How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.

How the CNS masters the many degrees of freedom of the musculoskeletal system to control goal-directed movements is a long-standing question. We have recently provided support to the hypothesis that the CNS relies on a modular control architecture by showing that the phasic muscle patterns for fast reaching movements in different directions are generated by combinations of a few time-varying muscle synergies: coordinated recruitment of groups of muscles with specific activation profiles. However, natural reaching movements occur at different speeds and require the control of both movement and posture. Thus we have investigated whether muscle synergies also underlie reaching at different speeds as well as the maintenance of stable arm postures. Hand kinematics and shoulder and elbow muscle surface EMGs were recorded in five subjects during reaches to eight targets in the frontal plane at different speeds. We found that the amplitude modulation of three time-invariant synergies captured the variations in the postural muscle patterns at the end of the movement. During movement, three phasic and three tonic time-varying synergies could reconstruct the time-normalized muscle pattern in all conditions. Phasic synergies were modulated in both amplitude and timing by direction and speed. Tonic synergies were modulated only in amplitude by direction. The directional tuning of both types of synergies was well described by a single or a double cosine function. These results suggest that muscle synergies are basic control modules that allow generating the appropriate muscle patterns through simple modulation and combination rules.

We have recently shown that the muscle patterns for reaching are well described by the combination of a few time-varying muscle synergies supporting the notion of a modular architecture for arm control. Here we investigated whether the muscle patterns for reaching movements involving online corrections are also generated by the combination of the same set of time-varying muscle synergies used for point-to-point movements. We recorded endpoint kinematics and EMGs from up to 16 arm muscles of 5 subjects reaching from a central location to 8 peripheral targets in the frontal plane, from each peripheral target to 1 of the 2 adjacent targets, and from the central location initially to 1 peripheral target and, after a delay of either 50, 150, or 250 ms from the go signal, to 1 of the 2 adjacent targets. Time-varying muscle synergies were extracted from the averaged, phasic, normalized EMGs of point-to-point movements and fit to the patterns of target change movements using an iterative optimization algorithm. In all subjects, three time-varying muscle synergies explained a large fraction of the data variation of point-to-point movements. The superposition and modulation of the same three synergies reconstructed the muscle patterns for target change movements better than the superposition and modulation of the corresponding point-to-point muscle patterns, appropriately aligned. While at the kinematic level the corrective trajectory for reaching during a change in target location can be obtained by the delayed superposition of the trajectory from the initial to the final target, at the muscle level the underlying phasic muscle patterns are captured by the amplitude and timing modulation of the same time-varying muscle synergies recruited for point-to-point movements. These results suggest that a common modular architecture is used for the control of unperturbed arm movement and for its visually guided online corrections.

Muscle activities underlying many motor behaviors can be generated by a small number of basic activation patterns with specific features shared across movement conditions. Such low-dimensionality suggests that the central nervous system (CNS) relies on a modular organization to simplify control. However, the relationship between the dimensionality of muscle patterns and that of joint torques is not fixed, because of redundancy and non-linearity in mapping the former into the latter, and needs to be investigated. We compared the torques acting at four arm joints during fast reaching movements in different directions in the frontal and sagittal planes and the underlying muscle patterns. The dimensionality of the non-gravitational components of torques and muscle patterns in the spatial, temporal, and spatiotemporal domains was estimated by multidimensional decomposition techniques. The spatial organization of torques was captured by two or three generators, indicating that not all the available coordination patterns are employed by the CNS. A single temporal generator with a biphasic profile was identified, generalizing previous observations on a single plane. The number of spatiotemporal generators was equal to the product of the spatial and temporal dimensionalities and their organization was essentially synchronous. Muscle pattern dimensionalities were higher than torques dimensionalities but also higher than the minimum imposed by the inherent non-negativity of muscle activations. The spatiotemporal dimensionality of the muscle patterns was lower than the product of their spatial and temporal dimensionality, indicating the existence of specific asynchronous coordination patterns. Thus, the larger dimensionalities of the muscle patterns may be required for CNS to overcome the non-linearities of the musculoskeletal system and to flexibly generate endpoint trajectories with simple kinematic features using a limited number of building blocks.