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Lubel E, Rohlen R, Sgambato BG, Barsakcioglu DY, Ibanez J, Tang MX, Farina D. Accurate Identification of Motoneuron Discharges From Ultrasound Images Across the Full Muscle Cross-Section. IEEE Trans Biomed Eng 2024; 71:1466-1477. [PMID: 38055363 DOI: 10.1109/tbme.2023.3340019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
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 Ten 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.
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Lundsberg J, Björkman A, Malesevic N, Antfolk C. Inferring position of motor units from high-density surface EMG. Sci Rep 2024; 14:3858. [PMID: 38360967 PMCID: PMC10869353 DOI: 10.1038/s41598-024-54405-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/12/2024] [Indexed: 02/17/2024] Open
Abstract
The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.
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Affiliation(s)
- Jonathan Lundsberg
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
| | - Anders Björkman
- Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Nebojsa Malesevic
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
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Rohlén R, Lubel E, Grandi Sgambato B, Antfolk C, Farina D. Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A comparative study with intramuscular and surface EMG. J Electromyogr Kinesiol 2023; 73:102825. [PMID: 37757604 DOI: 10.1016/j.jelekin.2023.102825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023] Open
Abstract
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.
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Affiliation(s)
- Robin Rohlén
- Department of Biomedical Engineering, Lund University, Lund, Sweden.
| | - Emma Lubel
- Department of Bioengineering, Imperial College London, London, UK
| | | | | | - Dario Farina
- Department of Bioengineering, Imperial College London, London, UK.
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Lubel E, Sgambato BG, Rohlen R, Ibanez J, Barsakcioglu DY, Tang MX, Farina D. Non-Linearity in Motor Unit Velocity Twitch Dynamics: Implications for Ultrafast Ultrasound Source Separation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3699-3710. [PMID: 37703141 DOI: 10.1109/tnsre.2023.3315146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
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.
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Rohlén R, Carbonaro M, Cerone GL, Meiburger KM, Botter A, Grönlund C. Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions . J Neural Eng 2023; 20:046016. [PMID: 37437598 DOI: 10.1088/1741-2552/ace6fc] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/12/2023] [Indexed: 07/14/2023]
Abstract
Objective.Ultrafast ultrasound (UUS) imaging has been used to detect intramuscular mechanical dynamics associated with single motor units (MUs). Detecting MUs from ultrasound sequences requires decomposing a velocity field into components, each consisting of an image and a signal. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the signals with MU firings obtained from needle electromyography (EMG). Here, we examined whether the repeatability of the images over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions.Approach.UUS images and high-density surface EMG (HDsEMG) were recorded simultaneously from 99 MUs in the biceps brachii of five healthy subjects. The MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based components. For each contraction, velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. To evaluate the repeatability of components' images across epochs, we calculated the Jaccard similarity coefficient (JSC). JSC compares the similarity between two images providing values between 0 and 1. Finally, the association between the components and the MUs from HDsEMG was assessed.Main results.All the MU-matched components had JSC > 0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8 ± 1.6 matches over 4.9 ± 1.8 MUs). The repeatable components (JSC > 0.38) represented 14% of the total components (6.5 ± 3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from noise and can be used for data reduction.Significance.This study provides the foundation for developing stand-alone methods to identify MU in UUS sequences and towards real-time imaging of MUs. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.
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Affiliation(s)
- Robin Rohlén
- Department of Biomedical Engineering, Lund University, Lund, Sweden
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
| | - Marco Carbonaro
- Department of Electronics and Telecommunication, Laboratory for Engineering of the Neuromuscular System (LISiN), Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Giacinto L Cerone
- Department of Electronics and Telecommunication, Laboratory for Engineering of the Neuromuscular System (LISiN), Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Kristen M Meiburger
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Alberto Botter
- Department of Electronics and Telecommunication, Laboratory for Engineering of the Neuromuscular System (LISiN), Politecnico di Torino, Turin, Italy
- PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy
| | - Christer Grönlund
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
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Grönlund C, Rohlén R. Ultrafast ultrasound imaging can be used to access single motor units in deep muscles, but the underlying biomechanical source remains to be understood. J Electromyogr Kinesiol 2023; 71:102797. [PMID: 37348262 DOI: 10.1016/j.jelekin.2023.102797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/24/2023] [Accepted: 06/16/2023] [Indexed: 06/24/2023] Open
Affiliation(s)
- Christer Grönlund
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
| | - Robin Rohlén
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden; Department of Biomedical Engineering, Lund University, Lund, Sweden.
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Rohlén R, Lundsberg J, Antfolk C. Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation. Biomed Eng Online 2023; 22:10. [PMID: 36750855 PMCID: PMC9906860 DOI: 10.1186/s12938-023-01076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
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.
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Affiliation(s)
- Robin Rohlén
- Department of Biomedical Engineering, Lund University, 221 00, Lund, Sweden. .,Department of Radiation Sciences, Biomedical Engineering, Radiation Physics, Umeå University, Umeå, Sweden.
| | - Jonathan Lundsberg
- grid.4514.40000 0001 0930 2361Department of Biomedical Engineering, Lund University, 221 00 Lund, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Lund University, 221 00, Lund, Sweden.
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Rohlén R, Raikova R, Stålberg E, Grönlund C. Estimation of contractile parameters of successive twitches in unfused tetanic contractions of single motor units - A proof-of-concept study using ultrafast ultrasound imaging in vivo. J Electromyogr Kinesiol 2022; 67:102705. [PMID: 36155330 DOI: 10.1016/j.jelekin.2022.102705] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 12/14/2022] Open
Abstract
During a voluntary contraction, motor units (MUs) fire a train of action potentials, causing summation of the twitch forces, resulting in fused or unfused tetanus. Twitches have been important in studying whole-muscle contractile properties and differentiation between MU types. However, there are still knowledge gaps concerning the voluntary force generation mechanisms. Current methods rely on the spike-triggered averaging technique, which cannot track changes in successive twitches' properties in response to individual neural firings. This study proposes a method that estimates successive twitches contractile parameters of single MUs during low force voluntary isometric contractions in human biceps brachii. We used a previously developed ultrafast ultrasound imaging method to estimate unfused tetanic activity signals of single MUs. A twitch decomposition model was used to decompose unfused tetanic activity signals into individual twitches. This study found that the contractile parameters varied within and across MUs. There was an association between the inter-spike interval and the contraction time (r = 0.49,p < 0.001) and the half-relaxation time (r = 0.58,p < 0.001), respectively. The method shows the proof-of-concept to study MU contractile properties of individual twitches in vivo, which can provide further insights into the force generation mechanisms of voluntary contractions and response to individual neural discharges.
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Affiliation(s)
- Robin Rohlén
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden; Department of Biomedical Engineering, Lund University, Lund, Sweden.
| | - Rositsa Raikova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Erik Stålberg
- Department of Clinical Neurophysiology, University Hospital, Uppsala, Sweden
| | - Christer Grönlund
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
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Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units. J Electromyogr Kinesiol 2022; 67:102714. [PMID: 36209700 DOI: 10.1016/j.jelekin.2022.102714] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/02/2022] [Accepted: 09/28/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Recent findings have shown that imaging voluntarily activated motor units (MUs) by decomposing ultrasound-based displacement images provides estimates of unfused tetanic signals evoked by spinal motoneurons' neural discharges (spikes). Two methods have been suggested to estimate its spike trains: band-pass filter (BPM) and Haar wavelet transform (HWM). However, the methods' optimal parameters and which method performs the best are unknown. This study will answer these questions. METHOD HWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and 21 experimental datasets, considering their rate of agreement, spike offset, and spike offset variability to the simulated or experimental spikes. RESULTS A range of parameter sets that resulted in the highest possible agreement with simulated spikes was provided. Both methods highly agreed with simulated and experimental spikes, but HWM was a better spike estimation method than BPM because it had a higher agreement, less bias, and less variation (p < 0.001). CONCLUSIONS The optimized HWM will be an important contributor to further developing the identification and analysis of MUs using imaging, providing indirect access to the neural drive of the spinal cord to the muscle by the unfused tetanic signals.
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de Oliveira J, de Souza MA, Assef AA, Maia JM. Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9232. [PMID: 36501933 PMCID: PMC9740760 DOI: 10.3390/s22239232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human-machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.
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Affiliation(s)
- Jonathan de Oliveira
- Graduate Program in Health Technology (PPGTS), Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil
| | - Mauren Abreu de Souza
- Graduate Program in Health Technology (PPGTS), Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil
| | - Amauri Amorin Assef
- Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
| | - Joaquim Miguel Maia
- Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
- Electronics Engineering Department (DAELN), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
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Lubel E, Grandi-Sgambato B, Barsakcioglu DY, Ibanez J, Tang MX, Farina D. Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound. J Neural Eng 2022; 19. [PMID: 36001952 DOI: 10.1088/1741-2552/ac8c6c] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/24/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e., the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions. APPROACH We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay. MAIN RESULTS Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9 ± 5.3 ms, 56.6 ± 8.4 ms, and 3.8 ± 3.0 ms (n = 390 across 10 participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions. SIGNIFICANCE The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.
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Affiliation(s)
- Emma Lubel
- Department of Bioengineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Bruno Grandi-Sgambato
- Department of Bioengineering, Imperial College London, Exhibition road, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Deren Y Barsakcioglu
- Department of Bioengineering, Imperial College London, Exhibition road, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Jaime Ibanez
- Bioengineering Group, Imperial College London, Engineering, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Meng-Xing Tang
- Department of Bioengineering, Imperial College London, Department of Bioeng, London, -- Select One --, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Dario Farina
- Department of Bioengineering, Imperial College London, Exhibition road, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Ali H, Umander J, Rohlén R, Röhrle O, Grönlund C. Modelling intra-muscular contraction dynamics using in silico to in vivo domain translation. Biomed Eng Online 2022; 21:46. [PMID: 35804415 PMCID: PMC9270806 DOI: 10.1186/s12938-022-01016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/20/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Advances in sports medicine, rehabilitation applications and diagnostics of neuromuscular disorders are based on the analysis of skeletal muscle contractions. Recently, medical imaging techniques have transformed the study of muscle contractions, by allowing identification of individual motor units' activity, within the whole studied muscle. However, appropriate image-based simulation models, which would assist the continued development of these new imaging methods are missing. This is mainly due to a lack of models that describe the complex interaction between tissues within a muscle and its surroundings, e.g., muscle fibres, fascia, vasculature, bone, skin, and subcutaneous fat. Herein, we propose a new approach to overcome this limitation. METHODS In this work, we propose to use deep learning to model the authentic intra-muscular skeletal muscle contraction pattern using domain-to-domain translation between in silico (simulated) and in vivo (experimental) image sequences of skeletal muscle contraction dynamics. For this purpose, the 3D cycle generative adversarial network (cycleGAN) models were evaluated on several hyperparameter settings and modifications. The results show that there were large differences between the spatial features of in silico and in vivo data, and that a model could be trained to generate authentic spatio-temporal features similar to those obtained from in vivo experimental data. In addition, we used difference maps between input and output of the trained model generator to study the translated characteristics of in vivo data. RESULTS This work provides a model to generate authentic intra-muscular skeletal muscle contraction dynamics that could be used to gain further and much needed physiological and pathological insights and assess and overcome limitations within the newly developed research field of neuromuscular imaging.
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Affiliation(s)
- Hazrat Ali
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | | | - Robin Rohlén
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Oliver Röhrle
- Stuttgart Center for Simulation Technology (SC SimTech), University of Stuttgart, Stuttgart, Germany.,Institute for Modelling and Simulation of Biomechanical Systems, Chair for Computational Biophysics and Biorobotics, University of Stuttgart, Stuttgart, Germany
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Carbonaro M, Zaccardi S, Seoni S, Meiburger KM, Botter A. Detecting anatomical characteristics of single motor units by combining high density electromyography and ultrafast ultrasound: a simulation study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:748-751. [PMID: 36086608 DOI: 10.1109/embc48229.2022.9871578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Muscle force production is the result of a sequence of electromechanical events that translate the neural drive issued to the motor units (MUs) into tensile forces on the tendon. Current technology allows this phenomenon to be investigated non-invasively. Single MU excitation and its mechanical response can be studied through high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) imaging respectively. In this study, we propose a method to integrate these two techniques to identify anatomical characteristics of single MUs. Specifically, we tested two algorithms, combining the tissue velocity sequence (TVS, obtained from ultrafast US images), and the MU firings (extracted from HDsEMG decomposition). The first is the Spike Triggered Averaging (STA) of the TVS based on the occurrences of individual MU firings, while the second relies on the correlation between the MU firing patterns and the TVS spatio-temporal independent components (STICA). A simulation model of the muscle contraction was adapted to test the algorithms at different degrees of neural excitation (number of active MUs) and MU synchronization. The performances of the two algorithms were quantified through the comparison between the simulated and the estimated characteristics of MU territories (size, location). Results show that both approaches are negatively affected by the number of active MU and synchronization levels. However, STICA provides a more robust MU territory estimation, outperforming STA in all the tested conditions. Our results suggest that spatio-temporal independent component decomposition of TVS is a suitable approach for anatomical and mechanical characterization of single MUs using a combined HDsEMG and ultrafast US approach.
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Rohlén R, Yu J, Grönlund C. Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions. BMC Res Notes 2022; 15:207. [PMID: 35705997 PMCID: PMC9202224 DOI: 10.1186/s13104-022-06093-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/03/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components' temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms. RESULTS We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using spatial information, which was the best together with stSOBI using either spatial or temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units.
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Affiliation(s)
- Robin Rohlén
- Department of Radiation Sciences, Biomedical Engineering, Umeå University, 901 87, Umeå, Sweden.
| | - Jun Yu
- Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden
| | - Christer Grönlund
- Department of Radiation Sciences, Biomedical Engineering, Umeå University, 901 87, Umeå, Sweden
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Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging. Sci Rep 2022; 12:8855. [PMID: 35614312 PMCID: PMC9133081 DOI: 10.1038/s41598-022-12999-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 05/06/2022] [Indexed: 02/07/2023] Open
Abstract
Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.
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Cruz-Montecinos C, Cerda M, Becerra P, Tapia C, Núñez-Cortés R, Latorre-García R, Freitas SR, Cuesta-Vargas A. Qualitative ultrasonography scale of the intensity of local twitch response during dry needling and its association with modified joint range of motion: a cross-sectional study. BMC Musculoskelet Disord 2021; 22:790. [PMID: 34521384 PMCID: PMC8442322 DOI: 10.1186/s12891-021-04592-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/07/2021] [Indexed: 12/21/2022] Open
Abstract
Background The relevance of local twitch response (LTR) during dry needling technique (DNT) is controversial, and it is questioned whether LTR is necessary for successful outcomes. Furthermore, because the LTR during the deep DNT may be evoked with different intensities, it is unknown whether the magnitude of LTR intensity is associated with optimal clinical results, especially concerning to the effects of joint maximal range of motion (ROM). This study aimed to (i) determine whether visual inspections can quantify the LTR intensity during the DNT through a qualitative ultrasonography scale of LTR intensity (US-LTR scale), and (ii) assess the differences of US-LTR scale associated with changes in the maximal joint ROM. Methods Using a cross-sectional design, seven asymptomatic individuals were treated with DNT in the latent myofascial trigger point in both medial gastrocnemius muscles. During DNT, three consecutive LTRs were collected. The US-LTR scale was used to classify the LTRs into strong, medium, and weak intensities. The categories of US-LTR were differentiated by the velocity of LTRs using the optical flow algorithm. ROM changes in ankle dorsiflexion and knee extension were assessed before and immediately after DNT. Results The US-LTR scale showed the third LTR was significantly smaller than the first one (p < 0.05). A significant difference in velocity was observed between US-LTR categories (p < 0.001). A significant difference in the ROM was observed between the strong and weak-medium intensity (p < 0.05). Conclusions The present findings suggest that the LTR intensity can be assessed using a qualitative US-LTR scale, and the effects of DNT on joint maximal ROM is maximized with higher LTR intensity. This study reports a novel qualitative method for LTR analysis with potential applications in research and clinical settings. However, further research is needed to achieve a broader application.
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Affiliation(s)
- Carlos Cruz-Montecinos
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago, Chile.,Laboratory of Biomechanics and Kinesiology, San José Hospital, Santiago, Chile
| | - Mauricio Cerda
- Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago, Chile.,Center for Medical Informatics and Telemedicine, Faculty of Medicine, Universidad de Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Santiago, Chile
| | - Pablo Becerra
- Laboratory of Biomechanics and Kinesiology, San José Hospital, Santiago, Chile
| | - Claudio Tapia
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Rodrigo Núñez-Cortés
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Rodrigo Latorre-García
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Sandro R Freitas
- Neuromuscular Research Lab, CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal
| | - Antonio Cuesta-Vargas
- Departamento de Fisioterapia, Andalucía Tech, Catedra de Fisioterapia y Discapacidad, Instituto de Investigación Biomedica de Málaga (IBIMA), Clinimetria (F-14), Universidad de Málaga, Málaga, Spain. .,School of Clinical Science, Faculty of Health at Queensland University Technology, QLD, Brisbane, Australia.
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