Biology subjects

Borzelli, D.

Publications and source records attributed to Borzelli, D..

4 recordsLinked to original sources

bioRxiv2025

A modular architecture for trial-by-trial learning of redundant muscle activity patterns in novel sensorimotor tasks

The coordination of the multiple degrees-of-freedom of the human body may be simplified by muscle synergies, motor modules which can be flexibly combined to achieve various goals. Studies investigating adaptation to novel relationships between muscle activity and task outcomes found that altering the recruitment of such modules is faster than the learning of their structures de novo. However, how learning new synergy recruitments or new synergy structures may occur remains unclear. While trial-by-trial learning of novel sensorimotor tasks has been successfully modeled at the level of task variables, few models accounted for the redundancy of the motor system, particularly at the muscular level. However, these models either did not consider a modular architecture of the motor system, or assumed a priori knowledge of the sensorimotor task. Here, we present a computational model for the generation of redundant muscle activity where explicitly defined modules, implemented as spatial muscle synergies, can be updated together with their recruitment coefficients through an error-based learning process dependent on a forward model of the sensorimotor task, which is not assumed to be known a priori. Our model can qualitatively reproduce the experimental observations of slower learning and larger changes in the structure of the muscle activity under sensorimotor tasks that require the learning of novel patterns of muscle activity, providing further insights into the modular organization of the human motor system. Author summaryIt has been proposed that muscles are recruited in modules, called muscle synergies, rather than on a muscle-by-muscle basis. This modular organization was shown to affect the learning of novel tasks, where simulated remapping of the forces generated by the muscles ("virtual surgeries") that make the synergies ineffective are more difficult to learn. Previous models of trial-by-trial learning in a modular architecture have assumed prior knowledge of the motor task (in particular, how to modify the muscles / synergies recruitment given an error), which might not be the case for tasks in which novel patterns of muscle activity are required. In contrast, models which assumed no prior knowledge of the task have not investigated the role of modularity. Here, we propose a computational model of the trial-by-trial adaptation of muscle synergies, their recruitment, and the concurrent learning of the internal model of the musculoskeletal system responsible for error correction. Our results show that this model replicates experimental observations of slower learning and larger changes in the structure of the muscle activity in sensorimotor tasks that require the learning of novel patterns of muscle activity, providing insight into learning-related changes of muscle activity in novel sensorimotor tasks.

neuroscience↗

bioRxiv2023

Control limitations in the null-space of the wrist muscle system

The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmen tation, where the muscle-to-force null-space could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed i) if controlled variation of the muscle activation patterns in the wrist joints null-space is possible; and ii) whether force and null-space targets could be reached simultaneously. During the null-space target reaching condition, participants used EMG-to-force null-space muscle activation to move their cursor towards a displayed target while minimising the exerted force as visualised through the cursors size. Initial targets were positioned to require natural co-contraction in the null-space and if participants showed a consistent ability to reach for their current target, they would rotate 5{degrees} incrementally to generate muscle activation patterns further away from their natural co-contraction. In contrast, during the concurrent target reaching condition participants were required to match a target position and size, where their cursor position was instead controlled by their exerted flexion-extension and radial-ulnar deviation, while its size was changed by their natural co-contraction magnitude. The results collected from 10 participants suggest that while there was variation in each participants co-contraction behaviour, most did not possess the ability to control this variation for muscle-to-force null-space reaching. In contrast, participants did show a direction and target size dependent ability to vary isometric force and co-contraction activity concurrently. Our results show the limitations of using null-space activity in joints with a low level of redundancy.

neuroscience↗

bioRxiv2022

Independent synaptic inputs to motor neurons driving antagonist muscles

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 motor neurons involved in generating joint torque is still unclear. We hypothesized that co-contraction is controlled through a specific synaptic input, independent from that underlying the control of torque. To test this hypothesis, we asked participants to concurrently generate multi-directional isometric forces at the hand and to modulate the co-contraction of arm muscles to displace and stabilize a virtual end-effector. The firings of motor units were identified through decomposition of High-Density EMGs collected from two antagonist muscles, Biceps Brachii and Triceps Brachii. We found significant peaks in the coherence between the neural drive to the two muscles, suggesting the existence of a common input modulating co-contraction across different exerted forces. Moreover, the within-muscle coherence computed after removing the component synchronized with the drive to the antagonist muscle or with the exerted force revealed two subsets of motor neurons that were selectively recruited to generate joint torque or modulate co-contraction. This study is the first to directly investigate the extent of shared versus independent control of antagonist muscles at the motor neuron level in a task involving concurrent force generation and modulation of co-contraction. Significance StatementHow the CNS coordinates the activity of antagonist muscles to modulate limb mechanical impedance is still unclear. We hypothesized that a common synaptic input, shared by the motor neurons pools of antagonist muscles, and independent from the inputs underlying force generation, regulates co-contraction. We then analyzed the coherence between the firing trains of motor neurons to assess whether a common input drives antagonist muscles only during tasks requiring co-activation for impedance but not for force generation. Results highlighted the existence of separate neural pathways underlying the control of joint torque or impedance. Scientifically, this study addressed an important gap in understanding how neural drive is delivered to antagonist muscles, disentangling the control of muscles for joint torque or impedance modulation.

neuroscience↗

bioRxiv2021

Task space exploration improves adaptation after incompatible virtual surgeries

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns, but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies, required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study, but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than after compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time can learn new muscle synergies. NEW & NOTEWORTHYMotor skill learning requires the acquisition of novel muscle patterns, a slow adaptive process. Here we show that learning to control a cursor after an incompatible virtual surgery, a complex skill requiring new muscle synergies, is possible when enough time for task space exploration is provided. Our results suggest that learning new synergies is related to the exceptional human capacity to acquire a wide variety of novel motor skills with practice.

neuroscience↗