Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals.

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

Consensus for experimental design in electromyography (CEDE) project: Checklist for reporting and critically appraising studies using EMG (CEDE-Check).

The diversity in electromyography (EMG) techniques and their reporting present significant challenges across multiple disciplines in research and clinical practice, where EMG is commonly used. To address these challenges and augment the reproducibility and interpretation of studies using EMG, the Consensus for Experimental Design in Electromyography (CEDE) project has developed a checklist (CEDE-Check) to assist researchers to thoroughly report their EMG methodologies. Development involved a multi-stage Delphi process with seventeen EMG experts from various disciplines. After two rounds, consensus was achieved. The final CEDE-Check consists of forty items that address four critical areas that demand precise reporting when EMG is employed: the task investigated, electrode placement, recording electrode characteristics, and acquisition and pre-processing of EMG signals. This checklist aims to guide researchers to accurately report and critically appraise EMG studies, thereby promoting a standardised critical evaluation, and greater scientific rigor in research that uses EMG signals. This approach not only aims to facilitate interpretation of study results and comparisons between studies, but it is also expected to contribute to advancing research quality and facilitate clinical and other practical applications of knowledge generated through the use of EMG.

Horizontal foot orientation affects the distribution of neural drive between gastrocnemii during plantarflexion, without changing neural excitability.

The distribution of activation among muscles from the same anatomical group can be affected by the mechanical constraints of the task, such as limb orientation. For example, the distribution of activation between the gastrocnemius medialis (GM) and lateralis (GL) muscles during submaximal plantarflexion depends on the orientation of the foot in the horizontal plane. The neural mechanisms behind these modulations are not known. The overall aim of this study was to determine whether the excitability of the two gastrocnemius muscles is differentially affected by changes in foot orientation. Nineteen males performed isometric plantarflexions with their foot internally (toes-in) or externally (toes-out) rotated. GM and GL motor unit discharge characteristics were estimated from high-density surface electromyography to estimate neural drive. GM and GL corticospinal excitability and intracortical activity were assessed using transcranial magnetic stimulation through motor-evoked potentials. The efficacy of synaptic transmission between Ia-afferent fibers and α-motoneurons of the GM and GL was evaluated through the Hoffmann reflex. We observed a differential change in neural drive between GM (toes-out > toes-in) and GL (toes-out < toes-in). However, there was no foot orientation-related modulation in corticospinal excitability of the GM or GL, either at the cortical level or through modulation of the efficacy of Ia-α-motoneuron transmission. These results demonstrate that change in the motor pathway excitability is not the mechanism controlling the different distribution of neural drive between GM and GL with foot orientation.NEW & NOTEWORTHY Horizontal foot orientation affects the distribution of neural drive between the gastrocnemii during plantarflexion. There is no foot orientation-related modulation in the corticospinal excitability of the gastrocnemii, either at the cortical level or through modulation of the efficacy of Ia-α-motoneuron transmission. Change in motor pathway excitability is not the mechanism controlling the different distribution of neural drive between gastrocnemius medialis and lateralis with foot orientation.

Estimates of persistent inward currents in lower limb muscles are not different between inactive, resistance-trained, and endurance-trained young males.

Persistent inward currents (PICs) increase the intrinsic excitability of α-motoneurons. The main objective of this study was to compare estimates of α-motoneuronal PICs between inactive, chronic resistance-trained, and chronic endurance-trained young individuals. We also aimed to investigate whether there is a relationship in the estimates of α-motoneuronal PIC magnitude between muscles. Estimates of PIC magnitude were obtained in three groups of young individuals: resistance-trained (n = 12), endurance-trained (n = 12), and inactive (n = 13). We recorded high-density surface electromyography (HDsEMG) signals from tibialis anterior (TA), gastrocnemius medialis (GM), soleus (SOL), vastus medialis (VM), and vastus lateralis (VL). Then, signals were decomposed with convolutive blind source separation to identify motor unit (MU) spike trains. Participants performed triangular isometric contractions to a peak of 20% of their maximum voluntary contraction. A paired-motor-unit analysis was used to calculate ΔF, which is assumed to be proportional to PIC magnitude. Despite the substantial differences in physical training experience between groups, we found no differences in ΔF, regardless of the muscle. Significant correlations of estimates of PIC magnitude were found between muscles of the same group (VL-VM, SOL-GM). Only two correlations (out of 8) between muscles of different groups were found (TA-GM and VL-GM). Overall, our findings suggest that estimates of PIC magnitude from lower-threshold MUs at low contraction intensities in the lower limb muscles are not influenced by physical training experience in healthy young individuals. They also suggest muscle-specific and muscle group-specific regulations of the estimates of PIC magnitude.NEW & NOTEWORTHY Chronic resistance and endurance training can lead to specific adaptations in motor unit activity. The contribution of α-motoneuronal persistent inward currents (PICs) to these adaptations is currently unknown in healthy young individuals. Therefore, we studied whether estimates of α-motoneuronal PIC magnitude are higher in chronically trained endurance- and resistance-trained individuals. We also studied whether there is a relationship between the estimates of α-motoneuronal PIC magnitude of different lower limb muscles.

Consistency of muscle activation signatures across different walking speeds.

BACKGROUND: Using a machine learning algorithm, individuals can be accurately identified from their muscle activation patterns during gait, leading to the concept of individual muscle activation signatures. RESEARCH QUESTION: Are muscle activation signatures robust across different walking speeds? METHODS: We used an open dataset containing electromyographic (EMG) signals from 8 lower limb muscles in 50 asymptomatic adults walking at 5 speeds (extremely slow, very slow, slow, spontaneous, and fast). A machine learning approach classified the EMG profiles based on similar (intra-speed classification) or different (inter-speed classification) walking speeds as training and testing conditions. RESULTS: Intra-speed median classification rates of muscle activation profiles increased with walking speed, from 92 % for extremely slow, to 100 % for self-selected fast walking conditions. Inter-speed median classification rates increased when the speed of the training condition was closer to that of the testing condition. Higher median classification rates were found across slow, spontaneous, and fast walking speed conditions, from 56 % to 96 %, compared with classification rates involving extremely and very slow walking speed conditions, from 6 % to 62 %. SIGNIFICANCE: Our findings reveal that i) muscle activation signatures are detectable for a large range of walking speeds, even those involving different gait strategies (intra-speed median classification rates from 92 % to 100 %), and ii) muscle activation signatures observed during very low walking speeds are not consistent with those observed at higher speeds, suggesting a difference in motor control strategy. Caution should therefore be exercised when assessing gait deviations of a slow walking patient against a normative database obtained at higher speed. Identifying the robustness of individual muscle activation signatures across different movements could help in detecting changes in motor control, otherwise difficult to detect on classical time-varying EMG patterns.

Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion.

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In part 2, 16 individuals performed plantarflexion at three ankle angles while we recorded EMG signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. The PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable, with no significant differences in the distribution of motor neuron weights across ankle angles (P = 0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. KEY POINTS: The CNS might generate movements by activating groups of motor neurons (synergies) with common inputs. We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions. We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e. the ankle angle. These results suggest the functional relevance of the modular organization of the CNS to control movements.

Neuromechanical Properties of the Vastus Medialis and Vastus Lateralis in Adolescents With Patellofemoral Pain.

Background: An alteration in the force distribution among quadriceps heads is one possible underlying mechanism of patellofemoral pain. However, this hypothesis cannot be directly tested as there are currently no noninvasive experimental techniques to measure individual muscle force or torque in vivo in humans. In this study, the authors considered a combination of biomechanical and muscle activation measures, which enabled us to estimate the mechanical impact of the vastus medialis (VM) and vastus lateralis (VL) on the patella. Purpose/Hypothesis: The purpose of this study was to determine whether the relative index of torque distribution for the VM and VL differs between adolescents with and without patellofemoral pain. It was hypothesized that, relative to the VL, the VM would contribute less to knee extension torque in adolescents with patellofemoral pain compared with controls. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Twenty adolescents with patellofemoral pain and 20 matched control participants were included (38 female; age, 15.3 ± 1.8 years; weight, 58 ± 13 kg; height, 164 ± 8 cm). Muscle volumes and resting moment arms were quantified from magnetic resonance images, and fascicle lengths were obtained from panoramic B-mode ultrasonography. Muscle activation was estimated using surface electromyography during submaximal isometric tasks (wall-squat and seated tasks). Muscle torque was estimated as the product of muscle physiological cross-sectional area (ie, muscle volume/fascicle length), muscle activation (normalized to maximal activation), and moment arm. Results: Across tasks and force levels, the relative contribution of the VM to the overall medial and lateral vastii torque was 31.0% ± 8.6% for controls and 31.5 ± 7.6% for adolescents with patellofemoral pain (group effect, P > .34). Conclusion: For the tasks and positions investigated in this study, the authors found no evidence of lower VM torque generation (relative to the VL) in adolescents with patellofemoral pain compared with controls.

A comparison of neural control of the biarticular gastrocnemius muscles between knee flexion and ankle plantar flexion.

We aimed to determine whether the neural control of the biarticular gastrocnemius medialis (GM) and lateralis (GL) muscles is joint-specific, that is, whether their control differs between isolated knee flexion and ankle plantar flexion tasks. Twenty-one male participants performed isometric knee flexion and ankle plantar flexion tasks while we recorded high-density surface electromyography (HDsEMG). First, we estimated the distribution of activation both within- and between muscles using two complementary approaches: surface EMG amplitude and motor unit activity identified from HDsEMG decomposition. Second, we estimated the level of common synaptic input between GM and GL motor units using a coherence analysis. The distribution of EMG amplitude between GM and GL was not different between tasks, which was confirmed by the analysis of motor units' discharge rate. Even though there was a significant proximal shift in GM and GL EMG amplitude during knee flexion compared with ankle plantar flexion, the magnitude of this shift was small and not confirmed via the inspection of the spatial distribution of motor unit action potentials. A significant coherence between GM and GL motor units was only observed for four (knee flexion) and three (ankle plantar flexion) participants, with no difference in the level of coherence between the two tasks. We were able to track only a few motor units across tasks, which raises the question as to whether the same motor units were activated across tasks. Our results suggest that the neural control of the GM and GL muscles is similar across their two main functions.NEW & NOTEWORTHY Several studies have focused on the neural strategies used to control the gastrocnemius medialis (GM) and lateralis (GL) during plantar flexion. However, their secondary function, i.e., knee flexion, is not often explored. We observed a robustness of the GM and GL activation strategy across tasks, which was confirmed with an analysis of the motor unit discharge characteristics. The level of common synaptic input between GM and GL motor units was low, regardless of the task.

Influence of internal muscle properties on muscle shape change and gearing in the human gastrocnemii.

Skeletal muscles bulge when they contract. These three-dimensional shape changes, coupled with fiber rotation, influence a muscle's mechanical performance by uncoupling fiber velocity from muscle belly velocity (i.e., gearing). Muscle shape change and gearing are likely mediated by the interaction between internal muscle properties and contractile forces. Muscles with greater stiffness and intermuscular fat, due to aging or disuse, may limit a muscle's ability to bulge in width, subsequently causing higher gearing. The aim of this study was to determine the influence of internal muscle properties on shape change, fiber rotation, and gearing in the medial (MG) and lateral gastrocnemii (LG) during isometric plantar flexion contractions. Multimodal imaging techniques were used to measure muscle shear modulus, intramuscular fat, and fat-corrected physiological cross-sectional area (PCSA) at rest, as well as synchronous muscle architecture changes during submaximal and maximal contractions in the MG and LG of 20 young (24 ± 3 yr) and 13 older (70 ± 4 yr) participants. Fat-corrected PCSA was positively associated with fiber rotation, gearing, and changes in thickness during submaximal contractions, but it was negatively associated with changes in thickness at maximal contractions. Muscle stiffness and intramuscular fat were related to muscle bulging and reduced fiber rotation, respectively, but only at high forces. Furthermore, the MG and LG had varied internal muscle properties, which may relate to the differing shape changes, fiber rotations, and gearing behaviors observed at each contraction level. These results indicate that internal muscle properties may play an important role in mediating in vivo muscle shape change and gearing, especially during high-force contractions.NEW & NOTEWORTHY Here, we measured internal muscle properties in vivo to determine their influence on the varying shape change and gearing behaviors in the synergistic gastrocnemii muscles. These relationships have previously only been hypothesized or examined within isolated muscles during supramaximal contractions. Our results contribute to a more comprehensive understanding of the factors that influence a muscle's mechanical response to force with implications for preventing or treating muscle deficits associated with aging, disease, and disuse.

Advances in imaging for assessing the design and mechanics of skeletal muscle in vivo.

Skeletal muscle is the engine that powers what is arguably the most essential and defining feature of human and animal life-locomotion. Muscles function to change length and produce force to enable movement, posture, and balance. Despite this seemingly simple role, skeletal muscle displays a variety of phenomena that still remain poorly understood. These phenomena are complex-the result of interactions between active and passive machinery, as well as mechanical, chemical and electrical processes. The emergence of imaging technologies over the past several decades has led to considerable discoveries regarding how skeletal muscles function in vivo where activation levels are submaximal, and the length and velocity of contracting muscle fibres are transient. However, our knowledge of the mechanisms of muscle behaviour during everyday human movements remains far from complete. In this review, we discuss the principal advancements in imaging technology that have led to discoveries to improve our understanding of in vivo muscle function over the past 50 years. We highlight the knowledge that has emerged from the development and application of various techniques, including ultrasound imaging, magnetic resonance imaging, and elastography to characterise muscle design and mechanical properties. We emphasize that our inability to measure the forces produced by skeletal muscles still poses a significant challenge, and that future developments to accurately and reliably measure individual muscle forces will promote newfrontiers in biomechanics, physiology, motor control, and robotics. Finally, we identify critical gaps in our knowledge and future challenges that we hope can be solved as a biomechanics community in the next 50 years.

Adaptability of the load sharing between the longissimus and components of the multifidus muscle during isometric trunk extension in healthy individuals.

PURPOSE: Redundancy of the musculoskeletal system implies multiple strategies are theoretically available to coordinate back extensor muscles. This study investigated whether coordination between back muscles during a tightly constrained isometric trunk extension task varies within and between individuals, and whether this changes following brief exposure to activation feedback of a muscle. METHODS: Nine healthy participants performed three blocks of two repetitions of ramped isometric trunk extension in side-lying against resistance from 0-30% of maximum voluntary contraction over 30 s (force feedback). Between blocks, participants repeated contractions with visual feedback of electromyography (EMG) from either superficial (SM) or deep multifidus (DM), in two conditions; 'After SM' and 'After DM'. Intramuscular EMG was recorded from SM, DM, and longissimus (LG) simultaneously with shear wave elastography (SWE) from SM or DM. RESULTS: In the 'Natural' condition (force feedback only), group data showed incremental increases in EMG with force, with minor changes in distribution of activation between muscles as force increased. SM was the most active muscle during the 'Natural' condition, but with DM most active in some participants. Individual data showed that coordination between muscles differed substantially between repetitions and individuals. Brief exposure to EMG feedback altered coordination. SWE showed individual variation, but findings differed from EMG. CONCLUSION: This study revealed substantial variation in coordination between back extensor muscles within and between participants, and after exposure to feedback, in a tightly constrained task. Shear modulus revealed similar variation, but with an inconsistent relationship to EMG. These data highlight highly flexible control of back muscles.

The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies.

The purpose of our study was to identify the low-dimensional latent components, defined hereafter as motor unit modes, underlying the discharge rates of the motor units in two knee extensors (vastus medialis and lateralis, eight men) and two hand muscles (first dorsal interossei and thenars, seven men and one woman) during submaximal isometric contractions. Factor analysis identified two independent motor unit modes that captured most of the covariance of the motor unit discharge rates. We found divergent distributions of the motor unit modes for the hand and vastii muscles. On average, 75% of the motor units for the thenar muscles and first dorsal interosseus were strongly correlated with the module for the muscle in which they resided. In contrast, we found a continuous distribution of motor unit modes spanning the two vastii muscle modules. The proportion of the muscle-specific motor unit modes was 60% for vastus medialis and 45% for vastus lateralis. The other motor units were either correlated with both muscle modules (shared inputs) or belonged to the module for the other muscle (15% for vastus lateralis). Moreover, coherence of the discharge rates between motor unit pools was explained by the presence of shared synaptic inputs. In simulations with 480 integrate-and-fire neurons, we demonstrate that factor analysis identifies the motor unit modes with high levels of accuracy. Our results indicate that correlated discharge rates of motor units that comprise motor unit modes arise from at least two independent sources of common input among the motor neurons innervating synergistic muscles.SIGNIFICANCE STATEMENT It has been suggested that the nervous system controls synergistic muscles by projecting common synaptic inputs to the engaged motor neurons. In our study, we reduced the dimensionality of the output produced by pools of synergistic motor neurons innervating the hand and thigh muscles during isometric contractions. We found two neural modules, each representing a different common input, that were each specific for one of the muscles. In the vastii muscles, we found a continuous distribution of motor unit modes spanning the two synergistic muscles. Some of the motor units from the homonymous vastii muscle were controlled by the dominant neural module of the other synergistic muscle. In contrast, we found two distinct neural modules for the hand muscles.

Consensus for experimental design in electromyography (CEDE) project: Single motor unit matrix.

The analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants.

Objective.High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants.Approach.We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train and validate the deep CNN.Main results.On average, the correlation coefficients between CST from the BSS and that from deep CNN were0.983±0.006for leave-one-out across-sessions-and-muscles validation and0.989±0.002for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at0.984±0.001for cross validations across muscles, sessions, and participants.Significance.We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation.

Understanding how movement is controlled by the CNS remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons and muscle synergies are usually addressed separately and therefore seen as independent features of motor control. In this review, we analyse the body of literature in a broader perspective and we identify a unified approach to explain apparently divergent observations at different scales of motor control. Specifically, we propose a new conceptual framework of the neural control of movement, which merges the concept of common input to motor neurons and modular control, together with the constraints imposed by recruitment order. This framework is based on the following assumptions: (1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; (2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; (3) clusters represent functional modules used by the CNS to reduce the dimensionality of the control; and (4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. Here, we discuss this framework and its underlying theoretical and experimental evidence.

Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task.

Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi-joint task. Specifically, high-density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analysed these activities by identifying their common low-frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles-including distant muscles-received common inputs. The study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons. KEY POINTS: A central and unresolved question is how spinal motor neurons are controlled to generate movement. We decoded the spiking activities of dozens of spinal motor neurons innervating six muscles during a multi-joint task, and we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity (considered as common input). The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). Groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles, including distant muscles, received common inputs. The study supports the theory that movement is produced through the control of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy.

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.

Strength capacity of lower-limb muscles in world-class cyclists: new insights into the limits of sprint cycling performance.

This study aimed to determine the relationship between the torque-generating capacity in sprint cycling and the strength capacity of the six lower-limb muscle groups in male and female world-class sprint cyclists. Eleven female and fifteen male top-elite cyclists performed 5-s sprints at maximal power in seated and standing positions. They also performed a set of maximal voluntary ankle, knee and hip flexions and extensions to assess single-joint isometric and isokinetic torques. Isokinetic torques presented stronger correlations with cycling torque than isometric torques for both body positions, regardless of the group. In the female group, knee extension and hip flexion torques accounted for 81.2% of the variance in cycling torque, while the ability to predict cycling torque was less evident in males (i.e., 59% of variance explained by the plantarflexion torque only). The standing condition showed higher correlations than seated and a better predictive model in males (R2 = 0.88). In addition to the knee extensors and flexors and hip extensors, main power producers, the strength capacity of lower-limb distal plantarflexor (and to a lesser extent dorsiflexor) muscles, as well as other non-measured qualities (e.g., the upper body), might be determinants to produce such extremely high cycling torque in males.

A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.

OBJECTIVE: Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. METHODS: We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. RESULTS: With the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. CONCLUSION: The proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. SIGNIFICANCE: With the proposed deep CNN, we could potentially build a neural-drive-based human-machine interface that is generalizable to different contraction tasks without retraining.

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

Whether the neural control of manual behaviors differs between the dominant and nondominant 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 nondominant 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 nondominant 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 nondominant 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 nondominant arm is the cause or the consequence of handedness remains to be determined.NEW & NOTEWORTHY How the neural control of manual behaviors differs between the dominant and nondominant hand remains poorly understood. This study shows that there is less common synaptic input between motor neurons innervating different muscles in the dominant than in the nondominant hand during isometric rotation tasks. This lower level of common input could confer higher flexibility in the recruitment of motor units.