Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions

Abstract

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.

Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units

“…Both the MRI technique and the early UUS methods use electrical stimulation to generate muscle activity to image MUs. In contrast, a recently proposed UUS technique (MU-UUS) can identify single MUs in voluntary activations using a blind source separation algorithm of the velocity image sequences to extract spatiotemporal components (Rohlén et al, 2020b) in which a subset corresponds to the MU (Carbonaro et al, 2022;Rohlén et al, 2022Rohlén et al, , 2020a. Given this, MU-UUS provides estimates of MU territories in cross-section and the corresponding train of twitches evoked by the spinal motor neurons' neural discharges (spikes).…”

“…Previous studies involving the MU-UUS analysis have provided estimates of unfused tetanic signals of single MUs (Ali et al, 2020;Rohlén et al, 2022Rohlén et al, , 2020bRohlén et al, , 2020a. These unfused tetanic estimates have further been used to estimate their spike trains (neural discharges).…”

“…Although an optimized parameter set may have improved the performance, these studies did not focus on optimizing the parameters (Rohlén et al, 2020b). This spike estimation has been suggested to have lower performance for unfused tetanic signals with lower SNR (Rohlén, 2021), and the estimated spikes have been shown to vary with respect to the reference (experimental) spike (Rohlén et al, 2020a). For these reasons, an improved spike estimation method was suggested that consisted of the continuous Haar wavelet transform.…”

“…For these reasons, an improved spike estimation method was suggested that consisted of the continuous Haar wavelet transform. It was motivated by its property of detecting abrupt transitions and oscillations in the signal as the twitch onset provides relatively large wavelet coefficients (Rohlén, 2021). However, the question remains which of the two methods is better at recovering the true spike train with maximal accuracy?…”

Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separation

“…A recent study found that methodological improvements in velocity image separation (blind source separation, BSS) should lead to a higher identification rate 6 . A methodological improvement could include more information about the MU characteristics in the separation process 6 . The reasoning is that the current method neglects the temporal information in the separation process and focuses on sparse spatial (pixel) distributions as cost functions motivated by the physical muscle unit territories (Fig.…”