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The muscle spindle model presented in Maltenfort and Burke (2003) calculates muscle spindle primary afferent feedback depending on the muscle fibre stretch and fusimotor drive. The aim of this paper is to provide an updated version of the model, which is now capable of replicating the originally published data. This is achieved by modifying the equations describing the modulation of the muscle spindle output in response to dynamic fusimotor drive. EDITOR'S NOTE V2: Showing manuscript and model files.

OBJECTIVE: Utilizing shear wave elastography, we compared the stiffness of the neck extensor muscles and the stiffness in muscle-specific regions between women with chronic nonspecific neck pain and asymptomatic controls. DESIGN: Cross-sectional observational study. METHODS: We measured the average muscle stiffness over multiple neck extensor muscles and in regions corresponding approximately to the trapezius, splenius capitis, semispinalis capitis, semispinalis cervicis, and multifidus muscles using ultrasound shear wave elastography in 20 women with chronic nonspecific neck pain and 18 asymptomatic women during multiple tasks. The measurements were automatically quality controlled and computer processed over the complete visible neck region or a large muscle-specific region. RESULTS: Pooled over all tasks, neck muscle stiffness was not significantly different between those with neck pain and asymptomatic controls (neck pain median, 11.6 kPa; interquartile range, 8.9 kPa and control median, 13.3 kPa; interquartile range, 8.6 kPa; P = .175). The measure of neck muscle stiffness was not correlated with the intensity of neck pain or perceived disability. CONCLUSION: Shear wave elastography revealed similar muscle stiffness in people with and without chronic neck pain, despite the sensation of increased neck stiffness in those with chronic neck pain. Therapeutic interventions aiming to reduce neck muscle tone are often based on the assumption that perceived neck stiffness corresponds to objective muscle stiffness. The current results question this assumption. J Orthop Sports Phys Ther 2020;50(4):179-188. Epub 6 Jan 2020. doi:10.2519/jospt.2020.8821.

Motor neurons innervating antagonist muscles receive reciprocal inhibitory afferent inputs to facilitate the joint movement in the two directions. The present study investigates the mutual transmission of reciprocal inhibitory afferent inputs between the tibialis anterior (TA) and triceps surae (soleus and medial gastrocnemius) motor units. We assessed this mutual mechanism in large populations of motor units for building a statistical distribution of the inhibition amplitudes during standardized input to the motor neuron pools to minimize the effect of modulatory pathways. Single motor unit activities were identified using high-density surface electromyography (HDsEMG) recorded from the TA, soleus (Sol), and medial gastrocnemius (GM) muscles during isometric dorsi- and plantarflexion. Reciprocal inhibition on the antagonist muscle was elicited by electrical stimulation of the tibial (TN) or common peroneal nerves (CPN). The probability density distributions of reflex strength for each muscle were estimated to examine the strength of mutual transmission of reciprocal inhibitory input. The results showed that the strength of reciprocal inhibition in the TA motor units was fourfold greater than for the GM and the Sol motor units. This suggests an asymmetric transmission of reciprocal inhibition between ankle extensor and flexor muscles. This asymmetry cannot be explained by differences in motor unit type composition between the investigated muscles since we sampled low-threshold motor units in all cases. Therefore, the differences observed for the strength of inhibition are presumably due to a differential reciprocal spindle afferent input and the relative contribution of nonreciprocal inhibitory pathways. NEW & NOTEWORTHY We investigated the mutual transmission of reciprocal inhibition in large samples of motor units using a standardized input (electrical stimulation) to the motor neurons. The results demonstrated that the disynaptic reciprocal inhibition exerted between ankle flexor and extensor muscles is asymmetric. The functional implication of asymmetric transmission may be associated with the neural strategies of postural control.

Trans-spinal direct current stimulation (tsDCS) provides a non-invasive, clinically viable approach to potentially restore physiological neuromuscular function after neurological impairment, e.g., spinal cord injury (SCI). The use of tsDCS has been hampered by the inability of delivering stimulation patterns based on the activity of neural targets responsible for motor function, i.e., a-motor neurons (a-MNs). State-of-the-art modeling and experimental techniques do not provide information about how individual a-MNs respond to electrical fields. This is a major element hindering the development of neuro-modulative technologies highly tailored to an individual patient. For the first time, we propose the use of a signal-based approach to infer tsDCS effects on large a-MNs pools in four incomplete SCI individuals. We employ leg muscles spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate a-MNs discharges across multiple lumbosacral segments during isometric plantar flexion sub-maximal contractions. This is done before, immediately after and 30 min after sub-threshold cathodal stimulation. We deliver sham tsDCS as a control measure. First, we propose a new algorithm for removing compromised information from decomposed a-MNs spike trains, thereby enabling robust decomposition and frequency-domain analysis. Second, we propose the analysis of a-MNs spike trains coherence (i.e., frequency-domain) as an indicator of spinal response to tsDCS. Results showed that a-MNs spike trains coherence analysis sensibly varied across stimulation phases. Coherence analysis results suggested that the common synaptic input to a-MNs pools decreased immediately after cathodal tsDCS with a persistent effect after 30 min. Our proposed non-invasive decoding of individual a-MNs behavior may open up new avenues for the design of real-time closed-loop control applications including both transcutaneous and epidural spinal electrical stimulation where stimulation parameters are adjusted on-the-fly.

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.

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 ( 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 ( 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.

Introduction: the first bout of eccentric exercise is known to have a protective effect on the consequent bouts. This effect is still disputable as it is not known whether it protects muscle damage by reducing force production or by improving force recovery in the healing process. The underlying mechanisms of this protective effect have not been fully understood. Objectives: to determine the mechanisms of this protective effect, three different loads were used for the first eccentric bout. This was done to investigate whether the protective effect is related to the size of the load in the first bout. To determine the neural adaptations, voluntary activation was assessed and to determine the muscular adaptations, the resting twitch was measured. Method: thirty healthy participants were selectively allocated into three groups (low-, moderate-and high-load group) to match for maximal voluntary contraction (MVC) (n = 10 per group). Participants in each group performed only one of the three sets of ten eccentric (ECC) exercises of the elbow flexors (10%, 20% and 40% of MVC) as their first eccentric bout. The second bout of eccentric exercise was performed two weeks later and was identical for all the three groups, i.e., 40% ECC. Results: the results showed that for the first bout, MVC, voluntary activation and the resting twitch displayed significant (p < 0.0001) interaction (group x time). This was not the case however for the second bout as there was no significant (group x time) interaction in all outcome variables immediately after exercise. When the first and second bouts were compared, it was found that the high-load group had faster recovery in MVC at day 1 and 4 corresponding to voluntary activation and only at day 4 corresponding to the resting twitch. Conclusions: in this study, it was found that high-load exercise aids fast recovery either via neural or muscular adaptations., Thailand Research Fund and Commission on Higher Education; Research Fund from The Research Administration Center, Chiang Mai University

Key points Reflex responses of single motor units have been used for the study of spinal circuitries but the methods employed are invasive and limited to the assessment of a relatively small number of motor units. We propose a new approach to investigate reflexes on individual motor units based on high-density surface electromyography (HDsEMG) decomposition. The decomposition of HDsEMG has been previously validated in voluntary isometric contractions but never during reflex activities. The use of HDsEMG decomposition for reflex studies at the individual motor unit level, during constant force contractions, with excitatory and inhibitory stimuli, was validated here by the comparison of results with concurrently recorded intramuscular EMG signals. The validation results showed that HDsEMG decomposition allows an accurate quantification of reflex responses for a large number of individual motor units non-invasively, for both excitatory and inhibitory stimuli. Abstract We propose and validate a non-invasive method that enables accurate detection of the discharge times of a relatively large number of motor units during excitatory and inhibitory reflex stimulations. High-density surface electromyography (HDsEMG) and intramuscular EMG (iEMG) were recorded from the tibialis anterior muscle during ankle dorsiflexions performed at 5%, 10% and 20% of the maximum voluntary contraction (MVC) force, in nine healthy subjects. The tibial nerve (inhibitory reflex) and the peroneal nerve (excitatory reflex) were stimulated with constant current stimuli. In total, 416 motor units were identified from the automatic decomposition of the HDsEMG. The iEMG was decomposed using a state-of-the-art decomposition tool and provided 84 motor units (average of two recording sites). The reflex responses of the detected motor units were analysed using the peri-stimulus time histogram (PSTH) and the peri-stimulus frequencygram (PSF). The reflex responses of the common motor units identified concurrently from the HDsEMG and the iEMG signals showed an average disagreement (the difference between number of observed spikes in each bin relative to the mean) of 8.2 ± 2.2% (5% MVC), 6.8 ± 1.0% (10% MVC) and 7.5 ± 2.2% (20% MVC), for reflex inhibition, and 6.5 ± 4.1%, 12.0 ± 1.8% and 13.9 ± 2.4%, for reflex excitation. There was no significant difference between the characteristics of the reflex responses, such as latency, amplitude and duration, for the motor units identified by both techniques. Finally, reflex responses could be identified at higher force (4 of the 9 subjects performed contraction up to 50% MVC) using HDsEMG but not iEMG, because of the difficulty in decomposing the iEMG at high forces. In conclusion, single motor unit reflex responses can be estimated accurately and non-invasively in relatively large populations of motor units using HDsEMG. This non-invasive approach may enable a more thorough investigation of the synaptic input distribution on active motor units at various force levels.

It has been observed that muscle pain influences force variability and low-frequency (

Sensory information conveyed along afferent fibers from muscle and joint proprioceptors play an important role in the control of posture and gait in humans. In particular, proprioceptive information from the neck is fundamental in supplying the central nervous system with information about the orientation and movement of the head relative to the rest of the body. The previous studies have confirmed that proprioceptive afferences originating from the neck region, evoked via muscle vibration, lead to strong body-orienting effects during static conditions (e.g., leaning of the body forwards or backwards, depending on location of vibration). However, it is not yet certain in humans, whether the somatosensory receptors located in the deep skin (cutaneous mechanoreceptors) have a substantive contribution to postural control, as vibratory stimulation encompasses the receptive field of all the somatosensory receptors from the skin to the muscles. The aim of this study was to investigate the postural effect of cutaneous mechanoreceptor afferences using electro-tactile stimulation applied to the neck. Ten healthy volunteers (8M, 2F) were evaluated. The average position of their centre of foot pressure (CoP) was acquired before, during, and after a subtle electro-tactile stimulation over their posterior neck (mean ± SD = 5.1 ± 2.3 mA at 100 Hz-140% of the perception threshold) during upright stance with their eyes closed. The electro-tactile stimulation led to a body-orienting effect with the subjects consistently leaning forward. An average shift of the CoP of 12.1 ± 11.9 mm (mean ± SD) was reported, which significantly (p

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

In this study we investigated whether the reflex excitatory and inhibitory inputs to motor neuron pools are uniformly or differentially distributed. We found that the distribution of monosynaptic excitatory (H-reflex) and reciprocal inhibitory inputs to different size motor neurons fitted gamma distributions with different skew values. This result can be interpreted as due to a differential distribution of afferent inputs on motor neurons.

This abstract proposes a modeling methodology that enables reconstructing ankle joint mechanical function from muscle motor unit spike trains decomposed from high-density electromyography signals. The abstract outlines methods and results and discusses the implication that this approach can have for enhancing our understanding of the neuro-mechanical processes underlying human movement.

In this paper we analyze the role of corticomuscular transmission for the time-varying force control. Corticospinal coherence is assessed during frequency-modulated isometric ankle dorsiflexions. Our preliminary results show a significant coupling between EEG signals and motor unit spike trains at the target frequency, suggesting that low-frequency cortical oscillations may have an important functional role in force control.

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.

Electromyography (EMG) can be used to study the behaviour of the motor neurons and thus provides insights into the physiology of the central nervous system. However, due to the high complexity of neuromuscular control, EMG signals are challenging to interpret. While the exact knowledge of the excitation patterns of a specific muscle within an in vivo experimental setting remains elusive, simulations allow to systematically investigate EMG signals in a controlled environment. Within this context, simulations can provide virtual EMG data, which, for example, can be used to validate and optimise signal analysis methods that aim to estimate the relationship between EMG signals and the output of motor neuron pools. However, since existing methods, which are employed to compute EMG signals, exhibit deficiencies with respect to the physical model itself as well as with respect to numerical aspects, we propose a novel homogenised continuum model that closely resolves the electro-physiological behaviour of skeletal muscle tissue. The proposed model is based on an extension of the well-established bidomain model and includes a biophysically detailed description of the electrical activity within the tissue, which is due to the depolarisation of the muscle fibre membranes. In contrast to all other published EMG models, which assume that the electrical potential field for each muscle fibre can be calculated independently, the proposed model assumes that the electrical potential in the muscle fibres is coupled to the electrical potential in the extracellular space. We show that the newly proposed model is able to simulate realistic EMG signals and demonstrate the potential to employ the predicted virtual EMG signal in order to evaluate the goodness of automated decomposition algorithms.