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

Rohlén, Robin; Lubel, Emma; Grandi Sgambato, Bruno; Antfolk, Christian; Farina, Dario

doi:10.1016/j.jelekin.2023.102825

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…We have presented an US decomposition algorithm for estimating MU discharge times in voluntary contractions using convolutive BSS with an integrated blind deconvolution step. We theorized that this technique would separate the MU-related signals from the large noise more successfully than previous methods for US decomposition that rely on linear instantaneous mixture models [12], [15]. Furthermore, by combining traditional convolutional BSS methods with an integrated blind deconvolution, we achieved better signal separation for the noisy input than the convolutive BSS alone.…”

Section: Discussionmentioning

confidence: 99%

“…Using these approaches, estimates of MU locations have a high repeatability. However, the discharge times are estimated with relatively low accuracy (with an agreement between US and the gold standard EMG of only ~30% of the discharges) [15]. Spatial linear combinations of pixels (which is equivalent to spatial filtering of the velocity maps) cannot compensate for the long duration of the MU velocity twitch profiles.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we propose a method for decomposing US image series into MN activity using a convolutive BSS method. Unlike previous methods for US analysis [12]- [15], our method is completely stand-alone and requires no concurrent EMG recording. We validated the method using simultaneously recorded US and high-density sEMG (HDsEMG) signals at low force levels from 10 participants.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

Lubel,

Rohlén,

Sgambato

et al. 2023

Preprint

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ObjectiveNon-invasive identification of motoneuron (MN) activity is commonly done using (EMG). However, surface EMG (sEMG) signals detect 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.MethodsHere, 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.Results140 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.ConclusionThe proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images.SignificanceThe proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

Lubel,

Rohlén,

Sgambato

et al. 2023

Preprint

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“…The convolutional BSS method can identify MU spike trains in US-based velocity images up to 40% MVC. Previous studies using ultrafast US for MU identification and analysis have considered low forces of up to 10% [16,26], 20% [6], and 30% [27], and not with direct identification of the spike trains. This is the first study showing MU spike trains directly identified from US at 40% MVC.…”

Section: Discussionmentioning

confidence: 99%

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

Rohlén,

Lubel,

Farina

2024

Preprint

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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.

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“…In practice, the amplitude and waveform similarity of MUAPs are also taken into account to distinguish MUs that overlap in three-dimensional space. The distribution and structure of muscle fibers is complex, a large variation exists in the distribution of fibers within the territories of biceps brachii MUs [35][36][37], and different MUs may also have different structures. Those MU variations will lead to variations of their MUAP waveforms, therefore MUAP waveforms which convey MU-specific features, were employed in this study to distinguish MUs, jointly with 3D location information of MUs.…”

Section: Discussionmentioning

confidence: 99%

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

He,

Houston,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before / after, and during muscle fatigue were identified for analysis. The surface location of the innervation zones (IZs) of the MUs was identified from the HD-sEMG bipolar motor unit action potential (MUAP) map. The depth of the MU was also identified from the decay pattern of the MUAP along the muscle fiber transverse direction. Both the surface IZ location and the MU depth information were utilized to ensure the same MU was examined during the contraction before / after muscle fatigue. The MUAP similarity, defined as the correlation coefficient between MUAP morphology, was adopted to reveal the alterations in MU characteristics under the condition of fatigue. The biomarkers of the same MUs were compared before / after fatigue (task 1) at 5%, 10%, and 15% maximal voluntary contraction (MVC) and in the process of continuous fatigue (task 2) at 20% MVC. Our results indicate that the MUAP morphology similarity of the same MUs was 0.91 ± 0.06 (task 1) and 0.93 ± 0.04 (task 2). The results showed that MUAP morphology maintained good stability before / after, and during muscle fatigue. The findings of this study may advance our understanding of the mechanism of MU neuromuscular fatigue.

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

Cited by 5 publications

References 43 publications

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

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