The Volitional Control of Individual Motor Units Is Constrained within Low-Dimensional Neural Manifolds by Common Inputs.

The implementation of low-dimensional movement control by the central nervous system has been debated for decades. In this study, we investigated the dimensionality of the control signals received by spinal motor neurons when controlling either the ankle or knee joint torque. We first identified the low-dimensional latent factors underlying motor unit activity during torque-matched isometric contractions in male participants. Subsequently, we evaluated the extent to which motor units could be independently controlled. To this aim, we used an online control paradigm in which participants received the corresponding motor unit firing rates as visual feedback. We identified two main latent factors, regardless of the muscle group (vastus lateralis-medialis and gastrocnemius lateralis-medialis). The motor units of the gastrocnemius lateralis could be controlled largely independently from those of the gastrocnemius medialis during ankle plantarflexion. This dissociation of motor unit activity imposed similar behavior to the motor units that were not displayed in the feedback. Conversely, it was not possible to dissociate the activity of the motor units between the vastus lateralis and medialis muscles during the knee extension tasks. These results demonstrate that the number of latent factors estimated from linear dimensionality reduction algorithms does not necessarily reflect the dimensionality of volitional control of motor units. Overall, individual motor units were never controlled independently of all others but rather belonged to synergistic groups. Together, these findings provide evidence for a low-dimensional control of motor units constrained by common inputs, with notable differences between muscle groups.

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.

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.

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.

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.

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 1,874 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 the 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.NEW & NOTEWORTHY Redundancy of the neuromuscular system theoretically allows for a redistribution of the neural drive across muscles (i.e., between-muscle compensation) during a fatiguing contraction. Our results suggest that a high level of common input between muscles (e.g., vastus lateralis and medialis) represents a neural constraint making it less likely to redistribute the neural drive across these muscles. In this way, redistribution was only observed across muscles that share little common synaptic input (e.g., gastrocnemius lateralis and medialis).

Peripheral electrical stimulation to reduce pathological tremor: a review.

Interventions to reduce tremor in essential tremor (ET) and Parkinson's disease (PD) clinical populations often utilize pharmacological or surgical therapies. However, there can be significant side effects, decline in effectiveness over time, or clinical contraindications for these interventions. Therefore, alternative approaches must be considered and developed. Some non-pharmacological strategies include assistive devices, orthoses and mechanical loading of the tremorgenic limb, while others propose peripheral electrical stimulation. Specifically, peripheral electrical stimulation encompasses strategies that activate motor and sensory pathways to evoke muscle contractions and impact sensorimotor function. Numerous studies report the efficacy of peripheral electrical stimulation to alter tremor generation, thereby opening new perspectives for both short- and long-term tremor reduction. Therefore, it is timely to explore this promising modality in a comprehensive review. In this review, we analyzed 27 studies that reported the use of peripheral electrical stimulation to reduce tremor and discuss various considerations regarding peripheral electrical stimulation: the stimulation strategies and parameters, electrodes, experimental designs, results, and mechanisms hypothesized to reduce tremor. From our review, we identified a high degree of disparity across studies with regard to stimulation patterns, experimental designs and methods of assessing tremor. Having standardized experimental methodology is a critical step in the field and is needed in order to accurately compare results across studies. With this review, we explore peripheral electrical stimulation as an intervention for tremor reduction, identify the limitations and benefits of the current state-of-the-art studies, and provide ideas to guide the development of novel approaches based on the neural circuitries and mechanical properties implied in tremor generation.

Individual differences in the distribution of activation among the hamstring muscle heads during stiff-leg Deadlift and Nordic hamstring exercises.

The aim of this study was to compare the distribution of activation among the three heads of the hamstring between a knee flexion-oriented exercise (Nordic hamstring) and a hip extension-oriented exercise (stiff-leg Deadlift) at the group and individual level. Data were collected for 20 participants. Muscle activation of the semimembranosus (SM), semitendinosus (ST), and biceps femoris (BF) was estimated using surface electromyography (EMG) during Nordic hamstring and stiff-leg Deadlift exercises. Although Nordic hamstring exercise induced a higher normalized RMS EMG value for BF (64.5 ± 17.4%) compared to SM (48.6 ± 14.6%; P<0.001) and ST (55.9 ± 17.4%; P < 0.001), the greatest active muscle varied between individuals. Similar interindividual differences in the greatest active muscle were found for the stiff-leg Deadlift exercise. Regarding the distribution of activation, the stiff-leg Deadlift favoured the contribution of the SM compared to ST (P < 0.001, 18/20 participants) whereas the Nordic hamstring exercise favoured the contribution of the ST compared to SM (P < 0.001, 19/20 participants). Importantly, these tasks affected the contribution of the activation of BF in different ways between individuals. The distribution of activation across the three muscles was well correlated between the two exercises (r values ≥ 0.42).

Hamstring muscle elasticity differs in specialized high-performance athletes.

The effect of training on hamstring flexibility has been widely assessed through the measurement of the maximal range of motion or passive torque. However, these global measures do not provide direct information on the passive muscle mechanical properties of individual muscle. This characterization is crucial to better understand the effect of interventions as selective adaptations may occur among synergist muscles. Taking advantage of shear wave elastography, we aimed to determine whether elite sport athletes exhibit different passive shear modulus of hamstring heads compared to controls. Passive shear modulus was measured on semitendinosus (ST), semimembranosus (SM), and biceps femoris (BF) using shear wave elastography with the knee flexed at 60° and 90°, and 90° of hip flexion. A total of 97 elite athletes from various sports including running sprint, figure skating, fencing, field hockey, taekwondo, basketball, and soccer and 12 controls were evaluated. The shear modulus measured at 60° of knee flexion was lower in SM for figure skating (P < .001; d = 1.8), taekwondo (P < .001; d = 2.1), fencing (P = .024; d = 1.0), and soccer (P = .011; d = 0.9) compared to controls, while no difference was found for athletic sprinters, field hockey, and basketball players. Shear modulus of the BF and ST muscle was not significantly different between controls and elite athletes, regardless of the sport specialization (all P values = 1). We provide evidence that the shear modulus of the SM is altered in athletes involved in elite sport practice performed over large range of motion and/or including substantial stretching program in training content (taekwondo, figure skating, fencing, and soccer).

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.

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.

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.

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.

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.

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.

Muscles from the same muscle group do not necessarily share common drive: evidence from the human triceps surae.

It has been proposed that movements are produced through groups of muscles, or motor modules, activated by common neural commands. However, the neural origin of motor modules is still debated. Here, we used complementary approaches to determine: 1) whether three muscles of the same muscle group [soleus, gastrocnemius medialis (GM), and gastrocnemius lateralis (GL)] are activated by a common neural drive, and 2) whether the neural drive to GM and GL could be differentially modified by altering the mechanical requirements of the task. Eighteen human participants performed an isometric standing heel raise and submaximal isometric plantarflexions (10%, 30%, and 50% of maximal effort). High-density surface electromyography recordings were decomposed into motor unit action potentials and coherence analysis was applied on the motor unit spike trains. We identified strong common drive to each muscle but minimal common drive between the muscles. Further, large between-muscle differences were observed during the isometric plantarflexions, such as a delayed recruitment time of GL compared with GM and soleus motor units and opposite time-dependent changes in the estimates of neural drive to muscles during the torque plateau. Finally, the feet position adopted during the heel-raise task (neutral vs. internally rotated) affected only the GL neural drive with no change for GM. These results provide conclusive evidence that not all anatomically defined synergist muscles are controlled by strong common neural drive. Independent drive to some muscles from the same muscle group may allow for more flexible control to comply with secondary goals such as joint stabilization.NEW & NOTEWORTHY In this study, we demonstrated that the three muscles composing the human triceps surae share minimal common drive during isometric contractions. Our results suggest that reducing the number of effectively controlled degrees of freedom may not always be the strategy used by the central nervous system to control movements. Independent control of some, but not all, synergist muscles may allow for more flexible control to comply with secondary goals (e.g., joint stabilization).

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators.

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

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: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) 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 (3.2-21.4 s epoch-1vs 6.5-47.8 s epoch-1, respectively) and prediction time (0.04 vs 0.27 s sample-1, 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 ofr-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.

Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task.

The interindividual variability in the neural drive sent from the spinal cord to muscles is largely unknown, even during highly constrained motor tasks. Here, we investigated individual differences in the strength of neural drive received by the vastus lateralis (VL) and vastus medialis (VM) during an isometric task. We also assessed the proportion of common neural drive within and between these muscles. Twenty-two participants performed a series of submaximal isometric knee extensions at 25% of their peak torque. High-density surface electromyography recordings were decomposed into motor unit action potentials. Coherence analyses were applied on the motor unit spike trains to assess the degree of neural drive that was shared between motor neurons. Six participants were retested ∼20 mo after the first session. The distribution of the strength of neural drive between VL and VM varied between participants and was correlated with the distribution of normalized interference electromyography (EMG) signals (r > 0.56). The level of within- and between-muscle coherence varied across individuals, with a significant positive correlation between these two outcomes (VL: r = 0.48; VM: r = 0.58). We also observed a large interindividual variability in the proportion of muscle-specific drive, that is, the drive unique to each muscle (VL range: 6%-83%, VM range: 6%-86%). All the outcome measures were robust across sessions, providing evidence that the individual differences did not depend solely on the variability of the measures. Together, these results demonstrate that the neural strategies to control the VL and VM muscles widely vary across individuals, even during a constrained task.NEW & NOTEWORTHY We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of neural drive that was shared within and between these muscles also varied across participants. These results provide evidence that the neural strategies to control the vastus lateralis and vastus medialis muscles widely vary across individuals, even during a mechanically constrained task.

Bilateral differences in hamstring coordination in previously injured elite athletes.

Hamstring strain injuries (HSIs) involve tissue disruption and pain, which can trigger long-term adaptations of muscle coordination. However, little is known about the effect of previous HSIs on muscle coordination and in particular, after the completion of rehabilitation and in the absence of symptoms. This study aimed to determine if elite athletes with a prior unilateral HSI have bilateral differences in coordination between the hamstring muscle heads after returning to sport. Seventeen athletes with a unilateral history of biceps femoris (BF) injury participated in the experiment. Surface electromyography was recorded from three hamstring muscles [BF, semimembranosus (SM), and semitendinosus] during submaximal isometric torque-matched tasks at 20% and 50% of maximal voluntary contraction. The product of normalized electromyographic amplitude with functional physiological cross-sectional area (PCSA) and moment arm was considered as an index of individual muscle torque. The contribution of the injured muscle to total knee flexion torque was lower in the injured than the uninjured limb (-5.6 ± 10.2%, P = 0.038). This reduced contribution of BF was compensated by a higher contribution of the SM muscle in the injured limb (+5.6 ± 7.5%, P = 0.007). These changes resulted from a lower contribution of PCSA from the injured muscle (BF) and a larger contribution of activation from an uninjured synergist muscle (SM). In conclusion, bilateral differences in coordination were observed in previously injured athletes despite the completion of rehabilitation. Whether these bilateral differences in hamstring coordination could constitute an intrinsic risk factor that contributes to the high rate of hamstring injury recurrence remains to be investigated.NEW & NOTEWORTHY We used an experimental approach, combining the assessment of muscle activation, physiological cross-sectional area, and moment arm to estimate force-sharing strategies among hamstring muscles during isometric knee flexions. We tested athletes with a history of hamstring injury. We observed a lower contribution of the injured biceps femoris to the total knee flexor torque in the injured limb than in the contralateral limb. This decreased contribution was mainly due to selective atrophy of the injured biceps femoris muscle and was compensated by an increased activation of the semimembranosus muscle.