Biomedical subjects

Andrea d'Avella

Publications and source records attributed to Andrea d'Avella.

7 recordsLinked to original sources

PubMed2006

Control of fast-reaching movements by muscle synergy combinations.

How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.

Adult↗

PubMed2006

Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets.

Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis (FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization (NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints (pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms (FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.

Algorithms↗

PubMed2005

Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors.

Previous studies have suggested that the motor system may simplify control by combining a small number of muscle synergies represented as activation profiles across a set of muscles. The role of sensory feedback in the activation and organization of synergies has remained an open question. Here, we assess to what extent the motor system relies on centrally organized synergies activated by spinal and/or supraspinal commands to generate motor outputs by analyzing electromyographic (EMG) signals collected from 13 hindlimb muscles of the bullfrog during swimming and jumping, before and after deafferentation. We first established that, for both behaviors, the intact and deafferented data sets possess low and similar dimensionalities. Subsequently, we used a novel reformulation of the non-negative matrix factorization algorithm to simultaneously search for synergies shared by, and synergies specific to, the intact and deafferented data sets. Most muscle synergies were identified as shared synergies, suggesting that EMGs of locomotor behaviors are generated primarily by centrally organized synergies. Both the amplitude and temporal patterns of the activation coefficients of most shared synergies, however, were altered by deafferentation, suggesting that sensory inflow modulates activation of those centrally organized synergies. For most synergies, effects of deafferentation on the activation coefficients were not consistent across frogs, indicating substantial interanimal variability of feedback actions. We speculate that sensory feedback might adapt recruitment of muscle synergies to behavioral constraints, and the few synergies specific to the intact or deafferented states might represent afferent-specific modules or feedback reorganization of spinal neuronal networks.

Afferent Pathways↗

PubMed2005

Localization and connectivity in spinal interneuronal networks: the adduction-caudal extension-flexion rhythm in the frog.

We have previously reported that focal intraspinal N-methyl-d-aspartate (NMDA) iontophoresis in the frog elicits a motor output, which is organized in terms of its constituent isometric force directions at the ipsilateral ankle and its topography. Furthermore, the associated EMG patterns can be reconstructed as the linear combinations of seven muscle synergies, labeled A to G. We now focus on one of the most common NMDA-elicited outputs, the adduction-caudal extension-flexion rhythm, and examine the relationship between the different force phases in terms of synergies and topography. Two distinct EMG patterns produce caudal extensions, and only one of the two patterns is used at most sites. The key synergy combinations for the two patterns are B + e and D + c (strongest synergies capitalized). These two patterns map at distinct locations in the lumbar cord. Within individual sites rhythms, we find linkages among the synergies used to produce adductions, the onsets of flexions after caudal extensions, and the synergy pattern producing the caudal extensions. For example, the synergy composition of adductions at B + e caudal extension sites is dominated by E + b and at D + c caudal extension sites by C + d. The two types of adductions map at distinct locations, situated between the two caudal extension regions. Specifically the linked patterns of caudal extension-adduction interleave rostrocaudally in a CE2-ADD1-ADD2-CE1 sequence, where 1 and 2 refer to the two pattern types. The implications of this topography and connectivity with respect to motor systems organization and behaviors are discussed.

Analysis of Variance↗

PubMed2005

Shared and specific muscle synergies in natural motor behaviors.

Selecting the appropriate muscle pattern to achieve a given goal is an extremely complex task because of the dimensionality of the search space and because of the nonlinear and dynamical nature of the transformation between muscle activity and movement. To investigate whether the central nervous system uses a modular architecture to achieve motor coordination we characterized the motor output over a large set of movements. We recorded electromyographic activity from 13 muscles of the hind limb of intact and freely moving frogs during jumping, swimming, and walking in naturalistic conditions. We used multidimensional factorization techniques to extract invariant amplitude and timing relationships among the muscle activations. A decomposition of the instantaneous muscle activations as combinations of nonnegative vectors, synchronous muscle synergies, revealed a spatial organization in the muscle patterns. A decomposition of the same activations as a combination of temporal sequences of nonnegative vectors, time-varying muscle synergies, further uncovered specific characteristics in the muscle activation waveforms. A mixture of synergies shared across behaviors and synergies for specific behaviors captured the invariances across the entire dataset. These results support the hypothesis that the motor controller has a modular organization.

Animals↗

PubMed2003

Combinations of muscle synergies in the construction of a natural motor behavior.

A central issue in motor control is how the central nervous system generates the muscle activity patterns necessary to achieve a variety of behavioral goals. The many degrees of freedom of the musculoskeletal apparatus provide great flexibility but make the control problem extremely complex. Muscle synergies--coherent activations, in space or time, of a group of muscles--have been proposed as building blocks that could simplify the construction of motor behaviors. To evaluate this hypothesis, we developed a new method to extract invariant spatiotemporal components from the simultaneous recordings of the activity of many muscles. We used this technique to analyze the muscle patterns of intact and unrestrained frogs during kicking, a natural defensive behavior. Here we show that combinations of three time-varying muscle synergies underlie the variety of muscle patterns required to kick in different directions, that the recruitment of these synergies is related to movement kinematics, and that there are similarities among the synergies extracted from different behaviors.

Animals↗

PubMed2002

Coordination and localization in spinal motor systems.

We review here experiments examining the hypothesis that vertebrate spinal motor systems produce movement through the flexible combination of a small number of units of motor output. Using a variety of preparations and techniques, these experiments provide evidence for such spinally generated units and for the localization of the networks responsible for producing them within different regions of the spinal cord. Such an organization might help to simplify the production of movement, reducing the degrees of freedom that need to be specified by providing a set of units involved in regulating features common to a range of behaviors.

Animals↗