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

Increased trial-to-trial similarity and reduced temporal overlap of muscle synergy activation coefficients manifest during learning and with increasing movement proficiency

Muscle synergy analyses are used to enhance our understanding of motor control. Spatially fixed synergy weights coordinate multiple co-active muscles through activation commands, known as activation coefficients. To gain a more comprehensive understanding of motor learning, it is essential to understand how activation coefficients vary during a learning task and at different levels of movement proficiency. Participants walked on a line, a beam, and learned to walk on a tightrope-tasks that represent different levels of proficiency. Muscle synergies were extracted from electromyography signals across all conditions and the number of synergies was determined by the knee-point of the total variance accounted for (tVAF) curve. The results indicated that the tVAF of one synergy decreased with task proficiency, with the tightrope task resulting in the highest tVAF compared to the line and beam tasks. Furthermore, with increasing proficiency and after a learning process, trial-to-trial similarity increased and temporal overlap of synergy activation coefficients decreased. Consequently, we propose that precise adjustment and refinement of synergy activation coefficients play a pivotal role in motor learning.

A comparative study of muscle activity and synergies during walking in baboons and humans

Bipedal locomotion was a major functional change during hominin evolution, yet, our understanding of this gradual and complex process remains strongly debated. Based on fossil discoveries, it is possible to address functional hypotheses related to bipedal anatomy, however, motor control remains intangible with this approach. Using comparative models which occasionally walk bipedally has proved to be relevant to shed light on the evolutionary transition toward habitual bipedalism. Here, we explored the organization of the neuromuscular control using surface electromyography (sEMG) for six extrinsic muscles in two baboon individuals when they walk quadrupedally and bipedally on the ground. We compared their muscular coordination to five human subjects walking bipedally. We extracted muscle synergies from the sEMG envelopes using the non-negative matrix factorization algorithm which allows decomposing the sEMG data in the linear combination of two non-negative matrixes (muscle weight vectors and activation coefficients). We calculated different parameters to estimate the complexity of the sEMG signals, the duration of the activation of the synergies, and the generalizability of the muscle synergy model across species and walking conditions. We found that the motor control strategy is less complex in baboons when they walk bipedally, with an increased muscular activity and muscle coactivation. When comparing the baboon bipedal and quadrupedal pattern of walking to human bipedalism, we observed that the baboon bipedal pattern of walking is closer to human bipedalism for both baboons, although substantial differences remain. Overall, our findings show that the muscle activity of a non-adapted biped effectively fulfills the basic mechanical requirements (propulsion and balance) for walking bipedally, but substantial refinements are possible to optimize the efficiency of bipedal locomotion. In the evolutionary context of an expanding reliance on bipedal behaviors, even minor morphological alterations, reducing muscle coactivation, could have faced strong selection pressure, ultimately driving bipedal evolution in hominins.

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.

Muscle synergy patterns as altered coordination strategies in individuals with chronic low back pain: a cross-sectional study

BACKGROUND

Chronic low back pain (CLBP) is a highly prevalent disease with poorly understood underlying mechanisms. In particular, altered trunk muscle coordination in response to specific trunk tasks remains largely unknown.

METHODS

We investigated the muscle synergies during 11 trunk movement and stability tasks in 15 healthy individuals (8 females and 7 males, aged 21. 3 (20.1-22.8) ± 0.6 years) and in 15 CLBP participants (8 females and 7 males, aged 20. 9 (20.2-22.6) ± 0.7 years) by recording the surface electromyographic activities of 12 back and abdominal muscles (six muscles unilaterally). Non-negative matrix factorization was performed to extract the muscle synergies.

RESULTS

We found six trunk muscle synergies and temporal patterns in both groups. The high similarity of the trunk synergies and temporal patterns in the groups suggests that both groups share the common feature of the trunk coordination strategy. We also found that trunk synergies related to the lumbar erector spinae showed lower variability in the CLBP group. This may reflect the impaired back muscles that reshape the trunk synergies in the fixed structure of CLBP. Furthermore, the higher variability of trunk synergies in the other muscle regions such as in the latissimus dorsi and oblique externus, which were activated in trunk stability tasks in the CLBP group, represented more individual motor strategies when the trunk tasks were highly demanding.

CONCLUSION

Our work provides the first demonstration that individual modular organization is fine-tuned while preserving the overall structures of trunk synergies and temporal patterns in the presence of persistent CLBP.

Muscle synergies in Parkinson's disease before and after the deep brain stimulation of the bilateral subthalamic nucleus

The aim of this study is to quantitatively assess motor control changes in Parkinson's disease (PD) patients after bilateral deep brain stimulation of the subthalamic nucleus (STN-DBS), based on a novel muscle synergy evaluation approach. A group of 20 PD patients evaluated at baseline (before surgery, T₀), at 3 months (T₁), and at 12 months (T₂) after STN-DBS surgery, as well as a group of 20 age-matched healthy control subjects, underwent an instrumented gait analysis, including surface electromyography recordings from 12 muscles. A smaller number of muscle synergies was found in PD patients (4 muscle synergies, at each time point) compared to control subjects (5 muscle synergies). The neuromuscular robustness of PD patients-that at T₀ was smaller with respect to controls (PD T₀: 69.3 ± 2.2% vs. Controls: 77.6 ± 1.8%, p = 0.004)-increased at T₁ (75.8 ± 1.8%), becoming not different from that of controls at T₂ (77.5 ± 1.9%). The muscle synergies analysis may offer clinicians new knowledge on the neuromuscular structure underlying PD motor types of behavior and how they can improve after electroceutical STN-DBS therapy.

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT), and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data were used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via opensim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. There was a decrease in JRF in the medial compartment during the loading phase of gait for TIG (6.6%) and LTL (4.9%) and an increasing JRF for MKT (2.6%). but none was statistically significant. A significant increase from baseline was found for TIG (5.8%) in the medial second peak. We found a large variation in individual responses to gait interventions, which may help explain the lack of statistically significant results. Possible factors influencing these wide ranges of responses to gait modifications include static alignment and the impacts of variation in muscle coordination strategies used, by participants, to implement gait modifications.

Evolution of muscle coordination and mechanical output following four weeks of arm cranking submaximal training

Muscle synergies is extensively studied to understand how the neuromusculoskeletal system deals with abundancy. The synergies represent covariant muscles that acts as building blocks for movement production. Nevertheless, little is known on how those synergies evolve following training, learning and expertise. This study reports the influence a 4-weeks submaximal training of arm-cranking on novice participants' muscle synergies.

METHODS

12 participants performed 8 sessions of submaximal training for 4 weeks. One session consisted in two 30-second-maximal power tests followed by six 2-minutes-bouts at 30% of maximal recorded power. Cranking torque and EMG of 11 muscles were recorded during the entire protocol. After EMG normalization, muscle synergies were extracted using NNMF. Similarity was computed using cross-correlation and cosine similarities and statistical evolution across training was tested using repeated measured ANOVA.

RESULTS

While maximal power increased across training days nor torque management, EMG or muscle synergies were significantly affected by submaximal training. Nevertheless, results suggest slights modifications of muscle synergies across day despite to non-significant differences.

DISCUSSION

Despite the strong complexity of the upper limbs anatomy, our results showed that training didn't induce significant changes in movement realization (mechanical and coordination level). A low-dimensional organization of muscle synergies is selected from the first day and kept through the following training days, despite slight but non-significant modifications.This study supports the hypothesis that motor control for movement production could be simplify using low-dimensional building blocks (muscle synergies). Such building blocks allow stability in movement execution and are slightly adjusted to fit movement requirements with training.

Variability of trunk muscle synergies underlying the multidirectional movements and stability trunk motor tasks in healthy individuals

Muscle synergy analysis is useful for investigating trunk coordination patterns based on the assumption that the central nervous system reduces the dimensionality of muscle activation to simplify movement. This study aimed to quantify the variability in trunk muscle synergy during various trunk motor tasks in healthy participants to provide reference data for evaluating trunk control strategies in patients and athletes. Sixteen healthy individuals performed 11 trunk movement and stability tasks with electromyography (EMG) recording of their spinal and abdominal muscles (6 bilaterally). Non-negative matrix factorization applied to the concatenated EMG of all tasks identified the five trunk muscle synergies (W) with their corresponding temporal patterns (C). The medians of within-cluster similarity defined by scalar products in W and rmax coefficient using the cross-correlation function in C were 0.73-0.86 and 0.64-0.75, respectively, while the inter-session similarities were 0.81-0.96 and 0.74-0.84, respectively. However, the lowest and highest values of both similarity indices were broad, reflecting the musculoskeletal system's redundancy within and between participants. Furthermore, the significant differences in the degree of variability between the trunk synergies may represent the different neural features of synergy organization and strategies to overcome the various mechanical demands of a motor task.

Complexity of locomotion activities in an outside-of-the-lab wearable motion capture dataset

Gait complexity is widely used to understand risk factors for injury, rehabilitation, the performance of assistive devices, and other matters of clinical interest. We analyze the complexity of out-of-the-lab locomotion activities via measures that have previously been used in gait analysis literature, as well as measures from other domains of data analysis. We categorize these broadly as quantifying either the intrinsic dimensionality, the variability, or the regularity, periodicity, or self-similarity of the data from a nonlinear dynamical systems perspective. We perform this analysis on a novel full-body motion capture dataset collected in out-of-the-lab conditions for a variety of indoor environments. This is a unique dataset with a large amount (over 24 h total) of data from participants behaving without low-level instructions in out-of-the-lab indoor environments. We show that reasonable complexity measures can yield surprising, and even profoundly contradictory, results. We suggest that future complexity analysis can use these guidelines to be more specific and intentional about what aspect of complexity a quantitative measure expresses. This will become more important as wearable motion capture technology increasingly allows for comparison of ecologically relevant behavior with lab-based measurements.

Evaluation of Methods for the Extraction of Spatial Muscle Synergies

Muscle synergies have been largely used in many application fields, including motor control studies, prosthesis control, movement classification, rehabilitation, and clinical studies. Due to the complexity of the motor control system, the full repertoire of the underlying synergies has been identified only for some classes of movements and scenarios. Several extraction methods have been used to extract muscle synergies. However, some of these methods may not effectively capture the nonlinear relationship between muscles and impose constraints on input signals or extracted synergies. Moreover, other approaches such as autoencoders (AEs), an unsupervised neural network, were recently introduced to study bioinspired control and movement classification. In this study, we evaluated the performance of five methods for the extraction of spatial muscle synergy, namely, principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), nonnegative matrix factorization (NMF), and AEs using simulated data and a publicly available database. To analyze the performance of the considered extraction methods with respect to several factors, we generated a comprehensive set of simulated data (ground truth), including spatial synergies and temporal coefficients. The signal-to-noise ratio (SNR) and the number of channels (NoC) varied when generating simulated data to evaluate their effects on ground truth reconstruction. This study also tested the efficacy of each synergy extraction method when coupled with standard classification methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and Random Forest (RF). The results showed that both SNR and NoC affected the outputs of the muscle synergy analysis. Although AEs showed better performance than FA in variance accounted for and PCA in synergy vector similarity and activation coefficient similarity, NMF and ICA outperformed the other three methods. Classification tasks showed that classification algorithms were sensitive to synergy extraction methods, while KNN and RF outperformed the other two methods for all extraction methods; in general, the classification accuracy of NMF and PCA was higher. Overall, the results suggest selecting suitable methods when performing muscle synergy-related analysis.

Central Commands to the Elbow and Shoulder Muscles During Circular Planar Movements of Hand With Simultaneous Generation of Tangential Forces

This study examines some of the non-linear effects of signal transduction in the human motor system, with particular emphasis on muscle hysteresis. The movement tests were analyzed in a group of eight subjects, which were asked to develop tangential force using visual biofeedback while performing slow, externally imposed, circular movements of right hand holding a moving handle operated by a computerized mechatronic system. The positional changes in the averaged EMGs of the elbow and shoulder muscles were compared for all combinations of direction of movement and generated force. Additionally, for one of the subjects, there was carried out MRI identification and 3D printing of the bones of the forelimb, shoulder, scapula and collarbone, which made it possible to reconstruct for him the length and force traces of all the muscles under study. The averaged EMG traces in muscles of both joints show their close correspondence to the related force traces, however, the co-activation patterns of activity in agonists and antagonists were also often encountered. The EMG waves related to the respective force waves were strongly dependent on the predominant direction of the muscle length changes within the correspondent force wave locations: the EMG intensities were higher for the shortening muscle movements (concentric contractions) and lower during muscle lengthening (eccentric contractions). The data obtained allows to suggest that for two-joint movements of the forelimbs, it is sufficient to consider the force and activation synergies (patterns of simultaneous activity in different muscles), ignoring at the first stage the effects associated with kinematic synergy. On the other hand, the data obtained indicate that the movement kinematics has a strong modulating effect on the activation synergy, dividing it into concentric and eccentric subtypes, in accordance with the known non-linear features of the muscle dynamics. It has been shown that the concentric and eccentric differences in the responses of the shoulder muscles are more clearly distinguishable than those in the elbow muscles. The shoulder muscles also have a more pronounced symmetry of the averaged EMG responses with respect to the ascending and descending phases of force waves, while demonstrating a lower degree of antagonist cocontraction. The data obtained suggest that the central commands in two-joint movements are determined mainly by the interdependence of force and activation synergies including both intra- and inter-joint components, while kinematic synergy can be interpreted as a potent modulator of activation synergy.

Variable Stiffness Shoes for Knee Osteoarthritis: An Evaluation of 3-Dimensional Gait Mechanics and Medial Joint Contact Forces

The study aim was to quantify the impact of a commercially available variable stiffness shoe (VSS) on 3-dimensional ankle, knee, and hip mechanics and estimated knee contact forces compared with a control shoe. Fourteen participants (10 females) with knee osteoarthritis completed gait analysis after providing informed consent. Shoe conditions tested were control shoe (New Balance MW411v2) and VSS (Abeo SMART3400). An OpenSim musculoskeletal model with static optimization was used to estimate knee contact forces. There were no differences in joint kinematics or in the knee adduction or flexion moments (P = .06; P = .2). There were increases in the knee internal and external rotation (P = .02; P = .03) and hip adduction and internal rotation moments for VSS versus control (P = .03; P = .02). The estimated contact forces were not different between shoes (total P = .3, medial P = .1, and lateral P = .8), but contact force changes were correlated with changes in the knee adduction moment (medial r2 = .61; P < .007). High variability in knee flexion moment changes and increases in the internal rotation moment combined with small decreases in the knee adduction moment did not lead to decreases in estimated contact forces. These results suggest that evaluation of VSS using only the knee adduction moment may not adequately capture its impact on osteoarthritis.

Evidence for basic units of upper limb muscle synergies underlying a variety of complex human manipulations

Manipulations require complex upper-limb movements in which the central nervous system (CNS) must deal with many degrees of freedom. Evidence suggests that the CNS utilizes motor primitives called muscle synergies to simplify the production of movements. However, the exact neural mechanism underlying muscle synergies to control a wide array of manipulations is not fully understood. Here, we tested whether there are basic units of muscle synergies that can explain a diverse range of manipulations. We measured the electromyographic activities of 20 muscles across the shoulder, elbow, and wrist and fingers during 24 manipulation tasks. As a result, non-negative matrix factorization identified nine basic units of muscle synergies derived from the upper limb muscles that are shared across all tasks. The high similarity between muscle synergies of each of the 24 tasks and various combinations of nine basic unit muscle synergies in a single and/or merging state provides evidence that the CNS flexibly selects and modifies the degree of contribution of the nine basic units of muscle synergies to overcome different mechanical demands of tasks.

Synergies are minimally affected during emulation of cerebral palsy gait patterns

Muscle synergy analysis is commonly used to characterize motor control during dynamic tasks like walking. For clinical populations, such as children with cerebral palsy (CP), synergies are altered compared to nondisabled (ND) peers and have been associated with both function and treatment outcomes. However, the factors that contribute to altered synergies remain unclear. In particular, the extent to which synergies reflect altered biomechanics (e.g., changes in gait) or underlying neurologic injury is debated. To evaluate the effect that altered biomechanics have on synergies, we compared synergy complexity and structure while ND individuals (n = 14) emulated four common CP gait patterns (equinus, equinus-crouch, mild-crouch, and moderate crouch). Secondarily, we compared the similarity of ND synergies during emulation to synergies from a retrospective cohort of individuals with CP walking in similar gait patterns (n = 28 per pattern). During emulation, ND individuals recruited similar synergies as baseline walking. However, pattern-specific deviations in synergy activations and complexity emerged. In particular, equinus gait altered plantarflexor activation timing and reduced synergy complexity. Importantly, ND synergies during emulation were distinct from those observed in CP for all gait patterns. These results suggest that altered gait patterns are not primarily driving the changes in synergies observed in CP, highlighting the value of using synergies as a tool to capture patient-specific differences in motor control. However, they also highlight the sensitivity of both synergy activations and complexity to altered biomechanics, which should be considered when using these measures in clinical care.

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

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

Muscle synergies for the control of single-limb stance with and without visual information in young individuals

Purpose

Single-limb stance is a demanding postural task featuring a high number of daily living and sporting activities. Thus, it is widely used for training and rehabilitation, as well as for balance assessment. Muscle activations around single joints have been previously described, however, it is not known which are the muscle synergies used to control posture and how they change between conditions of normal and lack of visual information.

Methods

Twenty-two healthy young participants were asked to perform a 30 s single-limb stance task in open-eyes and closed-eyes condition while standing on a force platform with the dominant limb. Muscle synergies were extracted from the electromyographical recordings of 13 muscles of the lower limb, hip, and back. The optimal number of synergies, together with the average recruitment level and balance control strategies were analyzed and compared between the open- and the closed-eyes condition.

Results

Four major muscle synergies, two ankle-dominant synergies, one knee-dominant synergy, and one hip/back-dominant synergy were found. No differences between open- and closed-eyes conditions were found for the recruitment level, except for the hip/back synergy, which significantly decreased (p = 0.02) in the closed-eyes compared to the open-eyes condition. A significant increase (p = 0.03) of the ankle balance strategy was found in the closed-eyes compared to the open-eyes condition.

Conclusion

In healthy young individuals, single-limb stance is featured by four major synergies, both in open- and closed-eyes condition. Future studies should investigate muscle synergies in participants with other age groups, as well as pathological conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-021-00392-z.

Forward and backward walking share the same motor modules and locomotor adaptation strategies

Forward and backward walking are remarkably similar motor behaviors to the extent that backward walking has been described as a time-reversed version of forward walking. However, because they display different muscle activity patterns, it has been questioned if forward and backward walking share common control strategies. To investigate this point, we used a split-belt treadmill experimental paradigm designed to elicit healthy individuals' motor adaptation by changing the speed of one of the treadmill belts, while keeping the speed of the other belt constant. We applied this experimental paradigm to both forward and backward walking. We analyzed several adaptation parameters including step symmetry, stability, and energy expenditure as well as the characteristics of the synergies of lower-limb muscles. We found that forward and backward walking share the same muscle synergy modules. We showed that these modules are marked by similar patterns of adaptation driven by stability and energy consumption minimization criteria, both relying on modulating the temporal activation of the muscle synergies. Our results provide evidence that forward and backward walking are governed by the same control and adaptation mechanisms.

Muscle synergy differences between voluntary and reactive backward stepping

Reactive stepping responses are essential to prevent falls after a loss of balance. It has previously been well described that both voluntary and reactive step training could improve the efficacy of reactive stepping in different populations. However, the effect of aging on neuromuscular control during voluntary and reactive stepping remains unclear. Electromyography (EMG) signals during both backward voluntary stepping in response to an auditory cue and backward reactive stepping elicited by a forward slip-like treadmill perturbation during stance were recorded in ten healthy young adults and ten healthy older adults. Using muscle synergy analysis, we extracted the muscle synergies for both voluntary and reactive stepping. Our results showed that fewer muscle synergies were used during reactive stepping than during voluntary stepping in both young and older adults. Minor differences in the synergy structure were observed for both voluntary and reactive stepping between age groups. Our results indicate that there is a low similarity of muscle synergies between voluntary stepping and reactive stepping and that aging had a limited effect on the structure of muscle synergies. This study enhances our understanding of the neuromuscular basis of both voluntary and reactive stepping as well as the potential effect of aging on neuromuscular control during balance tasks.

Common and distinct muscle synergies during level and slope locomotion in the cat

Although it is well established that the motor control system is modular, the organization of muscle synergies during locomotion and their change with ground slope are not completely understood. For example, typical reciprocal flexor-extensor muscle synergies of level walking in cats break down in downslope: one-joint hip extensors are silent throughout the stride cycle, whereas hindlimb flexors demonstrate an additional stance phase-related electromyogram (EMG) burst (Smith JL, Carlson-Kuhta P, Trank TV. J Neurophysiol 79: 1702-1716, 1998). Here, we investigated muscle synergies during level, upslope (27°), and downslope (-27°) walking in adult cats to examine common and distinct features of modular organization of locomotor EMG activity. Cluster analysis of EMG burst onset-offset times of 12 hindlimb muscles revealed five flexor and extensor burst groups that were generally shared across slopes. Stance-related bursts of flexor muscles in downslope were placed in a burst group from level and upslope walking formed by the rectus femoris. Walking upslope changed swing/stance phase durations of level walking but not the cycle duration. Five muscle synergies computed using non-negative matrix factorization accounted for at least 95% of variance in EMG patterns in each slope. Five synergies were shared between level and upslope walking, whereas only three of those were shared with downslope synergies; these synergies were active during the swing phase and phase transitions. Two stance-related synergies of downslope walking were distinct; they comprised a mixture of flexors and extensors. We suggest that the modular organization of muscle activity during level and slope walking results from interactions between motion-related sensory feedback, CPG, and supraspinal inputs.NEW & NOTEWORTHY We demonstrated that the atypical EMG activities during cat downslope walking, silent one-joint hip extensors and stance-related EMG bursts in flexors, have many features shared with activities of level and upslope walking. Majority of EMG burst groups and muscle synergies were shared among these slopes, and upslope modulated the swing/stance phase duration but not cycle duration. Thus, synergistic EMG activities in all slopes might result from a shared CPG receiving somatosensory and supraspinal inputs.

Muscle Synergies in Children Walking and Running on a Treadmill

Muscle synergies reflect the presence of a common neural input to multiple muscles. Steering small sets of synergies is commonly believed to simplify the control of complex motor tasks like walking and running. When these locomotor patterns emerge, it is likely that synergies emerge as well. We hence hypothesized that in children learning to run the number of accompanying synergies increases and that some of the synergies' activities display a temporal shift related to a reduced stance phase as observed in adults. We investigated the development of locomotion in 23 children aged 2-9 years of age and compared them with seven young adults. Muscle activity of 15 bilateral leg, trunk, and arm muscles, ground reaction forces, and kinematics were recorded during comfortable treadmill walking and running, followed by a muscle synergy analysis. We found that toddlers (2-3.5 years) and preschoolers (3.5-6.5 years) utilize a "walk-run strategy" when learning to run: they managed the fastest speeds on the treadmill by combining double support (DS) and flight phases (FPs). In particular the activity duration of the medial gastrocnemius muscle was weakly correlated with age. The number of synergies across groups and conditions needed to cover sufficient data variation ranged between four and eight. The number of synergies tended to be smaller in toddlers than it did in preschoolers and school-age children but the adults had the lowest number for both conditions. Against our expectations, the age groups did not differ significantly in the timing or duration of synergies. We believe that the increase in the number of muscle synergies in older children relates to motor learning and exploration. The ability to run with a FP is clearly associated with an increase in the number of muscle synergies.

Approaches to revealing the neural basis of muscle synergies: a review and a critique

The central nervous system (CNS) may produce coordinated motor outputs via the combination of motor modules representable as muscle synergies. Identification of muscle synergies has hitherto relied on applying factorization algorithms to multimuscle electromyographic data (EMGs) recorded during motor behaviors. Recent studies have attempted to validate the neural basis of the muscle synergies identified by independently retrieving the muscle synergies through CNS manipulations and analytic techniques such as spike-triggered averaging of EMGs. Experimental data have demonstrated the pivotal role of the spinal premotor interneurons in the synergies' organization and the presence of motor cortical loci whose stimulations offer access to the synergies, but whether the motor cortex is also involved in organizing the synergies has remained unsettled. We argue that one difficulty inherent in current approaches to probing the synergies' neural basis is that the EMG generative model based on linear combination of synergies and the decomposition algorithms used for synergy identification are not grounded on enough prior knowledge from neurophysiology. Progress may be facilitated by constraining or updating the model and algorithms with knowledge derived directly from CNS manipulations or recordings. An investigative framework based on evaluating the relevance of neurophysiologically constrained models of muscle synergies to natural motor behaviors will allow a more sophisticated understanding of motor modularity, which will help the community move forward from the current debate on the neural versus nonneural origin of muscle synergies.

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.

Muscle Synergy Assessment During Single-Leg Stance

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.

Shoulder muscle activation strategies differ when lifting or lowering a load

PURPOSE

Lowering a load could be associated with abnormal shoulder and scapular motion. We tested the hypothesis that lowering a load involves different shoulder muscle coordination strategies compared to lifting a load.

METHODS

EMG activity of 13 muscles was recorded in 30 healthy volunteers who lifted and lowered a 6, 12 or 18 kg box between three shelves. Kinematics, EMG levels and muscle synergies, extracted using non-negative matrix factorization, were analyzed.

RESULTS

We found greater muscle activity level during lowering in four muscles (+ 1-2% MVC in anterior deltoid, biceps brachii, serratus anterior and pectoralis major). The movements were performed faster during lifting (18.2 vs. 15.9 cm/s) but with similar hand paths and segment kinematics. The number of synergies was the same in both tasks. Two synergies were identified in ~ 75% of subjects, and one synergy in the others. Synergy #1 mainly activated prime movers' muscles, while synergy #2 co-activated several antagonist muscles. Synergies' structure was similar between lifting and lowering (Pearson's r ≈ 0.9 for synergy #1 and 0.7-08 for synergy #2). Synergy #2 was more activated during lowering and explained the greater activity observed in anterior deltoid, serratus anterior and pectoralis.

CONCLUSION

Lifting and lowering a load were associated with similar synergy structure. In 3/4 of subjects, lowering movements involved greater activation of a "multiple antagonists" synergy. The other subjects co-contracted all shoulder muscles as a unit in both conditions. These inter-individual differences should be investigated in the occurrence of shoulder musculoskeletal disorders.

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.

Older adults reduce the complexity and efficiency of neuromuscular control to preserve walking balance

Healthy aging modifies neuromuscular control of dynamic balance. Challenging tasks could amplify such modifications, providing clinical insights. We examined the effects of age and walking condition difficulty on neuromuscular control of walking balance. We analyzed whole-body kinematics and activity of 13 right leg and trunk muscles in 17 young (11 males and 6 females; age 24 ± 3 years) and 14 older adults (3 males and 11 females; age 69 ± 4 years) while walking on a taped line on the floor and a 6-cm wide beam. Spatiotemporal parameters of gait, margin of stability, motor performance, and muscle synergies were estimated. Regardless of age, maintaining walking balance was more difficult on the beam compared to the taped line as evidenced by a shorter distance walked (17.3%), a reduction in step length (5.8%) and speed (10.3%), as well as a 40.0% smaller margin of stability during beam vs. tape walking. The number of muscle synergies was also higher during beam vs. tape walking. Compared to younger adults, older adults had larger margin of stability during beam walking. Older adults also had higher muscle co-activity within each muscle synergy and greater variance accounted for by the first muscle synergy regardless of condition. Such age-effects may be interpreted as a safer, less efficient, and less complex neuromuscular modular control strategy. In conclusion, beam walking increased the difficulty of maintaining walking balance and induced adaptations in modular control. It seems that healthy older adults reduce the complexity and efficiency of neuromuscular control of walking to preserve walking balance.

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.

How to Improve Robustness in Muscle Synergy Extraction

The muscle synergy theory was widely used in literature to assess the modular organization of the central nervous system (CNS) during human locomotion. The extraction of muscle synergies may be strongly influenced by the preprocessing techniques applied to surface electromyographic (sEMG) signals. The aim of this contribution is to assess the robustness improvement in muscle synergy extraction obtained using an innovative pre-processing technique with respect to the standard procedure. The new pre-processing technique that we propose is based on the extraction of principal muscle activation intervals (necessary to accomplish a specific biomechanical task during gait) from the original sEMG signals, discarding the secondary muscle activation intervals (activations that occur only in some strides with auxiliary functions). Results suggest that the extraction of the principal activation intervals from sEMG provide a more consistent and stable description of the modular organization of the CNS with respect to the standard pre-processing procedure.

Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach.

Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking

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.

Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for five synergies). Constraining activations to SynSO caused the simulated muscle stress to increase compared to SO for all individuals, causing a 157% increase with two synergies. These results suggest that constraining simulated activations in inverse dynamic simulations to subject-specific synergies alone may not improve estimation of muscle activations during gait for generic musculoskeletal models.

Motor primitives are determined in early development and are then robustly conserved into adulthood

Motor patterns in legged vertebrates show modularity in both young and adult animals, comprising motor synergies or primitives. Are such spinal modules observed in young mammals conserved into adulthood or altered? Conceivably, early circuit modules alter radically through experience and descending pathways' activity. We analyze lumbar motor patterns of intact adult rats and the same rats after spinal transection and compare these with adult rats spinal transected 5 days postnatally, before most motor experience, using only rats that never developed hind limb weight bearing. We use independent component analysis (ICA) to extract synergies from electromyography (EMG). ICA information-based methods identify both weakly active and strongly active synergies. We compare all spatial synergies and their activation/drive strengths as proxies of spinal modules and their underlying circuits. Remarkably, we find that spatial primitives/synergies of adult injured and neonatal injured rats differed insignificantly, despite different developmental histories. However, intact rats possess some synergies that differ significantly, although modestly, in spatial structure. Rats injured as adults were more similar in modularity to rats that had neonatal spinal transection than to themselves before injury. We surmise that spinal circuit modules for spatial synergy patterns may be determined early, before postnatal day 5 (P5), and remain largely unaltered by subsequent development or weight-bearing experience. An alternative explanation but equally important is that, after complete spinal transection, both neonatal and mature adult spinal cords rapidly converge to common synergy sets. This fundamental or convergent synergy circuitry, fully determined by P5, is revealed after spinal cord transection.

Are there sex differences in muscle coordination of the upper girdle during a sustained motor task?

PURPOSE

The higher prevalence of work-related musculoskeletal disorders among women compared with men could be explained by sex-gender differences related to biological and physiological processes. The aim of this study was to evaluate sex differences in motor coordination during a sustained and repetitive motor task.

METHODS

Seventeen healthy females and 21 healthy males participated. The surface electromyography (sEMG) of the trapezius portions and serratus anterior were recorded. Root mean square (RMS) values were computed to assess the level of muscle activity. The standard deviation (SD) and coefficient of variation (CV) were computed as metrics of size of variability. The normalized mutual information (NMI) values were calculated as index of functional connectivity between muscles pairs.

RESULTS

Females had higher normalized RMS values for the upper trapezius (acromial fibers) and serratus anterior muscles compared with males. RMS decreased, SD and CV increased while NMI decreased for almost all muscle pairs over time.

CONCLUSION

The present work showed some signs of sex differences in muscle coordination of the shoulder girdle during a sustained motor task, performed with the upper limb positioned below of the shoulder level.

Modeling musculoskeletal kinematic and dynamic redundancy using null space projection

The coordination of the human musculoskeletal system is deeply influenced by its redundant structure, in both kinematic and dynamic terms. Noticing a lack of a relevant, thorough treatment in the literature, we formally address the issue in order to understand and quantify factors affecting the motor coordination. We employed well-established techniques from linear algebra and projection operators to extend the underlying kinematic and dynamic relations by modeling the redundancy effects in null space. We distinguish three types of operational spaces, namely task, joint and muscle space, which are directly associated with the physiological factors of the system. A method for consistently quantifying the redundancy on multiple levels in the entire space of feasible solutions is also presented. We evaluate the proposed muscle space projection on segmental level reflexes and the computation of the feasible muscle forces for arbitrary movements. The former proves to be a convenient representation for interfacing with segmental level models or implementing controllers for tendon driven robots, while the latter enables the identification of force variability and correlations between muscle groups, attributed to the system’s redundancy. Furthermore, the usefulness of the proposed framework is demonstrated in the context of estimating the bounds of the joint reaction loads, where we show that misinterpretation of the results is possible if the null space forces are ignored. This work presents a theoretical analysis of the redundancy problem, facilitating application in a broad range of fields related to motor coordination, as it provides the groundwork for null space characterization. The proposed framework rigorously accounts for the effects of kinematic and dynamic redundancy, incorporating it directly into the underlying equations using the notion of null space projection, leading to a complete description of the system.

Temporal and spatial asymmetries during stationary cycling cause different feedforward and feedback modifications in the muscular control of the lower limbs

Motor adaptations are useful for studying the way in which the lower limbs are controlled by the brain. However, motor adaptation paradigms for the lower limbs are typically based on locomotion tasks, where the necessity of maintaining postural stability is the main driver of adaptation and could possibly mask other underlying processes. In this study we investigated whether small temporal or spatial asymmetries can trigger motor adaptations during stationary cycling, where stability is not directly compromised. Fourteen healthy individuals participated in two experiments: in one of the experiments, the angle between the crank arms of the pedals was altered by 10° to induce a temporal asymmetry; in the other experiment, the length of the right pedal was shortened by 2.4 cm to induce a spatial asymmetry. We recorded the acceleration of the crank arms and the electromographic signals of 16 muscles (8 per leg). The analysis of the accelerometer data was used to investigate the presence of motor adaptations. Muscle synergy analysis was performed on each side to quantify changes in neuromuscular control. We found that motor adaptations are present in response to temporal asymmetries and are obtained by progressively shifting the activation patterns of two synergies on the right leg. Spatial asymmetries, on the other hand, appear to trigger a feedback-driven response that does not present an aftereffect. This response is characterized by a steplike decrease in activity in the right gastrocnemius when the asymmetry is present and likely reflects the altered task demands.

NEW & NOTEWORTHY The processes driving lower limb motor adaptations are not fully clear, and previous research appears to indicate that adaptations are mainly driven by stability. We show that lower limb adaptations can be obtained also in the absence of an explicit balance threat. We also show that adaptations are present when kinematic error cannot be compensated for, suggesting the presence of intrinsic error measures regulating the timing of activation of the two legs.

Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control

We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its ‘feasible activation space’). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes—in a complete way—the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D. The feasible activation space for a given task is the landscape where all neuromuscular learning, control, and performance must occur. This approach unifies current theories of neuromuscular control because the structure of feasible activation spaces can be separately approximated as either low-dimensional basis functions (synergies), high-dimensional joint probability distributions (Bayesian priors), or fitness landscapes (to optimize cost functions).

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.

Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization

The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

Low-Dimensional Motor Control Representations in Throwing Motions

In this study, we identified a low-dimensional representation of control mechanisms in throwing motions from a variety of subjects and target distances. The control representation was identified at the kinematic level in task and joint spaces, respectively, and at the muscle activation level using the theory of muscle synergies. Representative features of throwing motions in all of these spaces were chosen to be investigated. Features were extracted using factorization and clustering techniques from the muscle data of unexperienced subjects (with different morphologies and physical conditions) during a series of throwing tasks. Two synergy extraction methods were tested to assess their consistency. For the task features, the degrees of freedom (DoF), and the muscles under study, the results can be summarized as (1) a control representation across subjects consisting of only two synergies at the activation level and of representative features in the task and joint spaces, (2) a reduction of control redundancy (since the number of synergies are less than the number of actions to be controlled), (3) links between the synergies triggering intensity and the throwing distance, and finally (4) consistency of the extraction methods. Such results are useful to better represent mechanisms hidden behind such dynamical motions and could offer a promising control representation for synthesizing motions with muscle-driven characters.

Intra-Subject Consistency during Locomotion: Similarity in Shared and Subject-Specific Muscle Synergies

Human locomotion is a complex motor task. Previous research hypothesized that muscle synergies reflect the modular control of muscle groups operated by the Central Nervous System (CNS). Despite the high stride-to-stride variability characterizing human gait, most studies analyze only a few strides. This may be limiting, because the intra-subject variability of motor output is neglected. This gap could be filled by recording and analyzing many gait cycles during a single walking task. In this way, it can be investigated if CNS recruits the same muscle synergies consistently or if different strategies are adopted during the locomotion task. The aim of this work is to investigate the intra-subject consistency of muscle synergies during overground walking. Twelve young healthy volunteers were instructed to walk for 5 min at their natural pace. On the average, 181 ± 10 gait cycles were analyzed for each subject. Surface electromyography was recorded from 12 muscles of the dominant lower limb and the trunk. Gait cycles were grouped into subgroups containing 10 gait cycles each. The consistency of the muscle synergies extracted during the gait trial was assessed by measuring cosine similarity (CS) of muscle weights vectors, and zero-lag cross-correlation (CC) of activation signals. The average intra-subject CS and CC were 0.94 ± 0.10 and 0.96 ± 0.06, respectively. We found five synergies shared by all the subjects: high consistency values were found for these synergies (CS = 0.96 ± 0.05, CC = 0.97 ± 0.03). In addition, we found 10 subject-specific synergies. These synergies were less consistent (CS = 0.80 ± 0.20, CC = 0.89 ± 0.14). In conclusion, our results demonstrated that shared muscle synergies were highly consistent during walking. Subject-specific muscle synergies were also consistent, although to a lesser extent.

Inter- and Intrasubject Similarity of Muscle Synergies During Bench Press With Slow and Fast Velocity

We investigated the effect of low and high bar velocity on inter- and intrasubject similarity of muscle synergies during bench press. A total of 13 trained male subjects underwent two exercise conditions: a slow- and a fast-velocity bench press. Surface electromyography was recorded from 13 muscles, and muscle synergies were extracted using a nonnegative matrix factorization algorithm. The intrasubject similarity across conditions and intersubject similarity within conditions were computed for muscle synergy vectors and activation coefficients. Two muscle synergies were sufficient to describe the dataset variability. For the second synergy activation coefficient, the intersubject similarity within the fast-velocity condition was greater than the intrasubject similarity of the activation coefficient across the conditions. An opposite pattern was observed for the first muscle synergy vector. We concluded that the activation coefficients are robust within conditions, indicating a robust temporal pattern of muscular activity across individuals, but the muscle synergy vector seemed to be individually assigned.

Decoding finger movement in humans using synergy of EEG cortical current signals

The synchronized activity of neuronal populations across multiple distant brain areas may reflect coordinated interactions of large-scale brain networks. Currently, there is no established method to investigate the temporal transitions between these large-scale networks that would allow, for example, to decode finger movements. Here we applied a matrix factorization method employing principal component and temporal independent component analyses to identify brain activity synchronizations. In accordance with previous studies investigating “muscle synergies”, we refer to this activity as “brain activity synergy”. Using electroencephalography (EEG), we first estimated cortical current sources (CSs) and then identified brain activity synergies within the estimated CS signals. A decoding analysis for finger movement in eight directions showed that such CS synergies provided more information for dissociating between movements than EEG sensor signals, EEG synergy, or CS signals, suggesting that temporal activation patterns of the synchronizing CSs may contain information related to motor control. A quantitative analysis of features selected by the decoders further revealed temporal transitions among the primary motor area, dorsal and ventral premotor areas, pre-supplementary motor area, and supplementary motor area, which may reflect transitions in motor planning and execution. These results provide a proof of concept for brain activity synergy estimation using CSs.