Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

We studied the coordination of muscle activity during voluntary body sway performed by human subjects at different frequencies. Subjects stood on the force platform and performed cyclic shifts of the center of pressure (COP) while being paced by the metronome. A major question was: does the makeup of muscle synergies and their ability to assure reproducible sway trajectory vary with the speed of the sway? Principal component analysis was used to identify three muscle groups (M-modes) within the space of integrated indices of muscle activity. M-mode vectors were similar across both subjects and sway frequencies. There were also similar relations between changes in the magnitudes of all three M-modes and COP shifts (the Jacobians) across the sway frequencies. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect the average value of COP shift ("good variance") and the other that did. An index (DeltaV) was computed reflecting the relative amount of the "good variance"; this index has been interpreted as reflecting a multi-M-mode synergy stabilizing the COP trajectory. The average value of DeltaV was similar across all sway frequencies; DeltaV showed a within-a-cycle modulation at low but not at high sway frequencies. The modulation was mostly due to variations in the "good variance". We conclude that muscle modes and their mapping on COP shifts are robust across a wide range of rates of COP shifts. Multi-M-mode synergies stabilize COP shifts (assure its reproducibility) within a wide range of its speeds, but only during cyclic COP changes. Taken together with earlier studies that showed weak or absent multi-M-mode synergies during fast discrete COP shifts, the results suggest a basic difference between the neural control assuring stability of steady-state processes (postural or oscillatory) and transient processes (such as discrete actions). Current results provide the most comprehensive support for the notion of multi-M-mode synergies stabilizing time profiles of important performance variables in motor tasks involving large muscle groups.

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The results suggest that (a) balance deficits can be recognized as an effect of mTBI; (b) balance deficits induced by mTBI are multi-dimensional, affecting all three domains included in this study; and

We used the framework of the uncontrolled manifold hypothesis to quantify multi-muscle synergies stabilizing the moment of force about the frontal axis (MY) and the shear force in the anterior–posterior direction (FX) during voluntary body sway performed by standing subjects. We tested a hypothesis whether the controller could stabilize both MY and FX at the same time when the task and the visual feedback was provided only on one of the variables (MY). Healthy young subjects performed voluntary body sway in the anterior–posterior direction while different loads were attached at the ankle level producing horizontal forces acting forward or backwards. Principal component analysis was used to identify three M-modes within the space of integrated indices of muscle activation. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect a selected performance variable (MY or FX) and the other that did. Under all loading conditions and for each performance variable, a higher value for the former variance component was found. We interpret these results as reflections of two multi-M-mode synergies stabilizing both FX and MY. The indices of synergies were modulated within the sway cycle; both performance variables were better stabilized when the body moved forward than when it moved backward. The results show that the controller can use a set of three elemental variables (M-modes) to stabilize two performance variables at the same time. No negative interference was seen between the synergy indices computed for the two performance variables supporting the principle of superposition with respect to multi-muscle postural control.

Nine participants stood quietly for 30 s while the activity of the soleus, biceps femoris, lumbar erector spinae, tibialis anterior, rectus femoris, and rectus abdominis muscles were recorded using surface electrodes. Intermuscular (EMG-EMG) coherence was estimated for 12 muscle pairs formed by these muscles, including pairs formed solely by either posterior, anterior, or mixed (one posterior and one anterior) muscles. Intermuscular coherence was only found to be significant for muscle pairs formed solely by either posterior or anterior muscles, and no significant coherence was found for mixed muscle pairs. Significant intermuscular coherence was only found within a distinct frequency interval bounded between 1 and 10 Hz when visual input was available (OEs trials). The strength of correlated neural inputs was similar across muscle pairs located in different joints but executing a similar function (pushing body either backward or forward) suggesting that synergistic postural groups are likely formed based on their functional role instead of their anatomical location. Absence of visual information caused a significant decrease in intermuscular coherence. These findings are consistent with the hypothesis that correlated neural inputs are a mechanism used by the CNS to assemble synergistic muscle groups. Further, this mechanism is affected by interruption of visual input.

Posture and postural reactions to mechanical perturbations require the harmonic modulation of the activity of multiple muscles. This precision can become suboptimal in the presence of neuromuscular disorders and result in higher fall risk and associated levels of comorbidity. This study was designed to investigate neurophysiological principles related to the generation and distribution of inputs to skeletal muscles previously recognized as a synergistic group. Specifically, we investigated the current hypothesis that correlated neural inputs, as measured by intermuscular coherence, are the mechanism used by the central nervous system to coordinate the formation of postural muscle synergies. This hypothesis was investigated by analyzing the strength and distribution of correlated neural inputs to postural muscles during the execution of a quiet stance task. Nine participants, 4 females and 5 males, mean age 29.2 years old (±6.1 SD), performed the task of standing while holding a 5-kg barbell in front of their bodies at chest level. Subjects were asked to maintain a standing position for 10 s while the activity of three postural muscles was recorded by surface electrodes: soleus (SOL), biceps femoris (BF), and lumbar erector spinae (ERE). EMG-EMG coherence was estimated for three muscle pairs (SOL/BF, SOL/ERE, and BF/ERE). Our choice of studying these muscles was made based on the fact that they have been reported as components of a functional (synergistic) muscle group that emerges during the execution of bipedal stance. In addition, an isometric contraction can be easily induced in this muscle group by simply adding a weight to the body's anterior aspect. The experimental condition elicited a significant increase in muscle activation levels for all three muscles (p < 0.01 for all muscles). EMG-EMG coherence analysis revealed significant coherence within two distinct frequency bands, 0-5 and 5-20 Hz. Significant coherence within the later frequency band was also found to be significantly uniformly distributed across the three muscle pairs. These findings are interpreted as corroborative with the idea of a hierarchic system of control where the controller may use the generation of common neural inputs to reduce the number of variables it manipulates.

We used the idea of hierarchical control to study multi-muscle synergies during a whole-body sway task performed by a standing person. Within this view, at the lower level of the hierarchy, muscles are united into groups (M-modes). At the higher level, gains at the M-modes are co-varied by the controller in a task-specific way to ensure low variability of important physical variables. In particular, we hypothesized that (1) the composition of M-modes could adjust and (2) an index of M-mode co-variation would become weaker in more challenging conditions. Subjects were required to perform a whole-body sway at 0.5 Hz paced by a metronome. They performed the task with eyes open and closed, while standing on both feet or on one foot only, with and without vibration applied to the Achilles tendons. Integrated indices of muscle activation were subjected to principal component analysis to identify M-modes. An increase in the task complexity led to an increase in the number of principal components that contained significantly loaded indices of muscle activation from 3 to 5. Hence, in more challenging tasks, the controller manipulated a larger number of variables. Multiple regression analysis was used to define the Jacobian of the system mapping small changes in M-mode gains onto shifts of the center of pressure (COP) in the anterior-posterior direction. Further, the variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect an average across cycles COP coordinate and the other that did (good and bad variance, respectively). Under all conditions, the subjects showed substantially more good variance than bad variance interpreted as a multi-M-mode synergy stabilizing the COP trajectory. An index of the strength of the synergy was comparable across all conditions, and there was no modulation of this index over the sway cycle. Hence, our first hypothesis that the composition of M-modes could adjust under challenging conditions has been confirmed while the second hypothesis stating that the index of M-mode co-variation would become weaker in more challenging conditions has been falsified. We interpret the observations as suggesting that adjustments at the lower level of the hierarchy-in the M-mode composition-allowed the subjects to maintain a comparable level of stabilization of the COP trajectory in more challenging tasks. The findings support the (at least) two-level hierarchical control scheme of whole-body movements.