Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles

Russell L Hardesty,Matthew T Boots,Sergiy Yakovenko,Valeriya Gritsenko

doi:10.1038/s41598-020-67403-w

Russell L Hardesty, Matthew T Boots + Show 2 more

Open Access

https://doi.org/10.1038/s41598-020-67403-w

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Abstract

The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. The objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. To achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. The parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. The reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition.

Highlights

The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood
Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy[1,2,3,4]
The remaining unbalanced moments are often termed muscle torques; they produce motion. In this bottom-up reasoning, muscle contractions represent the output of the central nervous system (CNS) that reflects the mechanical properties of the limb being moved by these muscles

Summary

Introduction

The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. Given such complex and flexible Ia feedback that could be transmitted through the mono- and disynaptic pathways, the role that it plays in co-contraction and limb stiffness is unknown It has been suggested the co-contraction of antagonistic muscles can be modulated by descending signals through the concurrent fusimotor drive, e.g. C command in lambda-model[29]. We used the model of muscle spindle with the two types of fusimotor drive, static set and α-γ coactivation, to address the question of Ia afferent contribution to the co-contraction of multiple muscles during reaching movements. We expected that the activity of co-contracting antagonistic muscles will positively correlate with the activity of their Ia afferents shaped by static set and α-γ coactivation, which would be evident from observing these signals in the same clusters

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