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 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 mm 3) 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 (4 × 4 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.

Objective In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms. Results We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using spatial information, which was the best together with stSOBI using either spatial or temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units.

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.