Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

Abstract

Objective: Ultrasound (US) images during a muscle contraction can be decoded into individual motor unit (MU) activity, i.e., trains of neural discharges from the spinal cord. However, current decoding algorithms assume a stationary mixing matrix, i.e. equal mechanical twitches at each discharge. This study aimed to investigate the accuracy of these approaches in non-ideal conditions when the mechanical twitches in response to neural discharges vary over time and are partially fused in tetanic contractions. Methods: We performed an in silico experiment to study the decomposition accuracy for changes in simulation parameters, including the twitch waveforms, spatial territories, and motoneuron-driven activity. Then, we explored the consistency of the in silico findings with an in vivo experiment on the tibialis anterior muscle at varying contraction forces. Results: A large population of MU spike trains across different excitatory drives, and noise levels could be identified. The identified MUs with varying twitch waveforms resulted in varying amplitudes of the estimated sources correlated with the ground truth twitch amplitudes. The identified spike trains had a wide range of firing rates, and the later recruited MUs with larger twitch amplitudes were easier to identify than those with small amplitudes. Finally, the in silico and in vivo results were consistent, and the method could identify MU spike trains in US images at least up to 40% of the maximal voluntary contraction force. Conclusion: The decoding method was accurate irrespective of the varying twitch-like shapes or the degree of twitch fusion, indicating robustness, important for neural interfacing applications.

Model-based deconvolution of a force signal to estimate motor unit twitch parameters under low-force isometric contractions

“…In other words, the method is biased towards higher twitch amplitudes and, therefore, later recruited ones according to Henneman’s size principle (Henneman, 1957; Henneman et al, 1965). One should stress that this is relevant for many methods interested in decoding compound signals into MU activity, including high-density surface EMG decomposition (Holobar et al, 2009) and ultrafast ultrasound (Rohlén et al, 2024). However, the twitch parameter-dependent variability does not concern the twitch amplitude, which has the same variance across all ground truth twitch amplitudes (Fig.…”