Biology subjects

d'Avella, A.

Publications and source records attributed to d'Avella, A..

9 recordsLinked to original sources

bioRxiv2025

Whole-body coarticulation reflects expertise in sport climbing

Taking sport climbing as a testbed, we explored coarticulation in naturalistic motor-behavior at the level of whole-body kinematics. Participants were instructed to execute a series of climbing routes, each composed of two initial foot-moves equal in all routes, and two subsequent hand-moves differing across routes in a set of eight possible configurations. The goal was assessing whether climbers modulate the execution of a given move depending on which moves come next in the plan. Coarticulation was assessed by training a set of classifiers and estimating how well the whole-body (or single-joint) kinematics during a given stage of the climbing execution could predict its future unfolding. Results showed that most participants engage in coarticulation, with temporal and bodily patterns that depend on expertise. Non-climbers tend to prepare the next-to-come move right before its onset and only after the end of the previous move. Rather, expert-climbers (and to a smaller extent, beginner-climbers) show early coarticulation during the execution of the previous move and engage in adjustments that involve the coordination of a larger number of joints across the body. These results demonstrate coarticulation effects in whole-body naturalistic motor behavior and as a function of expertise. Furthermore, the enhanced coarticulation found in expert-climbers provides hints for experts engaging in more refined mental processes converting abstract instructions (e.g., move the right hand to a given location) into motor simulations involving whole-body coordination. Overall, these results contribute to advancing our current knowledge of the rich interplay between cognition and motor control. NEW & NOTEWORTHYThe current study explores the way in which having formed a plan for a sequential motor task affects its execution. We showed that climbing expertise increases the extent to which participants adjust their motor execution based on the moves that follow in a planned route. These results provide evidence of coarticulation in naturalistic motor behavior and suggest enhanced skills in mentalizing forward motor control and optimal-control strategies in expert climbers.

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bioRxiv2024

Changes in Muscle Synergy Structure and Activation Patterns Underlie Force Field Adaptation, Retention, and Generalization

1Humans can adapt their motor commands in response to errors when they perform reaching movements in new dynamic conditions, a process called motor adaptation. They acquire knowledge about the new dynamics, which they can use when they are re-exposed and, limitedly, generalize to untrained reaching directions. While force field adaptation, retention, and generalization have been thoroughly investigated at a kinematic and kinetic task level, the underlying coordination at a muscular level remains unclear. Many studies propose that the central nervous system uses low- dimensional control, i.e., coordinates muscles in functional groups: so-called muscle synergies. Accordingly, we hypothesized that changes in muscle synergy structure and activation patterns represent the acquired knowledge underlying force field adaptation, retention, and generalization. To test this, 36 male humans practiced reaching to a single target in a viscous force field and were tested for retention and generalization to new directions, while we simultaneously measured muscle activity from 13 upper-body muscles. We found that muscle synergies used for unperturbed reaching cannot explain the muscle patterns when adapted. Instead, muscle synergies specific to this adapted state were necessary, alongside a novel four-phasic pattern of muscle synergy activation. Furthermore, these structural changes and patterns were also evident during retention and generalization. Our results suggest that reaching in an environment with altered dynamics requires structural changes to muscle synergies compared to unperturbed reaching, and that these changes facilitate retention and generalization. These findings provide new insights into how the central nervous system coordinates the muscles underlying motor adaptation, retention, and generalization. 2 Significance StatementHumans can adapt their reaching movements to new dynamic conditions. They acquire knowledge about the new dynamics and can use it not only when re-exposed to these conditions but also, in part, to generalize unpracticed reaching directions. While adaptation, retention, and generalization in a force field with new dynamics have been thoroughly investigated at a kinematic and kinetic task level, coordination of the underlying muscles remains elusive. Our results show how muscle synergies - functional groupings of co-activated muscles - underlie adaptation, retention, and generalization. In particular, we observed structural changes in the muscle synergies after adaptation compared to unperturbed reaching. These changes facilitate retention and spatial generalization. Thus, muscle synergies provide new insights into human motor adaptation.

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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.

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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.

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bioRxiv2022

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

Muscle synergy analysis is commonly used for investigating the neurophysiological mechanisms that the central nervous system employs to control muscle activations. In the last two decades, several models have been developed to decompose EMG signals into spatial, temporal or spatiotemporal synergies. However, the presence of different approaches complicates the comparison and interpretation of results. Spatial synergies represent invariant activation weights in muscle groups modulated with variant temporal coefficients, while temporal synergies are based on invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization (NMF) allows to extract both spatial and temporal synergies, temporal synergies and the comparison between the two approaches have been barely investigated and so far no study targeted a large set of multi-joint upper limb movements. Here we present several analyses that highlight the duality of spatial and temporal synergies as a characterization of low-dimensional and intermittent motor coordination in the upper limb, allowing high flexibility and dexterity. First, spatial and temporal synergies were extracted from two datasets representing a comprehensive mapping of proximal (REACH PLUS) and distal (NINAPRO) upper limb movements, focusing on their differences in reconstruction accuracy and inter-individual variability. For both models, we extracted synergies achieving a given level of the goodness of reconstruction (R2), and we compared the similarity of the invariant components across participants. The two models provide a compact characterization of motor coordination at spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same R2 with a higher inter-subject similarity. Spatial and temporal synergies may thus capture different levels of motor control. Second, we showed the existence of both spatial and temporal structure in the EMG data, extracting spatial and temporal synergies from a surrogate dataset in which the phases were shuffled preserving the same frequency content of the original data. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to an intermittent control of the movement. These results may be useful to improve muscle synergy analysis in several fields such as rehabilitation, prosthesis control and motor control studies.

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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.

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bioRxiv2021

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

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bioRxiv2019

A Hessian-based decomposition to characterize how performance in complex motor skills depends on individual strategy and variability

In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and performance is still missing. Here we introduce a de-composition of performance into a small set of indicators that compactly and directly characterize the key performance-related features of the distribution of high-dimensional redundant actions. Central to the method is the observation that, if performance is quantified as a mean score, the Hessian (second order derivatives) of the action-to-score function determines how the noise of the action distribution affects the average score. We can then approximate the mean score as the sum of the score of the mean action and a tolerance-variability index which depends on both Hessian and action covariance. Such index can be expressed as the product of three terms capturing noise magnitude, noise sensitivity, and alignment of the most variable and most noise sensitive directions. We apply this method to the analysis of unconstrained throwing actions by non-expert participants and show that, consistently across four different throwing targets, each participant shows a specific selection of mean action score and tolerance-variability index as well as specific selection of noise magnitude and alignment indicators. Thus, participants with different strategies may display the same performance because they can trade off suboptimal mean action for better tolerance-variability and higher action variability for better alignment with more tolerant directions in action space. Author summaryWhy do people differ in their performance of complex motor skills? In many real-life motor tasks achieving a goal requires selecting an appropriate high-dimensional action out of infinitely many goal-equivalent actions. Because of sensorimotor noise, we are unable to execute the exact same movement twice and our performance depends on how accurate we are on average. Thus, to understand why people perform differently we need to characterize how their action distribution relates to their mean task score. While better performance is often associated to smaller variability around a more accurate mean action, performance also depends on the relationship between the directions of highest variability in action space and the directions in which action variability affects the most the outcome of the action. However, characterizing such geometric relationship when actions are high dimensional is challenging. In this work we introduce a method that allows to characterize the key performance-related features of the distribution of high-dimensional actions by a small set of indicators. We can then compare such indicators in different people performing a complex task (such as unconstrained throwing) and directly characterize the most skilled ones but also identify different strategies that distinguish people with similar performance.

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bioRxiv2017

An intermittent control model predicts the triphasic muscles activity during hand reaching

There are numerous ways to reach for an apple hanging from a tree. Yet, our motor system uses a specific muscle activity pattern to generate reaching movements that have similar characteristics. For many decades, we know that this pattern features activity bursts and silent periods. We suggest that these bursts are a strong evidence against the common view that the brain continuously controls the commands to the muscles. Instead, we suggest a model that changes these commands in a discrete way. We use unsupervised machine learning to identify transitions in the state of the muscles, and show that fitting a discrete model to the kinematics of movement using only one parameter predicts the transitions in the state of the muscles. Such discrete controller suggests that the brain reduces the complexity of the motor control problem as well as the wear-and-tear of the muscles by sending commands to the muscles at sparse times. Identifying this discrete controller can be applied in the control of prostheses and physical human-robot interaction systems such as exoskeletons and assistive devices.

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