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According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process. [ABSTRACT FROM AUTHOR]

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients’ locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross‐correlations between smoothed discharge rates of motor neurons. In part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross‐correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In part 2, 16 individuals performed plantarflexion at three ankle angles while we recorded EMG signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. The PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable, with no significant differences in the distribution of motor neuron weights across ankle angles (P = 0.62). When the cross‐correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. Key points: The CNS might generate movements by activating groups of motor neurons (synergies) with common inputs.We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions.We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e. the ankle angle.These results suggest the functional relevance of the modular organization of the CNS to control movements. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate balance performance and muscle synergies during a Single-Limb Stance (SLS) task in individuals with Chronic Ankle Instability (CAI) and a group of healthy controls. Twenty individuals with CAI and twenty healthy controls were asked to perform a 30-second SLS task in Open-Eyes (OE) and Closed-Eyes (CE) conditions while standing on a force platform with the injured or the dominant limb, respectively. The activation of 13 muscles of the lower limb, hip, and back was recorded by means of surface electromyography. Balance performance was assessed by identifying the number and the duration of SLS epochs, and the Root-Mean-Square (RMS) in Antero-Posterior (AP) and Medio-Lateral (ML) directions of the body-weight normalized ground reaction forces. The optimal number of synergies, weight vectors, and activation coefficients were also analyzed. CAI group showed a higher number and a shorter duration of SLS epochs and augmented ground reaction force RMS in both AP and ML directions compared to controls. Both groups showed an increase in the RMS in AP and ML forces in CE compared to OE. Both groups showed 4 optimal synergies in CE, while controls showed 5 synergies in OE. CAI showed a significantly higher weight of knee flexor muscles in both OE and CE. In conclusion, muscle synergies analysis provided an in-depth knowledge of motor control mechanisms in CAI individuals. They showed worse balance performance, a lower number of muscle synergies in a CE condition and abnormal knee flexor muscle activation compared to healthy controls.

Running is an exercise that can be performed in different environments that imposes distinct foot–floor interactions. For instance, running on grass may help reducing instantaneous vertical impact loading, while compromising natural speed. Inter-muscular coordination during running is an important factor to understand motor performance, but little is known regarding the impact of running surface hardness on inter-muscular coordination. Therefore, we investigated whether inter-muscular coordination during running is influenced by running surface. Surface electromyography (EMG) from 12 lower limb muscles were recorded from young male individuals (n = 9) while running on grass, concrete, and on a treadmill. Motor modules consisting of weighting coefficients and activation signals were extracted from the multi-muscle EMG datasets representing 50 consecutive running cycles using non-negative matrix factorization. We found that four motor modules were sufficient to represent the EMG from all running surfaces. The inter-subject similarity across muscle weightings was the lowest for running on grass (r = 0.76 ± 0.11) compared to concrete (r = 0.81 ± 0.07) and treadmill (r = 0.78 ± 0.05), but no differences in weighting coefficients were found when analyzing the number of significantly active muscles and residual muscle weightings (p > 0.05). Statistical parametric mapping showed no temporal differences between activation signals across running surfaces (p > 0.05). However, the activation duration (% time above 15% peak activation) was significantly shorter for treadmill running compared to grass and concrete (p < 0.05). These results suggest predominantly similar neuromuscular strategies to control multiple muscles across different running surfaces. However, individual adjustments in inter-muscular coordination are required when coping with softer surfaces or the treadmill's moving belt. [ABSTRACT FROM AUTHOR]

Understanding how movement is controlled by the CNS remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons and muscle synergies are usually addressed separately and therefore seen as independent features of motor control. In this review, we analyse the body of literature in a broader perspective and we identify a unified approach to explain apparently divergent observations at different scales of motor control. Specifically, we propose a new conceptual framework of the neural control of movement, which merges the concept of common input to motor neurons and modular control, together with the constraints imposed by recruitment order. This framework is based on the following assumptions: (1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; (2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; (3) clusters represent functional modules used by the CNS to reduce the dimensionality of the control; and (4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. Here, we discuss this framework and its underlying theoretical and experimental evidence. [ABSTRACT FROM AUTHOR]

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called "muscle excitations"), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called "included" muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called "excluded" muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called "synergy extrapolation"). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.

In the study of muscle synergies during the maintenance of single-leg stance there are several methodological issues that must be taken into account before muscle synergy extraction. In particular, it is important to distinguish between epochs of surface electromyography (sEMG) signals corresponding to “well-balanced” and “unbalanced” single-leg stance, since different motor control strategies could be used to maintain balance. The aim of this work is to present and define a robust procedure to distinguish between “well-balanced” and “unbalanced” single-leg stance to be chosen as input for the algorithm used to extract muscle synergies. Our results demonstrate that the proposed approach for the selection of sEMG epochs relative to “well-balanced” and “unbalanced” single-leg stance is robust with respect to the selection of the segmentation threshold, revealing a high consistency in the number of muscle synergies and high similarity among the weight vectors (correlation values range from 0.75 to 0.97). Moreover, differences in terms of average recruitment levels and balance control strategies were detected, suggesting a slightly different modular organization between “well-balanced” and “unbalanced” single-leg stance. In conclusion, this approach can be successfully used as a pre-processing step before muscle synergy extraction, allowing for a better assessment of motor control strategies during the single-leg stance task. [ABSTRACT FROM AUTHOR]

Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed "synergy optimization," or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2-6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability. [ABSTRACT FROM AUTHOR]

Background: Clinical gait analysis is commonly used in the evaluation and treatment of children with cerebral palsy (CP). While the repeatability of kinematic and kinetic measures of gait has previously been evaluated, the repeatability of electromyography (EMG) recordings or measures calculated from EMG data, such as muscle synergies, remains unclear for this population.Research Question: Are EMG recordings and muscle synergies from clinical gait analysis repeatable between visits for children with CP?Methods: We recruited 20 children with bilateral CP who had been referred for clinical gait analysis. The children completed two visits less than six weeks apart with EMG data collected bilaterally from five muscles (rectus femoris, medial hamstrings, vastus lateralis, anterior tibialis, and medial gastrocnemius). Variance ratio and cosine similarity were used to evaluate repeatability of EMG waveforms between visits. Nonnegative matrix factorization was used to calculate synergies from EMG data at each visit to compare synergy weights and activations.Results& Significance: The inter-visit variance ratios of EMG data for children with CP were similar to previously reported results for typically-developing children and unimpaired adults (range: 0.39 for vastus lateralis to 0.66 for rectus femoris). The average cosine similarity of the EMG waveforms between visits was greater than 0.9 for all muscles, while synergy weights and activations also had high similarity - greater than 0.8 and 0.9 between visits, respectively. These results demonstrate that EMG repeatability between visits during clinical gait analysis for children with CP is similar to unimpaired individuals. These results provide a baseline for evaluating whether observed changes in EMG recordings between visits reflect real changes in muscle activity or are within the range of inter-visit variability. [ABSTRACT FROM AUTHOR]

Muscle coordination of isometric force production can be explained by a smaller number of modules. Variability in force output, however, is higher during exploratory/transient force development phases than force maintenance phase, and it is not clear whether the same modular structure underlies both phases. In this study, eight neurologically-intact adults isometrically performed target force matches in 54 directions at hands, and electromyographic (EMG) data from eight muscles were parsed into four sequential phases. Despite the varying degree of motor complexity across phases (significant between-phase differences in EMG-force correlation, angular errors, and between-force correlations), the number/composition of motor modules were found equivalent across phases, suggesting that the CNS systematically modulated activation of the same set of motor modules throughout sequential force development. [ABSTRACT FROM AUTHOR]

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson’s disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

Balance training programs have been shown to reduce ankle sprain injuries in sports, but little is known about the transfer from this training modality to motor coordination and ankle joint biomechanics in sport‐specific movements. This study aimed to investigate the effects of wobble board training on motor coordination and ankle mechanics during early single‐leg landing from a lateral jump. Twenty‐two healthy men were randomly assigned to either a control or a training group, who engaged in 4 weeks of wobble board training. Full‐body kinematics, ground reaction force, and surface electromyography (EMG) from 12 lower limb muscles were recorded during landing. Ankle joint work in the sagittal, frontal, and transverse plane was calculated from 0 to 100 ms after landing. Non‐negative matrix factorization (NMF) was applied on the concatenated EMG Pre‐ and Post‐intervention. Wobble board training increased the ankle joint eccentric work 1.2 times in the frontal (P < .01) and 4.4 times in the transverse plane (P < .01) for trained participants. Wobble board training modified the modular organization of muscle recruitment in the early landing phase by separating the activation of plantar flexors and mediolateral ankle stabilizers. Furthermore, the activation of secondary muscles across motor modules was reduced after training, refocusing the activation on the main muscles involved in the mechanical main subfunctions for each module. These results suggest that wobble board training may modify motor coordination when landing from a lateral jump, focusing on the recruitment of specific muscles/muscle groups that optimize ankle joint stability during early ground contact in single‐leg landing. [ABSTRACT FROM AUTHOR]

Nordic walking is a form of walking that includes a poling action, and therefore an additional subtask, with respect to conventional walking. The aim of this study was to assess whether Nordic walking required a task‐specific muscle coordination with respect to conventional walking. We compared the electromyographic (EMG) activity of 15 upper‐ and lower‐limb muscles of 9 Nordic walking instructors, while executing Nordic walking and conventional walking at 1.3 ms−1 on a treadmill. Non‐negative matrix factorization method was applied to identify muscle synergies, representing the spatial and temporal organization of muscle coordination. The number of muscle synergies was not different between Nordic walking (5.2 ± 0.4) and conventional walking (5.0 ± 0.7, P = .423). Five muscle synergies accounted for 91.2 ± 1.1% and 92.9 ± 1.2% of total EMG variance in Nordic walking and conventional walking, respectively. Similarity and cross‐reconstruction analyses showed that 4 muscle synergies, mainly involving lower‐limb and trunk muscles, are shared between Nordic walking and conventional walking. One synergy acting during upper limb propulsion is specific to Nordic walking, modifying the spatial organization and the magnitude of activation of upper limb muscles compared to conventional walking. The inclusion of the poling action in Nordic walking does not increase the complexity of movement control and does not change the coordination of lower limb muscles. This makes Nordic walking a physical activity suitable also for people with low motor skill. [ABSTRACT FROM AUTHOR]

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requiresmethodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and afterMSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalizationmethod beforeMSA; (3) whether SVs were held constant across trials or allowed to vary duringMSA; and (4) synergy analysis output normalizationmethod after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90%variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies. [ABSTRACT FROM AUTHOR]

The neuromuscular control of human movement can be described by a set of muscle synergies factorized from myoelectric signals. There is some evidence that the selection, activation and flexible combination of these basic activation patterns are of a neural origin. We investigated the muscle synergies during incline and level walking to evaluate changes in the modular organization of neuromuscular control related to changes in the mechanical demands. Our results revealed five fundamental (not further factorizable) synergies for both walking conditions but with different frequencies of appearance of the respective synergies during incline compared with level walking. Low similarities across conditions were observed in the timing of the activation patterns (motor primitives) and the weightings of the muscles within the respective elements (motor modules) for the synergies associated with the touchdown, mid-stance and early push-off phase. The changes in neuromuscular control could be attributed to changes in the mechanical demands in support, propulsion and medio-lateral stabilization of the body during incline compared with level walking. Our findings provide further evidence that the central nervous system flexibly uses a consistent set of neural control elements with a flexible temporal recruitment and modifications of the relative muscle weightings within each element to provide stable locomotion under varying mechanical demands during walking. [ABSTRACT FROM AUTHOR]

Muscle synergies are hypothesized to reflect modular control of muscle groups via descending commands sent through multiple neural pathways. Recently, the number of synergies has been reported as a functionally relevant indicator of motor control complexity in individuals with neurological movement disorders. Yet the number of synergies extracted during a given activity, e.g. gait, varies within and across studies, even for unimpaired individuals. With no standardized methods for precise determination, this variability remains unexplained making comparisons across studies and cohorts difficult. Here, we utilize k-means clustering and intra-class and between-level correlation coefficients to precisely discriminate reliable from unreliable synergies. EMG was recorded bilaterally from eight leg muscles during treadmill walking at self-selected speed. Muscle synergies were extracted from 20 consecutive gait cycles using non-negative matrix factorization. We demonstrate that the number of synergies is highly dependent on the threshold when using the variance accounted for by reconstructed EMG. In contrast, our method utilized a quantitative metric to reliably identify four or five synergies underpinning walking in unimpaired adults and revealed synergies having poor reproducibility that should not be considered as true synergies. We show that robust and unreliable synergies emerge similarly, emphasizing the need for careful analysis in those with pathology.

We used a musculoskeletal model to investigate the possible biomechanical and neural bases of using consistent muscle synergy patterns to produce functional motor outputs across different biomechanical conditions, which we define as generalizability. Experimental studies in cats demonstrate that the same muscle synergies are used during reactive postural responses at widely varying configurations, producing similarly-oriented endpoint force vectors with respect to the limb axis. However, whether generalizability across postures arises due to similar biomechanical properties or to neural selection of a particular muscle activation pattern has not been explicitly tested. Here, we used a detailed cat hindlimb model to explore the set of feasible muscle activation patterns that produce experimental synergy force vectors at a target posture, and tested their generalizability by applying them to different test postures. We used three methods to select candidate muscle activation patterns: (1) randomly-selected feasible muscle activation patterns, (2) optimal muscle activation patterns minimizing muscle effort at a given posture, and (3) generalizable muscle activation patterns that explicitly minimized deviations from experimentally-identified synergy force vectors across all postures. Generalizability was measured by the deviation between the simulated force direction of the candidate muscle activation pattern and the experimental synergy force vectors at the test postures. Force angle deviations were the greatest for the randomly selected feasible muscle activation patterns (e.g. >100°), intermediate for effort-wise optimal muscle activation patterns (e.g. ~20°), and smallest for generalizable muscle activation patterns (e.g.

Muscle synergies have been investigated during different types of human movement using nonnegative matrix factorization. However, there are not any reports available on the reliability of the method. To evaluate between-day reliability, 21 subjects performed bench press, in two test sessions separated by approximately 7days. The movement consisted of 3 sets of 8 repetitions at 60% of the three repetition maximum in bench press. Muscle synergies were extracted from electromyography data of 13 muscles, using nonnegative matrix factorization. To evaluate between-day reliability, we performed a cross-correlation analysis and a cross-validation analysis, in which the synergy components extracted in the first test session were recomputed, using the fixed synergy components from the second test session. Two muscle synergies accounted for >90% of the total variance, and reflected the concentric and eccentric phase, respectively. The cross-correlation values were strong to very strong (r-values between 0.58 and 0.89), while the cross-validation values ranged from substantial to almost perfect (ICC3, 1 values between 0.70 and 0.95). The present findings revealed that the same general structure of the muscle synergies was present across days and the extraction of muscle synergies is thus deemed reliable. [ABSTRACT FROM AUTHOR]

The ability to perform forceful muscle contractions has important implications in sports performance and in activities of daily living. However, there is a lack of knowledge on adaptations in intermuscular coordination after strength training. The purpose of this study was therefore to assess muscle synergies before and after 5 weeks of bench press training. Thirty untrained male subjects were randomly allocated to a training group (TRA) or a control group (CON). After the pretest, TRA completed 5 weeks of bench press training, before completing a posttest, whereas subjects in CON continued their normal life. During test sessions, surface electromyography (EMG) was recorded from 13 different muscles. Muscle synergies were extracted from EMG data using nonnegative matrix factorization. To evaluate differences between pretest and posttest, we performed a cross-correlation analysis and a cross-validation analysis, in which the synergy components extracted in the pretest session were recomputed, using the fixed synergy components from the posttest session. Two muscle synergies accounted for '90% of the total variance and reflected the concentric and eccentric phase, respectively. TRA significantly increased 3 repetition maximum in bench press with 19.0% (25th; 75th percentile, 10.3%; 21.7%) (p < 0.001), whereas no change occurred in CON. No significant differences were observed in synergy components between groups. However, decreases in correlation values for intragroup comparisons in TRA may suggest that the synergy components changed, whereas this was not the case in CON. Strength and conditioning professionals may consider monitoring changes in muscle synergies in training and rehabilitation programs as a way to benchmark changes in intermuscular coordination. [ABSTRACT FROM AUTHOR]

Background: Robotic neurorehabilitation aims at promoting the recovery of lost function after neurological injury by leveraging strategies of motor learning. One important aspect of the rehabilitation process is the improvement of muscle coordination patterns, which can be drastically altered after stroke. However, it is not fully understood if and how robotic therapy can address these deficits. The aim of our study was to find how muscle coordination, analyzed from the perspective of motor modules, could change during motor adaptation to a dynamic environment generated by a haptic interface.Methods: In our experiment we employed the traditional paradigm of exposure to a viscous force field to subjects that grasped the handle of an actuated joystick during a reaching movement (participants moved directly forward and back by 30 c m). EMG signals of ten muscles of the tested arm were recorded. We extracted motor modules from the pooled EMG data of all subjects and analyzed the muscle coordination patterns.Results: We found that the participants reacted by using a coordination strategy that could be explained by a change in the activation of motor modules used during free motion and by two complementary modules. These complementary modules aggregated changes in muscle coordination, and evolved throughout the experiment eventually maintaining a comparable structure until the late phase of re-adaptation.Conclusions: This result suggests that motor adaptation induced by the interaction with a robotic device can lead to changes in the muscle coordination patterns of the subject. [ABSTRACT FROM AUTHOR]

We used a musculoskeletal model to investigate the possible biomechanical and neural bases of using consistent muscle synergy patterns to produce functional motor outputs across different biomechanical conditions, which we define as generalizability. Experimental studies in cats demonstrate that the same muscle synergies are used during reactive postural responses at widely varying configurations, producing similarly-oriented endpoint force vectors with respect to the limb axis. However, whether generalizability across postures arises due to similar biomechanical properties or to neural selection of a particular muscle activation pattern has not been explicitly tested. Here, we used a detailed cat hindlimb model to explore the set of feasible muscle activation patterns that produce experimental synergy force vectors at a target posture, and tested their generalizability by applying them to different test postures. We used three methods to select candidate muscle activation patterns: (1) randomly-selected feasible muscle activation patterns, (2) optimal muscle activation patterns minimizing muscle effort at a given posture, and (3) generalizable muscle activation patterns that explicitly minimized deviations from experimentally-identified synergy force vectors across all postures. Generalizability was measured by the deviation between the simulated force direction of the candidate muscle activation pattern and the experimental synergy force vectors at the test postures. Force angle deviations were the greatest for the randomly selected feasible muscle activation patterns (e.g., >100°), intermediate for effort-wise optimal muscle activation patterns (e.g., ~20°), and smallest for generalizable muscle activation patterns (e.g., <5°). Generalizable muscle activation patterns were suboptimal in terms of effort, often exceeding 50% of the maximum possible effort (cf. ~5% in minimum-effort muscle activation patterns). The feasible muscle activation ranges of individual muscles associated with producing a specific synergy force vector was reduced by ~45% when generalizability requirements were imposed. Muscles recruited in the generalizable muscle activation patterns had less sensitive torque-producing characteristics to changes in postures. We conclude that generalization of function across postures does not arise from limb biomechanics or a single optimality criterion. Muscle synergies may reflect acquired motor solutions globally tuned for generalizability across biomechanical contexts, facilitating rapid motor adaptation. [ABSTRACT FROM AUTHOR]

Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g. with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analysed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60 s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20 and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality > 90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity > 0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~ 40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~ 80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.

This study aimed to examine how acute muscle pain affects muscle coordination during gait with consideration of muscle synergies (i.e., group of muscles activated in synchrony), amplitude of muscle activity and kinematics. A secondary aim was to determine whether any adaptation was specific to pain location. Sixteen participants walked on a treadmill during 5 conditions [control, low back pain (LBP), washout LBP, calf pain (CalfP), and washout CalfP]. Five muscle synergies were identified for all of the conditions. Cross-validation analysis showed that muscle synergy vectors extracted for the control condition accounted for >81% of variance accounted for from the other conditions. Muscle synergies were altered very little in some participants (n = 7 for LBP; n = 10 for CalfP), but were more affected in the others (n = 9 for LBP; n = 6 for CalfP). No systematic differences between pain locations were observed. Considering all participants, synergies related to propulsion and weight acceptance were largely unaffected by pain, whereas synergies related to other functions (trunk control and leg deceleration) were more affected. Gastrocnemii activity was less during both CalfP and LBP than control. Soleus activity was further reduced during CalfP, and this was associated with reduced plantar flexion. Some lower leg muscles exhibited adaptations depending on pain location (e.g., greater vastus lateralis and rectus femoris activity during CalfP than LBP). Overall, these changes in muscle coordination involve a participant-specific strategy that is important to further explore, as it may explain why some people are more likely to develop persistence of a painful condition. [ABSTRACT FROM AUTHOR]

Muscle synergies are neural coordinative structures that function to alleviate the computational burden associated with the control of movement and posture. In this commentary, we address two critical questions: the explicit encoding of muscle synergies in the nervous system, and how muscle synergies simplify movement production. We argue that shared and task-specific muscle synergies are neurophysiological entities whose combination, orchestrated by the motor cortical areas and the afferent systems, facilitates motor control and motor learning.

The purpose of this study was to investigate the effects of dopaminergic therapy on neuromuscular complexity during gait and on the relationship between neuromuscular complexity and gait speed in persons with Parkinson's disease (PD). Nine persons with PD walked at self-selected speed for 5 min after having withdrawn from dopaminergic medication for at least 12 h and while optimally-medicated. Electromyographic recordings were taken from eight leg muscles bilaterally. Non-negative matrix factorization was applied to reduce the dimensionality of the electromyographic signals into motor modules. We assessed neuromuscular complexity by investigating the number, structure, and timing of the modules. We also investigated the influence of dopaminergic medication on the relationships between neuromuscular complexity and gait speed. Though gait speed increased significantly after medication intake, medication did not affect neuromuscular complexity. Neuromuscular complexity was significantly associated with gait speed only while the participants were medicated. Thus, the supraspinal structures that govern neuromuscular complexity during gait do not appear to be solely dopaminergically-influenced in PD. The lack of dopaminergic influence on neuromuscular complexity may explain why persons with PD exhibit gait slowness even while medicated, and an intervention that restores neuromuscular complexity may result in gait speed improvement in PD. [ABSTRACT FROM AUTHOR]

Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to determine whether subjects who have learned a complex motor skill exhibit similar neuromuscular control strategies. We studied a population of experienced gymnasts during backward giant swings on the high bar. This cyclic movement is interesting because it requires learning, as untrained subjects are unable to perform this task. 9 gymnasts were tested. Both kinematics and electromyographic (EMG) patterns of 12 upper-limb and trunk muscles were recorded. Muscle synergies were extracted by non-negative matrix factorization, providing two components: muscle synergy vectors and synergy activation coefficients. First, the coefficient of correlation (r) and circular cross-correlation (rmax) were calculated to assess similarities in the mechanical patterns, EMG patterns and muscle synergies between gymnasts. We performed a further analysis to verify that the muscle synergies (in terms of muscle synergy vectors or synergy activation coefficients) extracted for one gymnast accounted for the EMG patterns of the other gymnasts. 3 muscle synergies explained 89.9±2.0% of the variance accounted for (VAF). The coefficients of correlation of the muscle synergy vectors among the participants were 0.83±0.08, 0.86±0.09, and 0.66±0.28 for synergy #1, #2, and #3, respectively. By keeping the muscle synergy vectors constant, we obtained an averaged VAF across all pairwise comparisons of 79±4%. For the synergy activation coefficients, rmax-values were 0.96±0.03, 0.92±0.03, and 0.95±0.03, for synergy #1, #2, and #3, respectively. By keeping the synergy activation coefficients constant, we obtained an averaged VAF across all pairwise comparisons of 72±5%. Although variability was found (especially for synergy #3), the gymnasts exhibited gross similar neuromuscular strategies when performing backward giant swings. This confirms that the muscle synergies are consistent across participants, even during a skilled motor task that requires learning.

Abstract: Objective: Individual muscle activation patterns may be controlled by motor modules constructed by the central nervous system to simplify motor control. This study compared modular control of gait between persons with Parkinson’s disease (PD) and neurologically-healthy older adults (HOA) and investigated relationships between modular organization and gait parameters in persons with PD. Methods: Fifteen persons with idiopathic PD and fourteen HOA participated. Electromyographic recordings were made from eight leg muscles bilaterally while participants walked at their preferred walking speed for 10min on an instrumented treadmill. Non-negative matrix factorization techniques decomposed the electromyographic signals, identifying the number and nature of modules accounting for 95% of variability in muscle activations during treadmill walking. Results: Generally, fewer modules were required to reconstruct muscle activation patterns during treadmill walking in PD compared to HOA (p <.05). Control of knee flexor and ankle plantar flexor musculature was simplified in PD. Activation timing was altered in PD while muscle weightings were unaffected. Simplified neuromuscular control was related to decreased walking speed in PD. Conclusion: Neuromuscular control of gait is simplified in PD and may contribute to gait deficits in this population. Significance: Future studies of locomotor rehabilitation in PD should consider neuromuscular complexity to maximize intervention effectiveness. [Copyright &y& Elsevier]

Abstract: This study investigated the modular control of complex locomotor tasks that require fast changes in direction, i.e., cutting manoeuvres. It was hypothesized that such tasks are accomplished by an impulsive (burst-like) activation of a few motor modules, as observed during walking and running. It was further hypothesized that the performance in cutting manoeuvres would be associated to the relative timing of the activation impulses. Twenty-two healthy men performed 90° side-step cutting manoeuvres while electromyography (EMG) activity from 16 muscles of the supporting limb and trunk, kinematics, and ground reaction forces were recorded. Motor modules and their respective temporal activations were extracted from the EMG signals by non-negative matrix factorization. The kinematic analysis provided the velocity of the centre of mass and the external work absorbed during the load acceptance (negative work, external work during absorption (W-Abs)) and propulsion phases (positive work, external work during propulsion (W-Prp)) of the cutting manoeuvres. Five motor modules explained the EMG activity of all muscles and were driven in an impulsive way, with timing related to the initial contact (M2), load acceptance (M3), and propulsion (M4). The variability in timing between impulses across subjects was greater for cutting manoeuvres than for running. The timing difference between M2 and M3 in the cutting manoeuvres was significantly associated to W-Abs (r 2 =0.45) whereas the timing between M3 and M4 was associated to W-Prp (r 2 =0.43). These results suggest that complex locomotor tasks can be achieved by impulsive activation of muscle groups, and that performance is associated to the specific timing of the activation impulses. [Copyright &y& Elsevier]

The purpose of the present study was to determine whether subjects who have learned a complex motor skill exhibit similar neuromuscular control strategies. We studied a population of experienced gymnasts during backward giant swings on the high bar. This cyclic movement is interesting because it requires learning, as untrained subjects are unable to perform this task. Nine gymnasts were tested. Both kinematics and electromyographic (EMG) patterns of 12 upper-limb and trunk muscles were recorded. Muscle synergies were extracted by non-negative matrix factorization (NMF), providing two components: muscle synergy vectors and synergy activation coefficients. First, the coefficient of correlation (r) and circular cross-correlation (rmax) were calculated to assess similarities in the mechanical patterns, EMG patterns, and muscle synergies between gymnasts. We performed a further analysis to verify that the muscle synergies (in terms of muscle synergy vectors or synergy activation coefficients) extracted for one gymnast accounted for the EMG patterns of the other gymnasts. Three muscle synergies explained 89.9 ± 2.0% of the variance accounted for (VAF). The coefficients of correlation of the muscle synergy vectors among the participants were 0.83 ± 0.08, 0.86 ± 0.09, and 0.66 ± 0.28 for synergy #1, #2, and #3, respectively. By keeping the muscle synergy vectors constant, we obtained an averaged VAF across all pairwise comparisons of 79 ± 4%. For the synergy activation coefficients, rmax-values were 0.96 ± 0.03, 0.92 ± 0.03, and 0.95 ± 0.03, for synergy #1, #2, and #3, respectively. By keeping the synergy activation coefficients constant, we obtained an averaged VAF across all pairwise comparisons of 72 ± 5%. Although variability was found (especially for synergy #3), the gymnasts exhibited gross similar neuromuscular strategies when performing backward giant swings. This confirms that the muscle synergies are consistent across participants, even during a skilled motor task that requires learning. [ABSTRACT FROM AUTHOR]

Background: It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level. Methods: Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3km/h) and levels of body weight support (0 to 30%). Results: The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects. Conclusions: These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of the present study was to determine whether expertise in rowing is driven by a specific structure in muscular coordination. We compared seven experienced rowers and eight untrained (i.e., inexperienced) subjects during rowing on an ergometer. Both surface electromyography activity and mechanical patterns (forces exerted at the handle and the foot-stretcher) were recorded during a high intensity rowing exercise. A non-negative matrix factorization was applied to 23 electromyographic patterns to differentiate muscle synergies. Results showed that expertise was not associated with different dimensionality in the electromyographic data and that three muscle synergies were sufficient to explain the majority of the variance accounted for (i.e., >90% of the total variance) in the two populations. The synergies extracted were similar in the two populations, with identical functional roles. While the temporal organization of the propulsive synergies was very similar, slight differences were found in the composition of the muscle synergies (muscle synergy vectors) between the two populations. The results suggests that rowing expertise would not require the development of novel muscle synergies but would imply intrinsic synergies already used in different behaviors. Performance in rowing is more probably linked to adjustments in the mechanical output of the muscle synergies rather than to differences in the shape and timing of their activations. [Copyright &y& Elsevier]

Production of voluntary movements relies critically on the functional integration of several motor cortical areas, such as the primary motor cortex, and the spinal circuitries. Surprisingly, after almost 40 years of research, how the motor cortices specify descending neural signals destined for the downstream interneurons and motoneurons has remained elusive. In light of the many recent experimental demonstrations that the motor system may coordinate muscle activations through a linear combination of muscle synergies, we hypothesize that the motor cortices may function to select and activate fixed muscle synergies specified by the spinal or brainstem networks. To test this hypothesis, we recorded electromyograms (EMG5) from 12-16 upper arm and shoulder muscles from both the unaffected and the stroke-affected arms of stroke patients having moderate-to-severe unilateral ischemic lesions in the frontal motor cortical areas. Analyses of EMG5 using a nonnegative matrix factorization algorithm revealed that in seven of eight patients the muscular compositions of the synergies for both the unaffected and the affected arms were strikingly similar to each other despite differences in motor performance between the arms, and differences in cerebral lesion sizes and locations between patients. This robustness of muscle synergies that we observed supports the notion that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem. Our conclusion also suggests an approach to stroke rehabilitation by focusing on those synergies with altered activations after stroke. [ABSTRACT FROM AUTHOR]

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.