Assessing the impact of degree of fusion and muscle fibre twitch shape variation on the accuracy of motor unit discharge time identification from ultrasound images

• Published in: Biomedical signal processing and control Vol. 100; p. 107002
• Main Authors: Rohlén, Robin, Lubel, Emma, Farina, Dario
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Blind source separation; Motor units; Spike train; Ultrasound; Motor units; Spike train; Blind source separation; Ultrasound
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2024.107002

•By decoding ultrasound images, it is possible to detect neural discharges.•The decoding method is robust to degrees of fusion and varying successive twitches.•Neural discharges up to 40% of maximal voluntary force can be identified.•The results from the in silico and in vivo experiments were consistent.•This method is important for neural interfacing applications. 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. 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. 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. 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.