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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.

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.

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.

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 s

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, Session-ID: OP-BM20

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., P-L-176

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.

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.

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.

Originally included in thesis in manuscript form.

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.

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.

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.