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

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. Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy 1-4. The motor control problem is then solved within a system with coupled neural and mechanical dynamical elements 5-7. Therefore, the relationship between neural signals driving muscle contraction and the resulting motion is nonlinear. Muscle contractions generate forces that sum into active moments defined by the agonistic or antagonistic relationships between the muscle's moment arms around a given axis of rotation of the joint. The components of these forces that sum to zero moment, such as forces produced by balanced

Neuromechanical Coupling is Reflected in the Spatial Organization of the Spinal Motoneuron Pools

“…Such afferent-driven muscle activity has been shown for hindlimb extensor muscles in the cat during the stance phase of walking (Hiebert and Pearson 1999; Prochazka et al 1997a) and it likely underlies positive force feedback control and other similar types of muscle coactivation in humans (Hagbarth 1993; Prochazka et al 1997b). The afferent-driven coactivation of synergistic upper limb muscles during reaching in humans has also been suggested by simulations of primary afferent feedback and their relationships to muscle activation patterns (Hardesty et al 2020).…”

“…Musculotendon lengths (i.e., muscle lengths) were calculated for each forelimb posture across the physiological range of motion in the OpenSim model using Matlab. The correlation matrix of muscle lengths across all postures was converted to heterogeneous variance explained (HVE) to emphasize agonistic relationships (Hardesty et al 2020). Hierarchical clustering was applied to HVE using the linkage function in Matlab to quantify the functional relationships between muscles.…”

The Human Motor Cortex Contributes to Gravity Compensation to Maintain Posture and During Reaching

“…1A). Spherical virtual targets 8 cm in diameter were displayed in a sagittal plane and defined a set of postures and reaching tasks as described in detail in (Hardesty et al 2020) (Fig. 1B).…”

“…1B). To calculate active joint torques, we used a dynamical model similar to that used for the selection of tasks as described in (Hardesty et al 2020). The model was customized to individual morphology by scaling segment length and inertia based on individual’s height and weight using published average proportions (Winter 2009).…”

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

“…Recent studies have shown that to predict patient function under novel conditions, personalized neural control models are likely to be necessary [25], and furthermore, that pre-treatment muscle synergies may facilitate the prediction of posttreatment muscle excitations [144]. While numerous studies exist that describe complex, realistic sensorimotor control models incorporating elements such as supraspinal control, modularity, central pattern generators, and proprioceptive feedback [22,[145][146][147][148][149][150][151][152][153][154][155][156][157][158][159], these models are generic rather than personalized and are typically applied to simplified planar dynamic systems. More realistic musculoskeletal models have been coupled with simpler neural control models employing modular control [25][26][27]136,137,[160][161][162][163] or proprioceptive feedback [164][165][166], but these models have yet to be evaluated in clinical treatment design scenarios.…”