A computational model of torque generation: neural, contractile, metabolic and musculoskeletal components

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy

The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.

Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop

Changes in old age that contribute to the complex issue of an increased metabolic cost of walking (mass-specific energy cost per unit distance traveled) in older adults appear to center at least in part on changes in gait biomechanics. However, age-related changes in energy metabolism, neuromuscular function and connective tissue properties also likely contribute to this problem, of which the consequences are poor mobility and increased risk of inactivity-related disease and disability. The U.S. National Institute on Aging convened a workshop in September 2021 with an interdisciplinary group of scientists to address the gaps in research related to the mechanisms and consequences of changes in mobility in old age. The goal of the workshop was to identify promising ways to move the field forward toward improving gait performance, decreasing energy cost, and enhancing mobility for older adults. This report summarizes the workshop and brings multidisciplinary insight into the known and potential causes and consequences of age-related changes in gait biomechanics. We highlight how gait mechanics and energy cost change with aging, the potential neuromuscular mechanisms and role of connective tissue in these changes, and cutting-edge interventions and technologies that may be used to measure and improve gait and mobility in older adults. Key gaps in the literature that warrant targeted research in the future are identified and discussed.

The impact of submaximal fatiguing exercises on the ability to generate and sustain the maximal voluntary contraction

Neuromuscular fatigability is a failure to produce or maintain a required torque, and commonly quantified with the decrease of maximal torque production during a few seconds-long maximal voluntary contraction (MVC). The literature shows that the MVC reduction after exercises with different torque-time integral (TTI), is often similar. However, it was shown that after a fatiguing exercise, the decline in the capacity to sustain the maximal voluntary contraction for 1 min (MVC1-MIN) differs from the decrease in the capacity to perform a brief-MVC, suggesting that this latter can only partially assess neuromuscular fatigability. This study aims to highlight the relevance of using a sustained MVC to further explore the neuromuscular alterations induced by fatiguing exercises with different TTI. We used two contraction intensities (i.e., 20% and 40% MVC) to modulate the TTI, and two exercise modalities [i.e., voluntary (VOL) and electrical induced (NMES)], since the letter are known to be more fatiguing for a given TTI. Thirteen subjects performed a plantar-flexors MVC1-MIN before and after the fatiguing exercises. A similar MVC loss was obtained for the two exercise intensities despite a greater TTI at 40% MVC, regardless of the contraction modality. On the other hand, the torque loss during MVC1-MIN was significantly greater after the 40% compared to 20% MVC exercise. These findings are crucial because they demonstrate that maximal torque production and sustainability are two complementary features of neuromuscular fatigability. Hence, MVC1-MIN assessing simultaneously both capacities is essential to provide a more detailed description of neuromuscular fatigability.

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.

Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.

Computational Modeling: Human Dynamic Model

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

Sex differences in kinematic adaptations to muscle fatigue induced by repetitive upper limb movements

Background

Muscle fatigue induced by repetitive movements contributes to the development of musculoskeletal disorders. Men and women respond differently to muscle fatigue during isometric single-joint efforts, but sex differences during dynamic multi-joint tasks have not been clearly identified. Moreover, most studies comparing men and women during fatigue development assessed endurance time. However, none evaluated sex differences in kinematic adaptations to fatigue during multi-joint dynamic tasks. The objective of the study was to compare how men and women adapt their upper body kinematics during a fatiguing repetitive pointing task.

Methods

Forty men and 41 women performed repetitive pointing movements (one per second) between two targets while maintaining their elbow elevated at shoulder height. The task ended when participants rated a perceived level of fatigue of 8/10. Trunk, humerothoracic, and elbow angles were compared between the first and last 30 s of the experiment and between men and women. Linear positions of the index finger (distance from the target) and the elbow (arm elevation) as well as movement timing were documented as task performance measures.

Results

Men (7.4 ± 3.2 min) and women (8.3 ± 4.5 min) performed the repetitive pointing task for a similar duration. For both sex groups, trunk range of motion increased with fatigue while shoulder’s and elbow’s decreased. Moreover, participants modified their trunk posture to compensate for the decreased humerothoracic elevation. Movements at all joints also became more variable with fatigue. However, of the 24 joint angle variables assessed, only two Sex × Fatigue interactions were observed. Although average humerothoracic elevation angle decreased in both subgroups, this decrease was greater in men (standardized response mean [SRM] − 1.63) than in women (SRM − 1.44). Moreover, the movement-to-movement variability of humerothoracic elevation angle increased only in women (SRM 0.42).

Conclusion

Despite many similarities between men’s and women’s response to fatigue induced by repetitive pointing movements, some sex differences were observed. Those subtle differences may indicate that men’s shoulder muscles were more fatigued than women’s despite a similar level of perceived exertion. They may also indicate that men and women do not adapt the exact same way to a similar fatigue.

Electronic supplementary material

The online version of this article (10.1186/s13293-018-0175-9) contains supplementary material, which is available to authorized users.

No Muscle Is an Island: Integrative Perspectives on Muscle Fatigue

Muscle fatigue has been studied with a variety approaches, tools and technologies. The foci of these studies have ranged tremendously, from molecules to the entire organism. Single cell and animal models have been used to gain mechanistic insight into the fatigue process. The theme of this review is the concept that the mechanisms of muscle fatigue do not occur in isolation in vivo: muscular work is supported by many complex physiological systems, any of which could fail during exercise and thus contribute to fatigue. To advance our overall understanding of fatigue, a combination of models and approaches is necessary. In this review, we examine the roles that neuromuscular properties, intracellular glycogen, oxygen metabolism, and blood flow play in the fatigue process during exercise and pathological conditions.

ATP cost of muscle contraction is associated with motor unit discharge rate in humans

Although a neural component has been suggested to contribute to the energetic cost of muscle contraction in vivo, the association between neural and energetic factors has not been determined during voluntary contractions in humans. Twenty young (24±1years, 10 women) healthy individuals performed isometric ankle dorsiflexion contractions at 20%, 50% and 100% of maximal voluntary contraction torque on two occasions during which measures of either motor unit discharge rates (MUDR, by indwelling electromyography) or ATP cost of contraction (by (31)P magnetic resonance spectroscopy) were obtained. Both MUDR and ATP cost increased with increasing contraction intensity (p≤0.02). A strong, positive relationship (r(2)=0.70; p<0.001) was observed between MUDR and ATP cost. These results suggest that a substantial portion of the variability in ATP cost can be explained by MUDR, and thus demonstrate that motor unit rate coding is likely an important neural factor contributing to energetic cost in vivo.

Mechanisms of in vivo muscle fatigue in humans: investigating age-related fatigue resistance with a computational model

KEY POINTS

Muscle fatigue can be defined as the transient decrease in maximal force that occurs in response to muscle use. Fatigue develops because of a complex set of changes within the neuromuscular system that are difficult to evaluate simultaneously in humans. The skeletal muscle of older adults fatigues less than that of young adults during static contractions. The potential sources of this difference are multiple and intertwined. To evaluate the individual mechanisms of fatigue, we developed an integrative computational model based on neural, biochemical, morphological and physiological properties of human skeletal muscle. Our results indicate first that the model provides accurate predictions of fatigue and second that the age-related resistance to fatigue is due largely to a lower reliance on glycolytic metabolism during contraction. This model should prove useful for generating hypotheses for future experimental studies into the mechanisms of muscle fatigue.

ABSTRACT

During repeated or sustained muscle activation, force-generating capacity becomes limited in a process referred to as fatigue. Multiple factors, including motor unit activation patterns, muscle fibre contractile properties and bioenergetic function, can impact force-generating capacity and thus the potential to resist fatigue. Given that neuromuscular fatigue depends on interrelated factors, quantifying their independent effects on force-generating capacity is not possible in vivo. Computational models can provide insight into complex systems in which multiple inputs determine discrete outputs. However, few computational models to date have investigated neuromuscular fatigue by incorporating the multiple levels of neuromuscular function known to impact human in vivo function. To address this limitation, we present a computational model that predicts neural activation, biomechanical forces, intracellular metabolic perturbations and, ultimately, fatigue during repeated isometric contractions. This model was compared with metabolic and contractile responses to repeated activation using values reported in the literature. Once validated in this way, the model was modified to reflect age-related changes in neuromuscular function. Comparisons between initial and age-modified simulations indicated that the age-modified model predicted less fatigue during repeated isometric contractions, consistent with reports in the literature. Together, our simulations suggest that reduced glycolytic flux is the greatest contributor to the phenomenon of age-related fatigue resistance. In contrast, oxidative resynthesis of phosphocreatine between intermittent contractions and inherent buffering capacity had minimal impact on predicted fatigue during isometric contractions. The insights gained from these simulations cannot be achieved through traditional in vivo or in vitro experimentation alone.