Principles of Motor Unit Physiology Evolve With Advances in Technology

Explaining the influence of practice on the grooved pegboard times of older adults: role of force steadiness

The purpose was to identify the variables that can explain the variance in the grooved pegboard times of older adults categorized as either fast or slow performers. Participants (n = 28; 60-83 years) completed two experimental sessions, before and after 6 practice sessions of the grooved pegboard test. The 2 groups were identified based on average pegboard times during the practice sessions. Average pegboard time during practice was 73 ± 11 s for the fast group and 85 ± 13 s for the slow group. Explanatory variables for the pegboard times before and after practice were the durations of 4 peg-manipulation phases and 12 measures of force steadiness (coefficient of variation [CV] for force) during isometric contractions with the index finger abductor and wrist extensor muscles. Time to complete the grooved pegboard test after practice decreased by 25 ± 11% for the fast group and by 28 ± 10% for the slow group. Multiple regression models explained more of the variance in the pegboard times for the fast group before practice (Adjusted R2 = 0.85) than after practice (R2 = 0.51), whereas the variance explained for the slow group was similar before (Adjusted R2 = 0.67) and after (Adjusted R2 = 0.64) practice. The explanatory variables differed between before and after practice for the fast group but only slightly for the slow group. These findings indicate that performance-based stratification of older adults can identify unique adjustments in motor function that are independent of chronological age.

Reduced rate of force development under fatigued conditions is associated to the decline in force complexity in adult males

PURPOSE

This study aimed to verify whether the slowing of muscle contraction quickness, typically observed in states of fatigue, may worsen force control by decreasing the rate with which force fluctuations are modulated. Therefore, we investigated the relationship between rate of force development (RFD), and force fluctuations' magnitude (Coefficient of variation, CoV) and complexity (Approximate Entropy, ApEn; Detrended fluctuation analysis, DFAα).

METHODS

Fourteen participants performed intermittent ballistic isometric contractions of the plantar dorsiflexors at 70% of maximal voluntary force until task failure (under 60% twice).

RESULTS

Indices of RFD (RFDpeak, RFD50, RFD100, and RFD150) decreased over time by approximately 46, 32, 44, and 39%, respectively (p all ≤ 0.007). DFAα increased by 10% (p < 0.001), and CoV increased by 15% (p < 0.001), indicating decreased force complexity along with increased force fluctuations, respectively. ApEn decreased by just over a quarter (28%, p < 0.001). The linear hierarchical models showed negative associations between RFDpeak and DFAα (β =  - 3.6 10-4, p < 0.001), CoV (β =  - 1.8 10-3, p < 0.001), while ApEn showed a positive association (β = 8.2 × 10-5, p < 0.001).

CONCLUSION

The results suggest that exercise-induced reductions in contraction speed, lead to smoother force complexity and diminished force control due to slower adjustments around the target force. The fatigued state resulted in worsened force producing capacity and overall force control.

Foot-dominance does not influence force variability during ankle dorsiflexion and foot adduction

The aim of our study was to compare the force steadiness and the discharge characteristics of motor units in the tibialis anterior (TA) during ankle dorsiflexion and foot adduction produced by submaximal isometric contractions with the dominant and non-dominant foot. Fifteen young men performed maximal and submaximal contractions at five target forces with both legs, and motor unit activity in TA was recorded using high-density electromyography. Maximal force and the fluctuations in force during submaximal contractions were similar between the two legs (p > 0.05). Motor unit activity was characterized by measures of mean discharge rate (MDR), coefficient of variation for interspike interval (CoV for ISI), and standard deviation of the filtered cumulative spike train (SD of fCST). There were no statistically significant differences in motor unit activity between legs during ankle dorsiflexion. In contrast, the MDR and the CoV for ISI but not the SD of fCST, were greater for the non-dominant foot compared with the dominant foot during foot adduction. Nonetheless, these differences in motor unit activity were not sufficient to influence the force fluctuations during the submaximal contractions. These results indicate that control of the force produced by TA during the two actions was not influenced by limb dominance.

Motor unit modes in the calf muscles during a submaximal isometric contraction are changed by brief stretches

The purpose of our study was to investigate the influence of a stretch intervention on the common modulation of discharge rate among motor units in the calf muscles during a submaximal isometric contraction. The current report comprises a computational analysis of a motor unit dataset that we published previously (Mazzo et al., 2021). Motor unit activity was recorded from the three main plantar flexor muscles while participants performed an isometric contraction at 10% of the maximal voluntary contraction force before and after each of two interventions. The interventions were a control task (standing balance) and static stretching of the plantar flexor muscles. A factorization analysis on the smoothed discharge rates of the motor units from all three muscles yielded three modes that were independent of the individual muscles. The composition of the modes was not changed by the standing-balance task, whereas the stretching exercise reduced the average correlation in the second mode and increased it in the third mode. A centroid analysis on the correlation values showed that most motor units were associated with two or three modes, which were presumed to indicate shared synaptic inputs. The percentage of motor units adjacent to the seven centroids changed after both interventions: Control intervention, mode 1 decreased and the shared mode 1 + 2 increased; stretch intervention, shared modes either decreased (1 + 2) or increased (1 + 3). These findings indicate that the neuromuscular adjustments during both interventions were sufficient to change the motor unit modes when the same task was performed after each intervention. KEY POINTS: Based on covariation of the discharge rates of motor units in the calf muscles during a submaximal isometric contraction, factor analysis was used to assign the correlated discharge trains to three motor unit modes. The motor unit modes were determined from the combined set of all identified motor units across the three muscles before and after each participant performed a control and a stretch intervention. The composition of the motor unit modes changed after the stretching exercise, but not after the control task (standing balance). A centroid analysis on the distribution of correlation values found that most motor units were associated with a shared centroid and this distribution, presumably reflecting shared synaptic input, changed after both interventions. Our results demonstrate how the distribution of multiple common synaptic inputs to the motor neurons innervating the plantar flexor muscles changes after a brief series of stretches.

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

Motor unit activity and synaptic inputs to motoneurons in the caudal part of the injured spinal cord

Spinal cord injury (SCI) disrupts neuronal function below the lesion epicenter, causing disuse muscle atrophy. We investigated motor unit (MU) activity and synaptic inputs to motoneurons in the caudal region of the injured spinal cord. Participants with C4-C7 cervical injuries were studied. The extensor digitorum communis (EDC) muscle, which is mainly innervated by C8, was assessed for disuse muscle atrophy. Using advanced electromyography and signal-processing techniques, we examined the concurrent activation of a substantial population of MUs during force-tracking tasks. We found that in participants with SCI (n = 9), both MU discharge rates and the amplitudes of MU action potentials were significantly lower than in controls (n = 9). After SCI, MUs were recruited in a limited force range as the strength of muscle contractions increased, implying a disruption in the orderly MU recruitment pattern. Coherence analysis revealed reduced synaptic inputs to motoneurons in the delta band (0.5-5 Hz) for participants with SCI, suggesting diminished common synaptic inputs to the EDC muscle. In addition, participants with SCI exhibited greater muscle force variability. Using principal component analysis on low-frequency MU discharge rates, we found that the first common component (FCC) captured the most discharge variability in participants with SCI. The coefficients of variation (CV) of the FCC correlated with force signal CVs, suggesting force variability mainly results from common synaptic inputs to the EDC muscle after SCI. These results advance our understanding of the neurophysiology of disuse muscle atrophy in human SCI, paving the way for therapeutic interventions to restore muscle function.NEW & NOTEWORTHY This study analyzed motor unit (MU) function below the lesion epicenter in patients with spinal cord injury (SCI). We found reduced MU discharge rates and action potential amplitudes in participants with SCI compared with controls. The strength of common synaptic inputs to motoneurons was reduced in patients with SCI, with increased force variability primarily due to low-frequency oscillations of common inputs. This study enhances understanding of neurophysiological and behavioral changes in disuse muscle atrophy post-SCI.

The neural control of movement: a century of in vivo motor unit recordings is the legacy of Adrian and Bronk

In two papers dated 1928 to 1929 in The Journal of Physiology, Edgar Adrian and Detlev Bronk described recordings from motor nerve and muscle fibres. The recordings from motor nerve fibres required progressive dissection of the nerve until a few fibres remained, from which isolated single fibre activity could be detected. The muscle fibre recordings were performed in humans during voluntary contractions with an intramuscular electrode - the concentric needle electrode - that they describe for the first time in the second paper. They recognised that muscle fibres would respond to each impulse sent by the innervating motor neurone and that therefore muscle fibre recordings provided information on the times of activation of the motor nerve fibres which were as accurate as a direct record from the nerve. These observations and the description of the concentric needle electrode opened the era of motor unit recordings in humans, which have continued for almost a century and have provided a comprehensive view of the neural control of movement at the motor unit level. Despite important advances in technology, many of the principles of motor unit behaviour that would be investigated in the subsequent decades were canvassed in the two papers by Adrian and Bronk. For example, they described the concomitant motor neurones' recruitment and rate coding for force modulation, synchronisation of motor unit discharges, and the dependence of discharge rate on motor unit recruitment threshold. Here, we summarise their observations and discuss the impact of their work. We highlight the advent of the concentric needle, and its subsequent influence on motor control research.

Ageing reduces persistent inward current contribution to motor neurone firing: Potential mechanisms and the role of exercise

Nervous system deterioration is a primary driver of age-related motor impairment. The motor neurones, which act as the interface between the central nervous system and the muscles, play a crucial role in amplifying excitatory synaptic input to produce the desired motor neuronal firing output. For this, they utilise their ability to generate persistent (long-lasting) depolarising currents that increase cell excitability, and both amplify and prolong the output activity of motor neurones for a given synaptic input. Modulation of these persistent inward currents (PICs) contributes to the motor neurones' capacities to attain the required firing frequencies and rapidly modulate them to competently complete most tasks. Thus, PICs are crucial for adequate movement generation. Impairments in intrinsic motor neurone properties can impact motor unit firing capacity, with convincing evidence indicating that the PIC contribution to motor neurone firing is reduced in older adults. Indeed, this could be an important mechanism underpinning the age-related reductions in strength and physical function. Furthermore, resistance training has emerged as a promising intervention to counteract age-associated PIC impairments, with changes in PICs being correlated with improvements in muscular strength and physical function after training. In this review, we present the current knowledge of the PIC magnitude decline during ageing and discuss whether reduced serotonergic and noradrenergic input onto the motor neurones, voltage-gated calcium channel dysfunction or inhibitory input impairments are candidates that: (i) explain age-related reductions in the PIC contribution to motor neurone firing and (ii) underpin the enhanced PIC contribution to motor neurone firing following resistance training in older adults.

Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units

The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.

High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions

Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for mapping neural input to mechanical output, there is a lack of adaptability to dynamic environments due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126-electrode surface EMG grid. This data set can be used to (i) validate methods for extracting neural signals from surface EMG, (ii) develop models for predicting torque output, or (iii) develop classifiers for movement intent.

Semi-Automated Identification of Motor Units Concurrently Recorded in High-Density Surface and Intramuscular Electromyography

An increasing focus on extending automated surface electromyography (EMG) decomposition algorithms to operate under non-stationary conditions requires rigorous and robust validation. However, relevant benchmarks derived manually from iEMG are laborsome to obtain and this is further exacerbated by the need to consider multiple contraction conditions. This work demonstrates a semi-automatic technique for extracting motor units (MUs) whose activities are present in concurrently recorded high-density surface EMG (HD-sEMG) and intramuscular EMG (iEMG) during isometric contractions. We leverage existing automatic surface decomposition algorithms for initial identification of active MUs. Resulting spike times are then used to identify (trigger) the sources that are concurrently detectable in iEMG. We demonstrate this technique on recordings targeting the extensor carpi radialis brevis in five human subjects. This dataset consists of 117 trials across different force levels and wrist angles, from which the presented method yielded a set of 367 high-confidence decompositions. Thus, our approach effectively alleviates the overhead of manual decomposition as it efficiently produces reliable benchmarks under different conditions.Clinical Relevance- We present an efficient method for obtaining high-quality in-vivo decomposition particularly useful in the verification of new surface decomposition approaches.

Cognitive challenge as a probe to expose sex- and age-related differences during static contractions

Despite activities of daily living being frequently performed simultaneously with a cognitive task, motor function is often investigated in isolation, which can hinder the applicability of findings. This brief review presents evidence that 1) performing a cognitive challenge simultaneously with a motor task can negatively impact force steadiness and fatigability of limb muscles during a static contraction, 2) the negative impact on old adults (>65 years old), particularly older women is greater than young when a cognitive challenge is simultaneously performed with a static motor task, 3) age-related mechanisms potentially explain impairments in motor performance in the presence of a cognitive challenge, and 4) the mechanisms for the age-related decrements in motor performance can be distinct between men and women. These observations are highly relevant to the older adults, given the increased risk of accidents and injury when a motor task is performed with a high cognitive-demand task, especially in light of the expanding reliance on an aging workforce.

Non-invasive estimation of muscle fibre size from high-density electromyography

Because of the biophysical relation between muscle fibre diameter and the propagation velocity of action potentials along the muscle fibres, motor unit conduction velocity could be a non-invasive index of muscle fibre size in humans. However, the relation between motor unit conduction velocity and fibre size has been only assessed indirectly in animal models and in human patients with invasive intramuscular EMG recordings, or it has been mathematically derived from computer simulations. By combining advanced non-invasive techniques to record motor unit activity in vivo, i.e. high-density surface EMG, with the gold standard technique for muscle tissue sampling, i.e. muscle biopsy, here we investigated the relation between the conduction velocity of populations of motor units identified from the biceps brachii muscle, and muscle fibre diameter. We demonstrate the possibility of predicting muscle fibre diameter (R2  = 0.66) and cross-sectional area (R2  = 0.65) from conduction velocity estimates with low systematic bias (∼2% and ∼4% respectively) and a relatively low margin of individual error (∼8% and ∼16%, respectively). The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling. The non-invasive nature of high-density surface EMG for the assessment of muscle fibre size may be useful in studies monitoring child development, ageing, space and exercise physiology, although the applicability and validity of the proposed methodology need to be more directly assessed in these specific populations by future studies. KEY POINTS: Because of the biophysical relation between muscle fibre size and the propagation velocity of action potentials along the sarcolemma, motor unit conduction velocity could represent a potential non-invasive candidate for estimating muscle fibre size in vivo. This relation has been previously assessed in animal models and humans with invasive techniques, or it has been mathematically derived from simulations. By combining high-density surface EMG with muscle biopsy, here we explored the relation between the conduction velocity of populations of motor units and muscle fibre size in healthy individuals. Our results confirmed that motor unit conduction velocity can be considered as a novel biomarker of fibre size, which can be adopted to predict muscle fibre diameter and cross-sectional area with low systematic bias and margin of individual error. The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling.

Decoding firings of a large population of human motor units from high-density surface electromyogram in response to transcranial magnetic stimulation

We describe a novel application of methodology for high-density surface electromyography (HDsEMG) decomposition to identify motor unit (MU) firings in response to transcranial magnetic stimulation (TMS). The method is based on the MU filter estimation from HDsEMG decomposition with convolution kernel compensation during voluntary isometric contractions and its application to contractions elicited by TMS. First, we simulated synthetic HDsEMG signals during voluntary contractions followed by simulated motor evoked potentials (MEPs) recruiting an increasing proportion of the motor pool. The estimation of MU filters from voluntary contractions and their application to elicited contractions resulted in high (>90%) precision and sensitivity of MU firings during MEPs. Subsequently, we conducted three experiments in humans. From HDsEMG recordings in first dorsal interosseous and tibialis anterior muscles, we demonstrated an increase in the number of identified MUs during MEPs evoked with increasing stimulation intensity, low variability in the MU firing latency and a proportion of MEP energy accounted for by decomposition similar to voluntary contractions. A negative relationship between the MU recruitment threshold and the number of identified MU firings was exhibited during the MEP recruitment curve, suggesting orderly MU recruitment. During isometric dorsiflexion we also showed a negative association between voluntary MU firing rate and the number of firings of the identified MUs during MEPs, suggesting a decrease in the probability of MU firing during MEPs with increased background MU firing rate. We demonstrate accurate identification of a large population of MU firings in a broad recruitment range in response to TMS via non-invasive HDsEMG recordings. KEY POINTS: Transcranial magnetic stimulation (TMS) of the scalp produces multiple descending volleys, exciting motor pools in a diffuse manner. The characteristics of a motor pool response to TMS have been previously investigated with intramuscular electromyography (EMG), but this is limited in its capacity to detect many motor units (MUs) that constitute a motor evoked potential (MEP) in response to TMS. By simulating synthetic signals with known MU firing patterns, and recording high-density EMG signals from two human muscles, we show the feasibility of identifying firings of many MUs that comprise a MEP. We demonstrate the identification of firings of a large population of MUs in the broad recruitment range, up to maximal MEP amplitude, with fewer required stimuli compared to intramuscular EMG recordings. The methodology demonstrates an emerging possibility to study responses to TMS on a level of individual MUs in a non-invasive manner.

Modulation of spinal circuits following phase-dependent electrical stimulation of afferent pathways

Objective.Peripheral electrical stimulation (PES) of afferent pathways is a tool commonly used to induce neural adaptations in some neural disorders such as pathological tremor or stroke. However, the neuromodulatory effects of stimulation interventions synchronized with physiological activity (closed-loop strategies) have been scarcely researched in the upper-limb. Here, the short-term spinal effects of a 20-minute stimulation protocol where afferent pathways were stimulated with a closed-loop strategy named selective and adaptive timely stimulation (SATS) were explored in 11 healthy subjects.Approach. SATS was applied to the radial nerve in-phase (INP) or out-of-phase (OOP) with respect to the muscle activity of the extensor carpi radialis (ECR). The neural adaptations at the spinal cord level were assessed for the flexor carpi radialis (FCR) by measuring disynaptic Group I inhibition, Ia presynaptic inhibition, Ib facilitation from the H-reflex and estimation of the neural drive before, immediately after, and 30 minutes after the intervention.Main results.SATS strategy delivered electrical stimulation synchronized with the real-time muscle activity measured, with an average delay of 17 ± 8 ms. SATS-INP induced increased disynaptic Group I inhibition (77 ± 23% of baseline conditioned FCR H-reflex), while SATS-OOP elicited the opposite effect (125 ± 46% of baseline conditioned FCR H-reflex). Some of the subjects maintained the changes after 30 minutes. No other significant changes were found for the rest of measurements.Significance.These results suggest that the short-term modulatory effects of phase-dependent PES occur at specific targeted spinal pathways for the wrist muscles in healthy individuals. Importantly, timely recruitment of afferent pathways synchronized with specific muscle activity is a fundamental principle that shall be considered when tailoring PES protocols to modulate specific neural circuits. (NCT number 04501133).

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f₁ score. The LSTM models achieved 0.98 f₁ scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures

Advanced sensors/electrodes and signal processing techniques provide powerful tools to analyze surface electromyographic signals (sEMG) and their features, to decompose sEMG into the constituent motor unit action potential trains, and to identify synergies, neural muscle drive, and EEG-sEMG coherence. However, despite thousands of articles, dozens of textbooks, tutorials, consensus papers, and European and International efforts, the translation of this knowledge into clinical activities and assessment procedures has been very slow, likely because of lack of clinical studies and competent operators in the field. Understanding and using sEMG-based hardware and software tools requires a level of knowledge of signal processing and interpretation concepts that is multidisciplinary and is not provided by most academic curricula in physiotherapy, movement sciences, neurophysiology, rehabilitation, sport, and occupational medicine. The chasm existing between the available knowledge and its clinical applications in this field is discussed as well as the need for new clinical figures. The need for updating the training of physiotherapists, neurophysiology technicians, and clinical technologists is discussed as well as the required competences of trainers and trainees. Indications and examples are suggested and provide a basis for addressing the problem. Two teaching examples are provided in the Supplementary Material.

The acute effects of whole-body vibration on motor unit recruitment and discharge properties

Introduction: several studies have reported improved neuromuscular parameters in response to whole-body vibration (WBV). This is likely achieved by modulation of the central nervous system (CNS). Reduced recruitment threshold (RT), which is the % of Maximal Voluntary Force (%MVF) at which a given Motor Unit (MU) is recruited, may be responsible for the force/power improvements observed in several studies. Methods: 14 men (25 ± 2.3 years; BMI = 23.3 ± 1.5 kg m² MVF: 319.82 ± 45.74 N) performed trapezoidal isometric contractions of the tibialis anterior (TA) at 35-50-70 %MVF before and after three conditions: WBV, STAND (standing posture), and CNT (no intervention). The vibration was applied through a platform for targeting the TA. High-density surface electromyography (HDsEMG) recordings and analysis were used to detect changes in the RT and Discharge Rate (DR) of the MUs. Results: Mean motor unit recruitment threshold (MURT) reached 32.04 ± 3.28 %MVF before and 31.2 ± 3.72 %MVF after WBV, with no significant differences between conditions (p > 0.05). Additionally, no significant changes were found in the mean motor unit discharge rate (before WBV: 21.11 ± 2.94 pps; after WBV: 21.19 ± 2.17 pps). Discussion: The present study showed no significant changes in motor unit properties at the base of neuromuscular changes documented in previous studies. Further investigations are needed to understand motor unit responses to different vibration protocols and the chronic effect of vibration exposure on motor control strategies.

Common synaptic input between motor units from the lateral and medial posterior soleus compartments does not differ from that within each compartment

The human soleus muscle is anatomically divided into four separate anatomical compartments. The functional role of this compartmentalization remains unclear. Here, we tested the hypothesis that the common synaptic input to motor units between the medial and lateral posterior compartments is less than within each compartment. Fourteen male participants performed three different heel-raise tasks that were considered to place a different mechanical demand on the medial and lateral soleus compartments. High-density electromyography (EMG) signals from the medial and lateral soleus compartments and the medial gastrocnemius of the right leg were decomposed into individual motor unit spike trains. The coherence between cumulative spike trains of the motor units was estimated. The coherence analysis was also repeated for motor units that were matched across all three tasks. Furthermore, we calculated the ratio of significant correlations between the spike trains of pairs of motor units. We observed that the coherence between motor units of the two soleus compartments was similar as the coherence between motor units within each compartment, regardless of the task. The correlation analysis performed on pairs of motor units confirmed these results. We conclude that the level of common synaptic input between the motor units innervating the medial and lateral posterior soleus compartment is not different than the common synaptic input between motor units innervating each of these compartments, which contrasts with findings from previous studies on finger muscles. This suggests that there is no independent neural control for the individual posterior soleus compartments.NEW & NOTEWORTHY The human soleus muscle is anatomically subdivided into four compartments. The functional role for this compartmentalization remains unknown. Here, we showed that, contrary to previous findings in finger muscles, the common synaptic input between motor units innervating the medial and lateral posterior soleus compartment was similar as that between motor units within the individual compartments. We suggest that the contradictory findings with other compartmentalized muscles may be explained by differences in muscle-tendon anatomy and function.

Failure to improve task performance after visuomotor training with error reduction feedback for young adults

Visual feedback that reinforces accurate movements may motivate skill acquisition by promoting self-confidence. This study investigated neuromuscular adaptations to visuomotor training with visual feedback with virtual error reduction. Twenty-eight young adults (24.6 ± 1.6 years) were assigned to error reduction (ER) (n = 14) and control (n = 14) groups to train on a bi-rhythmic force task. The ER group received visual feedback and the displayed errors were 50% of the real errors in size. The control group was trained with visual feedback with no reduction in errors. Training-related differences in task accuracy, force behaviors, and motor unit discharge were contrasted between the two groups. The tracking error of the control group progressively declined, whereas the tracking error of the ER group was not evidently reduced in the practice sessions. In the post-test, only the control group exhibited significant task improvements with smaller error size (p = .015) and force enhancement at the target frequencies (p = .001). The motor unit discharge of the control group was training-modulated, as indicated by a reduction of the mean inter-spike interval (p = .018) and smaller low-frequency discharge fluctuations (p = .017) with enhanced firing at the target frequencies of the force task (p = .002). In contrast, the ER group showed no training-related modulation of motor unit behaviors. In conclusion, for young adults, ER feedback does not induce neuromuscular adaptations to the trained visuomotor task, which is conceptually attributable to intrinsic error dead-zones.

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.

The Neuromuscular Fatigue-Induced Loss of Muscle Force Control

Neuromuscular fatigue is characterised not only by a reduction in the capacity to generate maximal muscle force, but also in the ability to control submaximal muscle forces, i.e., to generate task-relevant and precise levels of force. This decreased ability to control force is quantified according to a greater magnitude and lower complexity (temporal structure) of force fluctuations, which are indicative of decreased force steadiness and adaptability, respectively. The “loss of force control” is affected by the type of muscle contraction used in the fatiguing exercise, potentially differing between typical laboratory tests of fatigue (e.g., isometric contractions) and the contractions typical of everyday and sporting movements (e.g., dynamic concentric and eccentric contractions), and can be attenuated through the use of ergogenic aids. The loss of force control appears to relate to a fatigue-induced increase in common synaptic input to muscle, though the extent to which various mechanisms (afferent feedback, neuromodulatory pathways, cortical/reticulospinal pathways) contribute to this remains to be determined. Importantly, this fatigue-induced loss of force control could have important implications for task performance, as force control is correlated with performance in a range of tasks that are associated with activities of daily living, occupational duties, and sporting performance.

People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations

We quantified the relationship between spatial oscillations in surface electromyographic (sEMG) activity and trunk-extension torque in individuals with and without chronic low back pain (CLBP), during two submaximal isometric lumbar extension tasks at 20% and 50% of their maximal voluntary torque. High-density sEMG (HDsEMG) signals were recorded from the lumbar erector spinae (ES) with a 64-electrode grid, and torque signals were recorded with an isokinetic dynamometer. Coherence and cross-correlation analyses were applied between the filtered interference HDsEMG and torque signals for each submaximal contraction. Principal component analysis was used to reduce dimensionality of HDsEMG data and improve the HDsEMG-based torque estimation. sEMG-torque coherence was quantified in the δ(0–5 Hz) frequency bandwidth. Regional differences in sEMG-torque coherence were also evaluated by creating topographical coherence maps. sEMG-torque coherence in the δ band and sEMG-torque cross-correlation increased with the increase in torque in the controls but not in the CLBP group (p = 0.018, p = 0.030 respectively). As torque increased, the CLBP group increased sEMG-torque coherence in more cranial ES regions, while the opposite was observed for the controls (p = 0.043). Individuals with CLBP show reductions in sEMG-torque relationships possibly due to the use of compensatory strategies and regional adjustments of ES-sEMG oscillatory activity.

Handedness is associated with less common input to spinal motor neurons innervating different hand muscles

Whether the neural control of manual behaviours differs between the dominant and non-dominant hand is poorly understood. This study aimed to determine whether the level of common synaptic input to motor neurons innervating the same or different muscles differs between the dominant and the non-dominant hand. Seventeen participants performed two motor tasks with distinct mechanical requirements: an isometric pinch and an isometric rotation of a pinched dial. Each task was performed at 30% of maximum effort and was repeated with the dominant and non-dominant hand. Motor units were identified from two intrinsic (flexor digitorum interosseous and thenar) and one extrinsic muscle (flexor digitorum superficialis) from high-density surface electromyography recordings. Two complementary approaches were used to estimate common synaptic inputs. First, we calculated the coherence between groups of motor neurons from the same and from different muscles. Then, we estimated the common input for all pairs of motor neurons by correlating the low-frequency oscillations of their discharge rate. Both analyses led to the same conclusion, indicating less common synaptic input between motor neurons innervating different muscles in the dominant hand than in the non-dominant hand, which was only observed during the isometric rotation task. No between-side differences in common input were observed between motor neurons of the same muscle. This lower level of common input could confer higher flexibility in the recruitment of motor units, and therefore, in mechanical outputs. Whether this difference between the dominant and non-dominant arm is the cause or the consequence of handedness remains to be determined.

Neuromuscular and structural tendon adaptations after 6 weeks of either concentric or eccentric exercise in individuals with non-insertional Achilles tendinopathy: protocol for a randomised controlled trial

INTRODUCTION

There is limited evidence on the neural strategies employed by the central nervous system to control muscle force in the presence of non-insertional Achilles tendinopathy (NIAT). Additionally, the neuromuscular mechanisms by which exercise may help to resolve tendon pain remain unclear.

OBJECTIVE

This study aims to first establish changes in the gastrocnemius-soleus motor unit firing properties after applying a training protocol of 6 weeks based on either controlled eccentric or concentric contractions in individuals with NIAT. Second, we want to determine changes in the level of pain and function and mechanical and structural properties of the Achilles tendon after applying the same training protocol. Additionally, we want to compare these variables at baseline between individuals with NIAT and asymptomatic controls.

METHODS AND ANALYSIS

A total of 26 individuals with chronic (>3 months) NIAT and 13 healthy controls will participate in the study. Individuals with NIAT will be randomised to perform eccentric or concentric training for 6 weeks. Motor unit firing properties of the medial gastrocnemius, lateral gastrocnemius and soleus muscles will be assessed using high-density surface electromyography, as well as Achilles tendon length, cross-sectional area, thickness and stiffness using B-mode ultrasonography and shear wave elastography. Moreover, participants will complete a battery of questionnaires to document their level of pain and function.

ETHICS AND DISSEMINATION

Ethical approval (ERN-20-0604A) for the study was obtained from the Science, Technology, Engineering and Mathematics Ethical Review Committee of the University of Birmingham. The results of the study will be published in peer-review journals.

TRIAL REGISTRATION NUMBER

ISRCTN46462385.

Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles

Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.

Estimating muscle activation from EMG using deep learning-based dynamical systems models

OBJECTIVE

To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.

APPROACH

Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation.

MAIN RESULTS

We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches.

SIGNIFICANCE

This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas.

Principles of human movement augmentation and the challenges in making it a reality

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

Online tracking of the phase difference between neural drives to antagonist muscle pairs in essential tremor patients

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

Less common synaptic input between muscles from the same group allows for more flexible coordination strategies during a fatiguing task

This study aimed to determine whether neural drive is redistributed between muscles during a fatiguing isometric contraction, and if so, whether the initial level of common synaptic input between these muscles constrains this redistribution. We studied two muscle groups: triceps surae (14 participants) and quadriceps (15 participants). Participants performed a series of submaximal isometric contractions and a torque-matched contraction maintained until task failure. We used high-density surface electromyography to identify the behavior of 1874 motor units from the soleus, gastrocnemius medialis (GM), gastrocnemius lateralis(GL), rectus femoris, vastus lateralis (VL), and vastus medialis(VM). We assessed the level of common drive between muscles in absence of fatigue using a coherence analysis. We also assessed the redistribution of neural drive between muscles during the fatiguing contraction through the correlation between their cumulative spike trains (index of neural drive). The level of common drive between VL and VM was significantly higher than that observed for the other muscle pairs, including GL-GM. The level of common drive increased during the fatiguing contraction, but the differences between muscle pairs persisted. We also observed a strong positive correlation of neural drive between VL and VM during the fatiguing contraction (r=0.82). This was not observed for the other muscle pairs, including GL-GM, which exhibited differential changes in neural drive. These results suggest that less common synaptic input between muscles allows for more flexible coordination strategies during a fatiguing task, i.e., differential changes in neural drive across muscles. The role of this flexibility on performance remains to be elucidated.

The anatomical, electrophysiological and histological observations of muscle contraction units in rabbits: a new perspective on nerve injury and regeneration

In the conventional view a muscle is composed of intermediate structures before its further division into microscopic muscle fibers. Our experiments in mice have confirmed this intermediate structure is composed of the lamella cluster formed by motor endplates, the innervating nerve branches and the corresponding muscle fibers, which can be viewed as an independent structural and functional unit. In this study, we verified the presence of these muscle construction units in rabbits. The results showed that the muscular branch of the femoral nerve sent out 4–6 nerve branches into the quadriceps and the tibial nerve sent out 4–7 nerve branches into the gastrocnemius. When each nerve branch of the femoral nerve was stimulated from the most lateral to the medial, the contraction of the lateral muscle, intermediate muscle and medial muscle of the quadriceps could be induced by electrically stimulating at least one nerve branch. When stimulating each nerve branch of the tibial nerve from the lateral to the medial, the muscle contraction of the lateral muscle 1, lateral muscle 2, lateral muscle 3 and medial muscle of the gastrocnemius could be induced by electrically stimulating at least one nerve branch. Electrical stimulation of each nerve branch resulted in different electromyographical waves recorded in different muscle subgroups. Hematoxylin-eosin staining showed most of the nerve branches around the neuromuscular junctions consisted of one individual neural tract, a few consisted of two or more neural tracts. The muscles of the lower limb in the rabbit can be subdivided into different muscle subgroups, each innervated by different nerve branches, thereby allowing much more complex muscle activities than traditionally stated. Together, the nerve branches and the innervated muscle subgroups can be viewed as an independent structural and functional unit. This study was approved by the Animal Ethics Committee of Peking University People’s Hospital (approval No. 2019PHE027) on October 20, 2019.

The Cellular Basis for the Generation of Firing Patterns in Human Motor Units

Motor units, which comprise a motoneuron and the set of muscle fibers it innervates, are the fundamental neuromuscular transducers for all motor commands. The one to one relationship between a motoneuron and its innervated muscle fibers allow motoneuron firing patterns to be readily measured in humans. In this chapter, we summarize the current understanding of the cellular basis for the generation of firing patterns in human motor units. We provide a brief review of landmark insights from classic studies and then proceed to consider the features of motor unit firing patterns that are most likely to be sensitive estimators of motoneuron inputs and properties. In addition, we discuss recent advances in technology for recording human motor unit firing patterns and highly realistic computer simulations of motoneurons. The final section presents our recent efforts to use the power of supercomputers for implementation of the motoneuron models, with a goal of achieving a true "reverse engineering" approach that maximizes the insights from motor unit firing patterns into the synaptic structure of motor commands.

Distribution of motor unit properties across human muscles

The purpose of our review was to compare the distribution of motor unit properties across human muscles of different sizes and recruitment ranges. Although motor units can be distinguished based on several different attributes, we focused on four key parameters that have a significant influence on the force produced by muscle during voluntary contractions: the number of motor units, average innervation number, the distributions of contractile characteristics, and discharge rates within motor unit pools. Despite relatively few publications on this topic, current data indicate that the most influential factor in the distribution of these motor unit properties between muscles is innervation number. Nonetheless, despite a fivefold difference in innervation number between a hand muscle (first dorsal interosseus) and a lower leg muscle (tibialis anterior), the general organization of their motor unit pools, and the range of discharge rates appear to be relatively similar. These observations provide foundational knowledge for studies on the control of movement and the changes that occur with aging and neurological disorders.

Neuromechanical Biomarkers for Robotic Neurorehabilitation

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

Behavior of motor units during submaximal isometric contractions in chronically strength-trained individuals

Neural and morphological adaptations combine to underpin the enhanced muscle strength following prolonged exposure to strength training, although their relative importance remains unclear. We investigated the contribution of motor unit (MU) behavior and muscle size to submaximal force production in chronically strength-trained athletes (ST) versus untrained controls (UT). Sixteen ST (age: 22.9 ± 3.5 yr; training experience: 5.9 ± 3.5 yr) and 14 UT (age: 20.4 ± 2.3 yr) performed maximal voluntary isometric force (MViF) and ramp contractions (at 15%, 35%, 50%, and 70% MViF) with elbow flexors, whilst high-density surface electromyography (HDsEMG) was recorded from the biceps brachii (BB). Recruitment thresholds (RTs) and discharge rates (DRs) of MUs identified from the submaximal contractions were assessed. The neural drive-to-muscle gain was estimated from the relation between changes in force (ΔFORCE, i.e. muscle output) relative to changes in MU DR (ΔDR, i.e. neural input). BB maximum anatomical cross-sectional area (ACSA_MAX) was also assessed by MRI. MViF (+64.8% vs. UT, P < 0.001) and BB ACSA_MAX (+71.9%, P < 0.001) were higher in ST. Absolute MU RT was higher in ST (+62.6%, P < 0.001), but occurred at similar normalized forces. MU DR did not differ between groups at the same normalized forces. The absolute slope of the ΔFORCE - ΔDR relationship was higher in ST (+66.9%, P = 0.002), whereas it did not differ for normalized values. We observed similar MU behavior between ST athletes and UT controls. The greater absolute force-generating capacity of ST for the same neural input demonstrates that morphological, rather than neural, factors are the predominant mechanism for their enhanced force generation during submaximal efforts.NEW & NOTEWORTHY In this study, we observed that recruitment strategies and discharge characteristics of large populations of motor units identified from biceps brachii of strength-trained athletes were similar to those observed in untrained individuals during submaximal force tasks. We also found that for the same neural input, strength-trained athletes are able to produce greater absolute muscle forces (i.e., neural drive-to-muscle gain). This demonstrates that morphological factors are the predominant mechanism for the enhanced force generation during submaximal efforts.

The force-generation capacity of the tibialis anterior muscle at different muscle-tendon lengths depends on its motor unit contractile properties

Purpose

Muscle–tendon length can influence central and peripheral motor unit (MU) characteristics, but their interplay is unknown. This study aims to explain the effect of muscle length on MU firing and contractile properties by applying deconvolution of high-density surface EMG (HDEMG), and torque signals on the same MUs followed at different lengths during voluntary contractions.

Methods

Fourteen participants performed isometric ankle dorsiflexion at 10% and 20% of the maximal voluntary torque (MVC) at short, optimal, and long muscle lengths (90°, 110°, and 130° ankle angles, respectively). HDEMG signals were recorded from the tibialis anterior, and MUs were tracked by cross-correlation of MU action potentials across ankle angles and torques. Torque twitch profiles were estimated using model-based deconvolution of the torque signal based on composite MU spike trains.

Results

Mean discharge rate of matched motor units was similar across all muscle lengths (P = 0.975). Interestingly, the increase in mean discharge rate of MUs matched from 10 to 20% MVC force levels at the same ankle angle was smaller at 110° compared with the other two ankle positions (P = 0.003), and the phenomenon was explained by a greater increase in twitch torque at 110° compared to the shortened and lengthened positions (P = 0.002). This result was confirmed by the deconvolution of electrically evoked contractions at different stimulation frequencies and muscle–tendon lengths.

Conclusion

Higher variations in MU twitch torque at optimal muscle lengths likely explain the greater force-generation capacity of muscles in this position.

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

Workers often develop low back pain due to manually lifting heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk in lifting activities. This study aims to verify the hypothesis that high-density surface electromyography (HDsEMG) allows an optimized discrimination of risk levels associated with different fatiguing lifting conditions compared to traditional bipolar sEMG. 15 participants performed three lifting tasks with a progressively increasing lifting index (LI) each lasting 15 min. Erector spinae (ES) activity was recorded using both bipolar and HDsEMG systems. The amplitude of both bipolar and HDsEMG can significantly discriminate each pair of LI. HDsEMG data could discriminate across the different LIs starting from the fourth minute of the task while bipolar sEMG could only do so towards the end. The higher discriminative power of HDsEMG data across the lifting tasks makes such methodology a valuable tool to be used to monitor fatigue while lifting and could extend the possibilities offered by currently available instrumental-based tools.

Synergistic Organization of Neural Inputs from Spinal Motor Neurons to Extrinsic and Intrinsic Hand Muscles

Our current understanding of synergistic muscle control is based on the analysis of muscle activities. Modules (synergies) in muscle coordination are extracted from electromyographic (EMG) signal envelopes. Each envelope indirectly reflects the neural drive received by a muscle; therefore, it carries information on the overall activity of the innervating motor neurons. However, it is not known whether the output of spinal motor neurons, whose number is orders of magnitude greater than the muscles they innervate, is organized in a low-dimensional fashion when performing complex tasks. Here, we hypothesized that motor neuron activities exhibit a synergistic organization in complex tasks and therefore that the common input to motor neurons results in a large dimensionality reduction in motor neuron outputs. To test this hypothesis, we factorized the output spike trains of motor neurons innervating 14 intrinsic and extrinsic hand muscles and analyzed the dimensionality of control when healthy individuals exerted isometric forces using seven grip types. We identified four motor neuron synergies, accounting for >70% of the variance of the activity of 54.1 ± 12.9 motor neurons, and we identified four functionally similar muscle synergies. However, motor neuron synergies better discriminated individual finger forces than muscle synergies and were more consistent with the expected role of muscles actuating each finger. Moreover, in a few cases, motor neurons innervating the same muscle were active in separate synergies. Our findings suggest a highly divergent net neural inputs to spinal motor neurons from spinal and supraspinal structures, contributing to the dimensionality reduction captured by muscle synergies.SIGNIFICANCE STATEMENT We addressed whether the output of spinal motor neurons innervating multiple hand muscles could be accounted for by a modular organization, i.e., synergies, previously described to account for the coordination of multiple muscles. We found that motor neuron synergies presented similar dimensionality (implying a >10-fold reduction in dimensionality) and structure as muscle synergies. Nonetheless, the synergistic behavior of subsets of motor neurons within a muscle was also observed. These results advance our understanding of how neuromuscular control arises from mapping descending inputs to muscle activation signals. We provide, for the first time, insights into the organization of neural inputs to spinal motor neurons which, to date, has been inferred through analysis of muscle synergies.

Wrist Torque Estimation via Electromyographic Motor Unit Decomposition and Image Reconstruction

Neural interface using decomposed motor units (MUs) from surface electromyography (sEMG) has allowed non-invasive access to the neural control signals, and provided a novel approach for intuitive human-machine interaction. However, most of the existing methods based on decomposed MUs merely adopted the discharge rate (DR) as the feature representations, which may lack local information around the discharge instant and ignore the subtle interactions of different MUs. In this study, we proposed an MU-specific image-based scheme for wrist torque estimation. Specifically, the high-density sEMG signals were decoded into motor unit spike trains (MUSTs), and then MU-specific images were reconstructed with MUSTs and corresponding motor unit action potential (MUAP). A convolutional neural network was used to learn representative features from MU-specific images automatically, and further to estimate wrist torques. The results demonstrated that the proposed method outperformed three conventional and a deep-learning regression approaches using DR features, with the estimation accuracy R² of 0.82 ± 0.09, 0.89 ± 0.06, and nRMSE of 12.6 ± 2.5%, 11.0 ± 3.1% for pronation/supination and flexion/extension, respectively. Further, the analysis of the extracted features from MU-specific images showed a higher correlation than DR for recorded torques, indicating the effectiveness of the proposed method. The outcomes of this study provide a novel and promising perspective for the intuitive control of neural interfacing.

Neural adaptations to long-term resistance training: evidence for the confounding effect of muscle size on the interpretation of surface electromyography

This study compared elbow flexor (EF; experiment 1) and knee extensor (KE; experiment 2) maximal compound action potential (M_max) amplitude between long-term resistance trained (LTRT; n = 15 and n = 14, 6 ± 3 and 4 ± 1 yr of training) and untrained (UT; n = 14 and n = 49) men, and examined the effect of normalizing electromyography (EMG) during maximal voluntary torque (MVT) production to M_max amplitude on differences between LTRT and UT. EMG was recorded from multiple sites and muscles of EF and KE, M_max was evoked with percutaneous nerve stimulation, and muscle size was assessed with ultrasonography (thickness, EF) and magnetic resonance imaging (cross-sectional area, KE). Muscle-electrode distance (MED) was measured to account for the effect of adipose tissue on EMG and M_max. LTRT displayed greater MVT (+66%-71%, P < 0.001), muscle size (+54%-56%, P < 0.001), and M_max amplitudes (+29%-60%, P ≤ 0.010) even when corrected for MED (P ≤ 0.045). M_max was associated with the size of both muscle groups (r ≥ 0.466, P ≤ 0.011). Compared with UT, LTRT had higher absolute voluntary EMG amplitude for the KE (P < 0.001), but not the EF (P = 0.195), and these differences/similarities were maintained after correction for MED; however, M_max normalization resulted in no differences between LTRT and UT for any muscle and/or muscle group (P ≥ 0.652). The positive association between M_max and muscle size, and no differences when accounting for peripheral electrophysiological properties (EMG/M_max), indicates the greater absolute voluntary EMG amplitude of LTRT might be confounded by muscle morphology, rather than providing a discrete measure of central neural activity. This study therefore suggests limited agonist neural adaptation after LTRT.NEW & NOTEWORTHY In a large sample of long-term resistance-trained individuals, we showed greater maximal M-wave amplitude of the elbow flexors and knee extensors compared with untrained individuals, which appears to be at least partially mediated by differences in muscle size. The lack of group differences in voluntary EMG amplitude when normalized to maximal M-wave suggests that differences in muscle morphology might impair interpretation of voluntary EMG as an index of central neural activity.

Design and Characterization of a Textile Electrode System for the Detection of High-Density sEMG

Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks.

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain?

Resistance training enhances muscular force due to a combination of neural plasticity and muscle hypertrophy. It has been well documented that the increase in strength over the first few weeks of resistance training (i.e. acute) has a strong underlying neural component and further enhancement in strength with long-term (i.e. chronic) resistance training is due to muscle hypertrophy. For obvious reasons, collecting long-term data on how chronic-resistance training affects the nervous system not feasible. As a result, the effect of chronic-resistance training on neural plasticity is less understood and has not received systematic exploration. Thus, the aim of this review is to provide rationale for investigating neural plasticity beyond acute-resistance training. We use cross-sectional work to highlight neural plasticity that occurs with chronic-resistance training at sites from the brain to spinal cord. Specifically, intra-cortical circuitry and the spinal motoneuron seem to be key sites for this plasticity. We then urge the need to further investigate the differential effects of acute versus chronic-resistance training on neural plasticity, and the role of this plasticity in increased strength. Such investigations may help in providing a clearer definition of the continuum of acute and chronic-resistance training, how the nervous system is altered during this continuum and the causative role of neural plasticity in changes in strength over the continuum of resistance training.

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

OBJECTIVES

This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated.

APPROACH

The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: 1) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and 2) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network.

MAIN RESULTS

The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2 to 21.4 s/epoch vs. 6.5 to 47.8 s/epoch, respectively) and prediction time (0.04 vs. 0.27 s/sample, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88-0.95).

SIGNIFICANCE

We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

Force Steadiness: From Motor Units to Voluntary Actions

Voluntary actions are controlled by the synaptic inputs that are shared by pools of spinal motor neurons. The slow common oscillations in the discharge times of motor units due to these synaptic inputs are strongly correlated with the fluctuations in force during submaximal isometric contractions (force steadiness) and moderately associated with performance scores on some tests of motor function. However, there are key gaps in knowledge that limit the interpretation of differences in force steadiness.