Abstract
Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to an accuracy level defined heuristically, often set to 90% of the variance. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is not entirely random and can influence the execution of motor tasks. Young healthy subjects performed an isometric reaching task in which the surface electromyography of 10 arm muscles was mapped onto a two-dimensional force used to control a cursor. Three to five synergies explained 90% of the variance in muscle activity. We altered the muscle-force mapping via "hard" and "easy" virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery; in contrast, it was as far as possible in the hard surgery. This design maximized potential differences in reaching errors attributable to residual activity. Results show that the easy surgery produced smaller directional errors than the hard surgery. Additionally, simulations of surgeries constructed with 1 to 10 synergies show that the errors in the easy and hard surgeries differ significantly for up to 8 synergies, which explains 98% of the variance on average. Our study thus indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient accuracy levels.NEW & NOTEWORTHY The muscle synergy hypothesis posits that the central nervous system simplifies motor control by grouping muscles into modules. Current techniques use dimensionality reduction, such that the identified synergies reconstruct 90% of the muscle activity. We show that residual muscle activity following such identification can have a large systematic effect on movements, even when the number of synergies approaches the number of muscles. Current synergy extraction techniques must therefore be updated to identify true physiological synergies.
Highlights
One of the most salient problems the central nervous system (CNS) faces when generating movements is the redundancy of the motor system [1]
Deviations from the line of action of the surgery were closer to the intended target during the easy surgery than during the hard surgery (Fig 2b)
These trajectories correspond to the last target set of the baseline subtask, and the first target set after the onset of the hard and easy incompatible virtual surgery tasks
Summary
One of the most salient problems the central nervous system (CNS) faces when generating movements is the redundancy of the motor system [1]. The CNS can generate an infinity of different motor commands to produce the same action This redundancy spans the length of the causal chain of motor control: from neuron to muscle to joint levels. In light of the complexity of this problem, the muscle synergy hypothesis posits that the CNS groups the control of functionally similar muscles into modules called muscle synergies [2]. This would reduce the number of variables that the CNS needs to control to produce a movement, decreasing the complexity of the computations necessary for motor control [3].
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