Adaptations in motor unit properties underlying changes in recruitment, rate coding, and maximum force.

Changes in the discharge characteristics of motor units as well as in the maximum force-producing capacity of the muscle are observed following training, aging, and fatiguability. The ability to measure the adaptations in the neuromuscular properties underlying these changes experimentally, however, is limited. In this study we used a computational model to systematically investigate the effects of various neural and muscular adaptations on motor unit recruitment thresholds, average motor unit discharge rates in submaximal contractions, and maximum force. The primary focus was to identify candidate adaptations that can explain experimentally observed changes in motor unit discharge characteristics after 4 wk of strength training (Del Vecchio A, Casolo A, Negro F, Scorcelletti M, Bazzucchi I, Enoka R, Felici F, Farina D. 597: 1873-1887, 2019). The simulation results indicated that multiple combinations of adaptations, likely involving an increase in maximum discharge rate across motor units, may occur after such training. On a more general level, we found that the magnitude of the adaptations scales linearly with the change in recruitment thresholds, discharge rates, and maximum force. In addition, the combination of multiple adaptations can be predicted as the linear sum of their individual effects. Together, this implies that the outcomes of the simulations can be generalized to predict the effect of any combination of neural and muscular adaptations. In this way, the study provides a tool for estimating potential underlying adaptations in neural and muscular properties to explain any change in commonly used measures of rate coding, recruitment, and maximum force. Our ability to measure adaptations in neuromuscular properties in vivo is limited. Using a computational model, we quantify the effect of multiple neuromuscular adaptations on common measures of motor unit recruitment, rate coding, and force-producing capacity. Scaling and combining adaptations had a near-linear effect on these measures, indicating that the results can explain and predict neuromuscular adaptations in a wide range of conditions, including, but not limited to, strength training.