Background Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors. Results This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%). Conclusions This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.
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.
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.
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