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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.

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.

Ultrasonic echolucent carotid intima-media (IM) complex and accelerated progression of carotid intima mediathickness (cIMT) have both separately been shown to predict future cardiovascular events. The aim of this studywas to evaluate if the echogenicity of the IM-complex is associated with the 3-year progression of cIMT. B-modeultrasound images captured at baseline and 3-year follow-up in the ‘Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention’ (VIPVIZA) trial were included (n: 3154). The bilateral mean cIMT and IM-echogenicity by greyscale median (GSM) were measured in the common carotid artery. Associations between IM-GSM at baseline and the 3-year cIMT progression were investigated using linear regression models for the whole population and stratified by sex, age and VIPVIZA study group (intervention versus control). In addition, adjusted analyses for confounding factors were performed. Unadjusted analysis showed that decreased IM-GSM at baseline was associated with increased progression of cIMT (p < 0.001). Stratified by age, the association was significant among 40 (p < 0.001) and 60 years old (p < 0.001). The association was statistically significant in both sexes and on comparison of VIPVIZA study subgroups. Adjustments for confounding factors did not alter the estimated relationship between IM-GSM and cIMT progression. Echolucent carotid intima media at baseline associates with increased 3-year cIMT progression among an asymptomatic, middle-aged population. Echogenicity of the intima media may identify individuals at risk for accelerated vascular aging.

Ultrasonic echolucent carotid intima-media (IM) complex and accelerated progression of carotid intima mediathickness (cIMT) have both separately been shown to predict future cardiovascular events. The aim of this studywas to evaluate if the echogenicity of the IM-complex is associated with the 3-year progression of cIMT. B-modeultrasound images captured at baseline and 3-year follow-up in the ‘Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention’ (VIPVIZA) trial were included (n: 3154). The bilateral mean cIMT and IM-echogenicity by greyscale median (GSM) were measured in the common carotid artery. Associations between IM-GSM at baseline and the 3-year cIMT progression were investigated using linear regression models for the whole population and stratified by sex, age and VIPVIZA study group (intervention versus control). In addition, adjusted analyses for confounding factors were performed. Unadjusted analysis showed that decreased IM-GSM at baseline was associated with increased progression of cIMT (p < 0.001). Stratified by age, the association was significant among 40 (p < 0.001) and 60 years old (p < 0.001). The association was statistically significant in both sexes and on comparison of VIPVIZA study subgroups. Adjustments for confounding factors did not alter the estimated relationship between IM-GSM and cIMT progression. Echolucent carotid intima media at baseline associates with increased 3-year cIMT progression among an asymptomatic, middle-aged population. Echogenicity of the intima media may identify individuals at risk for accelerated vascular aging.

Ultrasonic echolucent carotid intima-media (IM) complex and accelerated progression of carotid intima mediathickness (cIMT) have both separately been shown to predict future cardiovascular events. The aim of this studywas to evaluate if the echogenicity of the IM-complex is associated with the 3-year progression of cIMT. B-modeultrasound images captured at baseline and 3-year follow-up in the ‘Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention’ (VIPVIZA) trial were included (n: 3154). The bilateral mean cIMT and IM-echogenicity by greyscale median (GSM) were measured in the common carotid artery. Associations between IM-GSM at baseline and the 3-year cIMT progression were investigated using linear regression models for the whole population and stratified by sex, age and VIPVIZA study group (intervention versus control). In addition, adjusted analyses for confounding factors were performed. Unadjusted analysis showed that decreased IM-GSM at baseline was associated with increased progression of cIMT (p < 0.001). Stratified by age, the association was significant among 40 (p < 0.001) and 60 years old (p < 0.001). The association was statistically significant in both sexes and on comparison of VIPVIZA study subgroups. Adjustments for confounding factors did not alter the estimated relationship between IM-GSM and cIMT progression. Echolucent carotid intima media at baseline associates with increased 3-year cIMT progression among an asymptomatic, middle-aged population. Echogenicity of the intima media may identify individuals at risk for accelerated vascular aging.

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.

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

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

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

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

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

Ultrasonic echolucent carotid intima-media (IM) complex and accelerated progression of carotid intima mediathickness (cIMT) have both separately been shown to predict future cardiovascular events. The aim of this studywas to evaluate if the echogenicity of the IM-complex is associated with the 3-year progression of cIMT. B-modeultrasound images captured at baseline and 3-year follow-up in the ‘Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention’ (VIPVIZA) trial were included (n: 3154). The bilateral mean cIMT and IM-echogenicity by greyscale median (GSM) were measured in the common carotid artery. Associations between IM-GSM at baseline and the 3-year cIMT progression were investigated using linear regression models for the whole population and stratified by sex, age and VIPVIZA study group (intervention versus control). In addition, adjusted analyses for confounding factors were performed. Unadjusted analysis showed that decreased IM-GSM at baseline was associated with increased progression of cIMT (p < 0.001). Stratified by age, the association was significant among 40 (p < 0.001) and 60 years old (p < 0.001). The association was statistically significant in both sexes and on comparison of VIPVIZA study subgroups. Adjustments for confounding factors did not alter the estimated relationship between IM-GSM and cIMT progression. Echolucent carotid intima media at baseline associates with increased 3-year cIMT progression among an asymptomatic, middle-aged population. Echogenicity of the intima media may identify individuals at risk for accelerated vascular aging.

Ultrasonic echolucent carotid intima-media (IM) complex and accelerated progression of carotid intima mediathickness (cIMT) have both separately been shown to predict future cardiovascular events. The aim of this studywas to evaluate if the echogenicity of the IM-complex is associated with the 3-year progression of cIMT. B-modeultrasound images captured at baseline and 3-year follow-up in the ‘Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention’ (VIPVIZA) trial were included (n: 3154). The bilateral mean cIMT and IM-echogenicity by greyscale median (GSM) were measured in the common carotid artery. Associations between IM-GSM at baseline and the 3-year cIMT progression were investigated using linear regression models for the whole population and stratified by sex, age and VIPVIZA study group (intervention versus control). In addition, adjusted analyses for confounding factors were performed. Unadjusted analysis showed that decreased IM-GSM at baseline was associated with increased progression of cIMT (p < 0.001). Stratified by age, the association was significant among 40 (p < 0.001) and 60 years old (p < 0.001). The association was statistically significant in both sexes and on comparison of VIPVIZA study subgroups. Adjustments for confounding factors did not alter the estimated relationship between IM-GSM and cIMT progression. Echolucent carotid intima media at baseline associates with increased 3-year cIMT progression among an asymptomatic, middle-aged population. Echogenicity of the intima media may identify individuals at risk for accelerated vascular aging.

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.

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.

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.

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.

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.

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, Originally included in thesis in manuscript form with titel: "Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions"

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.

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.

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.

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, Originally included in thesis in manuscript form with titel: "Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions"

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, Originally included in thesis in manuscript form with titel: "Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions"

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

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.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

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.

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, Originally included in thesis in manuscript form with titel: "Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions"

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.

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, Originally included in thesis in manuscript form with titel: "Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of voluntary skeletal muscle contractions"

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.

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.

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.

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.

Objective: Non-adherence to guidelines and preventive measures is a major challenge, particularly so to ob- tain long-term adherence to lifestyle changes and recommended medication. The objective was to investigate if pictorial information regarding subclinical carotid atherosclerosis provided to individuals and physicians gave sustained effects on cardiovascular risk beyond the previously reported effect after 1 year and up to 3 years. Methods: A Prospective Randomized Open Blinded End-point (PROBE) trial. Within a CVD prevention program in Västerbotten County, Sweden, 3532 healthy individuals aged 40, 50 or 60 years were enrolled and 1:1 ran- domized to intervention ( n = 1749; pictorial information with additional prevention materials to participants and physicians) or control group ( n = 1783; no pictorial information to participants and physicians). Preventive measures were managed within primary care. Participants were investigated at baseline during 2013–2016 and at follow-up after 1 and 3 years. Results: A beneficial effect on cardiovascular risk was observed at 3-year follow-up; Framingham Risk Score (FRS) was 13.38 for the intervention group and 14.08 for the control group ( p = 0.047) and SCORE was 1.69 vs. 1.82 ( p = 0.022). The effect observed at 1-year was sustained over 3 years after adjustment for sex and education and more pronounced among participants with a severe atherosclerotic picture at baseline. Conclusions: This study provides evidence of sustained beneficial effects on the adherence to prevention guidelines over 3 years of pictorial information about subclinical carotid atherosclerosis, resulting in lower cardiovascular risk regardless of sex and educational level. Direct visualization of the underlying still subclinical atherosclerotic disease, rather than just indirect information about risk factors and statistical risk of future myocardial infarction, stroke and death, is one way to tackle the problem of non-adherence to prevention of, Originally included in thesis in manuscript form.

Objective: Non-adherence to guidelines and preventive measures is a major challenge, particularly so to ob- tain long-term adherence to lifestyle changes and recommended medication. The objective was to investigate if pictorial information regarding subclinical carotid atherosclerosis provided to individuals and physicians gave sustained effects on cardiovascular risk beyond the previously reported effect after 1 year and up to 3 years. Methods: A Prospective Randomized Open Blinded End-point (PROBE) trial. Within a CVD prevention program in Västerbotten County, Sweden, 3532 healthy individuals aged 40, 50 or 60 years were enrolled and 1:1 ran- domized to intervention ( n = 1749; pictorial information with additional prevention materials to participants and physicians) or control group ( n = 1783; no pictorial information to participants and physicians). Preventive measures were managed within primary care. Participants were investigated at baseline during 2013–2016 and at follow-up after 1 and 3 years. Results: A beneficial effect on cardiovascular risk was observed at 3-year follow-up; Framingham Risk Score (FRS) was 13.38 for the intervention group and 14.08 for the control group ( p = 0.047) and SCORE was 1.69 vs. 1.82 ( p = 0.022). The effect observed at 1-year was sustained over 3 years after adjustment for sex and education and more pronounced among participants with a severe atherosclerotic picture at baseline. Conclusions: This study provides evidence of sustained beneficial effects on the adherence to prevention guidelines over 3 years of pictorial information about subclinical carotid atherosclerosis, resulting in lower cardiovascular risk regardless of sex and educational level. Direct visualization of the underlying still subclinical atherosclerotic disease, rather than just indirect information about risk factors and statistical risk of future myocardial infarction, stroke and death, is one way to tackle the problem of non-adherence to prevention of, Originally included in thesis in manuscript form.

Objective: Non-adherence to guidelines and preventive measures is a major challenge, particularly so to ob- tain long-term adherence to lifestyle changes and recommended medication. The objective was to investigate if pictorial information regarding subclinical carotid atherosclerosis provided to individuals and physicians gave sustained effects on cardiovascular risk beyond the previously reported effect after 1 year and up to 3 years. Methods: A Prospective Randomized Open Blinded End-point (PROBE) trial. Within a CVD prevention program in Västerbotten County, Sweden, 3532 healthy individuals aged 40, 50 or 60 years were enrolled and 1:1 ran- domized to intervention ( n = 1749; pictorial information with additional prevention materials to participants and physicians) or control group ( n = 1783; no pictorial information to participants and physicians). Preventive measures were managed within primary care. Participants were investigated at baseline during 2013–2016 and at follow-up after 1 and 3 years. Results: A beneficial effect on cardiovascular risk was observed at 3-year follow-up; Framingham Risk Score (FRS) was 13.38 for the intervention group and 14.08 for the control group ( p = 0.047) and SCORE was 1.69 vs. 1.82 ( p = 0.022). The effect observed at 1-year was sustained over 3 years after adjustment for sex and education and more pronounced among participants with a severe atherosclerotic picture at baseline. Conclusions: This study provides evidence of sustained beneficial effects on the adherence to prevention guidelines over 3 years of pictorial information about subclinical carotid atherosclerosis, resulting in lower cardiovascular risk regardless of sex and educational level. Direct visualization of the underlying still subclinical atherosclerotic disease, rather than just indirect information about risk factors and statistical risk of future myocardial infarction, stroke and death, is one way to tackle the problem of non-adherence to prevention of, Originally included in thesis in manuscript form.

Objective: Non-adherence to guidelines and preventive measures is a major challenge, particularly so to ob- tain long-term adherence to lifestyle changes and recommended medication. The objective was to investigate if pictorial information regarding subclinical carotid atherosclerosis provided to individuals and physicians gave sustained effects on cardiovascular risk beyond the previously reported effect after 1 year and up to 3 years. Methods: A Prospective Randomized Open Blinded End-point (PROBE) trial. Within a CVD prevention program in Västerbotten County, Sweden, 3532 healthy individuals aged 40, 50 or 60 years were enrolled and 1:1 ran- domized to intervention ( n = 1749; pictorial information with additional prevention materials to participants and physicians) or control group ( n = 1783; no pictorial information to participants and physicians). Preventive measures were managed within primary care. Participants were investigated at baseline during 2013–2016 and at follow-up after 1 and 3 years. Results: A beneficial effect on cardiovascular risk was observed at 3-year follow-up; Framingham Risk Score (FRS) was 13.38 for the intervention group and 14.08 for the control group ( p = 0.047) and SCORE was 1.69 vs. 1.82 ( p = 0.022). The effect observed at 1-year was sustained over 3 years after adjustment for sex and education and more pronounced among participants with a severe atherosclerotic picture at baseline. Conclusions: This study provides evidence of sustained beneficial effects on the adherence to prevention guidelines over 3 years of pictorial information about subclinical carotid atherosclerosis, resulting in lower cardiovascular risk regardless of sex and educational level. Direct visualization of the underlying still subclinical atherosclerotic disease, rather than just indirect information about risk factors and statistical risk of future myocardial infarction, stroke and death, is one way to tackle the problem of non-adherence to prevention of, Originally included in thesis in manuscript form.

Objective: Non-adherence to guidelines and preventive measures is a major challenge, particularly so to ob- tain long-term adherence to lifestyle changes and recommended medication. The objective was to investigate if pictorial information regarding subclinical carotid atherosclerosis provided to individuals and physicians gave sustained effects on cardiovascular risk beyond the previously reported effect after 1 year and up to 3 years. Methods: A Prospective Randomized Open Blinded End-point (PROBE) trial. Within a CVD prevention program in Västerbotten County, Sweden, 3532 healthy individuals aged 40, 50 or 60 years were enrolled and 1:1 ran- domized to intervention ( n = 1749; pictorial information with additional prevention materials to participants and physicians) or control group ( n = 1783; no pictorial information to participants and physicians). Preventive measures were managed within primary care. Participants were investigated at baseline during 2013–2016 and at follow-up after 1 and 3 years. Results: A beneficial effect on cardiovascular risk was observed at 3-year follow-up; Framingham Risk Score (FRS) was 13.38 for the intervention group and 14.08 for the control group ( p = 0.047) and SCORE was 1.69 vs. 1.82 ( p = 0.022). The effect observed at 1-year was sustained over 3 years after adjustment for sex and education and more pronounced among participants with a severe atherosclerotic picture at baseline. Conclusions: This study provides evidence of sustained beneficial effects on the adherence to prevention guidelines over 3 years of pictorial information about subclinical carotid atherosclerosis, resulting in lower cardiovascular risk regardless of sex and educational level. Direct visualization of the underlying still subclinical atherosclerotic disease, rather than just indirect information about risk factors and statistical risk of future myocardial infarction, stroke and death, is one way to tackle the problem of non-adherence to prevention of, Originally included in thesis in manuscript form.

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.

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.

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.

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

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 cont