High-density Surface Electromyography Research Articles

Analysis of the suprahyoid muscles during tongue elevation: High-density surface electromyography as a novel tool for swallowing-related muscle assessment.

High-density surface electromyography (HD-sEMG) has enabled non-invasive analysis of motor unit (MU) activity and recruitment, but its application to swallowing-related muscles is limited. We aimed to investigate the utility of HD-sEMG for quantitatively evaluating the MU recruitment characteristics of the suprahyoid muscles during tongue elevation. We measured the sEMG activity of the suprahyoid muscles of healthy participants during tongue elevation using HD-sEMG. Maximum voluntary contraction (MVC) was measured, followed by data collection during sustained and ramp-up tasks to capture suprahyoid muscle activity. Changes in the temporal/spatial MU recruitment patterns within individual suprahyoid muscles were analysed. This study enrolled 16 healthy young adults (mean age: 27.8 ± 5.3 years; eight males and eight females). Increasing muscle force corresponded to a decrease in modified entropy and correlation coefficient and an increase in the coefficient of variation. No significant differences were observed between male and female participants. The results of this study, consistent with those observed in other muscles, such as the vastus lateralis muscle, suggest that HD-sEMG is a valuable and reliable tool for quantitatively evaluating MU recruitment in the suprahyoid muscles. This measurement technique holds promise for novel assessments of swallowing function.

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 (1-5Hz), alpha (5-15Hz), and beta (15-35Hz) 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 (5-15Hz). 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.

A memory-friendly class-incremental learning method for hand gesture recognition using HD-sEMG

Hand gesture recognition (HGR) plays a vital role in human-computer interaction. The integration of high-density surface electromyography (HD-sEMG) and deep neural networks (DNNs) has significantly improved the robustness and accuracy of HGR systems. These methods are typically effective for a fixed set of trained gestures. However, the need for new gesture classes over time poses a challenge. Introducing new classes to DNNs can lead to a substantial decrease in accuracy for previously learned tasks, a phenomenon known as "catastrophic forgetting," especially when the training data for earlier tasks is not retained and retrained. This issue is exacerbated in embedded devices with limited storage, which struggle to store the large-scale data of HD-sEMG. Class-incremental learning (CIL) is an effective method to reduce catastrophic forgetting. However, existing CIL methods for HGR rarely focus on reducing memory load. To address this, we propose a memory-friendly CIL method for HGR using HD-sEMG. Our approach includes a lightweight convolutional neural network, named SeparaNet, for feature representation learning, coupled with a nearest-mean-of-exemplars classifier for classification. We introduce a priority exemplar selection algorithm inspired by the herding effect to maintain a manageable set of exemplars during training. Furthermore, a task-equal-weight exemplar sampling strategy is proposed to effectively reduce memory load while preserving high recognition performance. Experimental results on two datasets demonstrate that our method significantly reduces the number of retained exemplars to only a quarter of that required by other CIL methods, accounting for less than 5% of the total samples, while still achieving comparable average accuracy.

Neuromodulatory Contribution to Muscle Force Production after Short-Term Unloading and Active Recovery.

Prior evidence has shown that neural factors contribute to the loss of muscle force after skeletal-muscle disuse. However, little is known about the specific neural mechanisms altered by disuse. Persistent inward current (PIC) is an intrinsic property of motoneurons responsible for prolonging and amplifying the synaptic input, proportionally to the level of neuromodulation, thus influencing motoneuron discharge rate and force production. Here, we hypothesized that short-term unilateral lower-limb suspension (ULLS) would reduce the neuromodulatory input associated with PICs, contributing to the reduction of force generation capacity. Additionally, we tested whether physical exercise would restore the force generation capacity by re-establishing the initial level of neuromodulatory input. In 12 young adults, we assessed maximal voluntary contraction (MVC) pre- and post- 10 days of ULLS and following 21 days of active recovery (AR) based on resistance exercise. PIC was estimated from high-density surface electromyograms of the vastus lateralis muscle as the delta frequency (∆F) of paired motor units calculated during isometric ramped contractions. The values of ∆F were reduced after 10 days of ULLS (-33%, p < 0.001), but were fully re-established after the AR (+29.4%, p < 0.001). The changes in estimated PIC values were correlated (r = 0.63, p = 0.004) with the reduction in MVC after ULLS (-29%, p = 0.002) and its recovery after the AR (+28.5%, p = 0.003). Our findings suggest that PIC estimates are reduced by muscle disuse and may contribute to the loss of force production and its recovery with exercise. Overall, this is the first study demonstrating that, in addition to peripheral neuromuscular changes, central neuromodulation is a major contributor to the loss of force generation capacity after disuse, and can be recovered after resistance exercise.

Motor unit discharge rate modulation during isometric contractions to failure is intensity- and modality-dependent.

The physiological mechanisms determining the progressive decline in the maximal muscle torque production capacity during isometric contractions to task failure are known to depend on task demands. Task-specificity of the associated adjustments in motor unit discharge rate (MUDR), however, remains unclear. This study examined MUDR adjustments during different submaximal isometric knee extension tasks to failure. Participants performed a sustained and an intermittent task at 20% and 50% of maximal voluntary torque (MVT), respectively (Experiment 1). High-density surface EMG signals were recorded from vastus lateralis (VL) and medialis (VM) and decomposed into individual MU discharge timings, with the identified MUs tracked from recruitment to task failure. MUDR was quantified and normalised to intervals of 10% of contraction time (CT). MUDR of both muscles exhibited distinct modulation patterns in each task. During the 20% MVT sustained task, MUDR decreased until ∼50% CT, after which it gradually returned to baseline. Conversely, during the 50% MVT intermittent task, MUDR remained stable until ∼40-50% CT, after which it started to continually increase until task failure. To explore the effect of contraction intensity on the observed patterns, VL and VM MUDR was quantified during sustained contractions at 30% and 50% MVT (Experiment 2). During the 30% MVT sustained task, MUDR remained stable until ∼80-90% CT in both muscles, after which it continually increased until task failure. During the 50% MVT sustained task the increase in MUDR occurred earlier, after ∼70-80% CT. Our results suggest that adjustments in MUDR during submaximal isometric contractions to failure are contraction modality- and intensity-dependent. KEY POINTS: During prolonged muscle contractions a constant motor output can be maintained by recruitment of additional motor units and adjustments in their discharge rate. Whilst contraction-induced decrements in neuromuscular function are known to depend on task demands, task-specificity of motor unit discharge behaviour adjustments is still unclear. In this study, we tracked and compared discharge activity of several concurrently active motor units in the vastii muscles during different submaximal isometric knee extension tasks to failure, including intermittent vs. sustained contraction modalities performed in the same intensity domain (Experiment 1), and two sustained contractions performed at different intensities (Experiment 2). During each task, motor units modulated their discharge rate in a distinct, biphasic manner, with the modulation pattern depending on contraction intensity and modality. These results provide insight into motoneuronal adjustments during contraction tasks posing different demands on the neuromuscular system.

376 Pathophysiology of voluntary motor commands in patients with multiple sclerosis identified using reverse engineering of motor unit population discharge.

OBJECTIVES/GOALS: Our objective is to characterize excitatory, inhibitory, and neuromodulatory components of the voluntary motor command at the level of the spinal motoneuron in people with multiple sclerosis (MS). This information will provide insight into neural mechanisms of motor dysfunction and their heterogeneity among patients with MS. METHODS/STUDY POPULATION: Due to advances in high-density surface EMG (HDsEMG) decomposition and the recent development of a paradigm for reverse engineering of motor unit population discharge, we can feasibly estimate aspects of excitatory, inhibitory, and neuromodulatory components of the voluntary motor command in humans on a person-specific basis. We tested 11 ambulatory patients with MS and mild-moderate disability. We recorded HDsEMG from tibialis anterior (TA) and soleus (SOL) during isometric plantarflexion and dorsiflexion, performed as slow triangle contractions. EMG was decomposed into motor unit spike trains using blind source separation. We calculated a number of motor unit variables, most notably delta-F, which estimates motoneuron excitability and the balance of neuromodulatory and inhibitory inputs. RESULTS/ANTICIPATED RESULTS: There were consistent differences in MS patients vs. controls. For TA, values were decreased for delta-F (3.9 vs. 5.9 pps), initial firing rate acceleration (5.8 vs. 7.1 pps), firing rate range (9.3 vs. 11.9 pps), and max firing rate (12.3 vs. 15.0 pps). SOL had more modest decreases in delta-F (3.0 vs. 3.8 pps) and firing rate range (4.8 vs. 5.6 pps). Self-sustained firing was longer for MS patients. Within a patient, abnormalities in motor unit variables were not consistent across muscles and legs. Interestingly, there were several abnormalities in the patients with a normal clinical motor exam, indicating that perhaps our measures are sensitive to subclinical changes in processing of voluntary motor commands. DISCUSSION/SIGNIFICANCE: Excitatory, inhibitory, and neuromodulatory components of the voluntary motor command must be appropriately balanced for skilled motor output. This study is the first to characterize how they are disrupted in MS, providing foundational information to inform the development of mechanistically-based rehabilitation interventions.

Adaptive Online Decomposition of Surface EMG Using Progressive FastICA Peel-Off.

This study presents a method for adaptive online decomposition of high-density surface electromyogram (SEMG) signals to overcome the performance degradation during long-term recordings. The proposed method utilized the progressive FastICA peel-off (PFP) method and integrated a practical double-thread-parallel algorithm into the conventional two-stage calculation approach. During the offline initialization stage, a set of separation vectors was computed. In the subsequent online decomposition stage, a backend thread was implemented to periodically update the separation vectors using the constrained FastICA algorithm and the automatic PFP method. Concurrently, the frontend thread employed the newly updated separation vectors to accurately extract motor unit (MU) spike trains in real time. To assess the effectiveness of the proposed method, simulated and experimental SEMG signals from abductor pollicis brevis muscles of ten subjects were used for evaluation. The results demonstrated that the proposed method outperformed the conventional method, which relies on fixed separation vectors. Specifically, the proposed method showed an improved matching rate by 3.63% in simulated data and 1.98% in experimental data, along with an increased motor unit number by 2.39 in simulated data and 1.30 in experimental data. These findings illustrated the feasibility of the proposed method to enhance the performance of online SEMG decomposition. As a result, this work holds promise for various applications that require accurate MU firing activities in decoding neural commands and building neural-machine interfaces.

Effects of repetition of a car-driving pedal maneuver and neural output in older adults

Understanding the ability of older adults to control pedal position angle and investigating whether this ability can be enhanced through practice may contribute to the prevention of traffic accidents. This study aimed to investigate repetitive effects on variability of the pedal position and neural drive during car-pedal operation in older adults. Thirteen older and 11 young adults performed 105 (21 sets × 5 repetitions) pedal angle control tasks with plantar flexor contraction. High-density surface electromyograms were recorded of triceps surae muscles. A cumulative spike train as a neural drive was calculated using continuously active motor unit activities. The coefficient of variation of the angle was higher in older (1.47 ± 1.06 %) than young (0.41 ± 0.21 %) adults in the first sets, and improved to 0.67 ± 0.51 % in the final sets in older adults only. There was no significant difference in neural drive variability between older and young adults. Our results suggest that repetition improves angular steadiness in older adults. However, this effect could not be explained by neural output which is estimated from lower threshold motor units that are continuously active.

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive.

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time.

To utilize surface electromyographics (sEMG) for control purposes, it is necessary to perform real-time estimation of the neural drive to the muscles, which involves real-time decomposition of the EMG signals. In this paper, we propose a Bidirectional Gate Recurrent Unit (Bi-GRU) network with attention to perform online decomposition of high-density sEMG signals. The model can give different levels of attention to different parts of the sEMG signal according to their importance using the attention mechanism. The output of gradient convolutional kernel compensation (gCKC) algorithm was used as the training label, and simulated and experimental sEMG data were divided into windows with 120 sample points for model training, the sampling rate of sEMG signal is 2048 Hz. We test different attention mechanisms and find out the ones that could bring the highest F1-score of the model. The simulated sEMG signal is synthesized from Fuglevand method (J. Neurophysiol., 1993). For the decomposition of 10 Motor Units (MUs), the network trained on simulated data achieved an average F1-score of 0.974 (range from 0.96 to 0.98), and the network trained on experimental data achieved an average F1-score of 0.876 (range from 0.82 to 0.97). The average decomposition time for each window was 28 ms (range from 25.6 ms to 30.5 ms), which falls within the lower bound of the human electromechanical delay. The experimental results show the feasibility of using Bi-GRU-Attention network for the real-time decomposition of Motor Units. Compared to the gCKC algorithm, which is considered the gold standard in the medical field, this model sacrifices a small amount of accuracy but significantly improves computational speed by eliminating the need for calculating the cross-correlation matrix and performing iterative computations.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

Sex differences in laterality of motor unit firing behavior of the first dorsal interosseous muscle in strength-matched healthy young males and females.

The purpose of this study was to compare laterality in motor unit firing behavior between females and males. Twenty-seven subjects (14 females) were recruited for this study. The participants performed ramp up and hold isometric index finger abduction at 10, 30, and 60% of their maximum voluntary contraction (MVC). High-density surface electromyography (HD-sEMG) signals were recorded in the first dorsal interosseous (FDI) muscle and decomposed into individual motor unit (MU) firing behavior using a convolution blind source separation method. In total, 769 MUs were detected (females, n = 318 and males, n = 451). Females had a significantly higher discharge rate than males at each relative torque level (10%: male dominant hand, 13.4 ± 2.7 pps vs. female dominant hand, 16.3 ± 3.4 pps; 30%: male dominant hand, 16.1 ± 3.9 pps vs. female dominant hand, 20.0 ± 5.0 pps; and 60%: male dominant hand, 19.3 ± 3.8 vs. female dominant hand, 25.3 ± 4.8 pps; p < 0.0001). The recruitment threshold was also significantly higher in females than in males at 30 and 60% MVC. Furthermore, males exhibited asymmetrical discharge rates at 30 and 60% MVC and recruitment thresholds at 30 and 60% MVC, whereas no asymmetry was observed in females. In the FDI muscle, compared to males, females exhibited different neuromuscular strategies with higher discharge rates and recruitment thresholds and no asymmetrical MU firing behavior. Notably, the findings that sex differences in neuromuscular activity also occur in healthy individuals provide important information for understanding the pathogenesis of various diseases.

Inferring position of motor units from high-density surface EMG

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference From High-Dimensional Biosignals

Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this paper proposes two PLS-based algorithms, Pseudo-Sparse PLS (PS-PLS) and iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3D trajectory from Electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the Electromyography (EMG) powers from recorded Magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the High-Density surface Electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches.

Motor Unit Firing Properties During Force Control Task and Associations With Neurological Tests in Children.

The present study aimed to clarify the development of motor unit (MU) firing properties and the association between those neural properties and force steadiness (FS)/neurological tests in 6- to 12-year-old children. Fifty-eight school-aged children performed maximal voluntary knee extension contraction, a submaximal FS test at 10% of maximal voluntary knee extension contraction, knee extension reaction time to light stimulus test, and single-leg standing test, and data from 38 children who passed the criteria were subject to analysis. During the FS test, high-density surface electromyography was recorded from the vastus lateralis muscle to identify individual MU firing activity. FS was improved with an increase in age (r = -.540, P < .001). The MU firing rate (MUFR) was significantly decreased with an increase in age (r = -.343, P = .035). MUFR variability was not associated with age. Although there was no significant correlation between FS and MUFR, FS was significantly correlated with MUFR variability even after adjustment for the effect of age (r = .551, P = .002). Neither the reaction time nor the single-leg standing test was correlated with any MU firing properties. These findings suggest that MUFR variability makes an important contribution to precise force control in children but does not naturally develop with age.

Time-of-day effects on motor unit firing and muscle contractile properties in humans.

Intrinsic factors related to neuromuscular function are time-of-day dependent, but diurnal rhythms in neural and muscular components of the human neuromuscular system remain unclear. The present study aimed to investigate the time-of-day effects on neural excitability and muscle contractile properties by assessing the firing properties of tracked motor units and electrically evoked twitch muscle contraction. In 15 young adults (22.9 ± 4.7 yr), neuromuscular function was measured in the morning (10:00), at noon (13:30), in the evening (17:00), and at night (20:30). Four measurements were completed within 24 h. The measurements consisted of maximal voluntary contraction (MVC) strength of knee extension, recording of high-density surface electromyography (HDsEMG) from the vastus lateralis during ramp-up contraction to 50% of MVC, and evoked twitch torque of knee extensors by electrical stimulation. Recorded HDsEMG signals were decomposed to individual motor unit firing behaviors and the same motor units were tracked among the times of day, and recruitment thresholds and firing rates were calculated. The number of detected and tracked motor units was 127. Motor unit firing rates significantly increased from morning to noon, evening, and night (P < 0.01), but there were no significant differences in recruitment thresholds among the times of day (P > 0.05). Also, there were no significant effects of time of day on evoked twitch torque (P > 0.05). Changes in the motor unit firing rate and evoked twitch torque were not significantly correlated (P > 0.05). These findings suggest that neural excitability may be affected by the time of day, but it did not accompany changes in peripheral contractile properties in a diurnal manner.NEW & NOTEWORTHY We investigated the variations of tracked motor unit firing properties and electrically evoked twitch contraction during the day within 24 h. The variation of motor unit firing rate was observed, and tracked motor unit firing rate increased at noon, in the evening, and at night compared with that in the morning. The variation in motor unit firing rate was independent of changes in twitch contraction. Motor unit firing rate may be affected by diurnal rhythms.

Common synaptic inputs and persistent inward currents of vastus lateralis motor units are reduced in older male adults

Although muscle atrophy may partially account for age-related strength decline, it is further influenced by alterations of neural input to muscle. Persistent inward currents (PIC) and the level of common synaptic inputs to motoneurons influence neuromuscular function. However, these have not yet been described in the aged human quadriceps. High-density surface electromyography (HDsEMG) signals were collected from the vastus lateralis of 15 young (mean ± SD, 23 ± 5 y) and 15 older (67 ± 9 y) men during submaximal sustained and 20-s ramped contractions. HDsEMG signals were decomposed to identify individual motor unit discharges, from which PIC amplitude and intramuscular coherence were estimated. Older participants produced significantly lower knee extensor torque (p < 0.001) and poorer force tracking ability (p < 0.001) than young. Older participants also had lower PIC amplitude (p = 0.001) and coherence estimates in the alpha frequency band (p < 0.001) during ramp contractions when compared to young. Persistent inward currents and common synaptic inputs are lower in the vastus lateralis of older males when compared to young. These data highlight altered neural input to the clinically and functionally important quadriceps, further underpinning age-related loss of function which may occur independently of the loss of muscle mass.

Acute effects of caffeine or quercetin ingestion on motor unit firing pattern before and after resistance exercise.

The aim of the present study was to investigate the acute effect of caffeine or quercetin ingestion on motor unit firing patterns and muscle contractile properties before and after resistance exercise. High-density surface electromyography (HDs-EMG) during submaximal contractions and electrically elicited torque in knee extensor muscles were measured before (PRE) and 60min after (POST1) ingestion of caffeine, quercetin glycosides, or placebo, and after resistance exercise (POST2) in ten young males. The Convolution Kernel Compensation technique was used to identify individual motor units of the vastus lateralis muscle for the recorded HDs-EMG. Ingestion of caffeine or quercetin induced significantly greater decreases in recruitment thresholds (RTs) from PRE to POST1 compared with placebo (placebo: 94.8 ± 9.7%, caffeine: 84.5 ± 16.2%, quercetin: 91.9 ± 36.7%), and there were significant negative correlations between the change in RTs (POST1-PRE) and RT at PRE for caffeine (rs = - 0.448, p < 0.001) and quercetin (rs = - 0.415, p = 0.003), but not placebo (rs = - 0.109, p = 0.440). Significant positive correlations between the change in firing rates (POST2-POST1) and RT at PRE were noted with placebo (rs = 0.380, p = 0.005) and quercetin (rs = 0.382, p = 0.007), but not caffeine (rs = 0.069, p = 0.606). No significant differences were observed in electrically elicited torque among the three conditions. These results suggest that caffeine or quercetin ingestion alters motor unit firing patterns after resistance exercise in different threshold-dependent manners in males.

Association between physical fitness tests and neuromuscular properties.

While various fitness tests have been developed to assess physical performances, it is unclear how these tests are affected by differences, such as, in morphological and neural factors. This study was aimed to investigate associations between individual differences in physical fitness tests and neuromuscular properties. One hundred and thirty-three young adults participated in various general physical fitness tests and neuromuscular measurements. The appendicular skeletal muscle mass (ASM) was estimated by bioelectrical impedance analysis. Echo intensity (EI) was evaluated from the vastus lateralis. During submaximal knee extension force, high-density surface electromyography of the vastus lateralis was recorded and individual motor unit firings were detected. Y-intercept (i-MU) and slope (s-MU) from the regression line between the recruitment threshold and motor unit firing rate were calculated. Stepwise multiple regression analyses revealed that knee extension strength could be explained (adjusted R2 = 0.712) by ASM (β = 0.723), i-MU (0.317), EI (-0.177), and s-MU (0.210). Five-sec stepping could be explained by ASM (adjusted R2 = 0.212). Grip strength, side-stepping, and standing broad jump could be explained by ASM and echo intensity (adjusted R2 = 0.686, 0.354, and 0.627, respectively). Squat jump could be explained by EI (adjusted R2 = 0.640). Counter-movement jump could be explained by EI and s-MU (adjusted R2 = 0.631). On the other hand, i-MU and s-MU could be explained by five-sec stepping and counter-movement jump, respectively, but the coefficients of determination were low (adjusted R2 = 0.100 and 0.045). Generally developed physical fitness tests were mainly explained by morphological factors, but were weakly affected by neural factors involved in performance.