<scp>Ultrasound‐guided</scp> motor unit scanning electromyography

Maitland, Stuart; Hall, Julie; McNeill, Andrew; Stenberg, Ben; Schofield, Ian; Whittaker, Roger

doi:10.1002/mus.27720

Cited by 6 publications

(5 citation statements)

References 30 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these parameters can be measured experimentally. For example, the location and the fibre length of a motor unit could be estimated by using ultrasound [22, 23]. The location of the innervation zone and the muscle fibre conduction velocity can be estimated, for example by using high-density EMG recordings [24, 25].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Ma,

Guerra,

Caillet

et al. 2023

Preprint

View full text Add to dashboard Cite

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, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three 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, such as 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. Here we also provide representative applications of NeuroMotion. We first show how simulated 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 regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate 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 full-spectrum model will complement experimental approaches and facilitate neuromechanical research.Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Some of these parameters can be measured experimentally. For example, the location and the fibre length of a motor unit could be estimated by using ultrasound [22,23].…”

Section: True / False Generatormentioning

confidence: 99%

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Ma,

Guerra,

Caillet

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The most commonly chosen muscles were the psoas and teres pronator muscles (40 versus 1 and 21 versus 13, respectively), the other muscles were too infrequently punctured to identify a difference between the two groups. USG muscle has been used to facilitate muscle biopsies increase the accuracy of standard needle electromyography (EMG) for non-routine muscles such as the extensor indicis muscle and tibialis posterior, for plegic muscles, for junior resident physicians or scanning EMG ( Billakota et al, 2016 , Connell et al, 2023 , Elleuch et al, 2021a , Elleuch et al, 2021b , Gentile et al, 2020 , Karvelas et al, 2016 , Maitland et al, 2022 , Padua et al, 2023 ). In the study on residents, needle electrode placement accuracy improved with residency years.…”

Section: Discussionmentioning

confidence: 99%

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

Laure Inghilleri,

Alonso,

Moron

et al. 2024

Clinical Neurophysiology Practice

View full text Add to dashboard Cite

“…Some of these parameters can be measured experimentally. For example, the location and the fibre length of a motor unit could be estimated by using ultrasound [ 24 , 25 ]. The location of the innervation zone and the muscle fibre conduction velocity can be estimated, for example by using high-density EMG recordings [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

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

Ma,

Mendez Guerra,

Caillet

et al. 2024

PLoS Comput Biol

View full text Add to dashboard Cite

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.

show abstract

Ultrasound‐guided motor unit scanning electromyography

Cited by 6 publications

References 30 publications

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

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

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

Contact Info

Product

Resources

About