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

Neuroplastic alterations in common synaptic inputs and synergistic motor unit clusters controlling the vastii muscles of individuals with ACL reconstruction

This cross-sectional study aims to elucidate the neural mechanisms underlying the control of knee extension forces in individuals with anterior cruciate ligament reconstruction (ACLR). Eleven soccer players with ACLR and nine control players performed unilateral isometric knee extensions at 10% and 30% of their maximum voluntary force (MVF). Simultaneous recordings of high-density surface electromyography (HDEMG) and force output were conducted for each lower limb, and HDEMG data from the vastus lateralis (VL) and vastus medialis (VM) muscles were decomposed into individual motor unit spike trains. Force steadiness was estimated using the coefficient of variation of force. An intramuscular coherence analysis was adopted to estimate the common synaptic input (CSI) converging to each muscle. A factor analysis was applied to investigate the neural strategies underlying the control of synergistic motor neuron clusters, referred to as motor unit modes. Force steadiness was similar between lower limbs. However, motor neurons innervating the VL on the reconstructed side received a lower proportion of CSI at low-frequency bandwidths (<5 Hz) compared with the unaffected lower limbs (P < 0.01). Furthermore, the reconstructed side demonstrated a higher proportion of motor units associated with the neural input common to the synergistic muscle, as compared with the unaffected lower limbs (P < 0.01). These findings indicate that the VL muscle of reconstructed lower limbs contribute marginally to force steadiness and that a plastic rearrangement in synergistic clusters of motor units involved in the control of knee extension forces is evident following ACLR.NEW & NOTEWORTHY Chronic quadriceps dysfunction is common after anterior cruciate ligament reconstruction (ACLR). We investigated voluntary force control strategies by estimating common inputs to motor neurons innervating the vastii muscles. Our results showed attenuated common inputs to the vastus lateralis and plastic rearrangements in functional clusters of motor neurons modulating knee extension forces in the reconstructed limb. These findings suggest neuroplastic adjustments following ACLR that may occur to fine-tune the control of quadriceps forces.

High-density EMG reveals atypical spatial activation of the gastrocnemius during walking in adolescents with Cerebral Palsy

Children with Cerebral Palsy (CP) exhibit less-selective, simplified muscle activation during gait due to injury of the developing brain. Abnormal motor unit recruitment, altered excitation-inhibition balance, and muscle morphological changes all affect the CP electromyogram. High-density surface electromyography (HDsEMG) has potential to reveal novel manifestations of CP neuromuscular pathology and functional deficits by assessing spatiotemporal details of myoelectric activity. We used HDsEMG to investigate spatial-EMG distribution and temporal-EMG complexity of gastrocnemius medialis (GM) muscle during treadmill walking in 11 adolescents with CP and 11 typically developed (TD) adolescents. Our results reveal more-uniform spatial-EMG amplitude distribution across the GM in adolescents with CP, compared to distal emphasis in TD adolescents. More-uniform spatial-EMG was associated with stronger ankle co-contraction and spasticity. CP adolescents exhibited a non-significant trend towards elevated EMG-temporal complexity. Homogenous spatial distribution and disordered temporal evolution of myoelectric activity in CP suggests less-structured and desynchronized recruitment of GM motor units, in combination with muscle morphological changes. Using HDsEMG, we uncovered novel evidence of atypical spatiotemporal activation during gait in CP, opening paths towards deeper understanding of motor control deficits and better characterization of changes in muscular activation from interventions.

Age-related breakdown in networks of inter-muscular coordination

Assessing inter-muscular coordination in older adults is crucial, as it directly impacts an individual's ability for independent functioning, injury prevention, and active engagement in daily activities. However, the precise mechanisms by which distinct muscle fiber types synchronize their activity across muscles to generate coordinated movements in older adults remain unknown. Our objective is to investigate how distinct muscle groups dynamically synchronize with each other in young and older adults during exercise. Thirty-five young adults and nine older adults performed one bodyweight squat set until exhaustion. Simultaneous surface electromyography (sEMG) recordings were taken from the left and right vastus lateralis, and left and right erector spinae. To quantify inter-muscular coordination, we first obtained ten time series of sEMG band power for each muscle, representing the dynamics of different muscle fiber types. Next, we calculated the bivariate equal-time Pearson's cross-correlation for each pair of sEMG band power time series across all leg and back muscles. The main results show (i) an overall reduction in the degree of inter-muscular coordination, and (ii) increased stratification of the inter-muscular network in older adults compared to young adults. These findings suggest that as individuals age, the global inter-muscular network becomes less flexible and adaptable, hindering its ability to reorganize effectively in response to fatigue or other stimuli. This network approach opens new avenues for developing novel network-based markers to characterize multilevel inter-muscular interactions, which can help target functional deficits and potentially reduce the risk of falls and neuro-muscular injuries in older adults.

The effect of trunk position and pain location on lumbar extensor muscle recruitment strategies

The aim of this study was to investigate the effect of trunk position and experimental lumbar pain location on lumbar extensor muscle recruitment strategies. Nineteen healthy participants (10 men and 9 women), aged 25.3 ± 4.7 yr, performed isometric back extension contractions in three positions (neutral, 45°, and 90° trunk flexion) and under three conditions (no pain, caudal pain, and cranial pain). Lumbar muscle activation strategies were recorded using high-density surface electromyography. The effect of position and pain condition on muscle activity amplitude and spatial redistributions was assessed. Muscle activity amplitude was 43% higher in 45° trunk flexion than in neutral position on both sides (P < 0.05). In the 90° trunk flexion, participants showed a more lateral spatial distribution than in the 45° trunk flexion on the left side (P < 0.01, 5.4 mm difference) and the neutral position on both sides (P < 0.05, 8.2 mm difference). In the 45° trunk flexion, participants exhibited a more lateral spatial distribution compared with the neutral position on the right side (P < 0.05, 3.7 mm difference). A lateral spatial redistribution of muscle activity was observed in the caudal pain condition compared with the no pain condition on the right side (P < 0.05, 3.0 mm difference). Individual responses to pain varied across all variables. Different trunk positions result in different distributions of activation within the lumbar extensor muscles, possibly based on regional mechanical advantage. No clear indication of location-specific pain adaptation and no effect of task-dependent pain adaptation were found, whereas individual-specific adaptations were observed.NEW & NOTEWORTHY Changes in muscle activity amplitude and spatial redistribution of lumbar extensor muscles were observed in different trunk positions, potentially due to changes in their mechanical advantage. The results complement the current pain-adaptation theory by illustrating individual spatial redistributions of activation within lumbar extensor muscles during pain. The study found no clear indication of location-specific pain adaptation and no effect of task-dependent pain adaptation.

Does pain influence control of muscle force? A systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

In the presence of pain, whether clinical or experimentally induced, individuals commonly show impairments in the control of muscle force (commonly known as force steadiness). In this systematic review and meta-analysis, we synthesized the available evidence on the influence of clinical and experimental pain on force steadiness.

DATABASES AND DATA TREATMENT

MEDLINE, EMBASE, PubMed, CINAHL Plus and Web of Science databases were searched from their inception to 19 December 2023, using MeSH terms and pre-selected keywords related to pain and force steadiness. Two independent reviewers screened studies for inclusion and assessed their methodological quality using a modified Newcastle-Ottawa risk of bias tool.

RESULTS

In total, 32 studies (19 clinical pain and 13 experimental pain) were included. Meta-analyses revealed reduced force steadiness in the presence of clinical pain as measured by the coefficient of variation (CoV) and standard deviation (SD) of force (standardized mean difference; SMD = 0.80, 95% CI = 0.31-1.28 and SMD = 0.61, 95% CI = 0.11-1.11). These findings were supported by moderate and low strength of evidence respectively. In the presence of experimental pain, meta-analyses revealed reductions in force steadiness when measured by the CoV of force but not by the SD of force (SMD = 0.50, 95% CI = 0.01-0.99; and SMD = 0.44, 95% CI = -0.04 to 0.92), each supported by very low strength of evidence.

CONCLUSIONS

This work demonstrates that pain, particularly clinical pain, impairs force steadiness. Such impairments likely have clinical relevance and could become targets for treatment when managing people experiencing musculoskeletal pain.

SIGNIFICANCE STATEMENT

This systematic review and meta-analyses enhances our understanding of motor impairments observed in people experiencing musculoskeletal pain. It underscores the significance of incorporating force steadiness assessment when managing individuals experiencing musculoskeletal pain. Additionally, it suggests that future research should explore the potential benefits of force steadiness training in alleviating patients' symptoms and enhancing their functional performance. This could potentially lead to the development of innovative therapeutic approaches for individuals suffering from musculoskeletal pain.

Integration of Motor Unit Filters for Enhanced Surface Electromyogram Decomposition During Varying Force Isometric Contraction

Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Objective.Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. This study aims to predict hand function of stroke survivors by combining interpretable features extracted from a wearable HD-EMG forearm sleeve.Approach.Here, able-bodied (N= 7) and chronic stroke subjects (N= 7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. A variety of HD-EMG features, or views, were decomposed to assess alterations in motor coordination.Main Results.Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was strongly correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants (R2= 0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations.Significance.These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care.

Impact of Stiffness of Quadriceps on the Pedaling Rate of Maximal Cycling

Propulsive power is one of the factors that determine the performance of sprint cycling. Pedaling rate is related to power output, and stiffness is associated with improving performance in athletic tasks.

PURPOSE

to investigate the relationship between musculoarticular stiffness and pedaling rate.

METHODS

twenty-two healthy, untrained male volunteers (19 ± 2 years, 175 ± 6 cm, 74 ± 16 kg) were divided into two groups after their musculoarticular (MA) stiffness was tested, and these groups were the stiffness group (SG) and compliant group (CG). A 6-s maximal cycling test was conducted in four cycling modes, which were levels 5 and 10 air-resistance, and levels 3 and 7 magnetic-resistance. Peak and average cadence, peak power output (POpeak), crank force (CFpeak), peak rate of crank force development (RCFD), and the angle of peak crank force were collected. The significance of differences between the two groups for these variables was assessed using an independent samples t-test. Pearson product-moment correlations were calculated to analyze the relationship between MA stiffness and each performance variable.

RESULTS

the SG had significantly higher peak cadence and average cadence at level 3 magnetic-resistance, peak crank force, and peak power output at level 10 air-resistance, peak rate of crank force development at levels 5 air-resistance, 10 air-resistance, and 3 magnetic-resistance (p < 0.05). MA stiffness was significantly correlated with average cadence at levels 5 and 10 air-resistance, peak crank force in all 4 modes, and RCFD and peak power output at level 10 air-resistance. There were no significant relationships between MA stiffness and the angle of peak crank force in each cycling mode.

CONCLUSION

results indicate that participants with relatively higher MA stiffness seemed to have a higher pedaling rate during a 6-s sprint cycling in these conditions. They also performed a superior crank force and rate of crank force development, producing greater power output when sprint cycling. Optimizing cycling resistance or gear ratio to enhance both RCFD and musculotendinous stiffness may be crucial for improving sprint cycling performance.

Investigation of the tradeoffs between tracking performance and energetics in heterogeneous variable recruitment fluidic artificial muscle bundles

In traditional hydraulic robotics, actuators must be sized for the highest possible load, resulting in significant energy losses when operating in lower force regimes. Variable recruitment fluidic artificial muscle (FAM) bundles offer a novel bio-inspired solution to this problem. Divided into individual MUs, each with its own control valve, a variable recruitment FAM bundle uses a switching control scheme to selectively bring MUs online according to load demand. To date, every dynamic variable recruitment study in the literature has considered homogeneous bundles containing MUs of equal size. However, natural mammalian muscle MUs are heterogeneous and primarily operate based on Henneman's size principle, which states that MUs are recruited from smallest to largest for a given task. Is it better for a FAM variable recruitment bundle to operate according to this principle, or are there other recruitment orders that result in better performance? What are the appropriate criteria for switching between recruitment states for these different recruitment orders? This paper seeks to answer these questions by performing two case studies exploring different bundle MU size distributions, analyzing the tradeoffs between tracking performance and energetics, and determining how these tradeoffs are affected by different MU recruitment order and recruitment state transition thresholds. The only difference between the two test cases is the overall force capacity (i.e. total size) of the bundle. For each test case, a Pareto frontier for different MU size distributions, recruitment orders, and recruitment state transition thresholds is constructed. The results show that there is a complex relationship between overall bundle size, MU size distributions, recruitment orders, and recruitment state transition thresholds corresponding to the best tradeoffs change along the Pareto frontier. Overall, these two case studies validate the use of Henneman's Size Principle as a variable recruitment strategy, but also demonstrate that it should not be the only variable recruitment method considered. They also motivate the need for a more complex variable recruitment scheme that dynamically changes the recruitment state transition threshold and recruitment order based on loading conditions and known system states, along with a co-design problem that optimizes total bundle size and MU size distribution.

Three Levels of Neural Control Contributing to Performance-stabilizing Synergies in Multi-finger Tasks

We tested a hypothesis on force-stabilizing synergies during four-finger accurate force production at three levels: (1) The level of the reciprocal and coactivation commands, estimated as the referent coordinate and apparent stiffness of all four fingers combined; (2) The level of individual finger forces; and (3) The level of firing of individual motor units (MU). Young, healthy participants performed accurate four-finger force production at a comfortable, non-fatiguing level under visual feedback on the total force magnitude. Mechanical reflections of the reciprocal and coactivation commands were estimated using small, smooth finger perturbations applied by the "inverse piano" device. Firing frequencies of motor units in the flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC) were estimated using surface recording. Principal component analysis was used to identify robust MU groups (MU-modes) with parallel changes in the firing frequency. The framework of the uncontrolled manifold hypothesis was used to compute synergy indices in the spaces of referent coordinate and apparent stiffness, finger forces, and MU-mode magnitudes. Force-stabilizing synergies were seen at all three levels. They were present in the MU-mode spaces defined for MUs in FDS, in EDC, and pooled over both muscles. No effects of hand dominance were seen. The synergy indices defined at different levels of analysis showed no correlations across the participants. The findings are interpreted within the theory of control with spatial referent coordinates for the effectors. We conclude that force stabilization gets contributions from three levels of neural control, likely associated with cortical, subcortical, and spinal circuitry.

ROLE OF FORELIMB MORPHOLOGY IN MUSCLE SENSORIMOTOR FUNCTIONS DURING LOCOMOTION IN THE CAT

Previous studies established strong links between morphological characteristics of mammalian hindlimb muscles and their sensorimotor functions during locomotion. Less is known about the role of forelimb morphology in motor outputs and generation of sensory signals. Here, we measured morphological characteristics of 46 forelimb muscles from 6 cats. These characteristics included muscle attachments, physiological cross-sectional area (PCSA), fascicle length, etc. We also recorded full-body mechanics and EMG activity of forelimb muscles during level overground and treadmill locomotion in 7 and 16 adult cats of either sex, respectively. We computed forelimb muscle forces along with force- and length-dependent sensory signals mapped onto corresponding cervical spinal segments. We found that patterns of computed muscle forces and afferent activities were strongly affected by the muscle's moment arm, PCSA, and fascicle length. Morphology of the shoulder muscles suggests distinct roles of the forelimbs in lateral force production and movements. Patterns of length-dependent sensory activity of muscles with long fibers (brachioradialis, extensor carpi radialis) closely matched patterns of overall forelimb length, whereas the activity pattern of biceps brachii matched forelimb orientation. We conclude that cat forelimb muscle morphology contributes substantially to locomotor function, particularly to control lateral stability and turning, rather than propulsion.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Synaptic architecture of leg and wing premotor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Onion skin is not a universal firing pattern for spinal motoneurons: simulation study

Muscle force is modulated by sequential recruitment and firing rates of motor units (MUs). However, discrepancies exist in the literature regarding the relationship between MU firing rates and their recruitment, presenting two contrasting firing-recruitment schemes. The first firing scheme, known as "onion skin," exhibits low-threshold MUs firing faster than high-threshold MUs, forming separate layers akin to an onion. This contradicts the other firing scheme, known as "reverse onion skin" or "afterhyperpolarization (AHP)," with low-threshold MUs firing slower than high-threshold MUs. To study this apparent dichotomy, we used a high-fidelity computational model that prioritizes physiological fidelity and heterogeneity, allowing versatility in the recruitment of different motoneuron types. Our simulations indicate that these two schemes are not mutually exclusive but rather coexist. The likelihood of observing each scheme depends on factors such as the motoneuron pool activation level, synaptic input activation rates, and MU type. The onion skin scheme does not universally govern the encoding rates of MUs but tends to emerge in unsaturated motoneurons (cells firing < their fusion frequency that generates peak force), whereas the AHP scheme prevails in saturated MUs (cells firing at their fusion frequency), which is highly probable for slow (S)-type MUs. When unsaturated, fast fatigable (FF)-type MUs always show the onion skin scheme, whereas S-type MUs do not show either one. Fast fatigue-resistant (FR)-type MUs are generally similar but show weaker onion skin behaviors than FF-type MUs. Our results offer an explanation for the longstanding dichotomy regarding MU firing patterns, shedding light on the factors influencing the firing-recruitment schemes.NEW & NOTEWORTHY The literature reports two contrasting schemes, namely the onion skin and the afterhyperpolarization (AHP) regarding the relationship between motor units (MUs) firing rates and recruitment order. Previous studies have examined these schemes phenomenologically, imposing one scheme on the firing-recruitment relationship. Here, we used a high-fidelity computational model that prioritizes biological fidelity and heterogeneity to investigate motoneuron firing schemes without bias toward either scheme. Our objective findings offer an explanation for the longstanding dichotomy on MU firing patterns.

Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics

The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.

Muscle contractile properties directly influence shared synaptic inputs to spinal motor neurons

Alpha band oscillations in shared synaptic inputs to the alpha motor neuron pool can be considered an involuntary source of noise that hinders precise voluntary force production. This study investigated the impact of changing muscle length on the shared synaptic oscillations to spinal motor neurons, particularly in the physiological tremor band. Fourteen healthy individuals performed low-level dorsiflexion contractions at ankle joint angles of 90° and 130°, while high-density surface electromyography (HDsEMG) was recorded from the tibialis anterior (TA). We decomposed the HDsEMG into motor units spike trains and calculated the motor units' coherence within the delta (1-5 Hz), alpha (5-15 Hz), and beta (15-35 Hz) bands. Additionally, force steadiness and force spectral power within the tremor band were quantified. Results showed no significant differences in force steadiness between 90° and 130°. In contrast, alpha band oscillations in both synaptic inputs and force output decreased as the length of the TA was moved from shorter (90°) to longer (130°), with no changes in delta and beta bands. In a second set of experiments (10 participants), evoked twitches were recorded with the ankle joint at 90° and 130°, revealing longer twitch durations in the longer TA muscle length condition compared to the shorter. These experimental results, supported by a simple computational simulation, suggest that increasing muscle length enhances the muscle's low-pass filtering properties, influencing the oscillations generated by the Ia afferent feedback loop. Therefore, this study provides valuable insights into the interplay between muscle biomechanics and neural oscillations. KEY POINTS: We investigated whether changes in muscle length, achieved by changing joint position, could influence common synaptic oscillations to spinal motor neurons, particularly in the tremor band (5-15 Hz). Our results demonstrate that changing muscle length from shorter to longer induces reductions in the magnitude of alpha band oscillations in common synaptic inputs. Importantly, these reductions were reflected in the oscillations of muscle force output within the alpha band. Longer twitch durations were observed in the longer muscle length condition compared to the shorter, suggesting that increasing muscle length enhances the muscle's low-pass filtering properties. Changes in the peripheral contractile properties of motor units due to changes in muscle length significantly influence the transmission of shared synaptic inputs into muscle force output. These findings prove the interplay between muscle mechanics and neural adaptations.

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.

Synaptic architecture of leg and wing premotor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Understanding optimal cadence dynamics: a systematic analysis of the power-velocity relationship in track cyclists with increasing exercise intensity

Background: This study aimed to investigate the changes in force-velocity (F/v) and power-velocity (P/v) relationships with increasing work rate up to maximal oxygen uptake and to assess the resulting alterations in optimal cadence, particularly at characteristic metabolic states. Methods: Fourteen professional track cyclists (9 sprinters, 5 endurance athletes) performed submaximal incremental tests, high-intensity cycling trials, and maximal sprints at varied cadences (60, 90, 120 rpm) on an SRM bicycle ergometer. Linear and non-linear regression analyses were used to assess the relationship between heart rate, oxygen uptake (V.O2), blood lactate concentration and power output at each pedaling rate. Work rates linked to various cardiopulmonary and metabolic states, including lactate threshold (LT1), maximal fat combustion (FATmax), maximal lactate steady-state (MLSS) and maximal oxygen uptake (V.O2max), were determined using cadence-specific inverse functions. These data were used to calculate state-specific force-velocity (F/v) and power-velocity (P/v) profiles, from which state-specific optimal cadences were derived. Additionally, fatigue-free profiles were generated from sprint data to illustrate the entire F/v and P/v continuum. Results: HR, V.O2 demonstrated linear relationships, while BLC exhibited an exponential relationship with work rate, influenced by cadence (p < 0.05, η2 ≥ 0.655). Optimal cadence increased sigmoidally across all parameters, ranging from 66.18 ± 3.00 rpm at LT1, 76.01 ± 3.36 rpm at FATmax, 82.24 ± 2.59 rpm at MLSS, culminating at 84.49 ± 2.66 rpm at V.O2max (p < 0.01, η2 = 0.936). A fatigue-free optimal cadence of 135 ± 11 rpm was identified. Sprinters and endurance athletes showed no differences in optimal cadences, except for the fatigue-free optimum (p < 0.001, d = 2.215). Conclusion: Optimal cadence increases sigmoidally with exercise intensity up to maximal aerobic power, irrespective of the athlete's physical condition or discipline. Threshold-specific changes in optimal cadence suggest a shift in muscle fiber type recruitment toward faster types beyond these thresholds. Moreover, the results indicate the need to integrate movement velocity into Henneman's hierarchical size principle and the critical power curve. Consequently, intensity zones should be presented as a function of movement velocity rather than in absolute terms.

Motor unit modes in the calf muscles during a submaximal isometric contraction are changed by brief stretches

The purpose of our study was to investigate the influence of a stretch intervention on the common modulation of discharge rate among motor units in the calf muscles during a submaximal isometric contraction. The current report comprises a computational analysis of a motor unit dataset that we published previously (Mazzo et al., 2021). Motor unit activity was recorded from the three main plantar flexor muscles while participants performed an isometric contraction at 10% of the maximal voluntary contraction force before and after each of two interventions. The interventions were a control task (standing balance) and static stretching of the plantar flexor muscles. A factorization analysis on the smoothed discharge rates of the motor units from all three muscles yielded three modes that were independent of the individual muscles. The composition of the modes was not changed by the standing-balance task, whereas the stretching exercise reduced the average correlation in the second mode and increased it in the third mode. A centroid analysis on the correlation values showed that most motor units were associated with two or three modes, which were presumed to indicate shared synaptic inputs. The percentage of motor units adjacent to the seven centroids changed after both interventions: Control intervention, mode 1 decreased and the shared mode 1 + 2 increased; stretch intervention, shared modes either decreased (1 + 2) or increased (1 + 3). These findings indicate that the neuromuscular adjustments during both interventions were sufficient to change the motor unit modes when the same task was performed after each intervention. KEY POINTS: Based on covariation of the discharge rates of motor units in the calf muscles during a submaximal isometric contraction, factor analysis was used to assign the correlated discharge trains to three motor unit modes. The motor unit modes were determined from the combined set of all identified motor units across the three muscles before and after each participant performed a control and a stretch intervention. The composition of the motor unit modes changed after the stretching exercise, but not after the control task (standing balance). A centroid analysis on the distribution of correlation values found that most motor units were associated with a shared centroid and this distribution, presumably reflecting shared synaptic input, changed after both interventions. Our results demonstrate how the distribution of multiple common synaptic inputs to the motor neurons innervating the plantar flexor muscles changes after a brief series of stretches.

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.

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.

Two classes of action-stabilizing synergies reflecting spinal and supraspinal circuitry

We explored force-stabilizing synergies during accurate four-finger constant force production tasks in spaces of finger modes (commands to fingers computed to account for the finger interdependence) and of motor unit (MU) firing frequencies. The main specific hypothesis was that the multifinger synergies would disappear during unintentional force drifts without visual feedback on the force magnitude, whereas MU-based synergies would be robust to such drifts. Healthy participants performed four-finger accurate cyclical force production trials followed by trials of constant force production. Individual MUs were identified in the flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC). Principal component analysis was applied to motor unit frequencies to identify robust MU groups (MU-modes) with parallel scaling of the firing frequencies in FDS, in EDC, and the combined MUs of FDS + EDC. The framework of the uncontrolled manifold hypothesis was used to quantify force-stabilizing synergies when visual feedback on the force magnitude was available and 15 s after turning the visual feedback off. Removing visual feedback led to a force drift toward lower magnitudes, accompanied by the disappearance of multifinger synergies. In contrast, MU-mode synergies were minimally affected by removing visual feedback off and continued to be robust for the FDS and for the EDC, while being absent for the (FDS + EDC) analysis. We interpret the findings within the theory of hierarchical control of action with spatial referent coordinates. The qualitatively different behavior of the multifinger and MU-mode-based synergies likely reflects the difference in the involved neural circuitry, supraspinal for the former and spinal for the latter.NEW & NOTEWORTHY Two types of synergies, in the space of commands to individual fingers and in the space of motor unit groups, show qualitatively different behaviors during accurate multifinger force-production tasks. After removing visual feedback, finger force synergies disappear, whereas motor unit-based synergies persist. These results point at different neural circuitry involved in these two basic classes of synergies: supraspinal for multieffector synergies, and spinal for motor unit-based synergies.

The neural control of movement: a century of in vivo motor unit recordings is the legacy of Adrian and Bronk

In two papers dated 1928 to 1929 in The Journal of Physiology, Edgar Adrian and Detlev Bronk described recordings from motor nerve and muscle fibres. The recordings from motor nerve fibres required progressive dissection of the nerve until a few fibres remained, from which isolated single fibre activity could be detected. The muscle fibre recordings were performed in humans during voluntary contractions with an intramuscular electrode - the concentric needle electrode - that they describe for the first time in the second paper. They recognised that muscle fibres would respond to each impulse sent by the innervating motor neurone and that therefore muscle fibre recordings provided information on the times of activation of the motor nerve fibres which were as accurate as a direct record from the nerve. These observations and the description of the concentric needle electrode opened the era of motor unit recordings in humans, which have continued for almost a century and have provided a comprehensive view of the neural control of movement at the motor unit level. Despite important advances in technology, many of the principles of motor unit behaviour that would be investigated in the subsequent decades were canvassed in the two papers by Adrian and Bronk. For example, they described the concomitant motor neurones' recruitment and rate coding for force modulation, synchronisation of motor unit discharges, and the dependence of discharge rate on motor unit recruitment threshold. Here, we summarise their observations and discuss the impact of their work. We highlight the advent of the concentric needle, and its subsequent influence on motor control research.

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.

Regional recruitment and differential behavior of motor units during postural control in older adults

Aging is associated with neuromuscular system changes that may have implications for the recruitment and firing behaviors of motor units (MUs). In previous studies, we observed that young adults recruit subpopulations of triceps surae MUs during tasks that involved leaning in five directions: common units that were active during different leaning directions and unique units that were active in only one leaning direction. Furthermore, the MU subpopulation firing behaviors [average firing rate (AFR), coefficient of variation (CoVISI), and intermittent firing] modulated with leaning direction. The purpose of this study was to examine whether older adults exhibited this regional recruitment of MUs and firing behaviors. Seventeen older adults (aged 74.8 ± 5.3 yr) stood on a force platform and maintained their center of pressure leaning in five directions. High-density surface electromyography recordings from the triceps surae were decomposed into single MU action potentials. A MU tracking analysis identified groups of MUs as being common or unique across the leaning directions. Although leaning in different directions did not affect the AFR and CoVISI of common units (P > 0.05), the unique units responded to the leaning directions by increasing AFR and CoVISI, albeit modestly (F = 18.51, P < 0.001). The unique units increased their intermittency with forward leaning (F = 9.22, P = 0.003). The mediolateral barycenter positions of MU activity in both subpopulations were found in similar locations for all leaning directions (P > 0.05). These neuromuscular changes may contribute to the reduced balance performance seen in older adults.NEW & NOTEWORTHY In this study, we observed differences in motor unit recruitment and firing behaviors of distinct subpopulations of motor units in the older adult triceps surae muscle from those observed in the young adult. Our results suggest that the older adult central nervous system may partially lose the ability to regionally recruit and differentially control motor units. This finding may be an underlying cause of balance difficulties in older adults during directionally challenging leaning tasks.

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.

Ageing reduces persistent inward current contribution to motor neurone firing: Potential mechanisms and the role of exercise

Nervous system deterioration is a primary driver of age-related motor impairment. The motor neurones, which act as the interface between the central nervous system and the muscles, play a crucial role in amplifying excitatory synaptic input to produce the desired motor neuronal firing output. For this, they utilise their ability to generate persistent (long-lasting) depolarising currents that increase cell excitability, and both amplify and prolong the output activity of motor neurones for a given synaptic input. Modulation of these persistent inward currents (PICs) contributes to the motor neurones' capacities to attain the required firing frequencies and rapidly modulate them to competently complete most tasks. Thus, PICs are crucial for adequate movement generation. Impairments in intrinsic motor neurone properties can impact motor unit firing capacity, with convincing evidence indicating that the PIC contribution to motor neurone firing is reduced in older adults. Indeed, this could be an important mechanism underpinning the age-related reductions in strength and physical function. Furthermore, resistance training has emerged as a promising intervention to counteract age-associated PIC impairments, with changes in PICs being correlated with improvements in muscular strength and physical function after training. In this review, we present the current knowledge of the PIC magnitude decline during ageing and discuss whether reduced serotonergic and noradrenergic input onto the motor neurones, voltage-gated calcium channel dysfunction or inhibitory input impairments are candidates that: (i) explain age-related reductions in the PIC contribution to motor neurone firing and (ii) underpin the enhanced PIC contribution to motor neurone firing following resistance training in older adults.

Network of muscle fibers activation facilitates inter-muscular coordination, adapts to fatigue and reflects muscle function

Fundamental movement patterns require continuous skeletal muscle coordination, where muscle fibers with different timing of activation synchronize their dynamics across muscles with distinct functions. It is unknown how muscle fibers integrate as a network to generate and fine tune movements. We investigate how distinct muscle fiber types synchronize across arm and chest muscles, and respond to fatigue during maximal push-up exercise. We uncover that a complex inter-muscular network of muscle fiber cross-frequency interactions underlies push-up movements. The network exhibits hierarchical organization (sub-networks/modules) with specific links strength stratification profile, reflecting distinct functions of muscles involved in push-up movements. We find network reorganization with fatigue where network modules follow distinct phase-space trajectories reflecting their functional role and adaptation to fatigue. Consistent with earlier observations for squat movements under same protocol, our findings point to general principles of inter-muscular coordination for fundamental movements, and open a new area of research, Network Physiology of Exercise.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing

Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.

Linking cortex and contraction-Integrating models along the corticomuscular pathway

Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons  and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.