Unit Spike Research Articles

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.

Voluntary co-contraction of ankle muscles alters motor unit discharge characteristics and reduces estimates of persistent inward currents.

Motoneuronal persistent inward currents (PICs) are facilitated by neuromodulatory inputs but are highly sensitive to local inhibitory circuits. Estimates of PICs are reduced by group Ia reciprocal inhibition, and increased with the diffuse actions of neuromodulators released during remote muscle contraction. However, it remains unknown how motoneurons function in the presence of simultaneous excitatory and inhibitory commands. To probe this topic, we investigated motor unit discharge patterns and estimated PICs during voluntary co-contraction of ankle muscles, which simultaneously demands the contraction of agonist-antagonist pairs. Twenty participants performed triangular ramps of both co-contraction (simultaneous dorsiflexion and plantar flexion) and isometric dorsiflexion to a peak of 30% of their maximum muscle activity from a maximal voluntary contraction. Motor unit spike trains were decomposed from high-density surface EMG activity recorded from tibialis anterior using blind source separation algorithms. Voluntary co-contraction altered motor unit discharge rate characteristics. Discharge rate at recruitment and peak discharge rate were modestly reduced (∼6% change; P<0.001; d=0.22) and increased (∼2% change; P=0.001, d=-0.19), respectively, in the entire dataset but no changes were observed when motor units were tracked across conditions. The largest effects during co-contraction were that estimates of PICs (ΔF) were reduced by ∼20% (4.47 vs. 5.57 pulses per second during isometric dorsiflexion; P<0.001, d=0.641). These findings suggest that, during voluntary co-contraction, the inhibitory input from the antagonist muscle overcomes the additional excitatory and neuromodulatory drive that may occur due to the co-contraction of the antagonist muscle, which constrains PIC behaviour. KEY POINTS: Voluntary co-contraction is a unique motor behaviour that concurrently provides excitatory and inhibitory synaptic input to motoneurons. Co-contraction of agonist-antagonist pairs alters agonist motor unit discharge characteristics, consistent with reductions in persistent inward current magnitude.

Acute intermittent hypoxia increases maximal motor unit discharge rates in people with chronic incomplete spinal cord injury.

Acute intermittent hypoxia (AIH) is an emerging technique for enhancing neuroplasticity and motor function in respiratory and limb musculature. Thus far, AIH-induced improvements in strength have been reported for upper and lower limb muscles after chronic incomplete cervical spinal cord injury (iSCI), but the underlying mechanisms have been elusive. We used high-density surface EMG (HDsEMG) to determine if motor unit discharge behaviour is altered after 15×60s exposures to 9% inspired oxygen, interspersed with 21% inspired oxygen (AIH), compared to breathing only 21% air (SHAM). We recorded HDsEMG from the biceps and triceps brachii of seven individuals with iSCI during maximal elbow flexion and extension contractions, and motor unit spike trains were identified using convolutive blind source separation. After AIH, elbow flexion and extension torque increased by 54% and 59% from baseline (P=0.003), respectively, whereas there was no change after SHAM. Across muscles, motor unit discharge rates increased by ∼4 pulses per second (P=0.002) during maximal efforts, from before to after AIH. These results suggest that excitability and/or activation of spinal motoneurons is augmented after AIH, providing a mechanism to explain AIH-induced increases in voluntary strength. Pending validation, AIH may be helpful in conjunction with other therapies to enhance rehabilitation outcomes after incomplete spinal cord injury, due to these enhancements in motor unit function and strength. KEY POINTS: Acute intermittent hypoxia (AIH) causes increases in muscular strength and neuroplasticity in people living with chronic incomplete spinal cord injury (SCI), but how it affects motor unit discharge rates is unknown. Motor unit spike times were identified from high-density surface electromyograms during maximal voluntary contractions and tracked from before to after AIH. Motor unit discharge rates were increased following AIH. These findings suggest that AIH can facilitate motoneuron function in people with incomplete SCI.

Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task.

The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of interspike interval (COVISI). We also quantified the post/pre ratio of motor units' coherence within delta, alpha, and beta bands. Force-matching improvements were accompanied by increased mean discharge rate and decreased COVISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% (TA) and ∼13% (FDI), with no delta or beta bands changes. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that short-term force-matching skill acquisition is mediated by attenuation of physiological tremor oscillations in the shared synaptic inputs. Supported by simulations, a plausible mechanism for alpha band reductions may involve spinal interneuron phase-cancelling descending oscillations. Therefore, during skill learning, the central nervous system acts as a matched filter, adjusting synaptic weights of shared inputs to suppress neural components unrelated to the specific task.

Decomposition strategy for surface EMG with few channels: a simulation study

Objective. In the specific use of electromyogram (EMG) driven prosthetics, the user’s disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability. Approach. The new framework contains a peel-off approach, a refining strategy for motor unit (MU) spike train and MU action potential and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework. Main results. The results show that the new algorithm has an average improvement of 19.97% in the number of MUs identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels. Significance. These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.

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.

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.

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Objective. The development of electrical pulse stimulations in brain, including deep brain stimulation, is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because the generation of neuronal firing in brain is a nonlinear process, investigating the characteristics of nonlinear dynamics induced by HFS could be helpful to reveal more mechanisms of brain stimulations. The aim of present study is to investigate the fractal properties in the neuronal firing generated by HFS. Approach. HFS pulse sequences with a constant frequency 100 Hz were applied in the afferent fiber tracts of rat hippocampal CA1 region. Unit spikes of both the pyramidal cells and the interneurons in the downstream area of stimulations were recorded. Two fractal indexes—the Fano factor and Hurst exponent were calculated to evaluate the changes of long-range temporal correlations (LRTCs), a typical characteristic of fractal process, in spike sequences of neuronal firing. Main results. Neuronal firing at both baseline and during HFS exhibited LRTCs over multiple time scales. In addition, the LRTCs significantly increased during HFS, which was confirmed by simulation data of both randomly shuffled sequences and surrogate sequences. Conclusion. The purely periodic stimulation of HFS pulses, a non-fractal process without LRTCs, can increase rather than decrease the LRTCs in neuronal firing. Significance. The finding provides new nonlinear mechanisms of brain stimulation and suggests that LRTCs could be a new biomarker to evaluate the nonlinear effects of HFS.

Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off.

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.

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.

Cluster Neuronal Firing Induced by Uniform Pulses of High-Frequency Stimulation on Axons in Rat Hippocampus.

High-frequency stimulation (HFS) of electrical pulse sequences has been used in various neuromodulation techniques to treat certain disorders. Here, we test the hypothesis that HFS sequences with purely periodic pulses could directly generate non-uniform firing in directly stimulated neurons. In vivo experiments were conducted in the rat hippocampal CA1 region. A stimulation electrode was placed on the alveus fibers, and a recording electrode array was inserted into the CA1 region upstream of the stimulation site. Antidromic-HFS (A-HFS) of 100 Hz pulses was applied to the alveus to antidromically activate the soma of pyramidal neurons around the recording site. By minimizing the interferences of population spikes, the evoked unit spikes of individual pyramidal neurons were obtained during A-HFS. Additionally, a computational model of pyramidal neuron was used to simulate the neuronal responses to A-HFS, revealing possible mechanisms underlying the different firing patterns. Of the total 54 pyramidal neurons recorded during 2-min 100 Hz A-HFS, 38 (70%) neurons fired in a cluster pattern with alternating periods of intensive spikes and silence. The remaining 16 (30%) neurons fired in a non-cluster pattern with regular spikes. Modeling simulations showed that under the situation of HFS-induced intermittent block, conduction failure and generation failure of action potentials along the axons resulted in the cluster and non-cluster firing. Sustained axonal A-HFS with periodic pulses can induce non-uniform firing in directly stimulated neurons. This finding provides new evidence for the nonlinear dynamics of neuronal firing, even under uniform stimulation.

Estimates of persistent inward currents in lower limb muscles are not different between inactive, resistance-trained, and endurance-trained young males.

Persistent inward currents (PICs) increase the intrinsic excitability of α-motoneurons. The main objective of this study was to compare estimates of α-motoneuronal PICs between inactive, chronic resistance-trained, and chronic endurance-trained young individuals. We also aimed to investigate whether there is a relationship in the estimates of α-motoneuronal PIC magnitude between muscles. Estimates of PIC magnitude were obtained in three groups of young individuals: resistance-trained (n = 12), endurance-trained (n = 12), and inactive (n = 13). We recorded high-density surface electromyography (HDsEMG) signals from tibialis anterior (TA), gastrocnemius medialis (GM), soleus (SOL), vastus medialis (VM), and vastus lateralis (VL). Then, signals were decomposed with convolutive blind source separation to identify motor unit (MU) spike trains. Participants performed triangular isometric contractions to a peak of 20% of their maximum voluntary contraction. A paired-motor-unit analysis was used to calculate ΔF, which is assumed to be proportional to PIC magnitude. Despite the substantial differences in physical training experience between groups, we found no differences in ΔF, regardless of the muscle. Significant correlations of estimates of PIC magnitude were found between muscles of the same group (VL-VM, SOL-GM). Only two correlations (out of 8) between muscles of different groups were found (TA-GM and VL-GM). Overall, our findings suggest that estimates of PIC magnitude from lower-threshold MUs at low contraction intensities in the lower limb muscles are not influenced by physical training experience in healthy young individuals. They also suggest muscle-specific and muscle group-specific regulations of the estimates of PIC magnitude.NEW & NOTEWORTHY Chronic resistance and endurance training can lead to specific adaptations in motor unit activity. The contribution of α-motoneuronal persistent inward currents (PICs) to these adaptations is currently unknown in healthy young individuals. Therefore, we studied whether estimates of α-motoneuronal PIC magnitude are higher in chronically trained endurance- and resistance-trained individuals. We also studied whether there is a relationship between the estimates of α-motoneuronal PIC magnitude of different lower limb muscles.

Functional force stimulation alters motor neuron discharge patterns.

Beneficial effects have been observed for mechanical vibration stimulation (MVS), which are mainly attributed to tonic vibration reflex (TVR). TVR is reported to elicit synchronized motor unit activation during locally applied vibration. Similar effects are also observed in a novel vibration system referred to as functional force stimulation (FFS). However, the manifestation of TVR in FFS is doubted due to the use of global electromyography (EMG) features in previous analysis. Our study aims to investigate the effects of FFS on motor unit discharge patterns of the human biceps brachii by analyzing the motor unit spike trains decoded from the high-density surface EMG. Eighteen healthy subjects volunteered in FFS training with different amplitudes and frequencies. One hundred and twenty-eight channel surface EMG was recorded from the biceps brachii and then decoded after motion-artifact removal. The discharge timings were extracted and the coherence between different motor unit spike trains was calculated to quantify synchronized activation. Significant synchronization within the vibration cycle and/or its integer multiples is observed for all FFS trials, which increases with increased FFS amplitude. Our results reveal the basic physiological mechanism involved in FFS, providing a theoretical foundation for analyzing and introducing FFS into clinical rehabilitation programs.

Estimates of persistent inward currents in lower limb motoneurons are larger in females than in males.

Noninvasive recordings of motor unit (MU) spike trains help us understand how the nervous system controls movement and how it adapts to various physiological conditions. The majority of participants in human and nonhuman animal physiology studies are male, and it is assumed that mechanisms uncovered in these studies are shared between males and females. However, sex differences in neurological impairment and physical performance warrant the study of sex as a biological variable in human physiology and performance. To begin addressing this gap in the study of biophysical properties of human motoneurons, we quantified MU discharge rates and estimates of persistent inward current (PIC) magnitude in both sexes. We decomposed MU spike trains from the tibialis anterior (TA), medial gastrocnemius (MG), and soleus (SOL) using high-density surface electromyography and blind source separation algorithms. Ten participants of each sex performed slow triangular (10 s up and down) isometric contractions to a peak of 30% of their maximum voluntary contraction. We then used linear mixed-effects models to determine if peak discharge rate and estimates of PICs were predicted by the fixed effects of sex, muscle, and their interaction. Despite a lack of sex-differences in peak discharge rates across all muscles, estimates of PICs were larger [χ2(1) = 6.26, P = 0.012] in females [4.73 ± 0.242 pulses per second (pps)] than in males (3.81 ± 0.240 pps). These findings suggest that neuromodulatory drive, inhibitory input, and/or biophysical properties of motoneurons differ between the sexes and may contribute to differences in MU discharge patterns.NEW & NOTEWORTHY Sex-related differences in motoneuron analyses have emerged with greater inclusion of female participants, however, mechanisms for these differences remain unclear. Estimates of persistent inward currents (i.e., ΔF) in motoneurons of the lower limb muscles were larger in females than in males. This suggests neuromodulatory drive, monoaminergic signaling, intrinsic motoneuron properties, and/or descending motor commands may differ between the sexes, which provides a potential mechanism underlying previously reported sex-related differences in motoneuron discharge patterns.

A peel-off convolution kernel compensation method for surface electromyography decomposition

Objective:Estimating discharge patterns of motor units by electromyography (EMG) decomposition has shown promising perspectives in neurophysiologic investigations and human–machine interfaces. However, the number of decoded motor units is still limited, especially under high excitation and noisy conditions, which is a major barrier to the broad application of EMG decomposition. In this work, we applied a peel-off decomposition strategy to the convolution kernel compensation (CKC) algorithm to extract more motor units non-invasively. Methods:EMG signals were firstly decomposed into motor unit spike trains (MUSTs) using the CKC. After each decomposition, the motor unit action potentials (MUAPs) were reconstructed and subtracted from the EMG signals. Then the same CKC decomposition was applied to the residual signals. This peel-off procedure was repeated until no valid motor units were identified. The proposed decomposition strategy was validated on synthetic EMG signals by convolving simulated MUSTs and experimentally extracted MUAPs under multiple excitations and noise conditions. Then the decomposition performance was evaluated on experimental data. Main results:Compared with the classic CKC method, the number of the identified motor units from synthetic signals was significantly increased by 7 to 43 in each decomposition. Moreover, the average agreement between identified MUST and ground truth was 0.80 ± 0.20, indicating a high decomposition accuracy. From experimental EMG signals, the peel-off method could identify more motor units (>20) with high confidence than the classic method (<5) across three excitation levels. Conclusion and Significance:These results demonstrate the efficiency of the proposed method in identifying more motor units from EMG signals, extending the potential applications of surface EMG decomposition for neural decoding.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants

Objective. High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants. Approach. We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train and validate the deep CNN. Main results. On average, the correlation coefficients between CST from the BSS and that from deep CNN were for leave-one-out across-sessions-and-muscles validation and for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at for cross validations across muscles, sessions, and participants. Significance. We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information.

Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

Common synaptic input between motor units from the lateral and medial posterior soleus compartments does not differ from that within each compartment.

The human soleus muscle is anatomically divided into four separate anatomical compartments. The functional role of this compartmentalization remains unclear. Here, we tested the hypothesis that the common synaptic input to motor units between the medial and lateral posterior compartments is less than within each compartment. Fourteen male participants performed three different heel-raise tasks that were considered to place a different mechanical demand on the medial and lateral soleus compartments. High-density electromyography (EMG) signals from the medial and lateral soleus compartments and the medial gastrocnemius of the right leg were decomposed into individual motor unit spike trains. The coherence between cumulative spike trains of the motor units was estimated. The coherence analysis was also repeated for motor units that were matched across all three tasks. Furthermore, we calculated the ratio of significant correlations between the spike trains of pairs of motor units. We observed that the coherence between motor units of the two soleus compartments was similar as the coherence between motor units within each compartment, regardless of the task. The correlation analysis performed on pairs of motor units confirmed these results. We conclude that the level of common synaptic input between the motor units innervating the medial and lateral posterior soleus compartment is not different than the common synaptic input between motor units innervating each of these compartments, which contrasts with findings from previous studies on finger muscles. This suggests that there is no independent neural control for the individual posterior soleus compartments.NEW & NOTEWORTHY The human soleus muscle is anatomically subdivided into four compartments. The functional role for this compartmentalization remains unknown. Here, we showed that, contrary to previous findings in finger muscles, the common synaptic input between motor units innervating the medial and lateral posterior soleus compartment was similar as that between motor units within the individual compartments. We suggest that the contradictory findings with other compartmentalized muscles may be explained by differences in muscle-tendon anatomy and function.

Acute Intermittent Hypoxia Increases Motor Unit Discharge Rates And Strength in Persons with SCI

<h3>Research Objectives</h3> To determine whether changes in motor unit discharge rates contribute to acute intermittent hypoxia (AIH)-induced improvements in volitional strength in persons with chronic incomplete spinal cord injury (iSCI) at the cervical level. <h3>Design</h3> Repeated-measures crossover study. <h3>Setting</h3> Rehabilitation hospital. <h3>Participants</h3> Seven individuals with iSCI completed two different sessions (AIH and SHAM) in a randomized order. <h3>Interventions</h3> AIH consisted of breathing 15 brief (∼60s) periods of low oxygen (9% O2) interspersed with 60s of normoxia (21% O2), whereas SHAM consisted of repeated exposures to normoxia. <h3>Main Outcome Measures</h3> We recorded high-density surface electromyograms from biceps and triceps brachii during maximal elbow flexion and extension torques, and decomposed these signals into individual motor unit spike trains before and 60 minutes after the interventions. <h3>Results</h3> After AIH, elbow flexion and extension torque increased by 54% (p = 0.0078; g = 0.58) and 59% (p = 0.04, g = 0.63) from baseline, respectively, whereas there was no change (i.e. < 10% change and p > 0.2 in both cases) after SHAM. Across muscles, motor unit discharge rates increased by 3.96 pulses per second (∼38%; p = 0.0015; G = 0.86) during maximal efforts, from pre to post AIH. Using generalized linear mixed models, we found that changes in motor unit discharge rates explained 54% (p = 0.0033) of the variance of changes in volitional torque. <h3>Conclusions</h3> These findings suggest that excitability and/or the activation of spinal motoneurons are augmented after AIH, providing a mechanism to explain AIH-induced increases in voluntary strength. Pending validation, AIH could potentially be used in conjunction with other therapies to enhance rehabilitation outcomes due to these enhancements in motor unit function and strength. <h3>Author(s) Disclosures</h3> The authors have nothing to disclose.

Compressed spike-triggered averaging in iterative decomposition of surface EMG

Background and ObjectiveAnalysis of motor unit activity is important for assessing and treating diseases or injuries affecting natural movement. State-of-the-art decomposition translates high-density surface electromyography (HDsEMG) into motor unit activity. However, current decomposition methods offer far from complete separation of all motor units. MethodsThis paper proposes a peel-off approach to automatic decomposition of HDsEMG into motor unit action potential (MUAP) trains, based on the Fast Independent Component Analysis algorithm (FastICA). The novel steps include utilizing compression by means of Principal Component Analysis and spike-triggered averaging, to estimate surface MUAP distributions with less noise, which are iteratively subtracted from the HDsEMG dataset. Furthermore, motor unit spike trains are estimated by high-dimensional density-based clustering of peaks in the FastICA source output. And finally, a new reliability measure is used to discard poor motor unit estimates by comparing the variance of the FastICA source output before and after the peel-off step. The method was validated using reconstructed synthetic data at three different signal-to-noise levels and was compared to an established deflationary FastICA approach. ResultsBoth algorithms had very high recall and precision, over 90%, for spikes from matching motor units, referred to as matched performance. However, the peel-off algorithm correctly identified more motor units for all noise levels. When accounting for unidentified motor units, total recall was up to 33 percentage points higher; and when accounting for duplicate estimates, total precision was up to 24 percentage points higher, compared to the state-of-the-art reference. In addition, a comparison was done using experimental data where the proposed algorithm had a matched recall of 97% and precision of 85% with respect to the reference algorithm. ConclusionThese results show a substantial performance increase for decomposition of simulated HDsEMG data and serve to validate the proposed approach. This performance increase is an important step towards complete decomposition and extraction of information of motor unit activity.