Persons with Parkinson's disease exhibit decreased neuromuscular complexity during gait

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.

Speed-dependent modulations of muscle modules in the gait of people with radiographical and asymptomatic knee osteoarthritis and elderly controls: Case-control pilot study

This study investigates the muscle modules involved in the increase of walking speed in radiographical and asymptomatic knee osteoarthritis (KOA) patients using tensor decomposition. The human body possesses redundancy, which is the property to achieve desired movements with more degrees of freedom than necessary. The muscle module hypothesis is a proposed solution to this redundancy. While previous studies have examined the pathological muscle activity modulations in musculoskeletal diseases such as KOA, they have focused on single muscles rather than muscle modules. Moreover, most studies have only examined the gait of KOA patients at a single speed, leaving the way in which gait speed affects gait parameters in KOA patients unclear. Assessing this influence is crucial for determining appropriate gait speed and understanding why preferred gait speed decreases in KOA patients. In this study, we apply tensor decomposition to muscle activity data to extract muscle modules in KOA patients and elderly controls during walking at different speeds. We found a muscle module comprising hip adductors and back muscles that activate bimodally in a gait cycle, specific to KOA patients when they increase their walking speed. These findings may provide valuable insights for rehabilitation for KOA patients.

Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model

How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.

Changes in walking function and neural control following pelvic cancer surgery with reconstruction

Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.

Age-related modifications of muscle synergies during daily-living tasks: A scoping review

BACKGROUND

Aging is associated with changes in neuromuscular control that can lead to difficulties in performing daily living tasks. Muscle synergy analysis allows the assessment of neuromuscular control strategies and functional deficits. However, the age-related changes of muscle synergies during functional tasks are scattered throughout the literature. This review aimed to synthesize the existing literature on muscle synergies in elderly people during daily-living tasks and examine how they differ from those exhibited by young adults.

METHODS

The Medline, CINAHL and Web of Science databases were searched. Studies were included if they focused on muscle synergies in elderly people during walking, sit-to-stand or stair ascent, and if muscle synergies were obtained by a matrix factorization algorithm.

FINDINGS

Seventeen studies were included after the screening process. The muscle synergies of 295 elderly people and 182 young adults were reported, including 5 to 16 muscles per leg, or leg and trunk. Results suggest that: 1) elderly people and young adults retain similar muscle synergies' number, 2) elderly people have higher muscles weighting during walking, and 3) an increased inter and intra-subject temporal activation variability during specific tasks (i.e., walking and stair ascent, respectively) was reported in elderly people compared to young adults.

INTERPRETATION

This review gives a comprehensive understanding of age-related changes in neuromuscular control during daily living tasks. Our findings suggested that although the number of synergies remains similar, metrics such as spatial and temporal structures of synergies are more suitable to identify neuromuscular control deficits between young adults and elderly people.

Evaluation of therapeutic benefits of botulinum toxin for foot dystonia associated with Parkinson's disease

BACKGROUND

Foot dystonia occurs in patients with Parkinson's disease (PD) and leads to pain, malformation, and difficulty with walking. Botulinum toxin injections may be effective for foot dystonia, but the extent of improvement and effects on motor function are unclear.

METHODS

In this study, we performed botulinum toxin injections for foot dystonia in 25 patients with PD. At 3 weeks and 3 months post-infection, we assessed changes in plantar pressure distribution utilizing the Pressure Plate system; dystonia using the Modified Ashworth Spasm score; pain using the visual analog scale (VAS) score; and lower extremity function using the Calf-raise Senior (CRS) test, Timed Up and Go (TUG) test, and gait parameters (eg, stride length, step length).

RESULTS

We found improved Modified Ashworth Spasm score (p < 0.01) and VAS score (p < 0.01) post-injection. CRS test score (3 weeks, p = 0.006; 3 months, p = 0.068), stride length (3 weeks, p = 0.012; 3 months, p = 0.715), and step length (3 weeks, p = 0.011; 3 months, p = 0.803) also improved. Plantar pressure distribution improved after botulinum toxin injection (metatarsal 1, 3 weeks, p = 0.031; 3 months, p = 0.144; metatarsal 2, 3 weeks, p = 0.049; 3 months, p = 0.065; metatarsal 3, 3 weeks, p = 0.002; 3 months, p = 0.017; metatarsal 4, 3 weeks, p = 0.017; 3 months, p = 0.144; medial heel, 3 weeks, p = 0.01; 3 months, p = 0.395; lateral heel, 3 weeks, p = 0.035; 3 months, p = 0.109).

CONCLUSION

Botulinum toxin injection for foot dystonia in patients with PD can reduce spasms and pain and normalize plantar pressure distribution, which improves balance and lower extremity function.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subjecťs skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 c m , and residual force magnitudes smaller than 2 % of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Motor control complexity can be dynamically simplified during gait pattern exploration using motor control-based biofeedback

Understanding how the central nervous system coordinates diverse motor outputs has been a topic of extensive investigation. Although it is generally accepted that a small set of synergies underlies many common activities, such as walking, whether synergies are equally robust across a broader array of gait patterns or can be flexibly modified remains unclear. Here, we evaluated the extent to which synergies changed as nondisabled adults (n = 14) explored gait patterns using custom biofeedback. Secondarily, we used Bayesian additive regression trees to identify factors that were associated with synergy modulation. Participants explored 41.1 ± 8.0 gait patterns using biofeedback, during which synergy recruitment changed depending on the type and magnitude of gait pattern modification. Specifically, a consistent set of synergies was recruited to accommodate small deviations from baseline, but additional synergies emerged for larger gait changes. Synergy complexity was similarly modulated; complexity decreased for 82.6% of the attempted gait patterns, but distal gait mechanics were strongly associated with these changes. In particular, greater ankle dorsiflexion moments and knee flexion through stance, as well as greater knee extension moments at initial contact, corresponded to a reduction in synergy complexity. Taken together, these results suggest that the central nervous system preferentially adopts a low-dimensional, largely invariant control strategy but can modify that strategy to produce diverse gait patterns. Beyond improving understanding of how synergies are recruited during gait, study outcomes may also help identify parameters that can be targeted with interventions to alter synergies and improve motor control after neurological injury.NEW & NOTEWORTHY We used a motor control-based biofeedback system and machine learning to characterize the extent to which nondisabled adults can modulate synergies during gait pattern exploration. Results revealed that a small library of synergies underlies an array of gait patterns but that recruitment from this library changes as a function of the imposed biomechanical constraints. Our findings enhance understanding of the neural control of gait and may inform biofeedback strategies to improve synergy recruitment after neurological injury.

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.

The investigation of an analysis method for co-activation of knee osteoarthritis utilizing normalization of peak dynamic method

BACKGROUND

Assessing co-activation characteristics in knee osteoarthritis (knee OA) using method of quantification of the activity ratio (such as the co-contraction index (CCI) or the directed co-activation ratios (DCAR)) for surface electromyography (EMG) has been reported. However, no studies have discussed the differences in results between non-negative matrix factorization (NNMF) and the DCAR.

RESEARCH QUESTION

Does DCAR or NNMF reflect the characteristic co-activation pattern of knee OA while using EMG normalized by the peak dynamic method?

METHODS

Ten elderly control participants (EC) and ten knee OA patients (KOA) volunteered to participate in this study. EMG data from 20 participants were obtained from our previous study. Patients with knee OA were recruited from a local orthopedic clinic. The DCAR of agonist and antagonist muscles and the number of modules using NNMF were calculated to evaluate multiple muscle co-activations. An independent t-test statistical parametric mapping approach was used to compare the DCAR between the two groups. The difference in the number of modules between EC and KOA was evaluated using the Wilcoxon rank-sum test.

RESULTS

There was no significant difference in the DCAR between the two groups. However, NNMF had significantly fewer modules with KOA than with EC.

SIGNIFICANCE

The NNMF with the ratio of the amplitude of each muscle and duration of activity as variables reflected the co-activation of KOA, characterized by the high synchronous and prolonged activity of each muscle. Therefore, the NNMF is suitable for extracting characteristic muscle activity patterns of knee OA independent of the normalization method.

Gait Event Prediction Using Surface Electromyography in Parkinsonian Patients

Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.

Modular organization of locomotor networks in people with severe spinal cord injury

Introduction

Previous studies support modular organization of locomotor circuitry contributing to the activation of muscles in a spatially and temporally organized manner during locomotion. Human spinal circuitry may reorganize after spinal cord injury; however, it is unclear if reorganization of spinal circuitry post-injury affects the modular organization. Here we characterize the modular synergy organization of locomotor muscle activity expressed during assisted stepping in subjects with complete and incomplete spinal cord injury (SCI) of varying chronicity, before any explicit training regimen. We also investigated whether the synergy characteristics changed in two subjects who achieved independent walking after training with spinal cord epidural stimulation.

Methods

To capture synergy structures during stepping, individuals with SCI were stepped on a body-weight supported treadmill with manual facilitation, while electromyography (EMGs) were recorded from bilateral leg muscles. EMGs were analyzed using non-negative matrix factorization (NMF) and independent component analysis (ICA) to identify synergy patterns. Synergy patterns from the SCI subjects were compared across different clinical characteristics and to non-disabled subjects (NDs).

Results

Results for both NMF and ICA indicated that the subjects with SCI were similar among themselves, but expressed a greater variability in the number of synergies for criterion variance capture compared to NDs, and weaker correlation to NDs. ICA yielded a greater number of muscle synergies than NMF. Further, the clinical characteristics of SCI subjects and chronicity did not predict any significant differences in the spatial synergy structures despite any neuroplastic changes. Further, post-training synergies did not become closer to ND synergies in two individuals.

Discussion

These findings suggest fundamental differences between motor modules expressed in SCIs and NDs, as well as a striking level of spatial and temporal synergy stability in motor modules in the SCI population, absent the application of specific interventions.

The Effect of Dual-Task Motor-Cognitive Training in Adults with Neurological Diseases Who Are at Risk of Falling

Falls are common in patients with neurological diseases and can be very problematic. Recently, there has been an increase in fall prevention research in people with neurological diseases; however, these studies are usually condition-specific (e.g., only MS, PD or stroke). Here, our aim was to evaluate and compare the efficacy of an advanced and innovative dual-task, motor-cognitive rehabilitation program in individuals with different neurological diseases who are at risk of falling. We recruited 95 consecutive adults with neurological diseases who are at risk of falling and divided them into four groups: 31 with cerebrovascular disease (CVD), 20 with Parkinson’s disease (PD), 23 with traumatic brain injury (TBI) and 21 with other neurological diseases (OND). Each patient completed a dual-task, motor-cognitive training program and underwent two test evaluations to assess balance, gait, fear of falling and walking performance at the pre-and post-intervention. We found that our experimental motor-cognitive, dual-task rehabilitation program was an effective method for improving walking balance, gait, walking endurance and speed, and fear of falling, and that it reduced the risk of falls in patients with different neurological diseases. This study presents an alternative approach for people with chronic neurological diseases and provides innovative data for managing this population.

Evaluation of Spatiotemporal Patterns of the Spinal Muscle Coordination Output during Walking in the Exoskeleton

Recent advances in the performance and evaluation of walking in exoskeletons use various assessments based on kinematic/kinetic measurements. While such variables provide general characteristics of gait performance, only limited conclusions can be made about the neural control strategies. Moreover, some kinematic or kinetic parameters are a consequence of the control implemented on the exoskeleton. Therefore, standard indicators based on kinematic variables have limitations and need to be complemented by performance measures of muscle coordination and control strategy. Knowledge about what happens at the spinal cord output level might also be critical for rehabilitation since an abnormal spatiotemporal integration of activity in specific spinal segments may result in a risk for abnormalities in gait recovery. Here we present the PEPATO software, which is a benchmarking solution to assess changes in the spinal locomotor output during walking in the exoskeleton with respect to reference data on normal walking. In particular, functional and structural changes at the spinal cord level can be mapped into muscle synergies and spinal maps of motoneuron activity. A user-friendly software interface guides the user through several data processing steps leading to a set of performance indicators as output. We present an example of the usage of this software for evaluating walking in an unloading exoskeleton that allows a person to step in simulated reduced (the Moon's) gravity. By analyzing the EMG activity from lower limb muscles, the algorithms detected several performance indicators demonstrating differential adaptation (shifts in the center of activity, prolonged activation) of specific muscle activation modules and spinal motor pools and increased coactivation of lumbar and sacral segments. The software is integrated at EUROBENCH facilities to benchmark the performance of walking in the exoskeleton from the point of view of changes in the spinal locomotor output.

Evidence for shared neural information between muscle synergies and corticospinal efficacy

Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH), and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters, including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

Individuals with Chronic Mild-to-Moderate Traumatic Brain Injury Exhibit Decreased Neuromuscular Complexity During Gait

BACKGROUND

Synergy analysis provides a means of quantifying the complexity of neuromuscular control during gait. Prior studies have shown evidence of reduced neuromuscular complexity during gait in individuals with neurological disorders associated with stroke, cerebral palsy, and Parkinson's disease.

OBJECTIVE

The purpose of this study was to investigate neuromuscular complexity during gait in individuals who experienced a prior traumatic brain injury (TBI) that resulted in chronic balance deficits.

METHODS

We measured and analyzed lower extremity electromyographic data during treadmill and overground walking for 44 individuals with residual balance deficits from a mild-to-moderate TBI at least 1 year prior. We also tested 20 unimpaired controls as a comparison. Muscle synergies were calculated for each limb using non-negative matrix factorization of the activation patterns for 6 leg muscles. We quantified neuromuscular complexity using Walk-DMC, a normalized metric of the total variance accounted for by a single synergy, in which a Walk-DMC score of 100 represents normal variance accounted for. We compared group average synergy structures and inter-limb similarity using cosine similarity. We also quantified each individual's gait and balance using the Sensory Organization Test, the Dynamic Gait Index, and the Six-Minute Walk Test.

RESULTS

Neuromuscular complexity was diminished for individuals with a prior TBI. Walk-DMC averaged 92.8 ± 12.3 for the TBI group during overground walking, which was significantly less than seen in controls (100.0 ± 10.0). Individuals with a prior TBI exhibited 13% slower overground walking speeds than controls and reduced performance on the Dynamic Gait Index (18.5 ± 4.7 out of 24). However, Walk-DMC measures were insufficient to stratify variations in assessments of gait and balance performance. Group average synergy structures were similar between groups, although there were considerable between-group differences in the inter-limb similarity of the synergy activation vectors.

CONCLUSIONS

Individuals with gait and balance deficits due to a prior TBI exhibit evidence of decreased neuromuscular complexity during gait. Our results suggest that individuals with TBI exhibit similar muscle synergy weightings as controls, but altered control of the temporal activation of these muscle weightings.

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.

The Cost of Gait Slowness: Can Persons with Parkinson's Disease Save Energy by Walking Faster?

BACKGROUND

Gait slowing is a common feature of Parkinson's disease (PD). Many therapies aim to improve gait speed in persons with PD, but goals are often imprecise. How fast should each patient walk? And how do persons with PD benefit from walking faster? There is an important need to understand how walking speed affects fundamental aspects of gait-including energy cost and stability-that could guide individualized therapy decisions in persons with PD.

OBJECTIVE

We investigated how changes in walking speed affected energy cost and spatiotemporal gait parameters in persons with PD. We compared these effects between dopaminergic medication states and to those observed in age-matched control participants.

METHODS

Twelve persons with PD and twelve control participants performed treadmill walking trials spanning at least five different speeds (seven speeds were desired, but not all participants could walk at the fastest speeds). Persons with PD participated in two walking sessions on separate days (once while optimally medicated, once after 12-hour withdrawal from dopaminergic medication). We measured kinematic and metabolic data across all trials.

RESULTS

Persons with PD significantly reduced energy cost by walking faster than their preferred speeds. This held true across medication conditions and was not observed in control participants. The patient-specific walking speeds that reduced energy cost did not significantly affect gait variability metrics (used as proxies for gait stability).

CONCLUSION

The gait slowing that occurs with PD results in energetically suboptimal walking. Rehabilitation strategies that target patient-specific increases in walking speed could result in a less effortful gait.

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects’ health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

Greater Reliance on Cerebral Palsy-Specific Muscle Synergies During Gait Relates to Poorer Temporal-Spatial Performance Measures

Children with cerebral palsy typically exhibit reduced complexity of muscle coordination patterns during walking; however, the specific patterns that characterize their gait abnormalities are still not well documented. This study aimed to identify the specific repertoire of muscle coordination patterns in children with CP during walking compared to same-aged peers without CP and their relationships to gait performance. To identify muscle coordination patterns, we extracted muscle synergies from 10 children with CP and 10 age-matched typically developing children (TD). K-mean clustering and discriminant analyses of all extracted synergies were used to group similar synergies. Then, weight-averaged z-scores were quantified for each cluster to determine their group-specific level. In this cohort, 10 of the 17 distinct clusters were largely CP-specific while six clusters were seen mainly in TD, and one was non-specific. CP-specific clusters generally showed merging of two TD synergies, excessive antagonist co-activation, decreased muscle activation compared to TD, and complex or atypical pattern. Significant correlations were found between weight-averaged z-scores and step length asymmetry, cadence asymmetry, self-selected treadmill speed and AP-COM displacement of the pelvis such that greater CP-specificity of muscle synergies was related to poorer performance, thus indicating that CP-specific synergies can influence motor dysfunction.

O tempo de balanço como variável preditiva da doença de Parkinson

RESUMO Atualmente, a doença de Parkinson (DP) tem seu diagnóstico baseado apenas na observação clínica de uma combinação de sintomas, o que pode levar ao diagnóstico tardio, uma vez que alguns indivíduos podem ter a doença por 5 a 10 anos antes de serem diagnosticados. O objetivo do estudo foi identificar variáveis cinemáticas temporais da marcha capazes de discriminar idosos com e sem DP. 40 indivíduos foram divididos em dois grupos: grupo de idosos sem DP (n=21) e com DP (n=19). Dez ciclos de marcha consecutivos foram obtidos durante a marcha em velocidade de preferência, e utilizados para a análise dos dados. Realizou-se uma análise discriminativa para determinar um modelo preditor de alterações na marcha característico da DP e calculado com base na especificidade e sensibilidade de cada variável analisada, utilizando-se variáveis cinemáticas temporais. A variável com valor discriminativo de sensibilidade e especificidade foi o tempo de balanço, o que pode classificá-la como a variável com grande potencial preditivo da presença ou não da DP; o ponto de corte encontrado para essa variável foi de 0,48segundos. A análise cinemática da marcha permite discriminar um grupo de indivíduos com DP de um grupo de indivíduos saudáveis com alta sensibilidade e especificidade, por meio do tempo de balanço, menor no grupo acometido pela doença (corte de 0,48 segundos).

Usefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait

OBJECTIVE

To clarify whether there are any muscle synergy changes in individuals with knee osteoarthritis, and to determine whether muscle synergy analysis could be applied to other musculoskeletal diseases.

METHODS

Subjects in this study included 11 young controls (YC), 10 elderly controls (EC), and 10 knee osteoarthritis patients (KOA). Gait was assessed on a split-belt treadmill at 3 km/h. A non-negative matrix factorization (NNMF) was applied to the electromyogram data matrix to extract muscle synergies. To assess the similarity of each module, we performed the NNMF analysis assuming four modules for all of the participants. Further, we calculated joint angles to compare the kinematic data between the module groups.

RESULTS

The number of muscle modules was significantly lower in the EC (2-3) and KOA (2-3) groups than in the YC group (3-4), which reflects the merging of late swing and early stance modules. The EC and KOA groups also showed greater knee flexion angles in the early stance phase. Contrarily, by focusing on the module structure, we found that the merging of early and late stance modules is characteristic in KOA.

CONCLUSION

The lower number of modules in the EC and KOA groups was due to the muscle co-contraction with increased knee flexion angle. Contrarily, the merging of early and late stance modules are modular structures specific to KOA and may be biomarkers for detecting KOA.

SIGNIFICANCE

Describing the changes in multiple muscle control associated with musculoskeletal degeneration can serve as a fundamental biomarker in joint disease.

Synergic control of action in levodopa-naïve Parkinson's disease patients: II. Multi-muscle synergies stabilizing vertical posture

Postural instability is a major disabling feature in Parkinson's disease (PD). We quantified the organization of leg and trunk muscles into synergies stabilizing the center of pressure (COP) coordinate within the uncontrolled manifold hypothesis in levodopa-naïve patients with PD and age-matched control subjects. The main hypothesis was that changes in the synergic control of posture are present early in the PD process even before levodopa exposure. Eleven levodopa-naïve patients with PD and 11 healthy controls performed whole-body cyclical voluntary sway tasks and a self-initiated load-release task during standing on a force plate. Surface electromyographic activity in 13 muscles on the right side of the body was analyzed to identify muscle groups with parallel scaling of activation levels (M-modes). Data were collected both before ("off-drug") and approximately 60 min after the first dose of 25/100 carbidopa/levodopa ("on-drug"). COP-stabilizing synergies were quantified for the load-release task. Levodopa-naïve patients with PD showed no COP-stabilizing synergy "off-drug", whereas controls showed posture-stabilizing multi-M-mode synergy. "On-drug", patients with PD demonstrated a significant increase in the synergy index. There were no significant drug effects on the M-mode composition, anticipatory postural adjustments, indices of motor equivalence, or indices of COP variability. The results suggest that levodopa-naïve patients with PD already show impaired posture-stabilizing multi-muscle synergies that may be used as promising behavioral biomarkers for emerging postural disorders in PD. Moreover, levodopa modified synergy metrics differently in these levodopa-naïve patients compared to a previous study of patients on chronic antiparkinsonian medications (Falaki et al. in J Electromyogr Kinesiol 33:20-26, 2017a), suggesting different neurocircuitry involvement.

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset

Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.

Generalization of motor module recruitment across standing reactive balance and walking is associated with beam walking performance in young adults

BACKGROUND

Recent studies provide compelling evidence that recruiting a common pool of motor modules across behaviors (i.e., motor module generalization) may facilitate motor performance. In particular, motor module generalization across standing reactive balance and walking is associated with both walking speed and endurance in neurologically impaired populations (e.g., stroke survivors and individual's with Parkinson's disease). To test whether this phenomenon is a general neuromuscular strategy associated with well-coordinated walking and not limited to motor impairment, this relationship must be confirmed in neurologically intact adults.

RESEARCH QUESTION

Is motor module generalization across standing reactive balance and walking related to walking performance in neurologically intact young adults?

METHODS

Two populations of young adults were recruited to capture a wide range of walking performance: professionally-trained ballet dancers (i.e., experts, n = 12) and novices (n = 8). Motor modules (a.k.a. muscle synergies) were extracted from muscles spanning the trunk, hip, knee and ankle during walking and multidirectional perturbations to standing. Motor module generalization was calculated as the number of modules common to these behaviors. Walking performance was assessed using self-selected walking speed and beam-walking proficiency (i.e., distance walked on a narrow beam). Motor module generalization between experts and novices was compared using rank-sum tests and the association between generalization and walking performance was assessed using correlation analyses.

RESULTS

Experts generalized more motor modules across standing reactive balance and walking than novices (p = 0.009). Across all subjects, motor module generalization was moderately associated with increased beam walking proficiency (r = 0.456, p = 0.022) but not walking speed (r = 0.092, p = 0.349).

SIGNIFICANCE

Similar relationships between walking performance and motor module generalization exist in neurologically intact and impaired populations, suggesting that motor module generalization across standing reactive balance and walking may be a general neuromuscular mechanism contributing to the successful control of walking.

Effect of Parkinson's disease and two therapeutic interventions on muscle activity during walking: a systematic review

Gait deficits are a common feature of Parkinson’s disease (PD) and predictors of future motor and cognitive impairment. Understanding how muscle activity contributes to gait impairment and effects of therapeutic interventions on motor behaviour is crucial for identifying potential biomarkers and developing rehabilitation strategies. This article reviews sixteen studies that investigate the electromyographic (EMG) activity of lower limb muscles in people with PD during walking and reports on their quality. The weight of evidence establishing differences in motor activity between people with PD and healthy older adults (HOAs) is considered. Additionally, the effect of dopaminergic medication and deep brain stimulation (DBS) on modifying motor activity is assessed. Results indicated greater proximal and decreased distal activity of lower limb muscles during walking in individuals with PD compared to HOA. Dopaminergic medication was associated with increased distal lower limb muscle activity whereas subthalamic nucleus DBS increased activity of both proximal and distal lower limb muscles. Tibialis anterior was impacted most by the interventions. Quality of the studies was not strong, with a median score of 61%. Most studies investigated only distal muscles, involved small sample sizes, extracted limited EMG features and lacked rigorous signal processing. Few studies related changes in motor activity with functional gait measures. Understanding mechanisms underpinning gait impairment in PD is essential for development of personalised rehabilitative interventions. Recommendations for future studies include greater participant numbers, recording more functionally diverse muscles, applying multi-muscle analyses, and relating EMG to functional gait measures.

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.

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.

Muscle Synergies in Parkinson's Disease

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

Deep brain stimulation enhances movement complexity during gait in individuals with Parkinson's disease

Deep brain stimulation (DBS) is associated with substantial improvements in motor symptoms of PD. Emerging evidence has suggested that nonlinear measures of complexity may provide greater insight into the efficacy of anti-PD treatments. This study investigated sample entropy and complexity index values in individuals with PD when DBS was OFF compared to ON. Five individuals with PD using DBS performed a four-minute treadmill walking task while 3D kinematics were collected over two periods of 30 s. Participants were tested in the DBS-ON and DBS-OFF conditions. Sample entropy (SE) and complexity index (CI) values were calculated for ankle, knee and hip joint angles. Paired samples t-tests were used to compare mean SE and CI values between the DBS-OFF and DBS-ON conditions, respectively. No differences in SE or CI were observed between the DBS-ON and DBS-OFF conditions at the ankle. At the knee, the DBS-ON was associated with greater SE and CI values than the DBS-OFF condition. At the hip, DBS-ON was associated with greater SE and CI values than the DBS-OFF condition. DBS enhances complexity of movement at the hip and knee joints while complexity at the ankle joint is not significantly altered. Greater complexity of knee and hip joint motion may represent increased adaptability and a greater number of available strategies to complete the gait task.

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction.

Using an unbiased symbolic movement representation to characterize Parkinson's disease states

Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.

Modulation of muscle synergies for multiple forearm movements under variant force and arm position constraints

OBJECTIVE

To promote clinical applications of muscle-synergy-based neurorehabilitation techniques, this study aims to clarify any potential modulations of both the muscular compositions and temporal activations of forearm muscle synergies for multiple movements under variant force levels and arm positions.

APPROACH

Two groups of healthy subjects participated in this study. Electromyography (EMG) signals were collected when they performed four hand and wrist movements under variant constraints-three different force levels for one group and five arm positions for the other. Muscle synergies were extracted from the EMGs, and their robustness across variant force levels and arm positions was separately assessed by evaluating their across-condition structure similarity, cross-validation, and cluster analysis. The synergies' activation coefficients across the variant constraints were also compared, and the coefficients were used to discriminate the different force levels and the arm positions, respectively.

MAIN RESULTS

Overall, the muscle synergies were relatively fixed across variant constraints, but they were more robust to variant forces than to changing arm positions. The activations of muscle synergies depended largely on the level of contraction force and could discriminate the force levels very well, but the coefficients corresponding to different arm positions discriminated the positions with lower accuracy. Similar results were found for all types of forearm movement analyzed.

SIGNIFICANCE

With our experiment and subject-specific analysis, only slight modulation of the muscular compositions of forearm muscle synergies was found under variant force and arm position constraints. Our results may shed valuable insights to future design of both muscle-synergy-based assistive robots and motor-function assessments.

Kinematic gait parameters for older adults with Parkinson's disease during street crossing simulation

Safe street crossing is important for older adults' social inclusion. We assessed gait kinematic adaptation under different simulated street crossing conditions in older adults with Parkinson's disease (PD) and made comparisons with older adults without PD to understand how PD interferes in outdoor task performance, helping in the development of strategies to reduce road traffic accident risk. In 20 older adults without PD (control group - CG) and 20 with PD (GPD), we assessed usual gait (C1), gait during street crossing simulation (C2), and gait during reduced-time street crossing simulation (C3). Velocity, step length, and step, swing, stance, and double support time were analyzed. Spatiotemporal differences in gait between groups and conditions were analyzed. The GPD walked 16% slower in C1 and 12% slower in C2 and C3 than the CG. GPD also took 11% shorter steps in C1 and 9.5% shorter steps in C2. The double support time was 8.5% greater in C1. In intragroup comparisons, there were significant differences in all gait conditions. The CG showed increased velocity (C2 15% > C1; C3 13% > C2; C3 26% > C1), step length (C2 8% > C1; C3 5% > C2; C3 13% > C1), and swing time (C2 2% > C1; C3 3.7% > C2; C3 6% > C1), and decreased step time (C2 7.5% < C1; C3 8% < C2; C3 15% < C1), stance time (C2 1.3% < C1; C3 2.5% < C2; C3 3.6% < C1), and double support time (C2 6.3% < C1; C3 10.5% < C2; C3 16% < C1). GPD showed increased velocity (C2 19% > C1; C3 13.5% > C2; C3 29.7% > C1), step length, (C2 6% > C1; C3 7% > C2; C3 16% > C1), and swing time (C2 3% > C1; C3 3% > C2; C3 5.5% > C1) and decreased step time (C2 10.3% < C1; C3 7.7% < C2; C3 17% < C1), stance time (C2 1.7% < C1; C3 1.7% < C2; C3 3.4% < C1), and double support time (C2 7% < C1; C3 9.5% < C2; C3 16% < C1). Kinematic changes observed in the intergroup comparison show that participants with PD had lower velocity in all conditions. However, per the intragroup results, both participants with and without PD managed to significantly modify gait variables to attempt to cross the street in the given time. It is necessary to assess whether this increases fall risk by exposing them to road traffic accidents.

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.

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD-CO, PD-PD and CO-CO. Four features reported in the literature-normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)-were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO-PD and PD-PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD-CO and PD-PD is higher compared with CO-CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

Temporal Features of Muscle Synergies in Sit-to-Stand Motion Reflect the Motor Impairment of Post-Stroke Patients

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people ( n = 12 ) and post-stroke patients ( n = 33 ), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.

Physiological complexity of gait between regular and non-exercisers with Parkinson's disease

BACKGROUND

Physiological complexity represents overall health of a system and its underlying capacity to adapt to stresses. The primary purpose of this study was to determine if physiological complexity of gait both ON and OFF anti-Parkinson medication differed between regular and non-exercisers with Parkinson's disease.

METHODS

Twenty participants with idiopathic Parkinson's disease were enrolled in this cross-sectional study (regular exercisers n = 10, non-exercisers n = 10). Two data collection sessions were completed during a single visit, first after a 12-hour overnight withdrawal from anti-Parkinson medications (OFF), and again one-hour after taking anti-Parkinson medications (ON). During each session participants completed a 2-minute walking task at their preferred pace while wearing wireless inertial measurement units on each lower extremity segment (thigh, shank, foot). Multivariate multiscale entropy was calculated from the tri-axial accelerometer signals and converted to a complexity index for analysis.

FINDINGS

Regular exercisers demonstrated significantly higher complexity indices ON and OFF anti-Parkinson medications compared to non-exercisers (ON F = 3.84 P = 0.02; OFF F = 3.61, P < 0.03). Regular exercisers did not significantly differ in complexity between OFF and ON states (most affected leg F = 0.15 P = 0.71; least affected leg F = 0.30 P = 0.60), but non-exercisers demonstrated significantly decreased complexity in the least affected leg OFF anti-Parkinson medications (F = 5.17 P < 0.04).

INTERPRETATION

Enhanced gait complexity in the regular exercisers may indicate that ongoing exercise is a key ingredient contributing to health in persons with Parkinson's disease. Exercising on a regular basis with Parkinson's disease may augment one's ability to adapt to barriers encountered during gait regardless of medication state.

Walking Along Curved Trajectories. Changes With Age and Parkinson's Disease. Hints to Rehabilitation

In this review, we briefly recall the fundamental processes allowing us to change locomotion trajectory and keep walking along a curved path and provide a review of contemporary literature on turning in older adults and people with Parkinson's Disease (PD). The first part briefly summarizes the way the body exploits the physical laws to produce a curved walking trajectory. Then, the changes in muscle and brain activation underpinning this task, and the promoting role of proprioception, are briefly considered. Another section is devoted to the gait changes occurring in curved walking and steering with aging. Further, freezing during turning and rehabilitation of curved walking in patients with PD is mentioned in the last part. Obviously, as the research on body steering while walking or turning has boomed in the last 10 years, the relevant critical issues have been tackled and ways to improve this locomotor task proposed. Rationale and evidences for successful training procedures are available, to potentially reduce the risk of falling in both older adults and patients with PD. A better understanding of the pathophysiology of steering, of the subtle but vital interaction between posture, balance, and progression along non-linear trajectories, and of the residual motor learning capacities in these cohorts may provide solid bases for new rehabilitative approaches.

Motor module generalization across balance and walking is impaired after stroke

Muscle coordination is often impaired after stroke, leading to deficits in the control of walking and balance. In this study, we examined features of muscle coordination associated with reduced walking performance in chronic stroke survivors using motor module (a.k.a. muscle synergy) analysis. We identified differences between stroke survivors and age-similar neurotypical controls in the modular control of both overground walking and standing reactive balance. In contrast to previous studies that demonstrated reduced motor module number poststroke, our cohort of stroke survivors did not exhibit a reduction in motor module number compared with controls during either walking or reactive balance. Instead, the pool of motor modules common to walking and reactive balance was smaller, suggesting reduced generalizability of motor module function across behaviors. The motor modules common to walking and reactive balance tended to be less variable and more distinct, suggesting more reliable output compared with motor modules specific to either behavior. Greater motor module generalization in stroke survivors was associated with faster walking speed, more normal step length asymmetry, and narrower step widths. Our work is the first to show that motor module generalization across walking and balance may help to distinguish important and clinically relevant differences in walking performance across stroke survivors that would have been overlooked by examining only a single behavior. Finally, because similar relationships between motor module generalization and walking performance have been demonstrated in healthy young adults and individuals with Parkinson's disease, this suggests that motor module generalization across walking and balance may be important for well-coordinated walking. NEW & NOTEWORTHY This is the first work to simultaneously examine neuromuscular control of walking and standing reactive balance in stroke survivors. We show that motor module generalization across these behaviors (i.e., recruiting common motor modules) is reduced compared with controls and is associated with slower walking speeds, asymmetric step lengths, and larger step widths. This is true despite no between-group differences in module number, suggesting that motor module generalization across walking and balance is important for well-coordinated walking.

Muscle synergies demonstrate only minimal changes after treatment in cerebral palsy

BACKGROUND

Children with cerebral palsy (CP) have altered synergies compared to typically-developing peers, reflecting different neuromuscular control strategies used to move. While these children receive a variety of treatments to improve gait, whether synergies change after treatment, or are associated with treatment outcomes, remains unknown.

METHODS

We evaluated synergies for 147 children with CP before and after three common treatments: botulinum toxin type-A injection (n = 52), selective dorsal rhizotomy (n = 38), and multi-level orthopaedic surgery (n = 57). Changes in synergy complexity were measured by the number of synergies required to explain > 90% of the total variance in electromyography data and total variance accounted for by one synergy. Synergy weights and activations before and after treatment were compared using the cosine similarity relative to average synergies of 31 typically-developing (TD) peers.

RESULTS

There were minimal changes in synergies after treatment despite changes in walking patterns. Number of synergies did not change significantly for any treatment group. Total variance accounted for by one synergy increased (i.e., moved further from TD peers) after botulinum toxin type-A injection (1.3%) and selective dorsal rhizotomy (1.9%), but the change was small. Synergy weights did not change for any treatment group (average 0.001 ± 0.10), but synergy activations after selective dorsal rhizotomy did change and were less similar to TD peers (- 0.03 ± 0.07). Only changes in synergy activations were associated with changes in gait kinematics or walking speed after treatment. Children with synergy activations more similar to TD peers after treatment had greater improvements in gait.

CONCLUSIONS

While many of these children received significant surgical procedures and prolonged rehabilitation, the minimal changes in synergies after treatment highlight the challenges in altering neuromuscular control in CP. Development of treatment strategies that directly target impaired control or are optimized to an individual's unique control may be required to improve walking function.

Neuromusculoskeletal model that walks and runs across a speed range with a few motor control parameter changes based on the muscle synergy hypothesis

Humans walk and run, as well as change their gait speed, through the control of their complicated and redundant musculoskeletal system. These gaits exhibit different locomotor behaviors, such as a double-stance phase in walking and flight phase in running. The complex and redundant nature of the musculoskeletal system and the wide variation in locomotion characteristics lead us to imagine that the motor control strategies for these gaits, which remain unclear, are extremely complex and differ from one another. It has been previously proposed that muscle activations may be generated by linearly combining a small set of basic pulses produced by central pattern generators (muscle synergy hypothesis). This control scheme is simple and thought to be shared between walking and running at different speeds. Demonstrating that this control scheme can generate walking and running and change the speed is critical, as bipedal locomotion is dynamically challenging. Here, we provide such a demonstration by using a motor control model with 69 parameters developed based on the muscle synergy hypothesis. Specifically, we show that it produces both walking and running of a human musculoskeletal model by changing only seven key motor control parameters. Furthermore, we show that the model can walk and run at different speeds by changing only the same seven parameters based on the desired speed. These findings will improve our understanding of human motor control in locomotion and provide guiding principles for the control design of wearable exoskeletons and prostheses.