A novel methodological framework for the assessment of the neural control of the shoulder using high-density surface electromyography

BACKGROUND: Complex shoulder function relies on coordinated activation of small and large muscles, including the deltoid, pectoralis major, trapezius, and latissimus dorsi. However, detailed knowledge of their neuromuscular control remains limited. This feasibility study aimed to develop a methodological framework to investigate the neural control of larger superficial shoulder muscles by combining a six-degree-of-freedom load cell, robotic arm and high-density surface electromyograms (HDsEMG). METHODS: Six healthy participants performed 30 % maximal voluntary isometric contractions (abduction, adduction, flexion, extension) at 30° and 65° of lateral abduction shoulder positions. HDsEMGs were recorded and analysed at the global activation, spatial distribution of activation and motoneurone levels. Global activation was quantified using averaged normalized root-mean-square (RMS) and spatial distribution using coefficient-of-variation of topographic maps. HDsEMGs were decomposed into individual motor unit spike trains using convolutive blind source separation. Motor unit (MU) behaviour was characterized by mean discharge rate and spatial distribution of MU action potentials (MUAPs). RESULTS: RMS maps revealed action-specific activation within and between muscles, with the trapezius active across all tasks, while the anterior, middle, and posterior deltoid, pectoralis major, and latissimus dorsi predominantly activated during abduction, flexion, and extension. MU discharge rate showed task-dependent activity. MUAP spatial distributions showed distinct MU territories within arrays, suggesting region-specific recruitment strategies across actions. CONCLUSION: The present framework provides a promising methodological approach for the non-invasive assessment of individual MU activity in superficial shoulder muscles. The approach provides a methodological basis for novel incorporation of neural control information into biomechanical models of shoulder function.