Motor Unit Action Potentials Research Articles

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

Quantification of high and low sEMG spectral components during sustained isometric contraction.

Superficial Electromyography (sEMG) spectrum contains aggregated information from several underlying physiological processes. Due to technological limitations, the isolation of these processes is challenging, and therefore, the interpretation of changes in muscle activity frequency is still controversial. Recent studies showed that the spectrum of sEMG signals recorded from isotonic and short‐term isometric contractions can be decomposed into independent components whose spectral features recall those of motor unit action potentials. In this paper sEMG spectral decomposition is tested during muscle fatigue induced by long‐term isometric contraction where sEMG spectral changes have been widely studied. The main goals of this work are to validate spectral component extraction during long‐term isometric muscle activation and the quantification of energy exchange between the low‐ and high‐frequency bands of sEMG signals during muscle fatigue.

Implications of median sensory study to the thumb and deltoid/biceps motor unit recruitment on identifying C6 root avulsion in upper neonatal brachial plexus palsy.

Anatomic representation suggests that a median sensory nerve conduction study recording the thumb (median D1 NCS) may effectively assess upper neonatal brachial plexus palsy (NBPP). We sought to determine the feasibility of technique, establish reference data, and assess its ability to: (a) identify focal upper plexus lesions; and (b) identify C6 root avulsion. In a secondary analysis, we explored the association between absence/presence of motor unit action potentials (MUAPs) during needle electromyography (EMG) of the deltoid and biceps brachii muscles and C6 avulsion status. A retrospective chart review was performed of surgical patients with severe upper NBPP who ultimately underwent surgical reconstruction (between 2017 and 2020). Median D1 sensory nerve action potential (SNAP) amplitude ranges were determined in affected and contralateral limbs and analyzed by C6 root avulsion status. Also, presence/absence of MUAPs during EMG of the deltoid and biceps brachii was compared between C6 avulsion patients and controls. Thirty-eight patients were included in our analysis. A median D1 NCS study was readily performed, showing a contralateral limb mean amplitude of 27.42 μV (range, 3.8-54.7μV). Most patients had a low ipsilateral median D1 SNAP amplitude, regardless of C6 avulsion status. Detectable MUAPs in either deltoid or biceps brachii on EMG were atypical in C6 root avulsion. The median D1 NCS identifies upper NBPP, but does not distinguish C6 avulsions from post-ganglionic lesions, likely due to the frequent co-occurrence of post-ganglionic axonal disruption. The presence of MUAPs on deltoid/biceps brachii EMG suggests C6 avulsion is unlikely.

Arthrogenic muscle inhibition manifests in thigh musculature motor unit characteristics after anterior cruciate ligament injury

ABSTRACT Joint trauma induces a presynaptic reflex inhibition termed arthrogenic muscle inhibition (AMI) that prevents complete activation of muscles. Reduced motor unit (MU) output is a hypothesised mechanism for persistent strength deficits. The objective of this study was to determine MU characteristics of thigh musculature and determine how they change with anterior cruciate ligament (ACL) injury compared to healthy controls. A randomised protocol of knee flexion/extension isometric contractions (10–50% maximal voluntary isometric contraction) was performed for each leg with surface EMG 5-pin array electrodes placed on the vastus medialis, vastus lateralis, semitendinosus and biceps femoris. Longitudinal assessments for average rate coding, recruitment thresholds and MU action potentials were acquired at 6-month intervals. With exception of the vastus medialis, all thigh musculature of ACL-injured demonstrated smaller MU action potential peak-to-peak amplitude. For average rate coding, ACL-injured demonstrated lower coding rates than Controls for the quadriceps (p < .05) and higher rates than Controls for the hamstrings (p < .05). These MU characteristics were different from Controls after ACL reconstruction up to 12 months post-surgery, yet maximal strength increased during this time frame. As thigh MU characteristics are known across phases of ACL rehabilitation, future studies can assess these patterns of motor control and their potential to determine risk of re-injury. Further, future rehabilitation can target specific intervention programmes to restore motor control. Highlights Motor unit strategies of arthrogenic muscle inhibition are characterised for the first time via decomposed EMG. Motor unit deficits of thigh musculature persist throughout all phases of ACL rehabilitation, even after return-to-sport. After ACL injury, motor unit sizes at similar recruitment thresholds were smaller than those of healthy controls.

Improved online decomposition of non-stationary electromyogram via signal enhancement using a neuron resonance model: a simulation study

Objective. Motor unit (MU) discharge information obtained via the online electromyogram (EMG) decomposition has shown promising prospects in multiple applications. However, the nonstationarity of EMG signals caused by the rotation (recruitment-derecruitment) of MUs and the variation of MU action potentials (MUAP) can significantly degrade the online decomposition performance. This study aimed to develop an independent component analysis-based online decomposition method that can accommodate the nonstationarity of EMG signals. Approach. The EMG nonstationarity can make the separation vectors obtained beforehand inaccurate, resulting in the reduced amplitudes of the peaks corresponding to firing events in the source signal (independent component) and then the decreased accuracy of firing events. Therefore, we utilized the FitzHugh–Nagumo (FHN) resonance model to enhance the firing peaks in the source signal in order to improve the decomposition accuracy. A two-session approach was used with the offline session to extract the separation vectors and train the FHN models. In the online session, the source signal was estimated and further processed using the FHN model before detecting the firing events in a real-time manner. The proposed method was tested on simulated EMG signals, in which MU rotation and MUAP variation were involved to mimic the nonstationarity of EMG recordings. Main results. Compared with the conventional method, the proposed method can improve the decomposition accuracy significantly (88.70% ± 4.17% vs. 92.43% ± 2.79%) by enhancing the firing peaks, and more importantly, the improvement was more prominent when the EMG signal had stronger background noises (87.00% ± 3.70% vs. 91.66% ± 2.63%). Conclusions. Our results demonstrated the effectiveness of the proposed method to utilize the FHN model to improve the online decomposition performance on the nonstationary EMG signals. Further development of our method has the potential to improve the performance of the neural decoding system that utilizes the MU discharge information and promote its application in the neural-machine interface.

Effects of hand muscle function and dominance on intra-muscle synergies.

The goal of the study was to explore the effects of hand dominance and muscle function (prime mover vs. supporting muscle) on recently discovered intra-muscle synergies as potential windows into their neural origin. Healthy right-handed subjects performed accurate cyclical force production tasks while pressing with the middle phalanges and distal phalanges of the fingers of the dominant and non-dominant hand. Surface electromyography was used to identify individual motor unit action potentials in two muscles, flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC). Stable motor unit groups (MU-modes) were defined in each muscle and in both muscles together. The composition of the MU-modes allowed linking them to the reciprocal and co-activation command. Force-stabilizing synergies were quantified in each hand and during force production at both sites using the framework of the uncontrolled manifold hypothesis. Force-stabilizing synergies were seen in the spaces of MU-modes from FDS and EDC separately, but not of MU-modes defined for both muscles together. Synergy indices were similar for both hands and both sites of force application. In contrast, force-stabilizing synergies in the space of finger forces were present in the non-dominant hand and absent in the dominant hand. The data suggest existence of distributed mechanisms of synergic control. Finger force synergies are likely to reflect functioning of subcortical loops involving the basal ganglia and cerebellum, while MU-mode synergies are likely to reflect spinal circuitry. Studies of both force-based and motor-unit-based synergies may be clinically valuable for distinguishing effects of spinal and supraspinal disorders.

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor.

Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor. Magnetic Resonance (MR) images were acquired to generate muscle fiber paths and to build the volume conductor. While the action potentials propagated along the identified curvilinear fibers, the conductivity tensor was approximated to be cylindrically anisotropic. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models with approximate or exact conductivity tensors were implemented. The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation 0.98, normalized root mean square error, nRMSE ≤ 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). Finally, the proposed model was used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark. The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths.

Abstract TP69: Change Over Time Feature Of A Range Of The Recruited Motor Unit In The Recovery Period Of Hemispheric Stroke

The paralysis after the stroke makes loss adjustment of the force in the voluntary movement and disturbs the physical activity. The voluntary force is regulated by the motor unit (MU) recruitment and firing rate. Therefore, we should evaluate MU firing patterns to understand a part of the mechanism of the force adjustment disorders of the paralysis side. It might be possible to evaluate the change of the force adjustment function on the paralysis side in the convalescence if we considered a range of the recruited MU over time. This pilot study hypothesizes that a range of the recruited MU spreads depending on a recovery stage. Two-stroke patients participated in this study. We measured the muscle activity of the biceps using surface EMG with three-time points during the recovery period after stroke. The raw EMG data were decomposed into motor unit action potential (MUAP). Then mean firing rate (MFR) and peak amplitude of MUAP were calculated by decomposed MUAP. Additionally, coefficients of the regression line were calculated by MFR and MUAP's peak amplitude. The coefficient of the linear regression line was adopted as the outcome to evaluate a range of the recruited MU size. We also analyzed the change over time of a range of the recruited MU size. The range of the recruited MU of the recovery stage changed over time but became wide during the recovery stage. These data showed that it varied from a restrictive style to a comprehensive recruitment style over time. Therefore, a range of the recruited MU with the stroke of the recovery stage was suggested to be a tendency to enlarge over time. Expanding the diachronic range of the recruited MU may let you enrich a variation of the force in the recovery process.

Automated Biomedical Signal Quality Assessment of Electromyograms: Current Challenges and Future Prospects

This work is an exposition of research supporting efforts to automate quality assessment in surface electromyography (sEMG). Electromyography measures electrical activity from skeletal muscles, which are the muscles associated with voluntary movements. Skeletal muscles can consist of tens to hundreds of thousands of contractile fibers which are grouped into functional units called Motor Units (MUs). Each MU is driven to contract its fibers by electrochemical impulses sent from a motor neuron, and a muscle may contain anywhere from tens to hundreds of MUs. When an MU is activated, all fibers associated with it are simultaneously activated. When fibers are activated, an electrochemical impulse propagates along each of them to cause contraction. This impulse is known as a Single Fiber Action Potential (SFAP). A Motor Unit Action Potential (MUAP) is the summation of all SFAPs corresponding to an activated MU. To generate a continuous contraction, MUs are repeatedly activated, producing a train of MUAPs, and this pulse train is called the Motor Unit Action Potential Train (MUAPT). The combined electrical activity of all of the MUAPTs associated with a contraction, as recorded on the surface of the skin using non-invasive electrodes, is a surface electromyography (sEMG) signal. In general, it is not possible to directly observe the SFAPs, MUAPs, and MUAPTs non-invasively (though there have been algorithmic efforts to decompose sEMG signals into its constituent MUAPTs). SFAPs and MUAPTs can be observed by inserting needle electrodes into specific muscle regions for high spatial selectivity; however, such techniques are invasive, making surface measurements a desirable alternative. sEMG signals have been used in a wide variety of applications, including fatigue assessment, myoelectric control, diagnosis of neuromuscular disorders, and tracking performance in sports.

Automatic detection of F waves and F-MUNE in two types of motor neuron diseases.

Motor unit number estimation by F waves (F-MUNE) is an uncommonly used MUNE technique. Our aim in this study was to assess the sensitivity of F-MUNE values elicited with newly developed software in motor neuron diseases. F waves were recorded by 300 submaximal stimuli from abductor digiti minimi and abductor pollicis brevis muscles of 35 patients with amyotrophic lateral sclerosis, 18 with previous poliomyelitis, and 20 controls. The software determined the surface motor unit action potentials (sMUAPs) and calculated the F-MUNE values. Compound muscle action potential scans were also recorded to obtain MScanFit. The sMUAP amplitudes were higher and F-MUNE values were lower in both muscles of the patients when compared with controls. F-MUNE values could distinguish patients from controls. Significant correlations were found between F-MUNE and MScanFit in the patient groups. The new F-MUNE software offered promising results in revealing motor unit loss caused by motor neuron diseases.

Unilateral transfemoral amputees exhibit altered strength and dynamics of muscular co-activation modulated by visual feedback

Objective. Somatosensory perception is disrupted in patients with a lower limb amputation. This increases the difficulty to maintain balance and leads to the development of neuromuscular adjustments. We investigated how these adjustments are reflected in the co-activation of lower body muscles and are modulated by visual feedback. Approach. We measured electromyography (EMG) signals of muscles from the trunk (erector spinae and obliquus external), and the lower intact/dominant leg (tibialis anterior and medial gastrocnemius) in 11 unilateral transfemoral amputees and 11 age-matched able-bodied controls during 30 s of upright standing with and without visual feedback. Muscle synergies involved in balance control were investigated using wavelet coherence analysis. We focused on seven frequencies grouped in three frequency bands, a low-frequency band (7.56 and 19.86 Hz) representing more sub-cortical and spinal inputs to the muscles, a mid-frequency band (38.26 and 62.63 Hz) representing more cortical inputs, and a high-frequency band (92.90, 129 and 170.90 Hz) associated with synchronizing motor unit action potentials. Further, the dynamics of changes in intermuscular coupling over time were quantified using the Entropic Half-Life. Main results. Amputees exhibited lower coherency values when vision was removed at 7.56 Hz for the muscle pair of the lower leg. At this frequency, the coherency values of the amputee group also differed from controls for the eyes closed condition. Controls and amputees exhibited opposite coherent behaviors with visual feedback at 7.56 Hz. For the eyes open condition at 129 Hz, the coherency values of amputees and controls differed for the muscle pair of the trunk, and at 170.90 Hz for the muscle pair of the lower leg. Amputees exhibited different dynamics of muscle co-activation at the low frequency band when vision was available. Significance. Altogether, these findings point to the development of neuromuscular adaptations reflected in the strength and dynamics of muscular co-activation.

Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study.

Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation. We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated. Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of signals. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels. The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix. Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.

Concordance between the international standards for neurological classification of spinal cord injury motor examination and needle electromyography findings in muscles with a motor power grade of zero or trace

Context/Objective To evaluate the accuracy of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) motor examination in individuals with spinal cord injury (SCI) with motor grade 0 or 1 and analyze its degree of concordance with needle electromyography (EMG) findings for each key muscle. Design Retrospective study. Setting University hospital in Goyang, Korea. Participants Individuals with SCI admitted to the Department of Rehabilitation from January 2013 to June 2019. Interventions In the enrolled persons, needle EMG was performed on muscles with motor grade 0 or 1 on ISNCSCI examination, and muscle contraction was confirmed through the detection of motor unit action potential. Outcome measures The agreement between motor examination and needle EMG findings was analyzed. Results In 175 key muscles, needle EMG findings in 115 and 60 muscles evaluated as grades 0 and 1 on ISNCSCI examination showed 80% and 50% agreements, respectively. We found a fair agreement between motor examination and needle EMG findings (κ = 0.309, P < 0.0001). Moreover, statistically significant agreement was seen only in T1, L2, and S1 key muscles (κ = 1, P < 0.0001; κ = 0.359, P = 0.019; and κ = 0.521, P = 0.004, respectively). Conclusions It is important to accurately distinguish between grade 0 and 1 motor power to maximize the positive outcomes from rehabilitation treatment and predict the possibility of recovery in individuals with SCI. Therefore, to improve the accuracy of motor examination and the American Spinal Injury Association Impairment Scale, needle EMG confirmation could be considered for muscles with motor grade 0 or 1 in individuals with SCI.

A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.

Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. With the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. The proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. With the proposed deep CNN, we could potentially build a neural-drive-based human-machine interface that is generalizable to different contraction tasks without retraining.

Effects of muscle shortening on single-fiber, motor unit, and compound muscle action potentials

Even under isometric conditions, muscle contractions are associated with some degree of fiber shortening. The effects of muscle shortening on extracellular electromyographic potentials have not been characterized in detail. Moreover, the anatomical, biophysical, and detection factors influencing the muscle-shortening effects have been neither identified nor understood completely. Herein, we investigated the effects of muscle shortening on the amplitude and duration characteristics of single-fiber, motor unit, and compound muscle action potentials. We found that, at the single-fiber level, two main factors influenced the muscle-shortening effects: (1) the electrode position and distance relative to the myotendinous zone and (2) the electrode distance to the maxima of the dipole field arising from the stationary dipole created at the fiber-tendon junction. Besides, at the motor unit and muscle level, two additional factors were involved: (3) the overlapping between the propagating component of some fibers with the non-propagating component of other fibers and (4) the spatial spreading of the fiber-tendon junctions. The muscle-shortening effects depend critically on the electrode longitudinal distance to the myotendinous zone. When the electrode was placed far from the myotendinous zone, muscle shortening resulted in an enlargement and narrowing of the final (negative) phase of the potential, and this enlargement became less pronounced as the electrode approached the fiber endings. For electrode locations close to the myotendinous zone, muscle shortening caused a depression of both the main (positive) and final (negative) phases of the potential. Beyond the myotendinous zone, muscle shortening led to a decrease of the final (positive) phase. The present results provide reference information that will help to identify changes in MUPs and M waves due to muscle shortening, and thus to differentiate these changes from those caused by muscle fatigue.Graphical abstract

Which Psychological and Electrodiagnostic Factors Are Associated With Limb Disability in Patients With Carpal Tunnel Syndrome?

It has been observed that patients with carpal tunnel syndrome (CTS) who also experience emotional distress, depression, or anxiety report more severe symptoms. As patients' own perspectives about their health increasingly are guiding treatment decisions, it seems important to study the simultaneous association of psychological distress and neuropathology with hand disability in patients who have CTS, as this may help prioritize and sequence management steps. What are the relationships among validated scores for (1) depression, (2) anxiety, (3) pain catastrophizing, and (4) nerve electrodiagnostic severity with measures of hand disability in patients with confirmed CTS? Between 2017 and 2019, we evaluated 116 patients for CTS in a referral urban hospital in Mashhad, Iran. Of those, we considered 85% (99) as potentially eligible by considering the following Electromyography-Nerve Conduction Study (EMG-NCS) diagnostic criteria: sensory latency ≥ 3.5 Ms, median-ulnar latency difference ≥ 0.5 Ms, motor latency ≥ 4.2 Ms, and abnormal EMG findings in the opponens pollicis muscle (neurogenic motor unit action potentials, positive sharp waves, or fibrillation). A further 13% (15 of 116) were excluded because of nonidiopathic CTS and prior surgery, and another 12% (14 of 116) were lost because of incomplete datasets, leaving 60% (70 of 116) for final inclusion in this cross-sectional study. In all, 89% of patients were women with total mean age of 47 years. We measured depression and anxiety using the Hospital Anxiety and Depression Scale (HADS) questionnaire (scored from 0 to 21, with a minimum clinically important difference [MCID] of 1.7 points), and we evaluated patients' state of mind regarding pain using the Pain Catastrophizing Scale (PCS) (scored from 0 to 52). Higher scores on these questionnaires represent more distress and pain catastrophizing. Hand disability was assessed with Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) questionnaire outcomes (scored from 0 [no disability] to 100 [most severe disability]; MCID of 15 points), Likert pain score (from 0 to 10), and grip/pinch dynamometry results. Correlational analyses were conducted once among HADS and PCS scores and again among EMG-NCS indices with pain and disability variables to answer our first, third, and fourth questions, respectively. Regression analysis was performed to assess the percentage of variance in QuickDASH and pain severity, which could be explained by psychological and electrodiagnostic factors. We did not include grip and pinch in our multivariable model (regression analysis) as dependent variables because they did not correlate with any of psychological or EMG-NCS variables (all p values > 0.1). Significance was set at p < 0.05. Correlational analysis showed that the scores of all three psychological questionnaires correlated with the QuickDASH score (r = 0.50, 0.42, and 0.53 for HADS-A, HADS-D, and PCS, respectively; p < 0.001 for all three), while EMG-NCS parameters had no correlation with QuickDASH and pain scores. We also found that 37% of the variance in QuickDASH score can be explained by HADS and PCS scores (r2 = 0.37; p < 0.001). Evaluation and treatment of psychological distress before deciding on elective surgery for CTS is important because patient-reported disability-often used as a factor in surgical decision-making-is substantially correlated with emotional distress. Future prospective, controlled studies on this topic are recommended; ideally, these should evaluate psychological interventions specifically to ascertain whether they improve patients' ratings of hand disability. Level III, prognostic study.

Decoding muscle force from individual motor unit activities using a twitch force model and hybrid neural networks

A deep learning method involving hybrid neural networks incorporated with a physiological twitch force model is presented for decoding muscle force from individual motor unit (MU) activities. A series of MU action potential (MUAP) trains were obtained using a progressive FastICA peel-off algorithm through high-density surface electromyogram (HD-sEMG) decomposition. The twitch force model was adopted to transform MUAP trains into twitch force sequences. The hybrid convolutional neural networks (CNNs) consisting of a spatial attention model (SA-CNN) and a bidirectional gated recurrent unit (Bi-GRU) were fed by the twitch force sequences to predict the muscle force. The performance of the proposed method was evaluated in predicting the thumb abduction force using both simulated data and experimental HD-sEMG recordings from abductor pollicis brevis muscles of ten subjects. The simulation approach assisted our method design in confirming a simplified relationship between MUAP amplitude and force contribution of each MU. When processing experimental data, the root mean square deviation (RMSD) values between the predicted and the actual forces were 6.57% ± 1.26% and 7.18% ± 0.96% under the user-specific testing scheme and the user-independent testing scheme, respectively. The proposed method outperformed other common sEMG amplitude-based or MU-based methods with significantly decreased RMSD values (p < 0.001). These findings indicate the advance of mining spatial information from individual MU activities using deep neural networks in decoding the muscle force. This study provides a useful tool for muscle force estimation, with wide applications in biomechanics and sports rehabilitation.

Reciprocal and coactivation commands at the level of individual motor units in an extrinsic finger flexor-extensor muscle pair.

We explored the synergic organization of motor units in extrinsic finger muscles, flexor digitorum superficialis (FDS), and extensor digitorum communis (EDC). Healthy subjects produced accurate cyclical force by pressing with the middle phalanges of one of the three fingers (Index, Middle, and Ring) and all three together. Two wireless sensor arrays were used to record and identify motor unit action potentials in FDS and EDC. Stable motor unit groups were identified within each muscle and across both muscles. Analysis of motor units combined over the two muscles showed one of the first two motor unit groups with consistently opposite signs of the loading factors for the FDS and EDC motor units, and the other group with consistently same signs of the loading factors for the two muscles. We interpret the two motor unit groups as reflections of the reciprocal and co-activation commands within the theory of control with spatial referent coordinates. Force changes within the cycle were primarily associated with the modulation of the co-activation motor unit group. Analysis of inter-cycle variance within the spaces of motor unit groups defined for FDS and EDC separately showed force-stabilizing synergies across both single-finger and three-finger tasks. In contrast, analysis within the motor unit groups defined across both muscles failed to show force-stabilizing synergies. We interpret these results as a reflection of the trade-off across levels within a hierarchical control system.

Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach.

High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a noninvasive model for estimating the depth of motor unit. We find that the depth of motor unit is linearly related to the Gaussian RMS width fitted by data points extracted from motor unit action potential. Simulated and experimental signals are used to evaluate the model performance. The correlation coefficient between reference depth and estimated depth is 0.92 ± 0.01 for simulated motor unit action potentials. Due to the symmetric nature of our model, no significant decrease is detected during the electrode selection procedure. We further checked the estimation results from decomposed motor units, the correlation coefficient between reference depth and estimated depth is 0.82 ± 0.07. For experimental signals, high discrimination of estimated depth vector is detected across gestures among trials. These results show the potential for a straightforward assessment of depth of motor units inside muscles. We discuss the potential of a non-invasive way for the location of decomposed motor units.

The force-generation capacity of the tibialis anterior muscle at different muscle\u2013tendon lengths depends on its motor unit contractile properties

PurposeMuscle–tendon length can influence central and peripheral motor unit (MU) characteristics, but their interplay is unknown. This study aims to explain the effect of muscle length on MU firing and contractile properties by applying deconvolution of high-density surface EMG (HDEMG), and torque signals on the same MUs followed at different lengths during voluntary contractions.MethodsFourteen participants performed isometric ankle dorsiflexion at 10% and 20% of the maximal voluntary torque (MVC) at short, optimal, and long muscle lengths (90°, 110°, and 130° ankle angles, respectively). HDEMG signals were recorded from the tibialis anterior, and MUs were tracked by cross-correlation of MU action potentials across ankle angles and torques. Torque twitch profiles were estimated using model-based deconvolution of the torque signal based on composite MU spike trains.ResultsMean discharge rate of matched motor units was similar across all muscle lengths (P = 0.975). Interestingly, the increase in mean discharge rate of MUs matched from 10 to 20% MVC force levels at the same ankle angle was smaller at 110° compared with the other two ankle positions (P = 0.003), and the phenomenon was explained by a greater increase in twitch torque at 110° compared to the shortened and lengthened positions (P = 0.002). This result was confirmed by the deconvolution of electrically evoked contractions at different stimulation frequencies and muscle–tendon lengths.ConclusionHigher variations in MU twitch torque at optimal muscle lengths likely explain the greater force-generation capacity of muscles in this position.