Conceptual models of synaptic inputs to motor neurons for the generation of force and the 86 modulation of co-contraction. A. An upper-limb task designed to dissociate co-activation of antagonist 87 muscles for impedance modulation and force generation. Force targets (black dots) arranged in two parallel 88 planes can be reached by applying different combinations of torques at the upper-limb joints. Horizontal 89 targets in the lower plane can be reached by different combinations of shoulder and elbow flexion or 90 extension torques, producing horizontal forces at the hand. Supination targets in the higher plane requires 91 the combination of shoulder and elbow torques (horizontal force) with wrist supination torque (producing 92 a torque at the hand mapped as vertical displacement in this task). Two muscles are considered: Triceps 93 Brachii (TB), an elbow extensor, i.e., with a pulling vector (cyan arrow) in the Horizontal plane (towards 94 target F2), and Biceps Brachii (BB), whose action is both elbow flexion and forearm supination, i.e., with a 95 pulling vector (red arrow) that has both a component (dashed arrows) in the Horizontal plane (towards 96

Conceptual models of synaptic inputs to motor neurons for the generation of force and the 86 modulation of co-contraction. A. An upper-limb task designed to dissociate co-activation of antagonist 87 muscles for impedance modulation and force generation. Force targets (black dots) arranged in two parallel 88 planes can be reached by applying different combinations of torques at the upper-limb joints. Horizontal 89 targets in the lower plane can be reached by different combinations of shoulder and elbow flexion or 90 extension torques, producing horizontal forces at the hand. Supination targets in the higher plane requires 91 the combination of shoulder and elbow torques (horizontal force) with wrist supination torque (producing 92 a torque at the hand mapped as vertical displacement in this task). Two muscles are considered: Triceps 93 Brachii (TB), an elbow extensor, i.e., with a pulling vector (cyan arrow) in the Horizontal plane (towards 94 target F2), and Biceps Brachii (BB), whose action is both elbow flexion and forearm supination, i.e., with a 95 pulling vector (red arrow) that has both a component (dashed arrows) in the Horizontal plane (towards 96

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The CNS may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of co-contraction, which does not contribute to joint torque generation but allows to modulate mechanical impedance. Whether co-contraction is controlled through the same synaptic input to...

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... 1A), the same synaptic inputs that drive motor neurons to generate endpoint force (Fig. 1B, red and 55 results provide, for the first time, evidence for the existence of a synaptic input to motoneurons that 83 specifically controls co-contraction, and that is independent from the input driving the generation of force. 84 85 A. An upper-limb task designed to dissociate co-activation of antagonist 87 muscles for impedance ...
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... control strategies exploited by CNS to regulate limb impedance are still debated. While some studies 488 have suggested an independent recruitment of individual muscles to optimally control both force and 489 stiffness (Berret & Jean, 2020;Forster et al., 2004;Hughes et al., 1995) (shared inputs in Figure 1), other 490 studies have proposed a separation of the control of force and stiffness, through shared common input to 491 antagonist muscles ( Borzelli et al., 2018;De Luca & Mambrito, 1987;Latash, 1992;Takagi et al., 2020) 492 (independent inputs in Figure 1). To address this question, we took advantage of the "virtual stiffness" 493 approach, which allowed the discrimination of the neural drives responsible for the co-activation for 494 . ...
Context 3
... control strategies exploited by CNS to regulate limb impedance are still debated. While some studies 488 have suggested an independent recruitment of individual muscles to optimally control both force and 489 stiffness (Berret & Jean, 2020;Forster et al., 2004;Hughes et al., 1995) (shared inputs in Figure 1), other 490 studies have proposed a separation of the control of force and stiffness, through shared common input to 491 antagonist muscles ( Borzelli et al., 2018;De Luca & Mambrito, 1987;Latash, 1992;Takagi et al., 2020) 492 (independent inputs in Figure 1). To address this question, we took advantage of the "virtual stiffness" 493 approach, which allowed the discrimination of the neural drives responsible for the co-activation for 494 . ...

Citations

... For instance, the taskredundant and -synergistic networks we extracted appeared to be structured around the coupling between a prime-mover muscle and several supporting muscles, supporting recent work showing the nonhomogeneous sharing of neural drives within modules (Del Vecchio et al., 2022). These novel spatial characteristics were driven by parallel temporal patterns representing endpoint trajectory and co-contraction-related mechanisms, an insight supportive of recent work showing their parallel innervation (Ronzano et al., 2021;Hardesty et al., 2020;Borzelli et al., 2022). Together, these representations encapsulated the functional interplay between task end-goal requirements and biomechanical affordances, a dynamic frequently highlighted in object manipulation experiments (Sartori et al., 2011). ...
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The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘ working together ’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘ work together ’ in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.
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Full-text available
The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns a specific functional role to each muscle coupling by characterising its task-relevance. Crucially, our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’ in different ways: a) irrespective of the task at hand but also b) redundantly or c) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (sharing the same task information) or synergistic (representing different aspects of the task). To demonstrate these types of interactions in real data, we apply the framework to large-scale datasets of human movements and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar (redundant) and complementary (synergistic) task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.
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Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R2 = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R2 = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.