Ultrasound-guided motor unit scanning electromyography

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

The value of ultrasound-guidance of nerves and muscles for patient tolerance and parameters electrodiagnostic studies

Objective

To assess impact of ultrasound guidance (USG) on patient's perception of nerve conduction studies (NCS).

Methods

In this single-center, randomized, sham-controlled, parallel, single-blind trial, we evaluated ultrasound (US) in identifying NCS stimulation site. Consecutive adults (18-80 old) without neuropathy referred for NCS were electronically randomized 1:1 to USG or Sham US. The primary outcome was sensory supramaximal intensity (SSMI) for each site/nerve; motor supramaximal intensity (MSMI), amplitudes, number of non-routine muscle punctured, Visual Analogue Scale (VAS), satisfaction were secondary outcomes.

Results

290 participants were randomized, with 145 in the USG and 144 Sham US groups, respectively. No difference in SSMI, CMAP or SNAP, VAS, satisfaction was recorded. With USG, the median at the elbow and fibular MMSI were lower (p = 0.04; p = 0.02). With normal NCS or overweight and obese subgroups patients had lower median SSMI (p = 0.05/ p = 0.02), higher median and sural SNAP with normal NCS (p = 0.04; p = 0.007) and the sural SNAP for the expert US subgroup (p = 0.02).

Conclusions

USG is useful for nerves, that are anatomically variable or in obesity. The sural SNAP gain with US in the normal NCS subgroup could facilitate routine NCS.

Significance

In standard NCS the USG does not modify the patient's tolerance.Trial Registration: clinicaltrials.gov (NCT03868189).

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A comparative study with intramuscular and surface EMG

The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images, using optimal separation spatial filters. However, this approach has yet to be fully compared with EMG techniques for a large population of unique MU spike trains. Here we identify individual MU activity in UUS images using the BSS method for 401 MU spike trains from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS method's ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 ± 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the convolutive and partly non-linear summation of velocities for recovering the full spike trains.