Motor patterns of patients with spinal muscular atrophy suggestive of sensory and corticospinal contributions to the development of locomotor muscle synergies.

Complex locomotor patterns are generated by combination of muscle synergies. How genetic processes, early sensorimotor experiences, and the developmental dynamics of neuronal circuits contribute to the expression of muscle synergies remains elusive. We shed light on the factors that influence development of muscle synergies by studying subjects with spinal muscular atrophy (SMA, types II/IIIa), a disorder associated with degeneration and deafferentation of motoneurons and possibly motor cortical and cerebellar abnormalities, from which the afflicted would have atypical sensorimotor histories around typical walking onset. Muscle synergies of children with SMA were identified from electromyographic signals recorded during active-assisted leg motions or walking, and compared with those of age-matched controls. We found that the earlier the SMA onset age, the more different the SMA synergies were from the normative. These alterations could not just be explained by the different degrees of uneven motoneuronal losses across muscles. The SMA-specific synergies had activations in muscles from multiple limb compartments, a finding reminiscent of the neonatal synergies of typically developing infants. Overall, while the synergies shared between SMA and control subjects may reflect components of a core modular infrastructure determined early in life, the SMA-specific synergies may be developmentally immature synergies that arise from inadequate activity-dependent interneuronal sculpting due to abnormal sensorimotor experience and other factors. Other mechanisms including SMA-induced intraspinal changes and altered cortical-spinal interactions may also contribute to synergy changes. Our interpretation highlights the roles of the sensory and descending systems to the typical and abnormal development of locomotor modules.NEW & NOTEWORTHY This is likely the first report of locomotor muscle synergies of children with spinal muscular atrophy (SMA), a subject group with atypical developmental sensorimotor experience. We found that the earlier the SMA onset age, the more the subjects' synergies deviated from those of age-matched controls. This result suggests contributions of the sensory/corticospinal activities to the typical expression of locomotor modules, and how their disruptions during a critical period of development may lead to abnormal motor modules.

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations.

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system's adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo-force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

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.

Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment.

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols.

A Quasi-Likelihood Approach to Nonnegative Matrix Factorization.

A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proved using the expectation-maximization algorithm. In addition, a measure to evaluate the goodness of fit of the resulting factorization is described. The proposed methods allow modeling of nonlinear effects using appropriate link functions and are illustrated using an application in biomedical signal processing.

Muscle synergy patterns as physiological markers of motor cortical damage.

The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination-including preservation, merging, and fractionation of muscle synergies-that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.

On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data.

Nonnegative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H, where V ~ WH. It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology, and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this letter, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates, and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness of fit on data. Our methods are demonstrated using experimental data from electromyography studies, as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise.

The effect of arm weight support on upper limb muscle synergies during reaching movements.

BACKGROUND: Compensating for the effect of gravity by providing arm-weight support (WS) is a technique often utilized in the rehabilitation of patients with neurological conditions such as stroke to facilitate the performance of arm movements during therapy. Although it has been shown that, in healthy subjects as well as in stroke survivors, the use of arm WS during the performance of reaching movements leads to a general reduction, as expected, in the level of activation of upper limb muscles, the effects of different levels of WS on the characteristics of the kinematics of motion and of the activity of upper limb muscles have not been thoroughly investigated before. METHODS: In this study, we systematically assessed the characteristics of the kinematics of motion and of the activity of 14 upper limb muscles in a group of 9 healthy subjects who performed 3-D arm reaching movements while provided with different levels of arm WS. We studied the hand trajectory and the trunk, shoulder, and elbow joint angular displacement trajectories for different levels of arm WS. Besides, we analyzed the amplitude of the surface electromyographic (EMG) data collected from upper limb muscles and investigated patterns of coordination via the analysis of muscle synergies. RESULTS: The characteristics of the kinematics of motion varied across WS conditions but did not show distinct trends with the level of arm WS. The level of activation of upper limb muscles generally decreased, as expected, with the increase in arm WS. The same eight muscle synergies were identified in all WS conditions. Their level of activation depended on the provided level of arm WS. CONCLUSIONS: The analysis of muscle synergies allowed us to identify a modular organization underlying the generation of arm reaching movements that appears to be invariant to the level of arm WS. The results of this study provide a normative dataset for the assessment of the effects of the level of arm WS on muscle synergies in stroke survivors and other patients who could benefit from upper limb rehabilitation with arm WS.

Task-based EEG and fMRI paradigms in a multimodal clinical diagnostic framework for disorders of consciousness.

Disorders of consciousness (DoC) are generally diagnosed by clinical assessment, which is a predominantly motor-driven process and accounts for up to 40 % of non-communication being misdiagnosed as unresponsive wakefulness syndrome (UWS) (previously known as prolonged/persistent vegetative state). Given the consequences of misdiagnosis, a more reliable and objective multimodal protocol to diagnosing DoC is needed, but has not been produced due to concerns regarding their interpretation and reliability. Of the techniques commonly used to detect consciousness in DoC, task-based paradigms (active paradigms) produce the most unequivocal result when findings are positive. It is well-established that command following (CF) reliably reflects preserved consciousness. Task-based electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can detect motor-independent CF and reveal preserved covert consciousness in up to 14 % of UWS patients. Accordingly, to improve the diagnostic accuracy of DoC, we propose a practical multimodal clinical decision framework centered on task-based EEG and fMRI, and complemented by measures like transcranial magnetic stimulation (TMS-EEG).

Concerto of movement: how expertise shapes the synergistic control of upper limb muscles in complex motor tasks with varying tempo and dynamics.

Objective. This research aims to reveal how the synergistic control of upper limb muscles adapts to varying requirements in complex motor tasks and how expertise shapes the motor modules.Approach. We study the muscle synergies of a complex, highly skilled and flexible task-piano playing-and characterize expertise-related muscle-synergy control that permits the experts to effortlessly execute the same task at different tempo and force levels. Surface EMGs (28 muscles) were recorded from adult novice (N= 10) and expert (N= 10) pianists as they played scales and arpeggios at different tempo-force combinations. Muscle synergies were factorized from EMGs.Main results. We found that experts were able to cover both tempo and dynamic ranges using similar synergy selections and achieved better performance, while novices altered synergy selections more to adapt to the changing tempi and keystroke intensities compared with experts. Both groups relied on fine-tuning the muscle weights within specific synergies to accomplish the different task styles, while the experts could tune the muscles in a greater number of synergies, especially when changing the tempo, and switch tempo over a wider range.Significance. Our study sheds light on the control mechanism underpinning expertise-related motor flexibility in highly skilled motor tasks that require decade-long training. Our results have implications on musical and sports training, as well as motor prosthetic design.

Gradient descent decomposition of force-field motor primitives optogenetically elicited for motor mapping of the murine lumbosacral spinal cord.

Generating diverse motor behaviors critical for survival is a challenge that confronts the central nervous system (CNS) of all animals. During movement execution, the CNS performs complex calculations to control a large number of neuromusculoskeletal elements. The theory of modular motor control proposes that spinal interneurons are organized in discrete modules that can be linearly combined to generate a variety of behavioral patterns. These modules have been previously represented as stimulus-evoked force fields (FFs) comprising isometric limb-endpoint forces across workspace locations. Here, we ask whether FFs elicited by different stimulations indeed represent the most elementary units of motor control or are themselves the combination of a limited number of even more fundamental motor modules. To probe for potentially more elementary modules, we optogenetically stimulated the lumbosacral spinal cord of intact and spinalized Thy1-ChR2 transgenic mice ( n=21), eliciting FFs from as many single stimulation loci as possible (20-70 loci per mouse) at minimally necessary power. We found that the resulting varieties of FFs defied simple categorization with just a few clusters. We used gradient descent to further decompose the FFs into their underlying basic force fields (BFFs), whose linear combination explained FF variability. Across mice, we identified 4-5 BFFs with partially localizable but overlapping representations along the spinal cord. The BFFs were structured and topographically distributed in such a way that a rostral-to-caudal traveling wave of activity across the lumbosacral spinal cord may generate a swing-to-stance gait cycle. These BFFs may represent more rudimentary submodules that can be flexibly merged to produce a library of motor modules for building different motor behaviors.

Stability of muscle synergies for voluntary actions after cortical stroke in humans.

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

Applying muscle synergy analysis to forearm high-density electromyography of healthy people.

Introduction: Muscle synergy is regarded as a motor control strategy deployed by the central nervous system (CNS). Clarifying the modulation of muscle synergies under different strength training modes is important for the rehabilitation of motor-impaired patients. Methods: To represent the subtle variation of neuromuscular activities from the smaller forearm muscles during wrist motion, we proposed to apply muscle synergy analysis to preprocessed high-density electromyographic data (HDEMG). Here, modulation of muscle synergies within and across the isometric and isotonic training modes for strengthening muscles across the wrist were investigated. Surface HDEMGs were recorded from healthy subjects (N = 10). Three different HDEMG electrode configurations were used for comparison and validation of the extracted muscle synergies. The cosine of principal angles (CPA) and the Euclidian distance (ED) between synergy vectors were used to evaluate the intra- and inter-mode similarity of muscle synergies. Then, how the activation coefficients modulate the excitation of specific synergy under each mode was examined by pattern recognition. Next, for a closer look at the mode-specific synergies and the synergies shared by the two training modes, k-means clustering was applied. Results: We observed high similarity of muscle synergies across different tasks within each training mode, but decreased similarity of muscle synergies across different training modes. Both intra- and intermode similarity of muscle synergies were consistently robust to electrode configurations regardless of the similarity metric used. Discussion: Overall, our findings suggest that applying muscle synergy analysis to HDEMG is feasible, and that the traditional muscle synergies defined by whole-muscle components may be broadened to include sub-muscle components represented by the HDEMG channels. This work may lead to an appropriate neuromuscular analysis method for motor function evaluation in clinical settings and provide valuable insights for the prescription of rehabilitation training therapies.

Age-Related Modifications of Muscle Synergies and Their Temporal Activations for Overground Walking.

Healthy ageing modifies neuromuscular control of human overground walking. Previous studies found that ageing changes gait biomechanics, but whether there is concurrent ageing-related modulation of neuromuscular control remains unclear. We analyzed gait kinematics and electromyographic signals (EMGs; 14 lower-limb and trunk muscles) collected at three speeds during overground walking in 11 healthy young adults (mean age of 23.4 years) and 11 healthy elderlies (67.2 years). Neuromuscular control was characterized by extracting muscle synergies from EMGs and the synergies of both groups were k -means-clustered. The synergies of the two groups were grossly similar, but we observed numerous cluster- and muscle-specific differences between the age groups. At the population level, some hip-motion-related synergy clusters were more frequently identified in elderlies while others, more frequent in young adults. Such differences in synergy prevalence between the age groups are consistent with the finding that elderlies had a larger hip flexion range. For the synergies shared between both groups, the elderlies had higher inter-subject variability of the temporal activations than young adults. To further explore what synergy characteristics may be related to this inter-subject variability, we found that the inter-subject variance of temporal activations correlated negatively with the sparseness of the synergies in elderlies but not young adults during slow walking. Overall, our results suggest that as humans age, not only are the muscle synergies for walking fine-tuned in structure, but their temporal activation patterns are also more heterogeneous across individuals, possibly reflecting individual differences in prior sensorimotor experience or ageing-related changes in limb neuro-musculoskeletal properties.

Modules in the brain stem and spinal cord underlying motor behaviors.

Previous studies using intact and spinalized animals have suggested that coordinated movements can be generated by appropriate combinations of muscle synergies controlled by the central nervous system (CNS). However, which CNS regions are responsible for expressing muscle synergies remains an open question. We address whether the brain stem and spinal cord are involved in expressing muscle synergies used for executing a range of natural movements. We analyzed the electromyographic (EMG) data recorded from frog leg muscles before and after transection at different levels of the neuraxis-rostral midbrain (brain stem preparations), rostral medulla (medullary preparations), and the spinal-medullary junction (spinal preparations). Brain stem frogs could jump, swim, kick, and step, while medullary frogs could perform only a partial repertoire of movements. In spinal frogs, cutaneous reflexes could be elicited. Systematic EMG analysis found two different synergy types: 1) synergies shared between pre- and posttransection states and 2) synergies specific to individual states. Almost all synergies found in natural movements persisted after transection at rostral midbrain or medulla but not at the spinal-medullary junction for swim and step. Some pretransection- and posttransection-specific synergies for a certain behavior appeared as shared synergies for other motor behaviors of the same animal. These results suggest that the medulla and spinal cord are sufficient for the expression of most muscle synergies in frog behaviors. Overall, this study provides further evidence supporting the idea that motor behaviors may be constructed by muscle synergies organized within the brain stem and spinal cord and activated by descending commands from supraspinal areas.

Muscle endurance time estimation during isometric training using electromyogram and supervised learning.

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. STUDY OBJECTIVE: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. METHOD: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. RESULTS: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. CONCLUSION: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.

Adapting to the Mechanical Properties and Active Force of an Exoskeleton by Altering Muscle Synergies in Chronic Stroke Survivors.

Chronic stroke survivors often suffer from gait impairment resistant to intervention. Recent rehabilitation strategies based on gait training with powered exoskeletons appear promising, but whether chronic survivors may benefit from them remains controversial. We evaluated the potential of exoskeletal gait training in restoring normal motor outputs in chronic survivors (N = 10) by recording electromyographic signals (EMGs, 28 muscles both legs) as they adapted to exoskeletal perturbations, and examined whether any EMG alterations after adaptation were underpinned by closer-to-normal muscle synergies. A unilateral ankle-foot orthosis that produced dorsiflexor torque on the paretic leg during swing was tested. Over a single session, subjects walked overground without exoskeleton (FREE), then with the unpowered exoskeleton (OFF), and finally with the powered exoskeleton (ON). Muscle synergies were identified from EMGs using non-negative matrix factorization. During adaptation to OFF, some paretic-side synergies became more dissimilar to their nonparetic-side counterparts. During adaptation to ON, in half of the subjects some paretic-side synergies became closer to their nonparetic references relative to their similarity at FREE as these paretic-side synergies became sparser in muscle components. Across subjects, level of inter-side similarity increase correlated negatively with the degree of gait temporal asymmetry at FREE. Our results demonstrate the possibility that for some survivors, exoskeletal training may promote closer-to-normal muscle synergies. But to fully achieve this, the active force must trigger adaptive processes that offset any undesired synergy changes arising from adaptation to the device's mechanical properties while also fostering the reemergence of the normal synergies.

Robustness of Muscle Synergies under Variant Muscle Contraction Force during Forearm Movements.

The Electromyography-based Pattern-Recognition (EMG-PR) framework has been investigated for almost three decades towards developing an intuitive myoelectric prosthesis. To utilize the knowledge of the underlying neurophysiological processes of natural movements, the concept of muscle synergy has been applied in prosthesis control and proved to be of great potential recently. For a muscle-synergy-based myoelectric system, the variation of muscle contraction force is also a confounding factor. This study evaluates the robustness of muscle synergies under a variant force level for forearm movements. Six channels of forearm surface EMG were recorded from six healthy subjects when they performed 4 movements (hand open, hand close, wrist flexion, and wrist extension) using low, moderate, and high force, respectively. Muscle synergies were extracted from the EMG using the alternating nonnegativity constrained least squares and active set (NNLS) algorithm. Three analytic strategies were adopted to examine whether muscle synergies were conserved under different force levels. Our results consistently showed that there exists fixed, robust muscle synergies across force levels. This outcome would provide valuable insights to the implementation of muscle- synergy-based assistive technology for the upper extremity.

Modulating the Structure of Motor Variability for Skill Learning Through Specific Muscle Synergies in Elderlies and Young Adults.

Objective: Motor variability - performance variations across task repetitions - has been assumed to be undesirable. But recent studies argue that variability facilitates early motor learning by allowing exploratory search of reward-generating motion, and that variability's structure may be modulated by neural circuits for furthering learning. What are the neural sources of learning-relevant motor variability and its modulation in humans of different ages? Methods: Elderlies and young adults played a 3-session virtual bowling while multi-muscle electromyographic signals were collected. We quantified trial-to-trial variability of muscle synergies - neuromotor control modules - and of their activations. Results: In elderlies, bowling-score gain correlated with change of activation timing variability of specific synergies, but in young adults, with variability changes of synergy-activation magnitude, and of the synergies themselves. Conclusions: Variability modulation of specific muscle synergies and their activations contribute to early motor learning. Elderly and young individuals may rely on different aspects of motor variability to drive learning.