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Lubel, E., Rohlén, R., Sgambato, B. G., Barsakcioglu, D. Y., Ibáñez, J., Tang, M.-X. & Farina, D. (2024). Accurate identification of motoneuron discharges from ultrasound images across the full muscle cross-section. IEEE Transactions on Biomedical Engineering, 71(5), 1466-1477
Open this publication in new window or tab >>Accurate identification of motoneuron discharges from ultrasound images across the full muscle cross-section
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2024 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 71, no 5, p. 1466-1477Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Medical Image Processing Physiology
Identifiers
urn:nbn:se:umu:diva-217558 (URN)10.1109/TBME.2023.3340019 (DOI)
Funder
EU, Horizon 2020, 899822The Swedish Brain Foundation, PS2022-0021
Available from: 2023-12-07 Created: 2023-12-07 Last updated: 2024-05-18
Rohlén, R., Lubel, E. & Farina, D. (2024). Identifying motor unit spike trains in ultrasound images comprised of varying successive twitch-like shapes and degrees of fusion in isometric contractions. In: ISEK XXIV Abstract book: . Paper presented at XXV ISEK Congress, International Society of Electrophysiology & Kinesiology, Nagoya, Japan, June 26-29, 2024. , Article ID O.13.7.
Open this publication in new window or tab >>Identifying motor unit spike trains in ultrasound images comprised of varying successive twitch-like shapes and degrees of fusion in isometric contractions
2024 (English)In: ISEK XXIV Abstract book, 2024, article id O.13.7Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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.

National Category
Medical Engineering Physiology
Identifiers
urn:nbn:se:umu:diva-227809 (URN)
Conference
XXV ISEK Congress, International Society of Electrophysiology & Kinesiology, Nagoya, Japan, June 26-29, 2024
Funder
Promobilia foundationSwedish Research CouncilStiftelsen Längmanska kulturfondenSwedish National Centre for Research in SportsThe Kempe FoundationsThe Swedish Brain Foundation
Available from: 2024-07-10 Created: 2024-07-10 Last updated: 2024-07-11Bibliographically approved
Rohlén, R., Lubel, E., Grönlund, C. & Farina, D. (2024). [Neuromechanical characterisation of muscles and their functional units using ultrasound imaging methods] State-of-the-art and future perspectives. In: ISEK XXIV Abstract book: . Paper presented at XXV ISEK Congress, International Society of Electrophysiology & Kinesiology, Nagoya, Japan, June 26-29, 2024. , Article ID S5.5.
Open this publication in new window or tab >>[Neuromechanical characterisation of muscles and their functional units using ultrasound imaging methods] State-of-the-art and future perspectives
2024 (English)In: ISEK XXIV Abstract book, 2024, article id S5.5Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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.

National Category
Physiology Medical Engineering
Identifiers
urn:nbn:se:umu:diva-227808 (URN)
Conference
XXV ISEK Congress, International Society of Electrophysiology & Kinesiology, Nagoya, Japan, June 26-29, 2024
Funder
Swedish Research CouncilStiftelsen Längmanska kulturfondenSwedish National Centre for Research in SportsThe Swedish Brain FoundationThe Kempe FoundationsPromobilia foundation
Note

Part of: Symposium 5: Neuromechanical characterisation of muscles and their functional units using ultrasound imaging methods: State-of-the-art and future perspectives

Available from: 2024-07-10 Created: 2024-07-10 Last updated: 2024-07-11Bibliographically approved
Rohlén, R., Lundsberg, J., Malesevic, N. & Antfolk, C. (2023). A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics. Journal of Neural Engineering
Open this publication in new window or tab >>A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics
2023 (English)In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552Article in journal (Refereed) Accepted
Abstract [en]

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.

National Category
Medical Engineering Neurosciences
Identifiers
urn:nbn:se:umu:diva-208291 (URN)10.1088/1741-2552/acd4e9 (DOI)
Funder
Swedish National Centre for Research in Sports, D2023-0003Promobilia foundationFoundation for Assistance to Disabled People in SkaneSwedish Research Council, 2019-05601
Available from: 2023-05-17 Created: 2023-05-17 Last updated: 2023-05-17
Carbonaro, M., Rohlén, R., Seoni, S., Meiburger, K., Vieira, T., Grönlund, C. & Botter, A. (2023). Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo. In: Convegno nazionale di bioingegneria: eight national congress of bioengineering: Proceedings. Paper presented at 8th National Congress of Bioengineering, GNB 2023, Padova, 21-23 June, 2023. (pp. 1-4). Patron Editore S.r.l.
Open this publication in new window or tab >>Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo
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2023 (English)In: Convegno nazionale di bioingegneria: eight national congress of bioengineering: Proceedings, Patron Editore S.r.l. , 2023, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

This study aims to compare two methods for the identification of anatomical and mechanical motor unit (MU) properties through the integration of high-density surface electromyography (HDsEMG) and ultrafast ultrasound (UUS). The two approaches rely on a combined analysis of the firing pattern of active MUs, identified from HDsEMG, and tissue velocity sequences of the muscle cross-section, obtained from UUS. The first method is the spike-triggered averaging (STA) of the tissue velocity sequence based on the occurrences of MU firings. The second is a method based on spatio-temporal independent component analysis (STICA) enhanced with the information of single MU firings. We compared the capability of these two approaches to identify the regions where single MU fibers are located within the muscle cross-section (MU displacement area) in vivo. HDsEMG signals and UUS images were detected simultaneously from biceps brachii in ten participants (6 males and 4 females) during low-level isometric elbow flexions. Experimental signals were processed by implementing both STA and STICA approaches. The medio-lateral distance between the estimated MU displacement areas and the centroid of the MU action potential distributions was used to compare the two methods. We found that STICA and STA are able to detect MU displacement areas. However, STICA provides more precise estimations to the detriment of higher computational complexity.

Place, publisher, year, edition, pages
Patron Editore S.r.l., 2023
Series
National Congress of Bioengineering. Proceedings., E-ISSN 27242129
Keywords
averaging, high-density surface emg, independent component analysis, motor unit, ultrafast ultrasound
National Category
Neurosciences
Identifiers
urn:nbn:se:umu:diva-216670 (URN)2-s2.0-85175855595 (Scopus ID)9788855580113 (ISBN)
Conference
8th National Congress of Bioengineering, GNB 2023, Padova, 21-23 June, 2023.
Available from: 2023-12-01 Created: 2023-12-01 Last updated: 2023-12-01Bibliographically approved
Rohlén, R., Lundsberg, J. & Antfolk, C. (2023). Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation. Biomedical engineering online, 22, Article ID 10.
Open this publication in new window or tab >>Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation
2023 (English)In: Biomedical engineering online, E-ISSN 1475-925X, Vol. 22, article id 10Article in journal (Refereed) Published
Abstract [en]

Background: Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors.

Results: This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%).

Conclusions: This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023
Keywords
Convolutive blind source separation, Spike train, Unfused tetanus, Twitch, Motor unit, Motoneuron
National Category
Medical Engineering Neurosciences
Identifiers
urn:nbn:se:umu:diva-204606 (URN)10.1186/s12938-023-01076-0 (DOI)000928601900001 ()36750855 (PubMedID)2-s2.0-85147640618 (Scopus ID)
Funder
Swedish National Centre for Research in Sports, D2023-0003Promobilia foundationFoundation for Assistance to Disabled People in SkaneSwedish Research Council, 2019-05601
Available from: 2023-02-07 Created: 2023-02-07 Last updated: 2023-09-05Bibliographically approved
Rohlén, R., Jiang, B., Nyman, E., Wester, P., Näslund, U. & Grönlund, C. (2023). Interframe Echo Intensity Variation of Subregions and Whole Plaque in Two-Dimensional Carotid Ultrasonography: Simulations and in Vivo Observations. Journal of ultrasound in medicine, 42(5), 1033-1046
Open this publication in new window or tab >>Interframe Echo Intensity Variation of Subregions and Whole Plaque in Two-Dimensional Carotid Ultrasonography: Simulations and in Vivo Observations
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2023 (English)In: Journal of ultrasound in medicine, ISSN 0278-4297, E-ISSN 1550-9613, Vol. 42, no 5, p. 1033-1046Article in journal (Refereed) Published
Abstract [en]

Objectives: The risk of cardiovascular disease is associated with the echo intensity of carotid plaques in ultrasound images and their cardiac cycle-induced intensity variations. In this study, we aimed to 1) explore the underlying origin of echo intensity variations by using simulations and 2) evaluate the association between the two-dimensional (2D) spatial distribution of these echo intensity variations and plaque vulnerability.

Methods: First, we analyzed how out-of-plane motion and compression of simulated scattering spheres of different sizes affect the ultrasound echo intensity. Next, we propose a method to analyze the features of the 2D spatial distribution of interframe plaque echo intensity in carotid ultrasound image sequences and explore their associations with plaque vulnerability in experimental data.

Results: The simulations showed that the magnitude of echo intensity changes was similar for both the out-of-plane motion and compression, but for scattering objects smaller than 1 mm radius, the out-of-plane motion dominated. In experimental data, maps of the 2D spatial distribution of the echo intensity variations had a low correlation with standard B-mode echo intensity distribution, indicating complementary information on plaque tissue composition. In addition, we found the existence of ∼1 mm diameter subregions with pronounced echo intensity variations associated with plaque vulnerability.

Conclusions: The results indicate that out-of-plane motion contributes to intra-plaque regions of high echo intensity variation. The 2D echo intensity variation maps may provide complementary information for assessing plaque composition and vulnerability. Further studies are needed to verify this method's role in identifying vulnerable plaques and predicting cardiovascular disease risk.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
National Category
Cardiac and Cardiovascular Systems Medical Engineering
Identifiers
urn:nbn:se:umu:diva-200460 (URN)10.1002/jum.16114 (DOI)000870331800001 ()36264181 (PubMedID)2-s2.0-85140218004 (Scopus ID)
Funder
Swedish Research Council, 2015-04461Västerbotten County Council, VLL-581211
Available from: 2022-10-20 Created: 2022-10-20 Last updated: 2024-07-02Bibliographically approved
Lubel, E., Sgambato, B. G., Rohlén, R., Ibáñez, J., Barsakcioglu, D. Y., Tang, M.-X. & Farina, D. (2023). Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation. IEEE transactions on neural systems and rehabilitation engineering
Open this publication in new window or tab >>Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation
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2023 (English)In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210Article in journal (Refereed) Epub ahead of print
Abstract [en]

Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Medical Image Processing Physiology
Identifiers
urn:nbn:se:umu:diva-214391 (URN)10.1109/tnsre.2023.3315146 (DOI)
Funder
EU, Horizon 2020, 899822Swedish National Centre for Research in Sports, D2023-0003
Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2023-09-14
Rohlén, R., Carbonaro, M., Botter, A., Grönlund, C. & Antfolk, C. (2023). Quantifying the spatial distribution of individual muscle units using high-density surface EMG and ultrafast ultrasound. In: Jakob Škarabot; Julian Alcazar (Ed.), ECSS Paris 2023 Oral presentations: Biomechanics & Motor control. Paper presented at ECSS Paris 2023, The 28th Annual Congress of the European College of Sport Science, Paris, France, July 4-7, 2023. , Article ID 2493.
Open this publication in new window or tab >>Quantifying the spatial distribution of individual muscle units using high-density surface EMG and ultrafast ultrasound
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2023 (English)In: ECSS Paris 2023 Oral presentations: Biomechanics & Motor control / [ed] Jakob Škarabot; Julian Alcazar, 2023, article id 2493Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

INTRODUCTION:Resistance training is a well-known intervention to improve muscle strength (1), with motor unit (MU) adaptation playing an important role (2). Recently, MUs were tracked in humans before and after resistance training using high-density surface electromyography (HDsEMG), showing a correlation between maximal force increase and MUs’ average discharge rate (3). Although these results demonstrate the relationship between an increase in strength and MU activity, only MU-level neural adaptation was considered. Indeed, neural and muscular information needs to be studied jointly to understand the exact adaptations of the MUs in response to resistance training (4).Recently, a method based on ultrafast ultrasound was presented, providing estimates of MU territories in cross-section and the train of twitches evoked by the spinal motoneurons’ discharges (5). In this study, as a proof-of-concept, we combined ultrafast ultrasound and HDsEMG to explore the spatial distribution of individual MUs.

METHODS:In a cross-sectional study, four participants performed low-force isometric contractions of the biceps brachii muscle while recording HDsEMG and ultrafast ultrasound signals from the biceps brachii muscle.The HDsEMG signals were decomposed into individual MU discharge timings (6), and the ultrafast ultrasound signals were decomposed into many components, each having a spatial map and temporal signal (5). We matched each discharge timing of a MU with a component based on spike-triggered averaging of the component’s temporal signal. Given a selected component, we applied a threshold to the spatial map and calculated the centroid and an equivalent diameter.

RESULTS:Out of 16 recordings from four subjects, we decomposed 82 MUs from HDsEMG. Given this, we found 32 matches between individual MU discharges and ultrasound components where the triggered twitches had a significant amplitude. The estimated territories were 4.6 ± 1.1 mm (ranging from 2.8 to 8.6 mm), in line with findings from previous research using scanning-EMG (7). Moreover, the components were located 12.7 ± 3.4 mm below the skin (ranging from 6.4 to 19.4 mm).

CONCLUSION:Our results show that using ultrafast ultrasound and HDsEMG in a strength training intervention, we should be able to quantify the relative contribution of the nervous system and skeletal muscle at the MU level. This information may provide the time course of both neural and hypertrophic adaptations to resistance training and elucidate the relative contributions of each to strength gain.

National Category
Physiology Medical Image Processing
Identifiers
urn:nbn:se:umu:diva-211508 (URN)
Conference
ECSS Paris 2023, The 28th Annual Congress of the European College of Sport Science, Paris, France, July 4-7, 2023
Funder
Wenner-Gren Foundations, RSh2022-0028Swedish National Centre for Research in Sports, D2023-0003
Note

Session-ID: OP-BM20

Available from: 2023-07-10 Created: 2023-07-10 Last updated: 2023-07-10Bibliographically approved
Rohlén, R., Lubel, E., Grandi Sgambato, B., Antfolk, C. & Farina, D. (2023). Spatial decomposition of ultrafast ultrasound images to identify motor unit activity: a comparative study with intramuscular and surface EMG. Journal of Electromyography & Kinesiology, Article ID 102825.
Open this publication in new window or tab >>Spatial decomposition of ultrafast ultrasound images to identify motor unit activity: a comparative study with intramuscular and surface EMG
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2023 (English)In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, article id 102825Article in journal (Refereed) In press
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
ultrafast ultrasound, electromyography, motor units, spike-triggered averaging, blind source separation
National Category
Physiology Medical Image Processing
Identifiers
urn:nbn:se:umu:diva-214673 (URN)10.1016/j.jelekin.2023.102825 (DOI)
Funder
EU, Horizon 2020, 899822Swedish National Centre for Research in Sports, D2023-0003
Available from: 2023-09-24 Created: 2023-09-24 Last updated: 2023-09-25
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4328-5467

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