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  • 1.
    Ali, Hazrat
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Umander, Johannes
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 170595-170608Article in journal (Refereed)
    Abstract [en]

    Skeletal muscles are functionally regulated by populations of so-called motor units (MUs). An MU comprises a bundle of muscle fibers controlled by a neuron from the spinal cord. Current methods to diagnose neuromuscular diseases and monitor rehabilitation, and study sports sciences rely on recording and analyzing the bio-electric activity of the MUs. However, these methods provide information from a limited part of a muscle. Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.

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  • 2.
    Ali, Hazrat
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
    Umander, Johannes
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Röhrle, Oliver
    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.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Modelling intra-muscular contraction dynamics using in silico to in vivo domain translation2022In: Biomedical engineering online, E-ISSN 1475-925X, Vol. 21, no 1, article id 46Article in journal (Refereed)
    Abstract [en]

    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 identifcation 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 fbres, 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 intramuscular 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 modifcations. The results show that there were large diferences 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 diference 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 feld of neuromuscular imaging.

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  • 3.
    Carbonaro, M.
    et al.
    LISiN, Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Seoni, S.
    PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy; Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
    Meiburger, K.M.
    PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy; Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
    Vieira, T.
    LISiN, Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Grönlund, C.
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Botter, A.
    LISiN, Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo2023In: Convegno nazionale di bioingegneria: eight national congress of bioengineering: Proceedings, Patron Editore S.r.l. , 2023, p. 1-4Conference 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.

  • 4.
    Grönlund, Christer
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Ultrafast ultrasound imaging can be used to access single motor units in deep muscles, but the underlying biomechanical source remains to be understood2023In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 71, article id 102797Article in journal (Refereed)
  • 5.
    Lubel, Emma
    et al.
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Rohlén, Robin
    Department of Biomedical Engineering, Lund University, Sweden .
    Sgambato, Bruno Grandi
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Barsakcioglu, Deren Y
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Ibáñez, Jaime
    I3A, University of Zaragoza, Spain.
    Tang, Meng-Xing
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Farina, Dario
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Accurate identification of motoneuron discharges from ultrasound images across the full muscle cross-section2024In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 71, no 5, p. 1466-1477Article in journal (Refereed)
    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.

  • 6.
    Lubel, Emma
    et al.
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Sgambato, Bruno Grandi
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Rohlén, Robin
    Department of Biomedical Engineering, Lund University, Sweden.
    Ibáñez, Jaime
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Barsakcioglu, Deren Y
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Tang, Meng-Xing
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Farina, Dario
    Department of Bioengineering, Imperial College, London, United Kingdom.
    Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation2023In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210Article in journal (Refereed)
    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.

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  • 7.
    Rohlén, Robin
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    A Statistical Analysis of Muscle Fiber Area2014Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    In the present study the cross sectional areas of individual muscle fibers were investigated with focus on statistical methodology. This thesis includes data from two studies; Resistance Study and Method Study. The Resistance Study analyzes the effect of exercise by comparing muscle fiber area before and after eight weeks of resistance training. Muscle biopsies from the vastus lateralis muscle were obtained from young male participants. The purpose of the Method Study was to examine the variation between right and left leg. Contrary to previous studies, this thesis focuses on individual data rather than on group-based data, and therefore takes a different approach than the previously published articles. This is proven to be successful since information is lost when analyzing group-wise, as the increase in small muscle fibers did not show when analyzing as a group. The results of the Resistance Study is similar to the results of the Method Study. Means and standard deviations have a wide spread both between subjects and between biopsies taken from the same subject. Inference on the 10th and 90th percentiles shows a positive pattern in the Resistance Study, in the sense that both the smallest and the largest muscle fibers have grown as a result of the resistance training. If muscle fiber area is used as a proxy for training effect, the conclusion is that many people seem to have responded well to the training. 

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  • 8.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Identification of motor units using ultrasound – From muscle to neural interface2022Conference paper (Refereed)
  • 9.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The central nervous system controls human force production by successive recruitment of motor units in the skeletal muscles and changing their neural firing rate. The motor unit comprises a motoneuron, its innervated muscle fibers, and its axons. The motor units’ function provides the basis for diagnosing neuromuscular diseases, analysis in exercise physiology and sports science, and prosthetic control. Electromyography is the gold standard to measure and analyze motor units under voluntary contractions, but the technique is limited in its field of view. Recent studies have demonstrated the possibility of using imaging techniques to study motor units providing a large field of view. However, these studies are based on electrical stimulation of the muscle, and therefore only provide partial information on the motor unit’s function in contrast to voluntary contractions. 

    The overall purpose of this thesis was to develop methods to identify and analyze motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions for neuromuscular diagnostics and muscle contraction characterization. The thesis is based on four studies. 

    In the first study, a methodological pipeline was developed to identify motor units by decomposing image sequences into spatiotemporal components. The firing pattern and territory of the components were evaluated using an in-house developed simulation model. It showed that this pipeline identified 75-95% of the simulated motor units at low force levels. The territory estimation had a 50-80% sensitivity and 100% specificity, and the firing pattern estimation had a 90% agreement with the true firing pattern. In general, the method’s performance decreased for more than 20 active motor units. Experimental isometric contractions from healthy subjects were recorded for feasibility assessment. The results showed that the number of components increased with force level, where the number of components at 1%, 2.5%, and 5% maximal voluntary contraction averaged 7, 9, and 12, respectively. The territory diameter (5-6 mm), contraction duration (40-50 ms), and firing rate (11-12 Hz) were similar for all force levels. Thus, the results were similar to motor units’ known characteristics, suggesting that these components could be motor units. 

    In the second study, the proposed pipeline was validated using ultrafast ultrasound and state-of-the-art needle electromyography simultaneously. The results showed that the method could identify 31% of the motor units in low force voluntary isometric contractions, and possible explanations for the unidentified 69% were discussed. The conclusion was that the proposed pipeline can identify motor units.

    The third study focused on evaluating the influence of different decomposition algorithms on performance of identifying single motor units in the data from study 2. The results showed that a decomposition algorithm is required for motor unit identification. The algorithms performed similarly in estimating firing patterns and they did not influence the motor unit twitch waveform. It was also shown that the algorithms identify different motor units, where some identified completely different units. These results suggest that the precise choice of decomposition algorithm is not critical, and there may be an improvement potential to detect more motor units. 

    In the fourth study, data from the second study was used to estimate single motor units’ contractile parameters based on a subset of the data (14 motor unit contractions). Multiple single motor unit’s contraction parameters were estimated using two models. Both models’ contractile parameters were consistent and agreed with previous literature. The former and more detailed model had a better experimental fit, whereas the latter model captured the “average behavior” with fewer parameters. It was found that the single twitch waveforms within a motor unit change shape during a voluntary isometric contraction at a low force level. These results suggest that the motor unit’s contractile parameters can be estimated using ultrafast ultrasound image sequences in voluntary isometric contractions. 

    In summary, a methodological pipeline to identify motor units was developed, evaluated, and validated. The key module in the pipeline, i.e., decomposition algorithm, was evaluated by comparing different algorithms’ influence on identifying single motor units. Finally, the pipeline output can be used for estimating motor units’ contractile parameters. This pipeline may contribute to neuromuscular diagnostics and muscle contraction characterization. In general, it may allow the study of various motor unit-related questions that previously were difficult or not possible to address. 

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  • 10.
    Rohlén, Robin
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Segmentation of motor units in ultrasound image sequences2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The archetypal modern comic book superhero, Superman, has two superpowers of interest: the ability to see into objects and the ability to see distant objects. Now, humans possess these powers as well, due to the medical ultrasound imaging and sound navigation. Ultrasound, a type of sound we cannot hear, has enabled us to see a world otherwise invisible to us.

    Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by so called motor units which are the smallest voluntarily activatable units and is of primary interest in this study.

    The major complexity in segmentation of motor units in skeletal muscle tissue in ultrasound image sequences is the aspect of overlapping objects. We propose a framework and evaluate the performance on simulated synthetic data.

    We have found that it is possible to segment motor units under an isometric contraction using high-end ultrasound scanners and we have proposed a framework which is robust when simulating up to 10 components when exposed to 20 dB Gaussian white noise. The framework is not satisfactory robust when exposed to significant amount of noise. In order to be able to segment a large number of components, decomposition is inevitable and together with development of a step including smoothing, the framework can be further improved. 

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  • 11.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden .
    Antfolk, Christian
    Department of Biomedical Engineering, Lund University, Lund, Sweden .
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor unitsManuscript (preprint) (Other academic)
    Abstract [en]

    Background: Human movement is generated by activating motor units (MUs), i.e., the smallest structures that can be voluntarily controlled. Recent findings have shown imaging of voluntarily activated MUs using ultrafast ultrasound based on displacement velocity images and a decomposition algorithm. Given this, estimates of trains of twitches (unfused tetanic signals) evoked by the neural discharges (spikes) of spinal motor neurons are provided. Based on these signals, a band-pass filter method (BPM) has been used to estimate its spike train. In addition, an improved spike estimation method consisting of a continuous Haar wavelet transform method (HWM) has been suggested. However, the parameters of the two methods have not been optimized, and their performance has not been compared rigorously.

    Method: HWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and two experimental datasets with 21 unfused tetanic contractions considering their rate of agreement, spike offset, and spike offset variability with respect 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.

  • 12.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Antfolk, Christian
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units2022In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 67, article id 102714Article in journal (Refereed)
    Abstract [en]

    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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  • 13.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Carbonaro, Marco
    Politecnico di Torino, Italy.
    Botter, Alberto
    Politecnico di Torino, Italy.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Antfolk, Christian
    Lund University, Lund, Sweden.
    Quantifying the spatial distribution of individual muscle units using high-density surface EMG and ultrafast ultrasound2023In: ECSS Paris 2023 Oral presentations: Biomechanics & Motor control / [ed] Jakob Škarabot; Julian Alcazar, 2023, article id 2493Conference paper (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.

  • 14.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Carbonaro, Marco
    Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Cerone, Giacinto Luigi
    Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Meiburger, Kristen M.
    PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy; Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
    Botter, Alberto
    Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy; PoliToBIOMed Lab, Politecnico di Torino, Turin, Italy.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions2023In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 20, no 4, p. 046016-Article in journal (Refereed)
    Abstract [en]

    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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  • 15.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jiang, Biao
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyman, Emma
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Medicine.
    Wester, Per
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Medicine.
    Näslund, Ulf
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Medicine.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Interframe Echo Intensity Variation of Subregions and Whole Plaque in Two-Dimensional Carotid Ultrasonography: Simulations and in Vivo Observations2023In: Journal of ultrasound in medicine, ISSN 0278-4297, E-ISSN 1550-9613, Vol. 42, no 5, p. 1033-1046Article in journal (Refereed)
    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.

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  • 16.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Medical and Translational Biology.
    Lubel, Emma
    Lund University, Lund, Sweden.
    Farina, Dario
    Imperial College London, UK.
    Identifying motor unit spike trains in ultrasound images comprised of varying successive twitch-like shapes and degrees of fusion in isometric contractions2024In: ISEK XXIV Abstract book, 2024, article id O.13.7Conference paper (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.

  • 17.
    Rohlén, Robin
    et al.
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Lubel, Emma
    Department of Bioengineering, Imperial College London, London, UK.
    Grandi Sgambato, Bruno
    Department of Bioengineering, Imperial College London, London, UK.
    Antfolk, Christian
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Farina, Dario
    Department of Bioengineering, Imperial College London, London, UK.
    Spatial decomposition of ultrafast ultrasound images to identify motor unit activity: a comparative study with intramuscular and surface EMG2023In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, article id 102825Article in journal (Refereed)
    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.

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  • 18.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Medical and Translational Biology.
    Lubel, Emma
    Lund University, Lund, Sweden.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention.
    Farina, Dario
    Imperial College London, UK.
    [Neuromechanical characterisation of muscles and their functional units using ultrasound imaging methods] State-of-the-art and future perspectives2024In: ISEK XXIV Abstract book, 2024, article id S5.5Conference paper (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.

  • 19.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Lundsberg, Jonathan
    Antfolk, Christian
    Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separationManuscript (preprint) (Other academic)
    Abstract [en]

    The central nervous system initiates voluntary force production by providing excitatory inputs to spinal motor neurons, each connected to a set of muscle fibres to form a motor unit. Motor units have been imaged and analysed using ultrafast ultrasound based on the separation of ultrasound images. Although this method has great potential to identify regions and trains of motor unit twitches (unfused tetanus) evoked by the spike trains, it currently has a limited motor unit identification rate. One potential explanation is that the current method neglects the temporal information in the separation process of ultrasound images, and including it could lead to significant improvement. Here, we take the first step by asking if it is possible to estimate the spike train of an unfused tetanic signal from simulated and experimental signals using convolutive blind source separation. This finding will provide a direction for ultrasound-based method improvement. In this study, we found that the estimated spike trains highly agreed with the simulated and reference spike trains. This result implies that the convolutive blind source separation of an unfused tetanic signal can be used to estimate its spike train. Although extending this approach to ultrasound images is promising, the translation remains to be investigated in future studies where spatial information is inevitable as a discriminating factor between different motor units.

  • 20.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Lundsberg, Jonathan
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Antfolk, Christian
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation2023In: Biomedical engineering online, E-ISSN 1475-925X, Vol. 22, article id 10Article in journal (Refereed)
    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.

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  • 21.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Lundsberg, Jonathan
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Malesevic, Nebojsa
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Antfolk, Christian
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematicsManuscript (preprint) (Other academic)
    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 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 spatial and temporal correlation between the MU-associated components from velBSS and stICA was high (0.86 +/- 0.05 and 0.87 +/- 0.06). The spike-triggered averaged twitch responses (using the MU spike trains from EMG) had an extremely high correlation (0.99 +/- 0.01). In addition, the computational time for velBSS was at least 50 times less than for stICA.

    Significance: The present algorithm (velBSS) outperforms the currently available method (stICA). 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.

  • 22.
    Rohlén, Robin
    et al.
    Lund University.
    Lundsberg, Jonathan
    Lund University.
    Malesevic, Nebojsa
    Lund University.
    Antfolk, Christian
    Lund University.
    A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics2023In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552Article in journal (Refereed)
    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.

  • 23.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Raikova, Rositsa
    Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
    Stålberg, Erik
    Department of Clinical Neurophysiology, University Hospital, Uppsala, Sweden.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    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 vivo2022In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 67, article id 102705Article in journal (Refereed)
    Abstract [en]

    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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  • 24.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Raikova, Rositsa
    Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
    Stålberg, Erik
    Department of Clinical Neurophysiology, Department of Neurosciences, University Hospital, Uppsala University, Sweden..
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Estimation of motor unit contractile properties in voluntary contractions of a skeletal muscle obtained by ultrafast ultrasoundManuscript (preprint) (Other academic)
  • 25.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Segmentation of Motor Unit Territories in Ultrasound Image Sequences of Contracting Skeletal Muscle Tissue2017Conference paper (Other academic)
    Abstract [en]

    Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by motor units, which are the smallest voluntarily activatable units. In order to capture a transient phenomenon, such as the contraction mechanism, a high sample rate is required. There has been a lot of research on whole-muscle aspects in terms of skeletal muscle contraction characteristics, neuromuscular disorders, and inter-muscle segmentation. Previous studies have shown that small-scale muscle twitches can be detected using ultrasound and there are several reports on ultrasound-based detection of electro-stimulated motor unit activity. However, methods for intra-muscular ultrasound-based analysis of muscle tissue are largely underdeveloped, in particular regarding the level of motor units.Diagnostics of skeletal muscle tissue is based on analyzing features of these units by invasive, non-imaging electrophysiological methods. Here,we make progress by using non-invasive ultrasound imaging to segment motor units, which have the potential to be a non-invasive substitute and where the imaging provides an important contribution.

  • 26.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Department of Clinical Neurophysiology, Department of Neurosciences, University Hospital, Uppsala University, Sweden.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Identification of single motor units in skeletal muscle under low force isometric voluntary contractions using ultrafast ultrasound2020In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, article id 22382Article in journal (Refereed)
    Abstract [en]

    The central nervous system (CNS) controls skeletal muscles by the recruitment of motor units (MUs). Understanding MU function is critical in the diagnosis of neuromuscular diseases, exercise physiology and sports, and rehabilitation medicine. Recording and analyzing the MUs’ electrical depolarization is the basis for state-of-the-art methods. Ultrafast ultrasound is a method that has the potential to study MUs because of the electrical depolarizations and consequent mechanical twitches. In this study, we evaluate if single MUs and their mechanical twitches can be identified using ultrafast ultrasound imaging of voluntary contractions. We compared decomposed spatio-temporal components of ultrasound image sequences against the gold standard needle electromyography. We found that 31% of the MUs could be successfully located and their firing pattern extracted. This method allows new non-invasive opportunities to study mechanical properties of MUs and the CNS control in neuromuscular physiology.

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  • 27.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ultrafast Ultrasound Imaging of Motor Units in Skeletal Muscle during Voluntary Contractions – A Pilot Validation Study by using Needle-EMG2019Conference paper (Other academic)
  • 28.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Department of Clinical Neurophysiology, Department of Neurosciences, University Hospital, Uppsala University, Sweden.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Method for Identification of Mechanical Response of Motor Units in Skeletal Muscle Voluntary Contractions using Ultrafast Ultrasound Imaging: Simulations and Experimental Tests2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 50299-50311Article in journal (Refereed)
    Abstract [en]

    The central nervous system coordinates movement through forces generated by motor units (MUs) in skeletal muscles. To analyze MUs function is essential in sports, rehabilitation medicine applications, and neuromuscular diagnostics. The MUs and their function are studied using electromyography. Typically, these methods study only a small muscle volume (1 mm3) or only a superficial (< 1 cm) volume of the muscle. Here we introduce a method to identify so-called mechanical units, i.e., the mechanical response of electrically active MUs, in the whole muscle (4x4 cm, cross-sectional) under voluntary contractions by ultrafast ultrasound imaging and spatiotemporal decomposition. We evaluate the performance of the method by simulation of active MUs' mechanical response under weak contractions. We further test the experimental feasibility on eight healthy subjects. We show the existence of mechanical units that contribute to the tissue dynamics in the biceps brachii at low force levels and that these units are similar to MUs described by electromyography with respect to the number of units, territory sizes, and firing rates. This study introduces a new potential neuromuscular functional imaging method, which could be used to study a variety of questions on muscle physiology that previously were difficult or not possible to address.

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  • 29.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Uppsala University.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ultrasound-based Imaging of Motor Units in Skeletal Muscle Tissue2018Conference paper (Other academic)
    Abstract [en]

    Neuromuscular diseases hinder muscle function and may be the outcome of damage and dysfunction of the smallest voluntarily activatable units in skeletal muscle tissue, the so-called motor units (MUs). MUs generate electrical signals and analyzing these signals gives a basis to assess and diagnose MUs. The signals are captured using needle electromyography, which is an invasive and non-imaging method. Here, we showultrasound-basedimaging of MUs, via an ultrasound-based spatiotemporal decomposition framework.

  • 30.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Uppsala University.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Imaging recruitment of motor units in voluntary skeletal muscle contractions using decomposition and ultrafast ultrasound imaging: A pilot study2020In: 2020 ISEK Virtual Congress Poster Abstract Booklet, International Society of Electrophysiology and Kinesiology , 2020, p. 141-142Conference paper (Other academic)
    Abstract [en]

    Recently our research group demonstrated a method to separate and identify the mechanical response of individual active MUs, from a large part of a muscle (4x4 cm, cross-sectional) under voluntary contractions. The method is based on ultrafast ultrasound imaging and spatiotemporal decomposition. In the present work we aimed to use this method to explore MU territory recruitment patterns at low force levels in the biceps brachii.

  • 31.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions2022In: BMC Research Notes, E-ISSN 1756-0500, article id 207Article in journal (Refereed)
    Abstract [en]

    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 quantifed 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 diferent algorithms.

    Results: We found that out of these four algorithms, no algorithm signifcantly improved the motor unit identifcation success compared to stICA using spatial information, which was the best together with stSOBI using either spatialor 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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