Are muscle synergies useful for neural control?

Aymar De Rugy,Gerald E Loeb,Timothy J Carroll

doi:10.3389/fncom.2013.00019

Abstract

The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.

Highlights

There is considerable evidence from a broad range of tasks and contexts that the activity of multiple muscles can appear to be well-approximated by only a few muscle synergies, each defined as a set of fixed relative levels of muscle activation (d’Avella et al, 2003, 2006; Torres-Oviedo et al, 2006; Tresch and Jarc, 2009; Dominici et al, 2011; Roh et al, 2012)
This figure illustrates that the muscle pattern reconstructed with synergy only starts to resemble the real pattern with three or four synergies, and that the remaining muscle-space differences translate into substantial aiming errors (AEs)
We showed that synergy decomposition on the optimal muscle pattern that minimizes summed-squared muscle activities for a representation of muscle biomechanics produces similar muscle approximations, with the same functional consequences

Summary

Introduction

There is considerable evidence from a broad range of tasks and contexts that the activity of multiple muscles can appear to be well-approximated by only a few muscle synergies, each defined as a set of fixed relative levels of muscle activation (d’Avella et al, 2003, 2006; Torres-Oviedo et al, 2006; Tresch and Jarc, 2009; Dominici et al, 2011; Roh et al, 2012). All synergy decomposition procedures (including, for example, those based on convenient optimization algorithms such as the non-negative matrix factorization; Lee and Seung, 2001), care only about explaining as much variance of muscle activity as possible These procedures are completely blind to any consideration of task achievement, ignoring the functional significance of the (typically modest) musclespace errors that are inevitably introduced when approximating an original muscle pattern using fewer synergies than muscles. Such errors would affect the utility of synergy decomposition as a potential control strategy to restore movement artificially such as with functional electrical stimulation (FES) or myoelectric controls (Davoodi et al, 2003; Parker et al, 2006; Hargrove et al, 2009)

Objectives

Results

Conclusion