<p dir="ltr">All motor commands converge onto motor units (MUs), which transduce the signals into mechanical actions of muscle fibres. This process is highly non-linear due to combinations of ionotropic (excitatory/inhibitory) and metabotropic (neuromodulatory) inputs. Neuromodulatory inputs facilitate dendritic persistent inward currents, which introduce non-linearities in MU discharge patterns and provide insights into the structure of motor commands. Here, we investigated the relative contribution of neuromodulation and the pattern of inhibition to modulate human MU discharge patterns with contraction forces up to 70% maximum. Leveraging MU discharge patterns identified from three human muscles (tibialis anterior – TA, and vastus lateralis and medialis), we show that with increased contraction force, the onset-offset discharge rate hysteresis (ΔF) increased whilst ascending MU discharge patterns become more linear, with lower slopes. In a follow-up experiment, we demonstrated that the observations of increased ΔF and more linear ascending MU discharge patterns with greater contraction force are maintained even when accounting for contraction duration and rate of force increase. We then reverse-engineered TA MU discharge patterns using highly realistic in silico motoneuron pools to substantiate the inferred physiological mechanisms from human recordings. We demonstrate a sharply restricted solution space, whereby the contraction force-induced changes in experimentally obtained MU discharge patterns can only be recreated with increased neuromodulation and a more reciprocal (i.e. push-pull) inhibitory pattern. In summary, our experimental and computational data suggest that neuromodulation and inhibitory patterns are uniquely shaped to generate discharge patterns that support force increases across a large proportion of the motor pool’s recruitment range.</p><p dir="ltr"><br></p>
Funding
Versus Arthritis Foundation Fellowship (reference no. 22569)
Natural Sciences and Engineering Research Council of Canada [Discovery Grant: RGPIN-2023-05862]
Natural Sciences and Engineering Research Council of Canada [Discovery Launch Supplement
DGECR-2023-00279]