Suboptimal Muscle Synergy Activation Patterns Generalize their Motor Function across Postures

Abstract

We used a musculoskeletal model to investigate the possible biomechanical and neural bases of using consistent muscle synergy patterns to produce functional motor outputs across different biomechanical conditions, which we define as generalizability. Experimental studies in cats demonstrate that the same muscle synergies are used during reactive postural responses at widely varying configurations, producing similarly-oriented endpoint force vectors with respect to the limb axis. However, whether generalizability across postures arises due to similar biomechanical properties or to neural selection of a particular muscle activation pattern has not been explicitly tested. Here, we used a detailed cat hindlimb model to explore the set of feasible muscle activation patterns that produce experimental synergy force vectors at a target posture, and tested their generalizability by applying them to different test postures. We used three methods to select candidate muscle activation patterns: (1) randomly-selected feasible muscle activation patterns, (2) optimal muscle activation patterns minimizing muscle effort at a given posture, and (3) generalizable muscle activation patterns that explicitly minimized deviations from experimentally-identified synergy force vectors across all postures. Generalizability was measured by the deviation between the simulated force direction of the candidate muscle activation pattern and the experimental synergy force vectors at the test postures. Force angle deviations were the greatest for the randomly selected feasible muscle activation patterns (e.g., >100°), intermediate for effort-wise optimal muscle activation patterns (e.g., ~20°), and smallest for generalizable muscle activation patterns (e.g., <5°). Generalizable muscle activation patterns were suboptimal in terms of effort, often exceeding 50% of the maximum possible effort (cf. ~5% in minimum-effort muscle activation patterns). The feasible muscle activation ranges of individual muscles associated with producing a specific synergy force vector was reduced by ~45% when generalizability requirements were imposed. Muscles recruited in the generalizable muscle activation patterns had less sensitive torque-producing characteristics to changes in postures. We conclude that generalization of function across postures does not arise from limb biomechanics or a single optimality criterion. Muscle synergies may reflect acquired motor solutions globally tuned for generalizability across biomechanical contexts, facilitating rapid motor adaptation.

A Neural Circuit Model of Proprioceptive Feedback from Muscles Recruited during an Isometric Knee Extension

“…These results are further supported by findings from a novel simulation of the local spinal circuits showing proprioception from muscles contribute to synergy level organization of motor control in humans during isometric tasks. Whilst our results are largely in line with previously established literature regarding synergy recruitment, we differ in our finding that joint angle and therefore afferent feedback regarding joint position alters synergy recruitment (Torres-Oviedo, Macpherson, and Ting 2006;Roh, Rymer, and Beer 2012;Sohn and Ting 2016). We propose that in this case, because the available synergies are constrained due to the nature of an isometric task, adaptation is achieved through changes in recruitment.…”

The cost of being stable: Trade-offs between effort and stability across a landscape of redundant motor solutions

“…Further, we took advantage of the finding that maximal effort solutions in arm postural control increases limb stability [39] by comprehensively exploring the continuum of solutions along the null-path between the minimum and maximum effort solutions. While we did not explicitly study effects of limb configuration [56][57][58], our prior work showed that non-minimum efforts solutions are more likely to generalize their function across different postures, potentially making them more robust for control [59]. Such robust properties of motor solutions could explain our prior observations that individual differences in motor module, a.k.a., muscle synergy, patterns are observed across limb postures and for different tasks [1,2,9,60,61].…”

“…For example, the "bumpiness" of the landscape may explain why habitual versus optimal patterns are preferred [43][44][45]. Although we used two functional properties of effort and stability, other multi-objective criteria can be used such as minimizing trajectory error, energetics [102], and ability to generalize [59,94,103] or switch [104] across motor tasks. Moreover, the multitude of solutions and local minima may support the observation of individual-specific patterns of muscle co-activation that are regularly observed during gait and balance tasks in healthy humans and animals [1,2,9,60,61,105].…”

The Effects of Motor Modularity on Performance, Learning, and Generalizability in Upper-Extremity Reaching: a Computational Analysis

“…Sohn and colleagues [44] present evidence that generalizability does not arise directly from musculoskeletal or optimality constraints. We complement this study by showing directly that synergies are generalizable for upper extremity reaching tasks.…”

“…Further, this generalizability of synergies computed from EMG might result from biomechanical constraints, although Sohn and colleagues [44] show that for a postural task in the cat hindlimb, generalizability must be explicitly accounted for in an objective function when computing muscle activations that are accurate across tasks. In simulation studies, researchers have shown that synergies computed for one walking task facilitate the generation of walking dynamics at different speeds [32] and different body weights [31], although both optimizations relied partially on tracking experimental kinematics or kinetics.…”