Physiology of the motor neuron and the motor unit

NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

Effect of Warm-Up Exercise on Functional Regulation of Motor Unit Activation during Isometric Torque Production

In this study, we tested several hypotheses related to changes in motor unit activation patterns after warm-up exercise. Fifteen healthy young men participated in the experiment and the main task was to produce voluntary torque through the elbow joint under the isometric condition. The experimental conditions consisted of two directions of torque, including flexion and extension, at two joint angles, 10° and 90°. Participants were asked to increase the joint torque to the maximal level at a rate of 10% of the maximum voluntary torque. The warm-up protocol followed the ACSM guidelines, which increased body temperature by approximately 1.5°C. Decomposition electromyography electrodes, capable of extracting multiple motor unit action potentials from surface signals, were placed on the biceps and triceps brachii muscles, and joint torque was measured on the dynamometer. The mean firing rate and the recruitment threshold of the decomposed motor units were quantified. In addition, a single motor unit activity from the spike train was quantified for each of five selected motor units. The magnitude of joint torque increased with the warm-up exercise for all the experimental conditions. The results of the motor unit analyses showed a positive and beneficial effect of the warm-up exercise, with an increase in both the mean firing rate and the recruitment threshold by about 56% and 33%, respectively, particularly in the agonist muscle. Power spectral density in the gamma band, which is thought to be the dominant voluntary activity, was also increased by the warm-up exercise only in the high threshold motor units.

Motor neurons and endothelial cells additively promote development and fusion of human iPSC-derived skeletal myocytes

BACKGROUND

Neurovascular cells have wide-ranging implications on skeletal muscle biology regulating myogenesis, maturation, and regeneration. Although several in vitro studies have investigated how motor neurons and endothelial cells interact with skeletal myocytes independently, there is limited knowledge about the combined effect of neural and vascular cells on muscle maturation and development.

METHODS

Here, we report a triculture system comprising human-induced pluripotent stem cell (iPSC)-derived skeletal myocytes, human iPSC-derived motor neurons, and primary human endothelial cells maintained under controlled media conditions. Briefly, iPSCs were differentiated to generate skeletal muscle progenitor cells (SMPCs). These SMPCs were seeded at a density of 5 × 104 cells/well in 12-well plates and allowed to differentiate for 7 days before adding iPSC-derived motor neurons at a concentration of 0.5 × 104 cells/well. The neuromuscular coculture was maintained for another 7 days in coculture media before addition of primary human umbilical vein endothelial cells (HUVEC) also at 0.5 × 104 cells/well. The triculture was maintained for another 7 days in triculture media comprising equal portions of muscle differentiation media, coculture media, and vascular media. Extensive morphological, genetic, and molecular characterization was performed to understand the combined and individual effects of neural and vascular cells on skeletal muscle maturation.

RESULTS

We observed that motor neurons independently promoted myofiber fusion, upregulated neuromuscular junction genes, and maintained a molecular niche supportive of muscle maturation. Endothelial cells independently did not support myofiber fusion and downregulated expression of LRP4 but did promote expression of type II specific myosin isoforms. However, neurovascular cells in combination exhibited additive increases in myofiber fusion and length, enhanced production of Agrin, along with upregulation of several key genes like MUSK, RAPSYN, DOK-7, and SLC2A4. Interestingly, more divergent effects were observed in expression of genes like MYH8, MYH1, MYH2, MYH4, and LRP4 and secretion of key molecular factors like amphiregulin and IGFBP-4.

CONCLUSIONS

Neurovascular cells when cultured in combination with skeletal myocytes promoted myocyte fusion with concomitant increase in expression of various neuromuscular genes. This triculture system may be used to gain a deeper understanding of the effects of the neurovascular niche on skeletal muscle biology and pathophysiology.

Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

Recent developments in dexterous myoelectric prosthetics have established a hardware base for human-machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control (SPC) for multiple degrees of freedom (DoFs) movements. In this paper, we introduce an SPC approach for multi-DoF wrist movements using the cumulative spike trains (CSTs) of motor unit pools, merely leveraging single-DoF training. The efficacy of our proposed approach was validated offline against existing methods respectively based on non-negative matrix factorization and motor unit spike trains, using experimental data. The experimental process includes both single-DoF (for training) and multi-DoF (for testing) movements. We evaluated the performance using Pearson correlation coefficient (R) and the normalized root mean square error (nRMSE). The results reveal that our method outperforms comparative approaches in force estimation for both testing datasets (3 and 4). On average, for dataset 3, R and nRMSE of the flexion/extension DoF (the pronation/supination DoF) are 0.923±0.037 (0.901±0.040) and 12.3±3.1% (12.9±2.2%); similarly, those of dataset 4 are 0.865±0.057 (0.837±0.053) and 14.9±2.9% (15.4±2.0%), respectively. The outcomes demonstrate the effectiveness of our method in simultaneous and proportional force estimation for multi-DoF wrist movements, showing a promising potential as a neural-machine interface for SPC of dexterous myoelectric prostheses.

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.

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC).

Estimation of contractile parameters of successive twitches in unfused tetanic contractions of single motor units - A proof-of-concept study using ultrafast ultrasound imaging in vivo

During a voluntary contraction, motor units (MUs) fire a train of action potentials, causing summation of the twitch forces, resulting in fused or unfused tetanus. Twitches have been important in studying whole-muscle contractile properties and differentiation between MU types. However, there are still knowledge gaps concerning the voluntary force generation mechanisms. Current methods rely on the spike-triggered averaging technique, which cannot track changes in successive twitches' properties in response to individual neural firings. This study proposes a method that estimates successive twitches contractile parameters of single MUs during low force voluntary isometric contractions in human biceps brachii. We used a previously developed ultrafast ultrasound imaging method to estimate unfused tetanic activity signals of single MUs. A twitch decomposition model was used to decompose unfused tetanic activity signals into individual twitches. This study found that the contractile parameters varied within and across MUs. There was an association between the inter-spike interval and the contraction time (r = 0.49,p < 0.001) and the half-relaxation time (r = 0.58,p < 0.001), respectively. The method shows the proof-of-concept to study MU contractile properties of individual twitches in vivo, which can provide further insights into the force generation mechanisms of voluntary contractions and response to individual neural discharges.

The Multifaceted Role of GPCRs in Amyotrophic Lateral Sclerosis: A New Therapeutic Perspective?

Amyotrophic lateral sclerosis (ALS) is a degenerating disease involving the motor neurons, which causes a progressive loss of movement ability, usually leading to death within 2 to 5 years from the diagnosis. Much effort has been put into research for an effective therapy for its eradication, but still, no cure is available. The only two drugs approved for this pathology, Riluzole and Edaravone, are onlyable to slow down the inevitable disease progression. As assessed in the literature, drug targets such as protein kinases have already been extensively examined as potential drug targets for ALS, with some molecules already in clinical trials. Here, we focus on the involvement of another very important and studied class of biological entities, G protein-coupled receptors (GPCRs), in the onset and progression of ALS. This workaimsto give an overview of what has been already discovered on the topic, providing useful information and insights that can be used by scientists all around the world who are putting efforts into the fight against this very important neurodegenerating disease.

Therapy development for spinal muscular atrophy: perspectives for muscular dystrophies and neurodegenerative disorders

BACKGROUND

Major efforts have been made in the last decade to develop and improve therapies for proximal spinal muscular atrophy (SMA). The introduction of Nusinersen/Spinraza™ as an antisense oligonucleotide therapy, Onasemnogene abeparvovec/Zolgensma™ as an AAV9-based gene therapy and Risdiplam/Evrysdi™ as a small molecule modifier of pre-mRNA splicing have set new standards for interference with neurodegeneration.

MAIN BODY

Therapies for SMA are designed to interfere with the cellular basis of the disease by modifying pre-mRNA splicing and enhancing expression of the Survival Motor Neuron (SMN) protein, which is only expressed at low levels in this disorder. The corresponding strategies also can be applied to other disease mechanisms caused by loss of function or toxic gain of function mutations. The development of therapies for SMA was based on the use of cell culture systems and mouse models, as well as innovative clinical trials that included readouts that had originally been introduced and optimized in preclinical studies. This is summarized in the first part of this review. The second part discusses current developments and perspectives for amyotrophic lateral sclerosis, muscular dystrophies, Parkinson's and Alzheimer's disease, as well as the obstacles that need to be overcome to introduce RNA-based therapies and gene therapies for these disorders.

CONCLUSION

RNA-based therapies offer chances for therapy development of complex neurodegenerative disorders such as amyotrophic lateral sclerosis, muscular dystrophies, Parkinson's and Alzheimer's disease. The experiences made with these new drugs for SMA, and also the experiences in AAV gene therapies could help to broaden the spectrum of current approaches to interfere with pathophysiological mechanisms in neurodegeneration.

Evaluation of Lower Limb Muscle Electromyographic Activity during 400 m Indoor Sprinting among Elite Female Athletes: A Cross-Sectional Study

The purpose of this cross-sectional study was to analyze changes in normalized surface electromyography (sEMG) signals for the gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, and vastus lateralis muscles occurring during a 400 m indoor sprint between subsequent curved sections of the track. Ten well-trained female sprinters (age: 21 ± 4 years; body mass: 47 ± 5 kg; body height: 161 ± 7 cm; 400 m personal best: 52.4 ± 1.1 s) performed an all-out 400 m indoor sprint. Normalized sEMG signals were recorded bilaterally from the selected lower limb muscles. The two-way ANOVA (curve × side) revealed no statistically significant interaction. However, the main effect analysis showed that normalized sEMG signals significantly increased in subsequent curves run for all the studied muscles: gastrocnemius medialis (p = 0.003), biceps femoris (p < 0.0001), gluteus maximus (p = 0.044), tibialis anterior (p = 0.001), and vastus lateralis (p = 0.023), but differences between limbs were significant only for the gastrocnemius medialis (p = 0.012). The results suggest that the normalized sEMG signals for the lower limb muscles increased in successive curves during the 400 m indoor sprint. Moreover, the gastrocnemius medialis of the inner leg is highly activated while running curves; therefore, it should be properly prepared for high demands, and attention should be paid to the possibility of the occurrence of a negative adaptation, such as asymmetries.

Approaches to revealing the neural basis of muscle synergies: a review and a critique

The central nervous system (CNS) may produce coordinated motor outputs via the combination of motor modules representable as muscle synergies. Identification of muscle synergies has hitherto relied on applying factorization algorithms to multimuscle electromyographic data (EMGs) recorded during motor behaviors. Recent studies have attempted to validate the neural basis of the muscle synergies identified by independently retrieving the muscle synergies through CNS manipulations and analytic techniques such as spike-triggered averaging of EMGs. Experimental data have demonstrated the pivotal role of the spinal premotor interneurons in the synergies' organization and the presence of motor cortical loci whose stimulations offer access to the synergies, but whether the motor cortex is also involved in organizing the synergies has remained unsettled. We argue that one difficulty inherent in current approaches to probing the synergies' neural basis is that the EMG generative model based on linear combination of synergies and the decomposition algorithms used for synergy identification are not grounded on enough prior knowledge from neurophysiology. Progress may be facilitated by constraining or updating the model and algorithms with knowledge derived directly from CNS manipulations or recordings. An investigative framework based on evaluating the relevance of neurophysiologically constrained models of muscle synergies to natural motor behaviors will allow a more sophisticated understanding of motor modularity, which will help the community move forward from the current debate on the neural versus nonneural origin of muscle synergies.

Reproducibility of muscle fibre conduction velocity during linearly increasing force contractions

Muscle fibre conduction velocity (MFCV) is a basic physiological parameter biophysically related to the diameter of muscle fibres and properties of the sarcolemma. The aim of this study was to assess the intersession reproducibility of the relation between voluntary force and estimates of average muscle fibre conduction velocity (MFCV) from multichannel high-density surface electromyographic recordings (HDsEMG). Ten healthy men performed six linearly increasing isometric ankle dorsiflexions on two separate experimental sessions, 4 weeks apart. Each session involved the recordings of voluntary force during maximal isometric (MViF) and submaximal ramp contractions at 35-50-70% of MViF. Concurrently, the HDsEMG activity was detected from the tibialis anterior muscle and MFCV estimates were derived in 250-ms epochs. Absolute and relative reproducibility of MFCV initial value (intercept) and rate of change (regression slope) as a function of force were assessed by within-subject coefficient of correlation (CV_w) and with intraclass correlation coefficient (ICC). MFCV was positively correlated with voluntary force (R² = 0.75 ± 0.12) in all individuals and test conditions (P < 0.001). Average CV_w for MFCV intercept and slope were of 2.6 ± 2.0% and 11.9 ± 3.2% and ICC values of 0.96 and 0.94, respectively. Overall, MFCV regression coefficients showed a high degree of intersession reproducibility in both absolute and relative terms. These results may have important practical implications in the tracking of training-induced neuromuscular changes and/or in the monitoring of the progress of neuromuscular disorders when a full sEMG signal decomposition is problematic or not possible.

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Voluntary activation and variability during maximal dynamic contractions with aging

Whether reduced supraspinal activation contributes to age-related reductions in maximal torque during dynamic contractions is not known. The purpose was to determine whether there are age differences in voluntary activation and its variability when assessed with stimulation at the motor cortex and the muscle during maximal isometric, concentric, and eccentric contractions. Thirty young (23.6 ± 4.1 years) and 31 old (69.0 ± 5.2 years) adults performed maximal isometric, shortening (concentric) and lengthening (eccentric) contractions with the elbow flexor muscles. Maximal isometric contractions were performed at 90° elbow flexion and dynamic contractions at a velocity of 60°/s. Voluntary activation was assessed by superimposing an evoked contraction with transcranial magnetic stimulation (TMS) or with electrical stimulation over the muscle during maximal voluntary contractions (MVCs). Old adults had lower MVC torque during isometric (- 17.9%), concentric (- 19.7%), and eccentric (- 9.9%) contractions than young adults, with less of an age difference for eccentric contractions. Voluntary activation was similar between the three contraction types when assessed with TMS and electrical stimulation, with no age group differences. Old adults, however, were more variable in voluntary activation than young (standard deviation 0.99 ± 0.47% vs. 0.73 ± 0.43%, respectively) to both the motor cortex and muscle, and had greater coactivation of the antagonist muscles during dynamic contractions. Thus, the average voluntary activation to the motor cortex and muscle did not differ with aging; however, supraspinal activation was more variable during maximal dynamic and isometric contractions in the old adults. Lower predictability of voluntary activation may indicate subclinical changes in the central nervous system with advanced aging.

The aging neuromuscular system and motor performance

Age-related changes in the basic functional unit of the neuromuscular system, the motor unit, and its neural inputs have a profound effect on motor function, especially among the expanding number of old (older than ∼60 yr) and very old (older than ∼80 yr) adults. This review presents evidence that age-related changes in motor unit morphology and properties lead to impaired motor performance that includes 1) reduced maximal strength and power, slower contractile velocity, and increased fatigability; and 2) increased variability during and between motor tasks, including decreased force steadiness and increased variability of contraction velocity and torque over repeat contractions. The age-related increase in variability of motor performance with aging appears to involve reduced and more variable synaptic inputs that drive motor neuron activation, fewer and larger motor units, less stable neuromuscular junctions, lower and more variable motor unit action potential discharge rates, and smaller and slower skeletal muscle fibers that coexpress different myosin heavy chain isoforms in the muscle of older adults. Physical activity may modify motor unit properties and function in old men and women, although the effects on variability of motor performance are largely unknown. Many studies are of cross-sectional design, so there is a tremendous opportunity to perform high-impact and longitudinal studies along the continuum of aging that determine 1) the influence and cause of the increased variability with aging on functional performance tasks, and 2) whether lifestyle factors such as physical exercise can minimize this age-related variability in motor performance in the rapidly expanding numbers of very old adults.

The human motor neuron pools receive a dominant slow-varying common synaptic input

KEY POINTS

Motor neurons in a pool receive both common and independent synaptic inputs, although the proportion and role of their common synaptic input is debated. Classic correlation techniques between motor unit spike trains do not measure the absolute proportion of common input and have limitations as a result of the non-linearity of motor neurons. We propose a method that for the first time allows an accurate quantification of the absolute proportion of low frequency common synaptic input (<5 Hz) to motor neurons in humans. We applied the proposed method to three human muscles and determined experimentally that they receive a similar large amount (>60%) of common input, irrespective of their different functional and control properties. These results increase our knowledge about the role of common and independent input to motor neurons in force control.

ABSTRACT

Motor neurons receive both common and independent synaptic inputs. This observation is classically based on the presence of a significant correlation between pairs of motor unit spike trains. The functional significance of different relative proportions of common input across muscles, individuals and conditions is still debated. One of the limitations in our understanding of correlated input to motor neurons is that it has not been possible so far to quantify the absolute proportion of common input with respect to the total synaptic input received by the motor neurons. Indeed, correlation measures of pairs of output spike trains only allow for relative comparisons. In the present study, we report for the first time an approach for measuring the proportion of common input in the low frequency bandwidth (<5 Hz) to a motor neuron pool in humans. This estimate is based on a phenomenological model and the theoretical fitting of the experimental values of coherence between the permutations of groups of motor unit spike trains. We demonstrate the validity of this theoretical estimate with several simulations. Moreover, we applied this method to three human muscles: the abductor digiti minimi, tibialis anterior and vastus medialis. Despite these muscles having different functional roles and control properties, as confirmed by the results of the present study, we estimate that their motor pools receive a similar and large (>60%) proportion of common low frequency oscillations with respect to their total synaptic input. These results suggest that the central nervous system provides a large amount of common input to motor neuron pools, in a similar way to that for muscles with different functional and control properties.

The optimal neural strategy for a stable motor task requires a compromise between level of muscle cocontraction and synaptic gain of afferent feedback

Increasing joint stiffness by cocontraction of antagonist muscles and compensatory reflexes are neural strategies to minimize the impact of unexpected perturbations on movement. Combining these strategies, however, may compromise steadiness, as elements of the afferent input to motor pools innervating antagonist muscles are inherently negatively correlated. Consequently, a high afferent gain and active contractions of both muscles may imply negatively correlated neural drives to the muscles and thus an unstable limb position. This hypothesis was systematically explored with a novel computational model of the peripheral nervous system and the mechanics of one limb. Two populations of motor neurons received synaptic input from descending drive, spinal interneurons, and afferent feedback. Muscle force, simulated based on motor unit activity, determined limb movement that gave rise to afferent feedback from muscle spindles and Golgi tendon organs. The results indicated that optimal steadiness was achieved with low synaptic gain of the afferent feedback. High afferent gains during cocontraction implied increased levels of common drive in the motor neuron outputs, which were negatively correlated across the two populations, constraining instability of the limb. Increasing the force acting on the joint and the afferent gain both effectively minimized the impact of an external perturbation, and suboptimal adjustment of the afferent gain could be compensated by muscle cocontraction. These observations show that selection of the strategy for a given contraction implies a compromise between steadiness and effectiveness of compensations to perturbations. This indicates that a task-dependent selection of neural strategy for steadiness is necessary when acting in different environments.

Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model

This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation-contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons.

Power spectrum of the rectified EMG: influence of motor unit action potential shapes

The rectification of EMG signals is a preprocessing method widely used for inferring neural connectivity by coherence analysis. The assumption for the use of this non-linear operator is that it enhances the neural information in the signal, i.e. the motor unit spike trains. However, because of non-linearity, it is difficult to predict the effect of rectification on the EMG power spectrum. In this study, we analyze the influence of the motor unit action potential properties on spectral content of the rectified EMG. The results show that changes in the action potential waveforms have a strong influence on the rectified EMG power spectrum, with an effect on estimated coherence functions. Knowledge on the properties of action potentials may be necessary for properly comparing the rectified EMG power spectrum across conditions.

Limitations of the spike-triggered averaging for estimating motor unit twitch force: a theoretical analysis

Contractile properties of human motor units provide information on the force capacity and fatigability of muscles. The spike-triggered averaging technique (STA) is a conventional method used to estimate the twitch waveform of single motor units in vivo by averaging the joint force signal. Several limitations of this technique have been previously discussed in an empirical way, using simulated and experimental data. In this study, we provide a theoretical analysis of this technique in the frequency domain and describe its intrinsic limitations. By analyzing the analytical expression of STA, first we show that a certain degree of correlation between the motor unit activities prevents an accurate estimation of the twitch force, even from relatively long recordings. Second, we show that the quality of the twitch estimates by STA is highly related to the relative variability of the inter-spike intervals of motor unit action potentials. Interestingly, if this variability is extremely high, correct estimates could be obtained even for high discharge rates. However, for physiological inter-spike interval variability and discharge rate, the technique performs with relatively low estimation accuracy and high estimation variance. Finally, we show that the selection of the triggers that are most distant from the previous and next, which is often suggested, is not an effective way for improving STA estimates and in some cases can even be detrimental. These results show the intrinsic limitations of the STA technique and provide a theoretical framework for the design of new methods for the measurement of motor unit force twitch.

Motor unit recruitment by size does not provide functional advantages for motor performance

It is commonly assumed that the orderly recruitment of motor units by size provides a functional advantage for the performance of movements compared with a random recruitment order. On the other hand, the excitability of a motor neuron depends on its size and this is intrinsically linked to its innervation number. A range of innervation numbers among motor neurons corresponds to a range of sizes and thus to a range of excitabilities ordered by size. Therefore, if the excitation drive is similar among motor neurons, the recruitment by size is inevitably due to the intrinsic properties of motor neurons and may not have arisen to meet functional demands. In this view, we tested the assumption that orderly recruitment is necessarily beneficial by determining if this type of recruitment produces optimal motor output. Using evolutionary algorithms and without any a priori assumptions, the parameters of neuromuscular models were optimized with respect to several criteria for motor performance. Interestingly, the optimized model parameters matched well known neuromuscular properties, but none of the optimization criteria determined a consistent recruitment order by size unless this was imposed by an association between motor neuron size and excitability. Further, when the association between size and excitability was imposed, the resultant model of recruitment did not improve the motor performance with respect to the absence of orderly recruitment. A consistent observation was that optimal solutions for a variety of criteria of motor performance always required a broad range of innervation numbers in the population of motor neurons, skewed towards the small values. These results indicate that orderly recruitment of motor units in itself does not provide substantial functional advantages for motor control. Rather, the reason for its near-universal presence in human movements is that motor functions are optimized by a broad range of innervation numbers.

Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (<10% of maximal force) for typical values of these parameters. Results indicate that motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates.

Linear transmission of cortical oscillations to the neural drive to muscles is mediated by common projections to populations of motoneurons in humans

Since the human central nervous system controls muscle contraction through inputs to spinal motoneurons, oscillations recorded on the primary motor cortex during voluntary movements are correlated with the electrical activity produced on the surface of the muscles. We show through theoretical derivations and experimental recordings that cortical input is transmitted partly in a linear way to the population of motoneurons. The results demonstrate the effective spread of the cortical projections to the motoneuron pool to allow an efficient control of the muscle force output.

Fluctuations in isometric muscle force can be described by one linear projection of low-frequency components of motor unit discharge rates

The aim of the study was to investigate the relation between linear transformations of motor unit discharge rates and muscle force. Intramuscular (wire electrodes) and high-density surface EMG (13 x 5 electrode grid) were recorded from the abductor digiti minimi muscle of eight healthy men during 60 s contractions at 5%, 7.5% and 10% of the maximal force. Spike trains of a total of 222 motor units were identified from the EMG recordings with decomposition algorithms. Principal component analysis of the smoothed motor unit discharge rates indicated that one component (first common component, FCC) described 44.2 +/- 7.5% of the total variability of the smoothed discharge rates when computed over the entire contraction interval and 64.3 +/- 10.2% of the variability when computed over 5 s intervals. When the FCC was computed from four or more motor units per contraction, it correlated with the force produced by the muscle (62.7 +/- 10.1%) by a greater degree (P < 0.001) than the smoothed discharge rates of individual motor units (41.4 +/- 7.8%). The correlation between FCC and the force signal increased up to 71.8 +/- 13.1% when the duration and the shape of the smoothing window for discharge rates were similar to the average motor unit twitch force. Moreover, the coefficients of variation (CoV) for the force and for the FCC signal were correlated in all subjects (R(2) range = 0.14-0.56; P < 0.05) whereas the CoV for force was correlated to the interspike interval variability in only one subject (R(2) = 0.12; P < 0.05). Similar results were further obtained from measures on the tibialis anterior muscle of an additional eight subjects during contractions at forces up to 20% of the maximal force (e.g. FCC explained 59.8 +/- 11.0% of variability of the smoothed discharge rates). In conclusion, one signal captures most of the underlying variability of the low-frequency components of motor unit discharge rates and explains large part of the fluctuations in the motor output during isometric contractions.

Motor units in cranial and caudal regions of the upper trapezius muscle have different discharge rates during brief static contractions

AIM

To compare the discharge patterns of motor unit populations from different locations within the upper trapezius muscle during brief submaximal constant-force contractions.

METHODS

Intramuscular and surface electromyographic (EMG) signals were collected from three sites of the right upper trapezius muscle distributed along the cranial-caudal direction in 11 volunteers during 10 s shoulder abduction at 25% of the maximum voluntary force.

RESULTS

A total of 38 motor units were identified at the cranial location, 36 from the middle location and 17 from the caudal location. Initial discharge rate was greatest at the caudal location (P < 0.05; mean +/- SD, cranial: 16.7 +/- 3.6 pps, middle: 16.9 +/- 4.0 pps, caudal: 19.2 +/- 3.3 pps). Discharge rate decreased during the contraction for the most caudal location only (P < 0.05). Initial estimates of surface EMG root mean square values were highest at the most caudal location (P < 0.05; cranial: 32.3 +/- 20.9 microV, middle: 41.3 +/- 21.0 microV, caudal: 51.6 +/- 23.6 microV).

CONCLUSION

This study demonstrates non-uniformity of motor unit discharge within the upper trapezius muscle during a brief submaximal constant-force contraction. Location-dependent modulation of discharge rate may reflect spatial dependency in the control of motor units necessary for the development and maintenance of force output.

Training adaptations in the behavior of human motor units

The purpose of this brief review is to examine the neural adaptations associated with training, by focusing on the behavior of single motor units. The review synthesizes current understanding on motor unit recruitment and rate coding during voluntary contractions, briefly describes the techniques used to record motor unit activity, and then evaluates the adaptations that have been observed in motor unit activity during maximal and submaximal contractions. Relatively few studies have directly compared motor unit behavior before and after training. Although some studies suggest that the voluntary activation of muscle can increase slightly with strength training, it is not known how the discharge of motor units changes to produce this increase in activation. The evidence indicates that the increase is not attributable to changes in motor unit synchronization. It has been demonstrated, however, that training can increase both the rate of torque development and the discharge rate of motor units. Furthermore, both strength training and practice of a force-matching task can evoke adaptations in the discharge characteristics of motor units. Because the variability in discharge rate has a significant influence on the fluctuations in force during submaximal contractions, the changes produced with training can influence motor performance during activities of daily living. Little is known, however, about the relative contributions of the descending drive, afferent feedback, spinal circuitry, and motor neuron properties to the observed adaptations in motor unit activity.