intralimb coordination. "Limb " referred to the nondominant versus dominant arm. "Intralimb Coordination Mode " referred to nonisodirectional (NonI) versus isodirectional (Iso) coordination between the shoulder and elbow joint within a limb. The remaining factors were similar to those of the previous ANOVA.
For all the analyses, the probability level was set at P < 0.05. When significant effects were found, post hoc tests (Tukey HSD) were conducted to identify the loci of these effects. Because AE and SD of relative phase measures showed similar tendencies, only the AE measures will be discussed in detail.
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RESULTS |
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Examples of raw data
Figure 3 shows representative examples of the shoulder–elbow combination (expt I) for an easy (IN–IN Iso–Iso, Fig. 3A) and difficult (AN–AN NonI–NonI, Fig. 3B) task condition. The IN–IN Iso–Iso condition, requiring the simultaneous activation of homologous muscles groups at all times, was performed with a high coordination quality both between and within limbs. This was not the case during the performance of the AN–AN NonI–NonI condition in which far less stable performance during the nonpreferred coordination patterns was noticed between as well as within limbs.
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ANALYSIS OF INTRALIMB COORDINATION RELATIVE PHASE MEASURES. The absolute errors (AE) of relative phase as a function of coordination conditions in the shoulders and elbows are shown in Fig. 4A. The lowest deviations from target relative phase were observed during the IN–IN Iso–Iso and IN–IN NonI–NonI conditions with similar levels of accuracy in both the shoulders and elbows. As soon as the antiphase coordination mode was performed in one or both bilateral joint pairs, interlimb coordination deteriorated. Higher deviations from required relative phase were found in the shoulders than in the elbows when the shoulders were prepared in the antiphase coordination mode (Fig. 4A, right), whereas smaller differences between both joints were observed when the shoulders were prepared in the inphase coordination mode (Fig. 4A, left).
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Similarly, the 2 x 2 (Joint x Coordination condition) ANOVA applied to the elbow–inphase conditions (Fig. 4A, conditions 1, 2, 5, 6) revealed that, relative to inphase, preparing the shoulder joints in the antiphase mode resulted in a deterioration of shoulder (234%) but also elbow (122%) coordinative accuracy [F(1,13) = 13.52, P < 0.01]. These findings suggest that shifting from the inphase to the antiphase mode in one joint not only affected the quality of coordination at this joint pair (local effect) but also had a detrimental influence on the quality of inphase coordination in the other joint pair (remote effect). In other words, one joint pair dragged the other pair into performance deterioration and this effect was exhibited in both proximal-to-distal and distal-to-proximal directions.
With respect to the effect of intralimb coordination modes on the quality of interlimb coordination, no significant effects were obtained (P > 0.05), which suggests that it did not matter for interlimb AE scores whether the ipsilateral joints were prepared according to the isodirectional versus nonisodirectional coordination mode.
ANALYSIS OF INTERLIMB COORDINATION RELATIVE PHASE MEASURES. Figure 4B displays the absolute error of intralimb relative phasing as a function of interlimb and intralimb coordination modes. As can be observed, the IN–IN NonI–NonI task was associated with the most accurate intralimb performance, both in the dominant and the nondominant limbs, as compared with the remaining task conditions. In order, we will first discuss the effect of intralimb and then interlimb coordination mode on the AE measures of intralimb coordination (Table 1, intralimb coordination).
The nonisodirectional coordination mode (M = 13.41°) was associated with lower error scores than the isodirectional mode (M = 19.78°, main effect of intralimb coordination mode, Table 1, intralimb coordination).
Adopting the antiphase coordination mode in the shoulders resulted in a higher disruption of overall intralimb coordination (M = 18.45°) than the inphase mode (M = 14.74°, main effect of shoulder coordination mode). No such effect was found for the elbows (P > 0.05). Moving according to the antiphase mode either in the elbow or shoulder invariably destabilized global intralimb coordinative behavior (Shoulder–INAN x Elbow–INAN, Table 1, intralimb coordination). Overall, this interaction and the significant main effect of shoulder coordination mode demonstrate that interlimb coordination modes had an effect on the quality of intralimb coordination. The remaining main effects and interactions were not significant (F < 4.79, P > 0.05).
Experiment II: shoulder–wrist coordination
ANALYSIS OF INTRALIMB COORDINATION RELATIVE PHASE MEASURES. The AE of interlimb relative phase as a function of coordination conditions in the shoulders and wrists are shown in Fig. 6A. The lowest error scores were observed during the IN–IN Iso–Iso and IN–IN NonI–NonI conditions with similar levels of accuracy in both the shoulders and wrists. The highest error scores were found in the IN–AN NonI–Iso, IN–AN Iso–NonI, and AN–AN NonI–NonI conditions for the wrist and in the AN–IN Iso–NonI and AN–AN NonI–NonI for the shoulder joints.
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There was no significant effect of intralimb coordination mode on the quality of interlimb coordination (P > 0.05). The only significant interaction containing the intralimb coordination mode was the Shoulder–INAN x ISON effect (Table 2, interlimb coordination). This effect suggested that the difference between inphase and antiphase shoulder coordination was more pronounced during nonisodirectional than during isodirectional coordination within the ipsilateral limb.
ANALYSIS OF INTRALIMB COORDINATION RELATIVE PHASE MEA-SURES. Figure 6B displays the absolute error of intralimb relative phasing as a function of interlimb and intralimb coordination modes. It is evident that the isodirectional mode was not associated with lower intralimb relative phasing error as compared with the nonisodirectional mode under all circumstances. Higher intralimb error scores were evident in the nondominant (M = 22.13°) compared with the dominant limb (M = 17.58°, main effect of limb, Table 2, intralimb coordination).
The effect of wrist interlimb coordination mode indicated that adopting antiphase coordination in the wrists (M = 22.37°) resulted in a higher disruption of overall intralimb coordination than the inphase mode (M = 17.34°). However, this effect also interacted with the coordination mode performed in the shoulder (Shoulder–INAN x Wrist–INAN, Table 2, intralimb coordination). As soon as the antiphase mode was adopted in one or both limbs, the intralimb error scores increased (relative to the ININ condition), reaching similar error levels across the three remaining conditions (INAN, ANIN, ANAN). This suggests that interlimb coordination mode influenced the quality of intralimb coordination.
The error scores of intralimb coordination were slightly lower for the nonisodirectional than for the isodirectional mode during inphase coordination of the wrists, whereas error scores were higher for nonisodirectional than for isodirectional coordination during the antiphase wrist coordination mode (ISON x Wrist–INAN, Table 2, intralimb coordination). Furthermore, the aforementioned effect was somewhat more pronounced in the dominant than in the nondominant limb (Limb x ISON x Wrist–INAN, Table 2, intralimb coordination, Fig. 7B). Thus these observations indicate that the difference in coordination quality between the isodirectional and nonisodirectional coordination mode was primarily affected by the coordination adopted between the bilateral wrists and was also modulated by limb dominance.
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DISCUSSION |
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Interlimb coordination
A general tendency emerged to converge toward symmetrical (inphase) movement patterns during the various coordination tasks, reflecting a general preference for mirror-image movements of the bilateral segments. This was inferred from the higher relative phase accuracy (and lower variability) between the limbs when both proximal and distal joints were prepared in the inphase versus the antiphase coordination mode. The present observations confirmed and extended previous findings underscoring the higher intrinsic stability of symmetrical (involving the simultaneous activation of homologous muscle groups) compared with asymmetrical bimanual movements (involving coactivation of nonhomologous muscle groups) to the context of multijoint bimanual coordination (Byblow et al. 1994, 1999; Carson et al. 1997; Kelso 1984; Lee et al. 2002; Li et al. 2004; Semjen et al. 1995; Swinnen et al. 1997, 1998). This is a hallmark of dynamic pattern theory in which the differential stability between coordination modes has been formalized mathematically for a single joint pair (Haken et al. 1985).
However, new insights were particularly obtained when inphase and antiphase coordination modes were combined across both joint couples. Whereas previous studies using simpler movements would allow us to extrapolate that a combination of inphase modes in both joint couples (IN–IN) would result in the best performance and a combination of antiphase modes in the worst (AN–AN), with the mixed conditions positioned in between (IN–AN, AN–IN), we observed that the impact of the interlimb coordination modes at both joint couples was not simply additive. As soon as the antiphase mode was introduced in only one of both bilateral joint pairs, interlimb coordination deteriorated and performance levels were comparable to, and sometimes even higher than, those obtained during antiphase coordination. This suggests that mixing the interlimb coordination modes across both joint couples was experienced as a rather difficult task combination.
Moreover, the interactions between coordination modes across proximal and distal joints also accounted for the nonadditive effects. More specifically, the proximal joints exhibited a stronger impact on the quality of interlimb coordination than the distal joints during the shoulder–wrist combination, whereas the interaction was mutual during production of the shoulder–elbow combination, i.e., from distal to proximal and vice versa. This exemplifies that the impact of interlimb coordination mode on the quality of overall coordinative performance was also dependent on the effector combination.
Multiple factors may account for these differences across both coordination tasks. First, joints were adjacent in the shoulder–elbow and nonadjacent in the shoulder–wrist task. In the former case, biarticular muscles (biceps and triceps brachii) could have a stronger modulatory impact on coordination between joints than in the latter case (because no muscle simultaneously spans shoulder and wrist in human beings). Second, segmental inertial parameters may also play a role, with larger effectors having a greater impact on global coordination than segments with smaller inertial parameters. This may account for the effect of bilateral shoulder on wrist coordination, but not vice versa, and for the mutual effects between shoulder and elbow coordination. Because of their larger mass, movements of larger effectors may have a more disturbing influence on the other effectors. More generally, the impact of inertial features on coordination during various types of interlimb tasks has been addressed in previous work (Kelso and Jeka 1992; Levin et al. 2004; Serrien and Swinnen 1999).
Finally, our observations revealed virtually no impact of the intralimb coordination modes on the quality of interlimb coordination in both experiments. More specifically, performing the isodirectional versus nonisodirectional mode between the segments within each limb did not significantly influence the quality of coordination between the homologous joint pairs. Thus the coalition of constraints that dominated bimanual multijoint coordination was mainly reflected by the interaction between the bilateral segments of the neuromuscular system while being less modulated by the interaction between the ipsilateral segments. In other words, the impact of the interlimb coordination mode was more powerful than that of the intralimb coordination mode on the performance measures of interlimb coordination.
Intralimb coordination
No consistent global picture emerged regarding the state of coordination between the segments within each limb and their consequences for accuracy and stability of intralimb coordination. With respect to the shoulder–elbow combination (expt I), performing the isodirectional mode between limb segments (simultaneous flexions and extensions) resulted in a lower quality of intralimb coordination than the nonisodirectional mode. With respect to the shoulder–wrist combination (expt II), the interaction between inter- and intralimb coordination mode revealed that isodirectional coordination was less successful than nonisodirectional coordination during inphase coordination between the wrists, whereas the converse effect was obtained during antiphase coordination.
These findings deviate from previous work in which isodirectional coordination modes were produced with higher stability than nonisodirectional modes (Dounskaia et al. 1998; Kelso et al. 1991; Putnam 1991; Virji-Babul and Cooke 1995). In a cyclical elbow–wrist coordination study, Kelso and coworkers demonstrated that the stability of intralimb coordination depended on hand posture (i.e., when the hand was supine) and the isodirectional mode was more stable than the nonisodirectional mode, whereas the opposite effect was observed with the hand in pronation (Kelso et al. 1991). Studying a similar task, Dounskaia et al. (1998) showed that the isodirectional pattern was more in agreement with interactive effects than the less-stable nonisodirectional pattern, thus causing their differential accuracy/stability under increasing cycling frequencies. The obtained relative differences in the accuracy/stability of isodirectional versus nonisodirectional intralimb coordination modes across the aforementioned tasks and those studied by us may be a consequence of the differential impact of interactive torques across these various segment combinations. However, other factors may also play a role, including neural, biomechanical, musculoskeletal, and cognitive factors. Additional research is warranted to assess the relative impact of each of these factors on the quality of intralimb coordination as well as the task-specific nature of these influences.
The role of limb dominance in the control of intralimb coordination was found to be prevalent during shoulder–wrist but not during shoulder–elbow coordination. The former finding is consistent with the dynamic-dominance hypothesis (Sainburg 2002), suggesting that the differences in the quality of control between the dominant and nondominant limb may modulate the quality of within-limb coordination. Because this phenomenon was observed only for the shoulder–wrist configuration, we hypothesize that the dynamic-dominance effect may have been masked by the supremacy of interlimb over intralimb coordination modes between adjacent segments during the production of shoulder–elbow movements.
Whereas the mode of intralimb coordination had a minor impact on the quality of interlimb coordination (see first section of DISCUSSION), the converse effect was more prevalent. There are several pieces of evidence to support this conclusion. First, the impact of interlimb on intralimb coordination was so powerful that the direction of the difference in performance quality between isodirectional and nonisodirectional coordination modes was determined by the coordination mode adopted between the bilateral distal joints (see Experiment II: shoulder–wrist coordination). Second, across both experiments, accuracy and stability of intralimb coordination was highest during inphase coordination in both bilateral joint pairs. As soon as antiphase coordination was introduced in one or both joints, the quality of intralimb coordination deteriorated. In the shoulder–elbow task, the coordination mode in the bilateral shoulders had a stronger impact on the quality of intralimb coordination than the elbow coordination mode. Conversely, in the shoulder–wrist task (expt II), the bilateral wrist coordination mode appeared to have a stronger impact on intralimb coordination than the bilateral shoulder coordination mode. In spite of these differences, the converging picture across both experiments is that the coordination mode adopted in either the bilateral proximal or distal joints (inphase vs. antiphase) induced a stronger impact on intralimb performance than the coordination mode adopted between the segments within the limbs themselves (isodirectional vs. nonisodirectional). Thus interlimb constraints ruled over intralimb constraints when evaluating intralimb coordination performance.
Neural correlates of coordination constraints
The present observations offer a different look at the general nature of human motor control. In studies of motor performance, we are often reminded of the phenomenon of hand preference/dominance. The dominant limb affords highly refined control, whereas performance with the nondominant limb is usually less than optimal (Sainburg 2002; Sainburg and Kalakanis 2000; Swinnen et al. 1996). These differential behavioral expressions are also associated with a higher degree of lateralized and more focused neural activation when moving with the dominant compared with the nondominant limb (Haaland et al. 2004). Yet, when moving both limbs together, control of the individual limbs becomes subordinate to a bilateral organization that harnesses the coordination between the ipsilateral limb segments.
This "symmetrical supremacy " in movement organization is most likely a direct consequence of the bilateral musculoskeletal organization that characterizes many species. Interestingly, there are dense interhemispheric connections between the homotopic motor networks of both hemispheres to support this symmetrical organizational supremacy (Cardoso de Oliveira et al. 2001; Donchin et al. 2001). The removal of these direct connections (such as during callosotomy) has important implications for bimanual control (Eliassen et al. 1999; Franz et al. 1996). Behavioral studies support the contention that connectivity between motor networks across hemispheres is often stronger than that within hemispheres. However, strong connectivity can also hamper the production of differentiated actions in the limbs, giving rise to patterns of mutual interference. As a consequence, producing the same movements simultaneously is easier, whereas producing different movements is often more difficult with the bilateral than with the ipsilateral limb segments (Serrien and Swinnen 1997a,b). More generally, this suggests that the supremacy of bilateral over ipsilateral coordination during upper limb movements, as inferred from our behavioral observations, is supported by a distinct neural organization in which the strength of interhemispheric interactions between motor control centers dominates over the intrahemispheric ones.
With respect to the observed differences among the interlimb coordination modes and their degree of compatibility across girdles (ININ and ANAN vs. INAN and ANIN), it is reasonable to assume that these behavioral effects are associated with neural correlates. Previous work has shown that bilateral coordination modes deviating from mirror symmetry are associated with higher and more extended brain activation patterns than inphase coordination modes (for reviews see Swinnen 2002; Wenderoth et al. 2004a). Basically, activations extend to prefrontal, parietal, and temporal areas when bimanual movements become more complex as a function of their temporal and/or spatial compatibility (Debaere et al. 2003, 2004a,b; Ullen et al. 2003; Wenderoth et al. 2004b; N Wenderoth, F Debaere, S Sunaert, and SP Swinnen, unpublished observations). These extended activation patterns are associated not only with generating more complex command structures that diverge across joint couples but also with suppression of preferred coordination modes to explore these more complex patterns (Puttemans et al. 2005; Swinnen 2002; Wenderoth et al. 2004b; Wenderoth et al. unpublished observations).
In conclusion, the present experiments underscore three main findings. First, the principle of muscle homology, giving rise to mirror-symmetrical movements with respect to the midsagittal plane, had a powerful influence on global interlimb coordination. Interactions between distal and proximal joint pairs were clearly evident and varied across coordination tasks. Second, the mode of coordination within limbs exhibited a variable impact on the quality of intralimb coordination between adjacent and nonadjacent ipsilateral joint combinations. Here, the impact of multiple variables, including musculoskeletal and dynamic, on the quality of intralimb coordination should be the focus of future investigation. Third, the mode of interlimb coordination had a much more powerful effect on the quality of intralimb coordination than vice versa. Taken together, these observations suggest a hierarchical control structure for multijoint bimanual movement whereby interlimb coordination constraints dominate over the constraints governing intralimb coordination. This is supported by a distinct neural organization with profound interhemispheric interactions during the production of bimanual movement.
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GRANTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: S. P. Swinnen, Laboratory of Motor Control, Department of Kinesiology, K.U. Leuven, Tervuursevest 101, B-3001 Heverlee, Belgium (E-mail: Stephan.Swinnen{at}faber.kuleuven.be)
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ABSTRACT
INTRODUCTION
