Practical limits on muscle synergy identification by non-negative matrix factorization in systems with mechanical constraints

The role of muscle synergies and task constraints on upper limb motor impairment after stroke

This study explores the role of task constraints over muscle synergies expression in the context of upper limb motor impairment after stroke. We recruited nine chronic stroke survivors with upper limb impairments and fifteen healthy controls, who performed a series of tasks designed to evoke muscle synergies through various spatial explorations. These tasks included an isometric force task, a dynamic reaching task, the clinical Fugl-Meyer (FM) assessment, and a pinch task. Electromyographic data from 16 upper limb muscles were collected during each task, alongside intermuscular coherence (IMC) measurements during the pinch task to assess neuromuscular connectivity. The findings confirm that motor impairment is inversely related to the diversity of muscle synergies, with fewer synergies and more stereotypical synergy structures observed post-stroke. The study further reveals that the nature of motor tasks significantly affects the number of identifiable muscle synergies, with less constrained tasks revealing a broader array of synergies. These findings highlight the importance of carefully selecting motor tasks in the context of clinical research and assessments to understand a patient's motor impairment, thus aiding in developing tailored rehabilitation strategies.

Analysis of muscle synergies and muscle network in sling exercise rehabilitation technique

The study assessed motor control strategies across the four sling exercises of supine sling exercise (SSE), prone sling exercise (PSE), left side-lying sling exercise (LLSE), and right side-lying sling exercise (RLSE) positions base on the muscle synergies and muscle network analyses. Muscle activities of bilateral transversus abdominis (TA), rectus abdominis, multifidus (MF), and erector spinae (ES) were captured via surface electromyography. Muscle synergies were extracted through principal components analysis (PCA) and non-negative matrix factorization (NNMF). Muscle synergies number, muscle synergies complexity, muscle synergies sparseness, muscle synergies clusters and muscle networks were calculated. PCA results indicated that SSE and PSE decomposed into 2.88 ± 0.20 and 2.82 ± 0.15 synergies respectively, while the LLSE and RLSE positions decomposed into 3.76 ± 0.14 and 3.71 ± 0.11 muscle synergies, respectively, which were more complex (P = 0.00) but less sparse (P = 0.01). Muscle synergies clusters analysis indicated common muscle synergies among different sling exercises. SSE position demonstrated specific muscle synergies with a strong contribution of the bilateral TA. LLSE-specific synergy has a strong contribution of the left erector spinae (ES). The RLSE-specific synergy has significant contributions from the right ES and multifidus. Muscle networks were functionally organized, with clustering coefficient (F(1.5, 24) = 6.041, P = 0.01) and global efficiency of the undirected network (F(1.5, 24) = 6.041, P = 0.01), and betweenness-centrality of the directed network (F(2.7, 44) = 6.453, P = 0.00). Our research highlights the importance of evaluating muscle synergies and network adaptation strategies in individuals with neuromuscular disorders and developing targeted therapeutic interventions accordingly.

Muscle synergies in upper limb stroke rehabilitation: a scoping review

INTRODUCTION

Upper limb impairment is a common consequence of stroke, significantly affecting the quality of life and independence of survivors. This scoping review assesses the emerging field of muscle synergy analysis in enhancing upper limb rehabilitation, focusing on the comparison of various methodologies and their outcomes. It aims to standardize these approaches to improve the effectiveness of rehabilitation interventions and drive future research in the domain.

EVIDENCE ACQUISITION

Studies included in this scoping review focused on the analysis of muscle synergies during longitudinal rehabilitation of stroke survivors' upper limbs. A systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, until September 2023, and was guided by the PRISMA for scoping review framework.

EVIDENCE SYNTHESIS

Fourteen studies involving a total of 247 stroke patients were reviewed, featuring varied patient populations and rehabilitative interventions. Protocols differed among studies, with some utilizing robotic assistance and others relying on traditional therapy methods. Muscle synergy extraction was predominantly conducted using Non-Negative Matrix Factorization from electromyography data, focusing on key upper limb muscles essential for shoulder, elbow, and wrist rehabilitation. A notable observation across the studies was the heterogeneity in findings, particularly in the changes observed in the number, weightings, and temporal coefficients of muscle synergies. The studies indicated varied and complex relationships between muscle synergy variations and clinical outcomes. This diversity underscored the complexity involved in interpreting muscle coordination in the stroke population. The variability in results was also influenced by differing methodologies in muscle synergy analysis, highlighting a need for more standardized approaches to improve future research comparability and consistency.

CONCLUSIONS

The synthesis of evidence presented in this scoping review highlights the promising role of muscle synergy analysis as an indicator of motor control recovery in stroke rehabilitation. By offering a comprehensive overview of the current state of research and advocating for harmonized methodological practices in future longitudinal studies, this scoping review aspires to advance the field of upper limb rehabilitation, ensuring that post-stroke interventions are both scientifically grounded and optimally beneficial for patients.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

How to improve the muscle synergy analysis methodology?

Muscle synergy analysis is increasingly used in domains such as neurosciences, robotics, rehabilitation or sport sciences to analyze and better understand motor coordination. The analysis uses dimensionality reduction techniques to identify regularities in spatial, temporal or spatio-temporal patterns of multiple muscle activation. Recent studies have pointed out variability in outcomes associated with the different methodological options available and there was a need to clarify several aspects of the analysis methodology. While synergy analysis appears to be a robust technique, it remain a statistical tool and is, therefore, sensitive to the amount and quality of input data (EMGs). In particular, attention should be paid to EMG amplitude normalization, baseline noise removal or EMG filtering which may diminish or increase the signal-to-noise ratio of the EMG signal and could have major effects on synergy estimates. In order to robustly identify synergies, experiments should be performed so that the groups of muscles that would potentially form a synergy are activated with a sufficient level of activity, ensuring that the synergy subspace is fully explored. The concurrent use of various synergy formulations-spatial, temporal and spatio-temporal synergies- should be encouraged. The number of synergies represents either the dimension of the spatial structure or the number of independent temporal patterns, and we observed that these two aspects are often mixed in the analysis. To select a number, criteria based on noise estimates, reliability of analysis results, or functional outcomes of the synergies provide interesting substitutes to criteria solely based on variance thresholds.

Reorganization of motor modules for standing reactive balance recovery following pyridoxine-induced large-fiber peripheral sensory neuropathy in cats

Task-level goals such as maintaining standing balance are achieved through coordinated muscle activity. Consistent and individualized groupings of synchronously activated muscles can be estimated from muscle recordings in terms of motor modules or muscle synergies, independent of their temporal activation. The structure of motor modules can change with motor training, neurological disorders, and rehabilitation, but the central and peripheral mechanisms underlying motor module structure remain unclear. To assess the role of peripheral somatosensory input on motor module structure, we evaluated changes in the structure of motor modules for reactive balance recovery following pyridoxine-induced large-fiber peripheral somatosensory neuropathy in previously collected data in four adult cats. Somatosensory fiber loss, quantified by postmortem histology, varied from mild to severe across cats. Reactive balance recovery was assessed using multidirectional translational support-surface perturbations over days to weeks throughout initial impairment and subsequent recovery of balance ability. Motor modules within each cat were quantified by non-negative matrix factorization and compared in structure over time. All cats exhibited changes in the structure of motor modules for reactive balance recovery after somatosensory loss, providing evidence that somatosensory inputs influence motor module structure. The impact of the somatosensory disturbance on the structure of motor modules in well-trained adult cats indicates that somatosensory mechanisms contribute to motor module structure, and therefore may contribute to some of the pathological changes in motor module structure in neurological disorders. These results further suggest that somatosensory nerves could be targeted during rehabilitation to influence pathological motor modules for rehabilitation.NEW & NOTEWORTHY Stable motor modules for reactive balance recovery in well-trained adult cats were disrupted following pyridoxine-induced peripheral somatosensory neuropathy, suggesting somatosensory inputs contribute to motor module structure. Furthermore, the motor module structure continued to change as the animals regained the ability to maintain standing balance, but the modules generally did not recover pre-pyridoxine patterns. These results suggest changes in somatosensory input and subsequent learning may contribute to changes in motor module structure in pathological conditions.

When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to an accuracy level defined heuristically, often set to 90% of the variance. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is not entirely random and can influence the execution of motor tasks. Young healthy subjects performed an isometric reaching task in which the surface electromyography of 10 arm muscles was mapped onto a two-dimensional force used to control a cursor. Three to five synergies explained 90% of the variance in muscle activity. We altered the muscle-force mapping via "hard" and "easy" virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery; in contrast, it was as far as possible in the hard surgery. This design maximized potential differences in reaching errors attributable to residual activity. Results show that the easy surgery produced smaller directional errors than the hard surgery. Additionally, simulations of surgeries constructed with 1 to 10 synergies show that the errors in the easy and hard surgeries differ significantly for up to 8 synergies, which explains 98% of the variance on average. Our study thus indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient accuracy levels.NEW & NOTEWORTHY The muscle synergy hypothesis posits that the central nervous system simplifies motor control by grouping muscles into modules. Current techniques use dimensionality reduction, such that the identified synergies reconstruct 90% of the muscle activity. We show that residual muscle activity following such identification can have a large systematic effect on movements, even when the number of synergies approaches the number of muscles. Current synergy extraction techniques must therefore be updated to identify true physiological synergies.

Muscle Synergies Obtained from Comprehensive Mapping of the Cortical Forelimb Representation Using Stimulus Triggered Averaging of EMG Activity

Neuromuscular control of voluntary movement may be simplified using muscle synergies similar to those found using non-negative matrix factorization. We recently identified synergies in electromyography (EMG) recordings associated with both voluntary movement and movement evoked by high-frequency long-duration intracortical microstimulation applied to the forelimb representation of the primary motor cortex (M1). The goal of this study was to use stimulus-triggered averaging (StTA) of EMG activity to investigate the synergy profiles and weighting coefficients associated with poststimulus facilitation, as synergies may be hard-wired into elemental cortical output modules and revealed by StTA. We applied StTA at low (LOW, ∼15 μA) and high intensities (HIGH, ∼110 μA) to 247 cortical locations of the M1 forelimb region in two male rhesus macaques while recording the EMG of 24 forelimb muscles. Our results show that 10-11 synergies accounted for 90% of the variation in poststimulus EMG facilitation peaks from the LOW-intensity StTA dataset while only 4-5 synergies were needed for the HIGH-intensity dataset. Synergies were similar across monkeys and current intensities. Most synergy profiles strongly activated only one or two muscles; all joints were represented and most, but not all, joint directions of motion were represented. Cortical maps of the synergy weighting coefficients suggest only a weak organization. StTA of M1 resulted in highly diverse muscle activations, suggestive of the limiting condition of requiring a synergy for each muscle to account for the patterns observed.SIGNIFICANCE STATEMENT Coordination of muscle activity and the neural origin of potential muscle synergies remains a fundamental question of neuroscience. We previously demonstrated that high-frequency long-duration intracortical microstimulation-evoked synergies were unrelated to voluntary movement synergies and were not clearly organized in the cortex. Here we present stimulus-triggered averaging facilitation-related muscle synergies, suggesting that when fundamental cortical output modules are activated, synergies approach the limit of single-muscle control. Thus, we conclude that if the CNS controls movement via linear synergies, those synergies are unlikely to be called from M1. This information is critical for understanding neural control of movement and the development of brain-machine interfaces.

Motor modules during adaptation to walking in a powered ankle exoskeleton

BACKGROUND

Modules of muscle recruitment can be extracted from electromyography (EMG) during motions, such as walking, running, and swimming, to identify key features of muscle coordination. These features may provide insight into gait adaptation as a result of powered assistance. The aim of this study was to investigate the changes (module size, module timing and weighting patterns) of surface EMG data during assisted and unassisted walking in an powered, myoelectric, ankle-foot orthosis (ankle exoskeleton).

METHODS

Eight healthy subjects wore bilateral ankle exoskeletons and walked at 1.2 m/s on a treadmill. In three training sessions, subjects walked for 40 min in two conditions: unpowered (10 min) and powered (30 min). During each session, we extracted modules of muscle recruitment via nonnegative matrix factorization (NNMF) from the surface EMG signals of ten muscles in the lower limb. We evaluated reconstruction quality for each muscle individually using R2 and normalized root mean squared error (NRMSE). We hypothesized that the number of modules needed to reconstruct muscle data would be the same between conditions and that there would be greater similarity in module timings than weightings.

RESULTS

Across subjects, we found that six modules were sufficient to reconstruct the muscle data for both conditions, suggesting that the number of modules was preserved. The similarity of module timings and weightings between conditions was greater then random chance, indicating that muscle coordination was also preserved. Motor adaptation during walking in the exoskeleton was dominated by changes in the module timings rather than module weightings. The segment number and the session number were significant fixed effects in a linear mixed-effect model for the increase in R2 with time.

CONCLUSIONS

Our results show that subjects walking in a exoskeleton preserved the number of modules and the coordination of muscles within the modules across conditions. Training (motor adaptation within the session and motor skill consolidation across sessions) led to improved consistency of the muscle patterns. Subjects adapted primarily by changing the timing of their muscle patterns rather than the weightings of muscles in the modules. The results of this study give new insight into strategies for muscle recruitment during adaptation to a powered ankle exoskeleton.

Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements

Background

A deep characterization of neurological patients is a crucial step for a detailed knowledge of the pathology and maximal exploitation and customization of the rehabilitation therapy. The muscle synergies analysis was designed to investigate how muscles coactivate and how their eliciting commands change in time during movement production. Few studies investigated the value of muscle synergies for the characterization of neurological patients before rehabilitation therapies. In this article, the synergy analysis was used to characterize a group of chronic poststroke hemiplegic patients.

Methods

Twenty-two poststroke patients performed a session composed of a sequence of 3D reaching movements. They were assessed through an instrumental assessment, by recording kinematics and electromyography to extract muscle synergies and their activation commands. Patients’ motor synergies were grouped by the means of cluster analysis. Consistency and characterization of each cluster was assessed and clinically profiled by comparison with standard motor assessments.

Results

Motor synergies were successfully extracted on all 22 patients. Five basic clusters were identified as a trade-off between clustering precision and synthesis power, representing: healthy-like activations, two shoulder compensatory strategies, two elbow predominance patterns. Each cluster was provided with a deep characterization and correlation with clinical scales, range of motion, and smoothness.

Conclusion

The clustering of muscle synergies enabled a pretherapy characterization of patients. Such technique may affect several aspects of the therapy: prediction of outcomes, evaluation of the treatments, customization of doses, and therapies.

Chronic pain alters spatiotemporal activation patterns of forearm muscle synergies during the development of grip force

It is largely unknown how the CNS regulates multiple muscle systems in the presence of pain. This study used muscle synergy analysis to investigate multiple forearm muscles in individuals with chronic elbow pain during the development of grip force. Eleven individuals with chronic elbow pain and 11 healthy age-matched control subjects developed grip force to 15% and 30% of maximum voluntary contraction (MVC). Surface electromyography was obtained from six forearm muscles during force development before nonnegative matrix factorization was performed. The relationship between muscle synergies and standard clinical tests of elbow pain were examined by linear regression. During grip force development to 15% MVC the pain group had a lower number of forearm muscle synergies, increased similarity in spatial activation patterns, increased cocontraction of forearm flexors, and a greater magnitude of muscle weightings across the forearm when performing the task. During the 30% MVC grip the numbers of muscle synergies were the same for both groups; however, the pain group had lower activation and reduced variability in the timing of peak activation. The timing of peak activation was delayed in the pain group regardless of the task, and performing the grip in different wrist postures did not affect muscle synergy characteristics in either group. Although localized pain causes direct dysfunction of an affected muscle, this study provides evidence that the timing and amplitude of agonist and antagonist muscle activity are also affected with chronic pain.NEW & NOTEWORTHY Muscle activation patterns of individuals with chronic elbow pain are simplified compared with healthy individuals. This is apparent as individuals with pain exhibit fewer forearm muscle synergies, and increased similarity of activation patterns between forearm muscles, when performing pain-free isometric gripping. As such, even during pain-free tasks it is possible to observe changes in motor control in people with chronic pain.

Muscle synergies obtained from comprehensive mapping of the primary motor cortex forelimb representation using high-frequency, long-duration ICMS

Simplifying neuromuscular control for movement has previously been explored by extracting muscle synergies from voluntary movement electromyography (EMG) patterns. The purpose of this study was to investigate muscle synergies represented in EMG recordings associated with direct electrical stimulation of single sites in primary motor cortex (M1). We applied single-electrode high-frequency, long-duration intracortical microstimulation (HFLD-ICMS) to the forelimb region of M1 in two rhesus macaques using parameters previously found to produce forelimb movements to stable spatial end points (90-150 Hz, 90-150 μA, 1,000-ms stimulus train lengths). To develop a comprehensive representation of cortical output, stimulation was applied systematically across the full extent of M1. We recorded EMG activity from 24 forelimb muscles together with movement kinematics. Nonnegative matrix factorization (NMF) was applied to the mean stimulus-evoked EMG, and the weighting coefficients associated with each synergy were mapped to the cortical location of the stimulating electrode. Synergies were found for three data sets including 1) all stimulated sites in the cortex, 2) a subset of sites that produced stable movement end points, and 3) EMG activity associated with voluntary reaching. Two or three synergies accounted for 90% of the overall variation in voluntary movement EMG whereas four or five synergies were needed for HFLD-ICMS-evoked EMG data sets. Maps of the weighting coefficients from the full HFLD-ICMS data set show limited regional areas of higher activation for particular synergies. Our results demonstrate fundamental NMF-based muscle synergies in the collective M1 output, but whether and how the central nervous system might coordinate movements using these synergies remains unclear.NEW & NOTEWORTHY While muscle synergies have been investigated in various muscle activity sets, it is unclear whether and how synergies may be organized in the cortex. We have investigated muscle synergies resulting from high-frequency, long-duration intracortical microstimulation (HFLD-ICMS) applied throughout M1. We compared HFLD-ICMS synergies to synergies from voluntary movement. While synergies can be identified from M1 stimulation, they are not clearly related to voluntary movement synergies and do not show an orderly topographic organization across M1.

Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules

The motor module hypothesis in motor control proposes that the nervous system can simplify the problem of controlling a large number of muscles in human movement by grouping muscles into a smaller number of modules. Here, we tested one prediction of the modular organization hypothesis by examining whether there is preferential exploration along these motor modules during the learning of a new gait pattern. Healthy college-aged participants learned a new gait pattern which required increased hip and knee flexion during the swing phase while walking in a lower-extremity robot (Lokomat). The new gait pattern was displayed as a foot trajectory in the sagittal plane and participants attempted to match their foot trajectory to this template. We recorded EMG from 8 lower-extremity muscles and we extracted motor modules during both baseline walking and target-tracking using non-negative matrix factorization (NMF). Results showed increased trajectory variability in the first block of learning, indicating that participants were engaged in exploratory behavior. Critically, when we examined the muscle activity during this exploratory phase, we found that the composition of motor modules changed significantly within the first few strides of attempting the new gait pattern. The lack of persistence of the motor modules under even short time scales suggests that motor modules extracted during locomotion may be more indicative of correlated muscle activity induced by the task constraints of walking, rather than reflecting a modular control strategy.

The effect of parameters of equilibrium-based 3-D biomechanical models on extracted muscle synergies during isometric lumbar exertion

A hallmark of more advanced models is their higher details of trunk muscles represented by a larger number of muscles. The question is if in reality we control these muscles individually as independent agents or we control groups of them called "synergy". To address this, we employed a 3-D biomechanical model of the spine with 18 trunk muscles that satisfied equilibrium conditions at L4/5, with different cost functions. The solutions of several 2-D and 3-D tasks were arranged in a data matrix and the synergies were computed by using non-negative matrix factorization (NMF) algorithms. Variance accounted for (VAF) was used to evaluate the number of synergies that emerged by the analysis, which were used to reconstruct the original muscle activations. It was showed that four and six muscle synergies were adequate to reconstruct the input data of 2-D and 3-D torque space analysis. The synergies were different by choosing alternative cost functions as expected. The constraints affected the extracted muscle synergies, particularly muscles that participated in more than one functional tasks were influenced substantially. The compositions of extracted muscle synergies were in agreement with experimental studies on healthy participants. The following computational methods show that the synergies can reduce the complexity of load distributions and allow reduced dimensional space to be used in clinical settings.

Neuromechanical principles underlying movement modularity and their implications for rehabilitation

Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e., motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson's disease. Recent studies provide insights into the yet-unknown underlying neural mechanisms of motor modules, motor impairment, and motor learning and may lead to better understanding of the causal nature of modularity and its underlying neural substrates.

Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses

Matrix factorization algorithms are commonly used to analyze muscle activity and provide insight into neuromuscular control. These algorithms identify low-dimensional subspaces, commonly referred to as synergies, which can describe variation in muscle activity during a task. Synergies are often interpreted as reflecting underlying neural control; however, it is unclear how these analyses are influenced by biomechanical and task constraints, which can also lead to low-dimensional patterns of muscle activation. The aim of this study was to evaluate whether commonly used algorithms and experimental methods can accurately identify synergy-based control strategies. This was accomplished by evaluating synergies from five common matrix factorization algorithms using muscle activations calculated from 1) a biomechanically constrained task using a musculoskeletal model and 2) without task constraints using random synergy activations. Algorithm performance was assessed by calculating the similarity between estimated synergies and those imposed during the simulations; similarities ranged from 0 (random chance) to 1 (perfect similarity). Although some of the algorithms could accurately estimate specified synergies without biomechanical or task constraints (similarity >0.7), with these constraints the similarity of estimated synergies decreased significantly (0.3-0.4). The ability of these algorithms to accurately identify synergies was negatively impacted by correlation of synergy activations, which are increased when substantial biomechanical or task constraints are present. Increased variability in synergy activations, which can be captured using robust experimental paradigms that include natural variability in motor activation patterns, improved identification accuracy but did not completely overcome effects of biomechanical and task constraints. These results demonstrate that a biomechanically constrained task can reduce the accuracy of estimated synergies and highlight the importance of using experimental protocols with physiological variability to improve synergy analyses.

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Effects of early and intensive neuro-rehabilitative treatment on muscle synergies in acute post-stroke patients: a pilot study

BACKGROUND

After a stroke, patients show significant modifications of neural control of movement, such as abnormal muscle co-activation, and reduced selectivity and modulation of muscle activity. Nonetheless, results reported in literature do not allow to unequivocally explain whether and, in case, how a cerebrovascular accident affects muscle synergies underlying the control of the upper limb. These discrepancies suggest that a complete understanding of the modular re-organization of muscle activity due to a stroke is still lacking. This pilot study aimed at investigating the effects of the conjunction between the natural ongoing of the pathology and the intense robot-mediated treatment on muscle synergies of the paretic upper limb of subacute post-stroke patients.

METHODS

Six subacute patients, homogenous with respect to the age and the time elapsed from the trauma, and ten healthy age-matched subjects were enrolled. The protocol consisted in achieving planar movement of the upper limb while handling the end-effector of a robotic platform. Patients underwent 6 weeks long treatment while clinical scores, kinematics of the end-effector and muscle activity were recorded. Then we verified whether muscle coordination underlying the motor task was significantly affected by the cerebrovascular accident and how muscle synergies were modified along the treatment.

RESULTS

Results show that although muscle synergies in subacute stroke patients were qualitatively comparable to those of healthy subjects, those underlying the movement of the shoulder can reflect the functional deficit induced by the pathology. Moreover, the improvement of motor performance due to the treatment was achieved in conjunction with slight modifications of muscle synergies. In this regard, modifications of muscle synergies appeared to be influenced by the different recovering mechanisms across patients presumably due to the heterogeneity of lesions, sides and location of the accident.

CONCLUSIONS

The results support the hypothesis that muscle synergies reflect the injury of the cerebrovascular accident and could document the effects of the functional recovery due to a suitable and customized treatment. Therefore, they open up new possibilities for the development of more effective neuro-rehabilitation protocols.

The number and choice of muscles impact the results of muscle synergy analyses

One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.

Are muscle synergies useful for neural control?

The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.