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Objective: Ultrasound (US) images during a muscle contraction can be decoded into individual motor unit (MU) activity, i.e., trains of neural discharges from the spinal cord. However, current decoding algorithms assume a stationary mixing matrix, i.e. equal mechanical twitches at each discharge. This study aimed to investigate the accuracy of these approaches in non-ideal conditions when the mechanical twitches in response to neural discharges vary over time and are partially fused in tetanic contractions.

Methods: We performed an in silico experiment to study the decomposition accuracy for changes in simulation parameters, including the twitch waveforms, spatial territories, and motoneuron-driven activity. Then, we explored the consistency of the in silico findings with an in vivo experiment on the tibialis anterior muscle at varying contraction forces.

Results: A large population of MU spike trains across different excitatory drives, and noise levels could be identified. The identified MUs with varying twitch waveforms resulted in varying amplitudes of the estimated sources correlated with the ground truth twitch amplitudes. The identified spike trains had a wide range of firing rates, and the later recruited MUs with larger twitch amplitudes were easier to identify than those with small amplitudes. Finally, the in silico and in vivo results were consistent, and the method could identify MU spike trains in US images at least up to 40% of the maximal voluntary contraction force.

Conclusion: The decoding method was accurate irrespective of the varying twitch-like shapes or the degree of twitch fusion, indicating robustness, important for neural interfacing applications.

Initial doublet discharges are important for quickly producing force due to the catch-like property of muscles, resulting in a non-linear summation of motor unit twitch forces (Burke et al., 1976). The doublet literature regarding healthy humans has focused primarily on repetitive doublets close to the minimal rhythmic firing rate (Bawa and Calancie, 1983) and ballistic contractions with high force levels (Desmedt and Godaux, 1977). However, whether a fast onset of synaptic input at lower force levels is sufficient to produce initial doublets is unclear. We performed a human experiment comprising a slow ramp followed by superimposed fast sinusoidal movements (5-15% MVC-range) at low force levels using the tibialis anterior while recording multichannel surface electromyography. Initial doublets were produced in some higher-threshold units that were phasically active during the superimposed sinusoidal contractions (mean inter-spike interval 9.9 ms). Motor units that were tonically active throughout the contraction did not exhibit doublets. In parallel intracellular recordings of high-threshold sacral motoneurons in adult mice, initial doublets (8-10 ms) were also produced in response to large rectangular current pulses (>3 nA), which allowed the afterdepolarization (AD) in the first action potential to reach the firing threshold. Once the afterhyperpolarization (AHP) was activated after the doublet spike, the size of subsequent afterdepolarizations was reduced, and no further doublets were produced. The outward SK currents activated during the AHP likely counteract the inward voltage-activated Ca+2 currents mediating the AD to restrict doublet discharges to the first action potential, thereby producing a firing pattern that maximizes force production.

Ultrasound (US) research scanners have recently been used to obtain motor unit (MU) activity. To improve clinical applicability, this study investigates whether similar MU activity can be detected using clinical US scanners.

A tibialis anterior muscle was simultaneously scanned using clinical US and surface electromyography (sEMG). Tissue velocity imaging (TVI) data was estimated from B-mode images, and spatial maps and twitch profiles were estimated from the TVI using spike-triggered averaging (STA).

The MU action potentials obtained from sEMG and US-derived spatial maps were estimated to be at approximately the same location. This demonstrates that clinical US may offer an accessible alternative for MU research

Neural source separation enables the extraction of individual spike trains from complex electrophysiological recordings. When applied to electromyographic (EMG) signals, it provides a unique window into the motor output of the nervous system by isolating the spiking activity of motor units (MUs). MU decomposition from EMG signals is currently the only scalable neural interfacing approach available in behaving humans and has become foundational in motor neuroscience and neuroprosthetics. However, unlike related domains such as spike sorting or electroencephalography (EEG) analysis, decomposition of EMG signals lacks open benchmarks that reflect the diversity of muscles, movement contexts, and noise sources encountered in practice. To address this gap, we introduce MUniverse, a modular simulation and benchmarking suite for decomposing EMG signals into individual MU spiking activity. MUniverse provides: (1) a simulation stack with a user-friendly interface to a state-of-the-art EMG generator; (2) a curated library of datasets across synthetic, hybrid synthetic-real data with ground truth spikes, and experimental EMG; (3) a set of internal and external decomposition pipelines; and (4) a unified benchmark with well-defined tasks, standard evaluation metrics, and baseline results from established decomposition pipelines. MUniverse is designed for extensibility, reproducibility, and community use, and all datasets are distributed with standardised metadata (Croissant, BIDS). By standardising evaluation and enabling dataset simulation at scale, MUniverse aims to catalyze progress on this long-standing neural signal processing problem.

Stretch reflex responses counteract sudden perturbations, and modulation of reflex gains can facilitate voluntary movement. Recent studies suggest movement preparation includes goal-directed tuning of muscle spindles and an equivalent modulation of both short- and long-latency stretch reflex responses (SLR and LLR), as long as the preparatory delay between ‘Cue’ and ‘Go’ exceeds 250 ms. The current study aimed to clarify the minimal preparation time required for goal-directed modulation of SLR and LLR responses and to determine how such modulation progressively evolves with extended preparation. We recorded bipolar electromyographic signals of healthy participants to assess reflex responses to mechanical perturbations induced by a robotic manipulandum in the context of a delayed-reach task. Specifically, we examined how multiple preparatory delays (250, 300, 350, 400, 450, and 500 ms) impact the goal-directed modulation of SLR and LLR responses from the loaded or unloaded pectoralis major, anterior deltoid, and posterior deltoid muscles. We found that preparatory delays of 300 ms and 350 ms are sufficient for goal-directed tuning of SLR responses in the posterior deltoid and pectoralis muscles, respectively. Our results also suggest that unloading (i.e., antagonist loading) may facilitate both the earlier emergence and more robust expression of goal-directed SLR tuning. Goal-directed tuning of LLR responses emerged as early as 250 ms of preparation, and such tuning was robust against muscle load conditions, in line with previous findings. We observed no consistent increase in SLR tuning at preparation delays that extended beyond the required minimum, whereas such enhancement was observed at the LLR epoch. These findings clarify the temporal characteristics of goal-directed stretch reflex gains, which likely emerge through the interplay of multiple feedback mechanisms.

Objective: Identifying the spiking activity of alpha motor neurons (MNs) non-invasively is possible by decomposing signals from active muscles, e.g., obtained with surface electromyography (EMG) or ultrasound. The theoretical background of MN identification using these techniques is convolutive blind source separation (cBSS), in which different algorithms have been developed and validated. However, the existence and identifiability of inverse solutions and the corresponding estimation errors are not fully understood. In addition, the guidelines for selecting appropriate parameters are often built on empirical observations, limiting the translation to clinical applications and other modalities.

Approach: We revisited the cBSS model for EMG-based MN identification, augmented it with new theoretical insights and derived a framework that can predict the existence of solutions for spike train estimates. This framework allows the quantification of source estimation errors due to the imperfect inversion of the motor unit action potentials (MUAP), physiological and non-physiological noise, and the ill-conditioning of the inverse problem. To bridge the gap between theory and practice, we used computer simulations.

Main results: (1) Increasing the similarity of MUAPs or the correlation between spike trains increases the bias for detecting MN spike trains linked with high amplitude MUAPs. (2) The optimal objective function depends on the expected spike amplitude, spike amplitude statistics and the amplitude of background spikes. (3) There is some wiggle room for MN detection given non-stationary MUAPs. (4) There is no connection between MUAP duration and extension factor, in contrast to previous guidelines. (5) Source quality metrics like the silhouette score (SIL) or the pulse-to-noise ratio (PNR) are highly correlated with a source's objective function output. (6) Considering established source quality measures, SIL is superior to PNR.

Significance: We expect these findings will guide cBSS algorithm developments tailored for MN identification and translation to clinical applications.

Objective: Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times.

Methods: Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth.

Results: 140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin.

Conclusion: The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. Significance: The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

Ultrasound can detect the activity of a large population of motoneurons, which may be used for neural interfacing purposes. Detecting motor unit (MU) spike trains from ultrafast ultrasound (US) images was first introduced using a linear blind source separation (BSS) method focused on instantaneous mixtures to provide an optimal spatial filter. Although this approach can accurately identify the location and average twitch of MUs, it has low spike train detection accuracy because it does not include the temporal evolution in the separation process.A solution was to use convolutive BSS, which has shown a very high spike train agreement for a large population of MUs in superficial and deep muscle parts. However, the assumption of equal successive twitches may not be fully accurate, as previous studies showed. Therefore, how the accuracy of the BSS algorithm is affected by varying twitch shapes needs to be clarified. In addition, a related question is whether the degree of fusion of the tetanic contraction reflects the accuracy of the decoding algorithm.In this work, we aimed to investigate the accuracy of the convolutive BSS method in estimating MU spike trains in US images comprised of varying twitch-like shapes in response to neural discharges of each MU and a varying degree of fusion of the tetanic contraction. For these purposes, we performed 30-second in-silico experiments based on a MU recruitment model using current knowledge about the experimental spatial distributions and twitch characteristics of MUs.We found that we could identify a large population of MU spike trains across different excitatory drive and noise levels, even when the individual MU had varying twitch-like shapes. The identified MU spike trains with varying twitch-like shapes resulted in varying amplitudes of the estimated sources, as opposed to equal twitch-like shapes, which resulted in estimated sources with similar amplitudes, and these varying amplitudes were correlated with the ground truth amplitudes of the twitches. The identified spike trains had a wide range (up to 35 Hz), i.e., the method is not selective to a higher degree of fusion. The spike train of MUs with larger twitch amplitudes was easier to identify than small amplitude ones unless the relative twitch amplitudes were not too large.Finally, we explored the consistency of the findings from the in-silico experiment with an in-vivo experiment on the TA muscle using thin- film intramuscular EMG as a reference for MU detection. We found a high spike train agreement between MU spike trains from US and EMG, as well as many spike trains not matched with EMG from 5% of maximum voluntary isometric force (MVIC) up to 40% MVIC. These identified MU spike trains showed features consistent with those in the in-silico experiments.These findings suggest the robustness of the BSS method for identifying MU spike trains under varying successive twitch-like shapes, degrees of fusion, and force levels.

Ultrasound imaging can be used to non-invasively assess muscle structure, musculoskeletal properties, and, more recently, neuromechanics in vivo. This technology can provide great spatial and temporal resolution, opening exciting avenues for investigating health, disease, and neural interfacing technology. This talk will build upon state-of-the-art ultrasound imaging technology and discuss future perspectives and translational capabilities of ultrasound imaging for the neuromechanical characterisation of muscle tissue.An ultrasound transducer on the skin parallel to the muscle fibres can be used to detect and analyse the muscle-tendon unit, muscle thickness, pennation angle, fascicle length, aponeuroses and muscle gearing. This is usually performed using a clinical ultrasound scanner with B-mode (grayscale) imaging, making it accessible to researchers, clinicians, etc. On the other hand, these scanners operate at relatively low frame rates and do not enable access to raw data to calculate displacement fields. These displacement fields are important for identifying transient events like the subtle displacements of muscle fibres in response to the neural discharges of a single motoneuron. Thus, for these applications, a programmable ultrasound research system is used. Moreover, the ultrasound transducer is usually placed perpendicular to the fibres to increase the identification yield.The above cannot all be done simultaneously due to probe positioning. However, it would enable the study of the musculoskeletal structure and properties along with the neuromechanical properties and motoneuron spike trains. Here, I will present the advancements in 3D imaging that could be applied and how they could further enable the study of dynamic contractions. For some translational activities, these systems and probes are too bulky, leading to the incentives for the rise of wearable systems. Finally, I will discuss the feasibility of studying neuromechanics and identifying neural spike trains using a clinical system through an innovative post- processing method. Such a method would increase the accessibility of neural information since a programmable ultrasound research system is currently needed.

Objective: Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g., the active MU displacements, arterial pulsations, bones, connective tissue, and noise.

Approach: This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e., the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings.

Main results: We found that the computational time for velBSS was at least 20 times less than for stICA, while the twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13).

Significance: The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.