Females typically live longer than males but, paradoxically, spend a greater number of later years in poorer health. The neuromuscular system is a critical component of the progression to frailty, and motor unit (MU) characteristics differ by sex in healthy young individuals and may adapt to ageing in a sex-specific manner due to divergent hormonal profiles. The purpose of this study was to investigate sex differences in vastus lateralis (VL) MU structure and function in early to late elderly humans. Intramuscular electromyography signals from 50 healthy older adults (M/F: 26/24) were collected from VL during standardized submaximal contractions and decomposed to quantify MU characteristics. Muscle size and neuromuscular performance were also measured. Females had higher MU firing rate (FR) than males (P = 0.025), with no difference in MU structure or neuromuscular junction transmission (NMJ) instability. All MU characteristics increased from low- to mid-level contractions (P < 0.05) without sex × level interactions. Females had smaller cross-sectional area of VL, lower strength and poorer force steadiness (P < 0.05). From early to late elderly, both sexes showed decreased neuromuscular function (P < 0.05) without sex-specific patterns. Higher VL MUFRs at normalized contraction levels previously observed in young are also apparent in old individuals, with no sex-based difference of estimates of MU structure or NMJ transmission instability. From early to late elderly, the deterioration of neuromuscular function and MU characteristics did not differ between sexes, yet function was consistently greater in males. These parallel trajectories underscore the lower initial level for older females and may offer insights into identifying critical intervention periods. KEY POINTS: Females generally exhibit an extended lifespan when compared to males, yet this is accompanied by a poorer healthspan and higher rates of frailty. In healthy young people, motor unit firing rate (MUFR) at normalized contraction intensities is widely reported to be higher in females than in age-matched males. Here we show in 50 people that older females have higher MUFR than older males with little difference in other MU parameters. The trajectory of decline from early to late elderly does not differ between sexes, yet function is consistently lower in females. These findings highlight distinguishable sex disparities in some MU characteristics and neuromuscular function, and suggest early interventions are needed for females to prevent functional deterioration to reduce the ageing health-sex paradox.

Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health.

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.

BACKGROUND: Surgical treatment has been increasingly performed in Hirayama disease (HD) patients to limit excessive neck flexion and restore cervical lordosis. However, postoperative recurrence of cervical lordosis loss may restart the progress of HD. Many studies have demonstrated a relationship between neck muscle strength and cervical lordosis, and it is widely accepted that leisure-time physical activity (LTPA) can increase muscle strength. However, there are few reports about the correlation between LTPA and maintenance of postoperative cervical curvature.
OBJECTIVE: To quantify the cervical lordosis and motor function before and after operation in HD patients and to analyze the impact of postoperative LTPA levels on the changes in these measurements.
METHODS: C2-7 Cobb were measured in 91 HD patients before, 2-5 days and approximately 2 years after operation. Motor unit number estimation (MUNE) and handgrip strength (HGS) were performed in all patients before and approximately 2 years after operation, and both cross-sectional area and fatty infiltration of posterior cervical muscles were measured in 62 patients. Long-form international Physical Activity Questionnaire and its different domains was administered to all patients at postoperative 2-year assessments.
RESULTS: The C2-7 Cobb was larger immediately and approximately 2 years after operation than that at preoperative assessment (P < 0.05). The preoperative to postoperative change in C2-7 Cobb was associated with postoperative changes in the symptomatic-side HGS and bilateral MUNE measurements (P < 0.05). Importantly, the patients performing LTPA had greater improvements in C2-7 Cobb from immediate to approximately 2 years after operation and greater C2-7 Cobb at last follow-up than those without LTPA, and postoperative improvements in both symptomatic-side MUNE measurements and symptomatic-side HGS were also greater in the former than in the latter (P < 0.05).
CONCLUSIONS: Postoperative LTPA has a positive effect on recovery/maintenance of cervical lordosis after operation, which may alleviate the motor unit loss of distal upper limbs in HD patients. Therefore, postoperative LTPA may be beneficial for postoperative rehabilitation or early conservative treatment of HD patients.

This study presents a novel compound muscle action potential (CMAP) examination of motor unit changes in paretic muscle post stroke. CMAP scan of the first dorsal interosseous (FDI) muscle was performed bilaterally in 16 chronic stroke subjects. Various parameters were derived from the CMAP scan to examine paretic muscle changes, including CMAP amplitude, D50, step index (STEPIX) and amplitude index (AMPIX). A significant decrease in CMAP amplitude and STEPIX was observed in paretic muscles compared with contralateral muscles (CMAP amplitude: paretic (9.0±0.5) mV, contralateral (11.3±0.9) mV, P=0.024; STEPIX: paretic 101.2±7.6, contralateral 121.9±6.5, P=0.020). No significant difference in D50 and AMPIX was observed between the paretic and contralateral sides (P>0.05). The findings revealed complex paretic muscle changes including motor unit degeneration, muscle fiber denervation, reinnervation and atrophy, providing useful insights to help understand neuromuscular mechanisms associated with weakness and other functional deterioration post stroke. The CMAP scan experimental protocols and the applied processing methods are noninvasive, convenient, and automated, offering practical benefits for clinical application.

This study investigated electromyography (EMG)-force relations using both simulated and experimental approaches. A motor neuron pool model was first implemented to simulate EMG-force signals, focusing on three different conditions that test the effects of small or large motor units located more or less superficially in the muscle. It was found that the patterns of the EMG-force relations varied significantly across the simulated conditions, quantified by the slope (b) of the log-transformed EMG-force relation. b was significantly higher for large motor units, which were preferentially located superficially rather than for random depth or deep depth conditions (p < 0.001). The log-transformed EMG-force relations in the biceps brachii muscles of nine healthy subjects were examined using a high-density surface EMG. The slope (b) distribution of the relation across the electrode array showed a spatial dependence; b in the proximal region was significantly larger than the distal region, whereas b was not different between the lateral and medial regions. The findings of this study provide evidence that the log-transformed EMG-force relations are sensitive to different motor unit spatial distributions. The slope (b) of this relation may prove to be a useful adjunct measure in the investigation of muscle or motor unit changes associated with disease, injury, or aging.

The adaptation of neural contractile properties has been observed in previous work. However, the neural changes on the motor unit (MU) level remain largely unknown. Voluntary movements are controlled through the precise activation of MU populations. In this work, we estimate the neural inputs from the spinal motor neurons to the muscles during isometric contractions and characterize the neural adaptation during training by comparing the MU properties decomposed from sprinters and nonathletes. Twenty subjects were recruited and divided into two groups. The high-density surface electromyography (EMG) signals were recorded from the lateralis vastus muscle during the isometric contraction of knee extension and were then decomposed into MU spike trains. Each MU\'s action potentials and discharge properties were extracted for comparison across subject groups and tasks. A total of 1097 MUs were identified from all subjects. Results showed that the discharge rates and amplitudes of MUAPs from athletes were significantly higher than those from nonathletes. These results demonstrate the neural adaptations in physical training at the MU population level and indicate the great potential of EMG decomposition in physiological investigations.

Objective.This study aims to characterize the motor units (MUs) distribution and recruitment pattern in the spastic and non-spastic bilateral biceps brachii muscles (BBMs) of chronic stroke survivors.Approach.High-density surface electromyography (HD-sEMG) signals were collected from both spastic and non-spastic BBMs of fourteen chronic stroke subjects during isometric elbow flexion at 10%, 30%, 50% and 100% maximal voluntary contractions (MVCs). By combining HD-sEMG decomposition and bioelectrical source imaging, MU innervation zones (MUIZs) of the decomposed MUs were first localized in the 3D space of spastic and non-spastic BBMs. The MU depth defined as the distance between the localized MUIZ and its normal projection on the skin surface was then normalized to the arm radius of each subject and averaged at given contraction level. The averaged MU depth at different contraction levels on a specific arm side (intra-side) and the bilateral depths under a specific contraction level (inter-side) were compared.Main results.The average depth of decomposed MUs increased with the contraction force and significant differences observed between 10% vs 50% (p< 0.0001), 10% vs 100% (p< 0.0001) and 30% vs 100% MVC (p= 0.0017) on the non-spastic side, indicating that larger MUs with higher recruitment threshold locate in deeper muscle regions. In contrast, no force-related difference in MU depth was observed on the spastic side, suggesting a disruption of orderly recruitment of MUs with increase of force level, or the MU denervation and the subsequent collateral reinnervation secondary to upper motor neuron lesions. Inter-side comparison demonstrated significant MU depth difference at 10% (p= 0.0048) and 100% force effort (p= 0.0026).Significance.This study represents the first effort to non-invasively characterize the MU distribution inside spastic and non-spastic bilateral BBM of chronic stroke patients by combining HD-sEMG recording, EMG signal decomposition and bioelectrical source imaging. The findings of this study advances our understanding regarding the neurophysiology of human muscles and the neuromuscular alterations following stroke. It may also offer important MU depth information for botulinum toxin injection in clinical post-stroke spasticity management.

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Recent development of surface electromyogram (sEMG) decomposition technique provides a good basis of decoding movements from individual motor unit (MU) activities that directly representing microscopic neural drives. How to interpret the function and contribution of each decomposed MU to macroscopic movements remains unclear. The objective of this study is to decode finger movement patterns by establishing a relationship between individual MU activities and movements. In this study, high-density sEMG (HD-sEMG) data were recorded by a 16 × 8 electrode array from finger extensor muscles of 10 subjects performing 10 finger movement patterns. The progressive FastICA peel-off algorithm was first applied to decompose the HD-sEMG data to obtain microscopic neural drives in terms of MU firing sequences and their corresponding action potential waveforms. Then, convolutional neural network was used for classification of the decomposed MUs by characterizing their spatial waveforms spanned over all channels of the array. On this basis, a fuzzy weighted decision strategy was designed to give a final decision of movement pattern recognition, where function of an individual MU was measured in the form of contributing into all movement patterns with different weights to solve the issue of MUs shared among multiple patterns due to muscle co-activation. The proposed method yielded an average accuracy approximating to 100%, and it outperformed other common MU-based methods or conventional myoelectric classification methods using macroscopic sEMG features (p <  0.05). The proposed method has a wide application prospect in the field of human-machine interaction and precise motor control.