Cortical Correlates of Locomotor Muscle Synergy Activation in Humans: An Electroencephalographic Decoding Study

Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions

Modularity (Muscle synergy) is the concept that has been used to answer the question of how the central nervous system (CNS) coordinates the body's high degrees of freedom. This study aimed to investigate the trunk muscle synergies in symmetrical and asymmetrical positions. Fourteen healthy males participated. Electromyographical activities of 16 muscles were recorded during maximum voluntary isometric contraction (MVIC) in six main directions with two repetitions and maximum voluntary isometric extension (MVIE) of the trunk in 23 different three-dimensional trunk positions. Muscle synergies were extracted separately using non-negative matrix factorization during MVIC (with one/two repetitions) and MVIE. The effect of position changes on synergies was investigated using response surface models and the Pearson correlation coefficient. The findings show that 6 synergies for 6 directions MVIC and 2 synergies for MVIE are suitable with the variance accounted for of 99.65 ± 0.65 96 and 94.14 ± 1.59, respectively. Trial repetition does not affect the synergies. In Conclusion, during the same activity in different positions and trials, the synergy of the main activity is preserved. These show the stability of synergies and their dependence on the activity type. This stability may help to determine the main damage caused and provide appropriate treatment protocol for trunk injuries.

Correlating Data-Driven Muscle Selection Approaches to Synergies for Gait Prediction

Optimizing sensors for physiological input is critical to enhance performance as well as minimize the cost and complexity of assistive devices (e.g. lower-limb exoskeletons). Electromyography (EMG) data can trace muscle activation for gait kinematics prediction. However, identifying optimal muscle groups for electrode placement and the potential variance between users has not yet been established. In this study, we use data-driven channel selection techniques on EMG signals to find muscle group combinations that maximize prediction performance. We apply greedy search (Recursive Feature Elimination, RFE) and variance-based (Principal Component Analysis, PCA) methods to select muscle groups during gait, without prior knowledge of musculoskeletal inter-connectivity. The selected muscle subsets are evaluated using the normalized accuracy of a Multi-Layer Perceptron (MLP), mapping muscle activity to knee flexion angle in a one-step-ahead scheme. The RFE selection led to an average predicted knee angle validation accuracy of % higher than the PCA approach, suggesting that dynamic search is more appropriate than a variance analysis of the signals. Whilst the RFE-selected muscle groups differed across subjects, the selected muscles were consistently spread out over more than 80% of the extracted synergy groups. This study underlines the value of incorporating synergistic information when developing gait prediction models, and reveals that maximizing the number of synergy groups could constitute the basis of muscle selection frameworks.

Differences in Ankle Neuromuscular Control Between the Preferred Speed and Fixed Speeds During Walking

Walking at the preferred speed, considered as a self-optimized gait pattern, is associated with improved energy conservation and cognitive abilities. However, the neuromuscular mechanisms underlying the benefits of the preferred walking speed remain unclear. Therefore, this study aimed to determine the differences in ankle neuromuscular control between the preferred and fixed speeds during walking. Eighteen healthy young adults were recruited to perform overground barefoot walking at the preferred speed, the prefer-matched control speed (PMCS), slower fixed speeds (1, 2, 3 and 4 km/h) and faster fixed speeds (5 and 6 km/h). Muscle synergies and intermuscular coherence were calculated using surface electromyography (EMG) signals of ankle muscles. Results showed that the preferred walking speed exhibited one less muscle synergy and higher intermuscular coherence in 8-42 Hz than the PMCS. Additionally, slow walking speeds performed more muscle synergies and weaker couplings between plantar flexors in 26-60 Hz than the preferred speed and faster fixed speeds. Our results demonstrate an impact of the preferred walking speed on ankle neuromuscular control during walking, which might influence energy consumption and brain resource occupation. Besides, the preferred walking speed and faster fixed speeds showed comparable modular control characteristics of ankle muscles, which might provide suggestions for experimental settings when examining individuals' natural neuromuscular control features.

Decoding temporal muscle synergy patterns based on brain activity for upper extremity in ADL movements

Muscle synergies have been hypothesized as specific predefined motor primitives that the central nervous system can reduce the complexity of motor control by using them, but how these are expressed in brain activity is ambiguous yet. The main purpose of this paper is to develop synergy-based neural decoding of motor primitives, so for the first time, brain activity and muscle synergy map of the upper extremity was investigated in the activity of daily living movements. To find the relationship between brain activities and muscle synergies, electroencephalogram (EEG) and electromyogram (EMG) signals were acquired simultaneously during activities of daily living. To extract the maximum correlation of neural commands with muscle synergies, application of a combined partial least squares and canonical correlation analysis (PLS-CCA) method was proposed. The Elman neural network was used to decode the relationship between extracted motor commands and muscle synergies. The performance of proposed method was evaluated with tenfold cross-validation and muscle synergy estimation of brain activity with R, VAF, and MSE of 84 ± 2.6%, 70 ± 4.7%, and 0.00011 ± 0.00002 were quantified respectively. Furthermore, the similarity between actual and reconstructed muscle activations was achieved more than 92% for correlation coefficient. To compare with the existing methods, our results showed significantly more accuracy of the model performance. Our results confirm that use of the expression of muscle synergies in brain activity can estimate the neural decoding performance for motor control that can be used to develop neurorehabilitation tools such as neuroprosthesis.

Auditory Cue Effects on Gait-Phase-Dependent Electroencephalogram (EEG) Modulations during Overground and Treadmill Walking

Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5-4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Age, Cognitive Task, and Arm Position Differently Affect Muscle Synergy Recruitment but have Similar Effects on Walking Balance

Age modifies walking balance and neuromuscular control. Cognitive and postural constraints can increase walking balance difficulty and magnify age-related differences. However, how such challenges affect neuromuscular control remains unknown. We determined the effects of age, cognitive task, and arm position on neuromuscular control of walking balance. Young (YA) and older adults (OA) walked on a 6-cm wide beam with and without arm crossing and a cognitive task. Walking balance was quantified by the distance walked on the beam. We also computed step speed, margin of stability, and cognitive errors. Neuromuscular control was determined through muscle synergies extracted from 13 right leg and trunk muscles. We analyzed neuromuscular complexity by the number of synergies and the variance accounted for by the first synergy, coactivity by the number of significantly active muscles in each synergy, and efficiency by the sum of the activation of each significantly active muscle in each synergy. OA vs. YA walked a 14% shorter distance, made 12 times more cognitive errors, and showed less complex and efficient neuromuscular control. Cognitive task reduced walking balance mainly in OA. Decreases in step speed and margin of stability, along with increased muscle synergy coactivity and reduced efficiency were observed in both age groups. Arm-crossing also reduced walking balance mostly in OA, but step speed decreased mainly in YA, in whom the margin of stability increased. Arm-crossing reduced the complexity of synergies. Age, cognitive task, and arm position affect differently muscle synergy recruitment but have similar effects on walking balance.

Context expectation influences the gait pattern biomechanics

Beyond classical aspects related to locomotion (biomechanics), it has been hypothesized that walking pattern is influenced by a combination of distinct computations including online sensory/perceptual sampling and the processing of expectations (neuromechanics). Here, we aimed to explore the potential impact of contrasting scenarios ("risky and potentially dangerous" scenario; "safe and comfortable" scenario) on walking pattern in a group of healthy young adults. Firstly, and consistently with previous literature, we confirmed that the scenario influences gait pattern when it is recalled concurrently to participants' walking activity (motor interference). More intriguingly, our main result showed that participants' gait pattern is also influenced by the contextual scenario when it is evoked only before the start of walking activity (motor expectation). This condition was designed to test the impact of expectations (risky scenario vs. safe scenario) on gait pattern, and the stimulation that preceded walking activity served as prior. Noteworthy, we combined statistical and machine learning (Support-Vector Machine classifier) approaches to stratify distinct levels of analyses that explored the multi-facets architecture of walking. In a nutshell, our combined statistical and machine learning analyses converge in suggesting that walking before steps is not just a paradox.

The effects of blurred visual inputs with different levels on the cerebral activity during free level walking

Objective

The aim of this study was to evaluate the effects of blurred vision on electrocortical activities at different levels during walking.

Materials and methods

A total of 22 healthy volunteers (all men; mean age: 24.4 ± 3.9 years) underwent an electroencephalography (EEG) test synchronous with free level walking. Visual status was simulated by goggles covered by the occlusion foil targeted at a Snellen visual acuity of 20/60 (V0.3), 20/200 (V0.1), and light perception (V0). At each of these conditions, the participants completed barefoot walking for five blocks of 10 m. The EEG signals were recorded by a wireless EEG system with electrodes of interest, namely, Cz, Pz, Oz, O1, and O2. The gait performances were assessed by the Vicon system.

Results

During walking with normal vision (V1.0), there were cerebral activities related to visual processing, characterized as higher spectral power of delta (Oz and O2 vs. Cz, Pz, and O1, p ≤ 0.033) and theta (Oz vs. Cz and O1, p = 0.044) bands in occipital regions. Moderately blurred vision (V0.3) would attenuate the predominance of delta- and theta-band activities at Oz and O2, respectively. At the statuses of V0.1 and V0, the higher power of delta (at V0.1 and V0, Oz, and O2 vs. Cz, Pz, and O1, p ≤ 0.047) and theta bands (at V0.1, Oz vs. Cz, p = 0.010; at V0, Oz vs. Cz, Pz, and O1, p ≤ 0.016) emerged again. The cautious gait pattern, characterized by a decrease in gait speed (p < 0.001), a greater amplitude of deviation from the right ahead (p < 0.001), a prolonged stance time (p = 0.001), a restricted range of motion in the hip on the right side (p ≤ 0.010), and an increased knee flexion during stance on the left side (p = 0.014), was only detected at the status of V0. The power of the alpha band at the status of V0 was higher than that at V1.0, V0.3, and V0.1 (p ≤ 0.011).

Conclusion

Mildly blurred visual inputs would elicit generalization of low-frequency band activity during walking. In circumstance to no effective visual input, locomotor navigation would rely on cerebral activity related to visual working memory. The threshold to trigger the shift might be the visual status that is as blurred as the level of Snellen visual acuity of 20/200.

Firing behavior of single motor units of the tibialis anterior in human walking as non-invasively revealed by HDsEMG decomposition

Objective.Investigation of the firing behavior of motor units (MUs) provides essential neuromuscular control information because MUs are the smallest organizational component of the neuromuscular system. The MUs activated during human infants' leg movements and rodent locomotion, mainly controlled by the spinal central pattern generator (CPG), show highly synchronous firing. In addition to spinal CPGs, the cerebral cortex is involved in neuromuscular control during walking in human adults. Based on the difference in the neural control mechanisms of locomotion between rodent, human infants and adults, MU firing behavior during adult walking probably has some different features from the other populations. However, so far, the firing activity of MUs in human adult walking has been largely unknown due to technical issues.Approach.Recent technical advances allow noninvasive investigation of MU firing by high-density surface electromyogram (HDsEMG) decomposition. We investigated the MU firing behavior of the tibialis anterior (TA) muscle during walking at a slow speed by HDsEMG decomposition.Main results.We found recruitment threshold modulation of MU between walking and steady isometric contractions. Doublet firings, and gait phase-specific firings were also observed during walking. We also found high MU synchronization during walking over a wide range of frequencies, probably including cortical and spinal CPG-related components. The amount of MU synchronization was modulated between the gait phases and motor tasks. These results suggest that the central nervous system flexibly controls MU firing to generate appropriate force of TA during human walking.Significance.This study revealed the MU behavior during walking at a slow speed and demonstrated the feasibility of noninvasive investigation of MUs during dynamic locomotor tasks, which will open new frontiers for the study of neuromuscular systems in the fields of neuroscience and biomedical engineering.

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.

Older adults with a history of falling exhibit altered cortical oscillatory mechanisms during continuous postural maintenance

Background and Aim

The significant risk of falling in older adults 65 years or older presents a substantial problem for these individuals, their caretakers, and the health-care system at large. As the proportion of older adults in the United States is only expected to grow over the next few decades, a better understanding of physiological and cortical changes that make an older adult more susceptible to a fall is crucial. Prior studies have displayed differences in postural dynamics and stability in older adults with a fall history (FH) and those who are non-fallers (NF), suggesting surplus alterations that occur in some older adults (i.e., FH group) in addition to the natural aging process.

Methods

The present study measured postural dynamics while the FH, NF, and young adult (YA) groups performed continuous postural maintenance. In addition, electroencephalography activity was recorded while participants performed upright postural stance to examine any group differences in cortical areas involved in postural control.

Results

As expected, older participants (FH and NF) exhibited worse postural stability, as evidenced by increased excursion, compared to the YA group. Further, while NF and YA show increased alpha activity in occipital areas during the most demanding postural task (eyes closed), the FH group did not show any differences in occipital alpha power between postural tasks.

Conclusions

As alpha activity reflects suppression of bottom-up processing and thus diversion of cognitive resources toward postural centers during more demanding postural maintenance, deficits in this regulatory function in the FH group are a possible impaired cortical mechanism putting these individuals at greater fall risk.

Relevance for Patients

Impaired inhibitory function in older adults may impact postural control and increase their risk of falling. Interventions that aim at addressing cortical processing deficits may improve postural stability and facilitate independent living in this population.

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

The age-related changes of gait symmetry in healthy children concerning individual joint and muscle activation data have previously been widely studied. Extending beyond individual joints or muscles, identifying age-related changes in the coordination of multiple joints or muscles (i.e., muscle synergies and kinematic synergies) could capture more closely the underlying mechanisms responsible for gait symmetry development. To evaluate the effect of age on the symmetry of the coordination of multiple joints or muscles during childhood, we measured gait symmetry by kinematic and EMG data in 39 healthy children from 2 years old to 14 years old, divided into three equal age groups: preschool children (G1; 2.0-5.9 years), children (G2; 6.0-9.9 years), pubertal children (G3; 10.0-13.9 years). Participants walked barefoot at a self-selected walking speed during three-dimensional gait analysis (3DGA). Kinematic synergies and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. Statistical analysis was performed to examine intergroup differences. The results showed that the effect of age was significant on the symmetry values extracted by kinematic synergies, while older children exhibited higher kinematic synergy symmetry values compared to the younger group. However, no significant age-related changes in symmetry values of muscle synergy were observed. It is suggested that kinematic synergy of lower joints can be asymmetric at the onset of independent walking and showed improving symmetry with increasing age, whereas the age-related effect on the symmetry of muscle synergies was not demonstrated. These data provide an age-related framework and normative dataset to distinguish age-related differences from pathology in children with neuromotor disorders.

Modulation of Muscle Synergies in Lower-Limb Muscles Associated With Split-Belt Locomotor Adaptation

Humans have great locomotor adaptability to environmental demands, which has been investigated using a split-belt treadmill with belts on both the left and right sides. Thus far, neuromuscular control in split-belt locomotor adaptation has been evaluated by analyzing muscle activities at the individual muscle level. Meanwhile, in the motor control field, the muscle synergy concept has been proposed. Muscle synergies are considered the fundamental building blocks of movement and are groups of coactive muscles and time-varying activation patterns, thereby, reflecting the neurophysiological characteristics of movement. To date, it remains unclear how such muscle synergies change during the adaptation and de-adaptation processes on the split-belt treadmill. Hence, we chronologically extracted muscle synergies while walking on the split-belt treadmill and examined changes in the number, muscle weightings, and temporal activation patterns of muscle synergies. Twelve healthy young males participated, and surface electromyography (EMG) signals were recorded bilaterally from 13 lower-limb muscles. Muscle synergies were extracted by applying non-negative matrix factorization to the EMG data of each leg. We found that during split-belt walking, the number of synergies in the slow leg increased while an extra synergy appeared and disappeared in the fast leg. Additionally, the areas under the temporal activation patterns in several synergies in both legs decreased. When both belts returned to the same speed, a decrease in the number of synergies and an increase in the areas under the temporal activation patterns of several synergies were temporally shown in each leg. Subsequently, the number of synergies and the areas under the temporal activation patterns returned to those of normal walking before split-belt walking. Thus, changes in the number, muscle weightings, and temporal activation patterns of synergies were noted in the split-belt locomotor adaptation, suggesting that the adaptation and de-adaptation occurred at the muscle synergy level.

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.

Effects of Transcranial Direct Current Electrical Stimulation over the Supplementary Motor Area Combined with Walking on the Intramuscular Coherence of the Tibialis Anterior in a Subacute Post-Stroke Patient: A Single-Case Study

Motor recovery is related to the corticospinal tract (CST) lesion in post-stroke patients. The CST originating from the supplementary motor area (SMA) affects the recovery of impaired motor function. We confirmed the effects of transcranial direct current stimulation (tDCS) over the SMA combined with walk training on CST excitability. This study involved a stroke patient with severe sensorimotor deficits and a retrospective AB design. Walk training was conducted only in phase A. Phase B consisted of anodal tDCS (1.5 mA) combined with walk training. Walking speed, stride time variability (STV; reflecting gait stability), and beta-band intramuscular coherence—derived from the paired tibialis anterior on the paretic side (reflecting CST excitability)—were measured. STV quantified the coefficient of variation in stride time using accelerometers. Intramuscular coherence during the early stance phase noticeably increased in phase B compared with phase A. Intramuscular coherence in both the stance and swing phases was reduced at follow-up. Walking speed showed no change, while STV was noticeably decreased in phase B compared with phase A. These results suggest that tDCS over the SMA during walking improves gait stability by enhancing CST excitability in the early stance phase.

Differences in muscle synergies among recovery responses limit inter-task generalisation of stability performance

Generalisation of adaptations is key to effective stability control facing variety of postural threats during daily life activity. However, in a previous study we could demonstrate that adaptations to stability control do not necessarily transfer to an untrained motor task. Here, we examined the dynamic stability and modular organisation of motor responses to different perturbations (i.e. unpredictable gait-trip perturbations and subsequent loss of anterior stability in a lean-and-release protocol) in a group of young and middle-aged adults (n = 57; age range 19-53 years) to detect potential neuromotor factors limiting transfer of adaptations within the stability control system. We hypothesized that the motor system uses different modular organisation in recovery responses to tripping and lean-and-release, which may explain lack in positive transfer of adaptations in stability control. After eight trip-perturbations participants increased their dynamic stability during the first recovery step (p < 0.001), yet they showed no significant improvement to the untrained lean-and-release transfer task compared to controls who did not undergo the perturbation exposure (p = 0.44). Regarding the neuromuscular control of responses, lower number of synergies (3 vs. 4) was found for the lean-and-release compared to the gait-trip perturbation task, revealing profound differences in both the timing and function of the recruited muscles to match the biomechanical specificity of different perturbations. Our results provide indirect evidence that the motor system uses different modular organisation in diverse perturbation responses, what possibly inhibits inter-task generalisation of adaptations in stability control.

Association between temporal asymmetry and muscle synergy during walking with rhythmic auditory cueing in stroke survivors living with impairments

•

We examined the effect of temporal gait asymmetry on muscle synergy post stroke.

•

In our design, the temporal asymmetry during gait was experimentally modulated.

•

The temporal asymmetry was modulated using rhythmic auditory cueing.

•

Rhythmic auditory cueing with gait immediately improved temporal asymmetry and muscle synergy deficits.

•

The temporal asymmetry affected muscle synergy more than kinematics.

Objective

To examine the relationship between temporal asymmetry and complexity of muscle synergy during walking using rhythmic auditory cueing (RAC) and the factors related to changes in muscle synergy during walking with RAC in survivors of stroke.

Design

Cross-sectional study.

Setting

Wards at 2 medical corporation hospitals.

Participants

Forty survivors of stroke (N=40; mean age, 70.4±10.3 years; time since stroke, 72.2±32.3 days) who could walk without physical assistance.

Interventions

Not applicable.

Main Outcome Measures

The participants were assessed in a random block design under 2 conditions: comfortable walking speed (CWS) and walking with RAC. Single-leg support time, kinematics, and electromyograms were measured. Factors related to the complexity of muscle synergy (variance accounted for by 1 synergy [VAF1]) between the walking conditions were examined using hierarchical multiple regression analysis.

Results

In the RAC condition, lower limb flexion and knee flexion angles, single-leg support time on the paretic side, and the symmetry index of single-leg support time were increased compared with those in the CWS condition. VAF1 was decreased in the RAC condition (73.9±0.15) compared with that in the CWS condition (76.9±0.13, P=.002). Hierarchical multiple regression analysis revealed that the change in VAF1 was explained by change in single-leg support time (R²=0.43, P=.002).

Conclusions

The RAC condition demonstrated a more complex representation of muscle synergy than the CWS condition; the change in single-leg support time on the paretic side related to the changes in muscle synergy more than changes in lower limb angle. These findings can help in the walking-training concept to improve muscle synergy deficits in survivors of stroke.

Basic locomotor muscle synergies used in land walking are finely tuned during underwater walking

Underwater walking is one of the most common hydrotherapeutic exercises. Therefore, understanding muscular control during underwater walking is important for optimizing training regimens. The effects of the water environment on walking are mainly related to the hydrostatic and hydrodynamic theories of buoyancy and drag force. To date, muscular control during underwater walking has been investigated at the individual muscle level. However, it is recognized that the human nervous system modularly controls multiple muscles through muscle synergies, which are sets of muscles that work together. We found that the same set of muscle synergies was shared between the two walking tasks. However, some task-dependent modulation was found in the activation combination across muscles and temporal activation patterns of the muscle synergies. The results suggest that the human nervous system modulates activation of lower-limb muscles during water walking by finely tuning basic locomotor muscle synergies that are used during land walking to meet the biomechanical requirements for walking in the water environment.

Common motor patterns of asymmetrical and symmetrical bipedal gaits

Background

Synergy modules have been used to describe activation of lower limb muscles during locomotion and hence to understand how the system controls movement. Walking and running have been shown shared synergy patterns suggesting common motor control of both symmetrical gaits. Unilateral skipping, an equivalent gait to the quadrupedal gallop in humans, has been defined as the third locomotion paradigm but the use by humans is limited due to its high metabolic cost. Synergies in skipping have been little investigated. In particular, to the best of our knowledge, the joint study of both trailing and leading limbs has never been addressed before.

Research question

How are organized muscle activation patterns in unilateral skipping? Are they organized in the same way that in symmetrical gaits? If yes, which are the muscle activation patterns in skipping that make it a different gait to walking or running? In the present research, we investigate if there are shared control strategies for all gaits in locomotion. Addressing these questions in terms of muscle synergies could suggest possible determinants of the scarce use of unilateral skipping in humans.

Methods

Electromyographic data of fourteen bilateral muscles were collected from volunteers while performing walking, running and unilateral skipping on a treadmill. Also, spatiotemporal gait parameters were computed from 3D kinematics. The modular composition and activation timing extracted by non-negative matrix factorization were analyzed to detect similarities and differences among symmetrical gaits and unilateral skipping.

Results

Synergy modules showed high similarity throughout the different gaits and between trailing and leading limbs during unilateral skipping. The synergy associated with the propulsion force operated by calf muscles was anticipated in bouncing gaits. Temporal features of synergies in the leading leg were very similar to those observed for running. The different role of trailing and leading legs in unilateral skipping was reflected by the different timing in two modules. Activation for weight acceptance was anticipated and extended in the trailing leg, preparing the body for landing impact after the flight phase. A different behaviour was detected in the leading leg, which only deals with a pendular weight transference.

Significance

The evidence gathered in this work supports the hypothesis of shared modules among symmetrical and asymmetrical gaits, suggesting a common motor control despite of the infrequent use of unilateral skipping in humans. Unilateral skipping results from phase-shifted activation of similar muscular groups used in symmetrical gaits, without the need for new muscular groups. The high and anticipated muscle activation in the trailing leg for landing could be the key distinctive event of unilateral skipping.

Neural decoding of gait phases during motor imagery and improvement of the decoding accuracy by concurrent action observation

Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous knowledge on decoding the MI of gait is limited to simple information (e.g. the classification of 'walking' and 'rest').Approach. Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy.Main results. We demonstrated that the stance and swing phases could be decoded from EEGs during MI or AO alone. We also demonstrated the decoding accuracy during MI was improved by concurrent AO. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations related to sensorimotor, visual, and action understanding systems associated with MI and AO.Significance. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO + MI of walking.Novelty and significanceBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. This study is the first to show the feasibility of EEG decoding of the stance versus swing phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for effective techniques for walking rehabilitation.

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.

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.

Phase dependent modulation of cortical activity during action observation and motor imagery of walking: An EEG study

Action observation (AO) and motor imagery (MI) are motor simulations which induce cortical activity related to execution of observed and imagined movements. Neuroimaging studies have mainly investigated where the cortical activities during AO and MI of movements are activated and if they match those activated during execution of the movements. However, it remains unclear how cortical activity is modulated; in particular, whether activity depends on observed or imagined phases of movements. We have previously examined the neural mechanisms underlying AO and MI of walking, focusing on the combined effect of AO with MI (AO+MI) and phase dependent modulation of corticospinal and spinal reflex excitability. Here, as a continuation of our previous studies, we investigated cortical activity depending on gait phases during AO and AO+MI of walking by using electroencephalography (EEG); 64-channel EEG signals were recorded in which participants observed walking with or without imagining it, respectively. EEG source and spectral analyses showed that, in the sensorimotor cortex during AO+MI and AO, the alpha and beta power were decreased, and power spectral modulations depended on walking phases. The phase dependent modulations during AO+MI, but not during AO, were like those which occur during actual walking as reported by previous walking studies. These results suggest that combinatory effects of AO+MI could induce parts of the phase dependent activation of the sensorimotor cortex during walking even without any movements. These findings would extend understanding of the neural mechanisms underlying walking and cognitive motor processes and provide clinically beneficial information towards rehabilitation for patients with neurological gait dysfunctions.

Defective corticomuscular connectivity during walking in patients with Parkinson's disease

Gait disturbances are common in individuals with Parkinson's disease (PD). Although the basic patterns of walking are thought to be controlled by the brainstem and spinal networks, recent studies have found significant corticomuscular coherence in healthy individuals during walking. However, it still remains unknown how PD affects the cortical control of muscles during walking. As PD typically develops in older adults, it is important to investigate the effects of both aging and PD when examining disorders in patients with PD. Here, we assessed the effects of PD and aging on corticomuscular communication during walking by investigating corticomuscular coherence. We recorded electroencephalographic and electromyographic signals in 10 individuals with PD, 9 healthy older individuals, and 15 healthy young individuals. We assessed the corticomuscular coherence between the motor cortex and two lower leg muscles, tibialis anterior (TA) and medial gastrocnemius, during walking. Older and young groups showed sharp peaks in muscle activation patterns at specific gait phases, whereas the PD group showed prolonged patterns. Smaller corticomuscular coherence was found in the PD group compared with the healthy older group in the α band (8-12 Hz) for both muscles, and in the β band (16-32 Hz) for TA. Older and young groups did not differ in the magnitude of corticomuscular coherence. Our results indicated that PD decreased the corticomuscular coherence during walking, whereas it was not affected by aging. This lower corticomuscular coherence in PD may indicate lower-than-normal corticomuscular communication, although direct or indirect communication is unknown, and may cause impaired muscle control during walking.NEW & NOTEWORTHY Mechanisms behind how Parkinson's disease (PD) affects cortical control of muscles during walking remain unclear. As PD typically develops in the elderly, investigation of aging effects is important to examine deficits regarding PD. Here, we demonstrated that PD causes weak corticomuscular synchronization during walking, but aging does not. This lower-than-normal corticomuscular communication may cause impaired muscle control during walking.

Gait-phase-dependent and gait-phase-independent cortical activity across multiple regions involved in voluntary gait modifications in humans

Modification of ongoing walking movement to fit changes in external environments requires accurate voluntary control. In cats, the motor and posterior parietal cortices have crucial roles for precisely adjusting limb trajectory during walking. In human walking, however, it remains unclear which cortical information contributes to voluntary gait modification. In this study, we investigated cortical activity changes associated with visually guided precision stepping using electroencephalography source analysis. Our results demonstrated frequency- and gait-event-dependent changes in the cortical power spectrum elicited by voluntary gait modification. The main differences between normal walking and precision stepping were as follows: (a) the alpha, beta or gamma power decrease during the swing phases in the sensorimotor, anterior cingulate and parieto-occipital cortices, and (b) a power decrease in the theta, alpha and beta bands and increase in the gamma band throughout the gait cycle in the parieto-occipital cortex. Based on the previous knowledge of brain functions, the former change was considered to be related to execution and planning of leg movement, while the latter change was considered to be related to multisensory integration and motor awareness. Therefore, our results suggest that the gait modification is achieved by higher cortical involvements associated with different sensorimotor-related functions across multiple cortical regions including the sensorimotor, anterior cingulate and parieto-occipital cortices. The results imply the critical importance of the cortical contribution to voluntary modification in human locomotion. Further, the observed cortical information related to voluntary gait modification would contribute to developing volitional control systems of brain-machine interfaces for walking rehabilitation.

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs.

Paving the way for a better understanding of the pathophysiology of gait impairment in myotonic dystrophy: a pilot study focusing on muscle networks

BACKGROUND

A proper rehabilitation program targeting gait is mandatory to maintain the quality of life of patients with Myotonic dystrophy type 1 (DM1). Assuming that gait and balance impairment simply depend on the degree of muscle weakness is potentially misleading. In fact, the involvement of the Central Nervous System (CNS) in DM1 pathophysiology calls into account the deterioration of muscle coordination in gait impairment. Our study aimed at demonstrating the presence and role of muscle connectivity deterioration in patients with DM1 by a CNS perspective by investigating signal synergies using a time-frequency spectral coherence and multivariate analyses on lower limb muscles while walking upright. Further, we sought at determining whether muscle networks were abnormal secondarily to the muscle impairment or primarily to CNS damage (as DM1 is a multi-system disorder also involving the CNS). In other words, muscle network deterioration may depend on a weakening in signal synergies (that express the neural drive to muscles deduced from surface electromyography data).

METHODS

Such an innovative approach to estimate muscle networks and signal synergies was carried out in seven patients with DM1 and ten healthy controls (HC).

RESULTS

Patients with DM1 showed a commingling of low and high frequencies among muscle at both within- and between-limbs level, a weak direct neural coupling concerning inter-limb coordination, a modest network segregation, high integrative network properties, and an impoverishment in the available signal synergies, as compared to HCs. These network abnormalities were independent from muscle weakness and myotonia.

CONCLUSIONS

Our results suggest that gait impairment in patients with DM1 depends also on a muscle network deterioration that is secondary to signal synergy deterioration (related to CNS impairment). This suggests that muscle network deterioration may be a primary trait of DM1 rather than a maladaptive mechanism to muscle degeneration. This information may be useful concerning the implementation of proper rehabilitative strategies in patients with DM1. It will be indeed necessary not only addressing muscle weakness but also gait-related muscle connectivity to improve functional ambulation in such patients.