Motor unit properties affect the excitation-force relation of a muscle: a simulation study

Abstract

Muscles consists of multiple motor units (MUs) that produce the force required for our movements. Motor unitproperties are responsible for the relation between neural excitation and muscle force (excitation-force relation). Theforce of the muscle is dictated by the recruitment and the force output of the different motor units. The recruitmentis physiologically determined by the recruitment thresholds of the motor units, where motor units with a higher forceoutput are thought to have a higher recruitment threshold, conform Henneman’s orderly recruitment principle. Thegoal of this study was to predict the effects of the MU-threshold and MU-force output distributions on the excitation-force relation of a muscle. Assuming that a muscle’s excitation-force relation depends on the values of the motor unitproperties present in the muscle, the recruitment threshold and maximum force distributions among the motor unitscan be derived from muscle force recordings. To achieve the goal a computer model was build to simulate motor unitrecruitment and force production of a group of MUs with different (combinations) of MU-threshold and maximum MU-force output distributions. Both MU-threshold distribution and maximum MU-force output distribution of the musclewere mathematically varied between linear and exponential curves. Input of the muscle model was a linear increasingneural activation profile and the force output profile of the muscle was simulated. The resultant MU-activation andforce patterns were compared with published results of simultaneous MU-recruitment and force recordings obtainedfrom literature. The simulation results showed that the excitation-force profile does not show characteristics thatcan be linked to either the MU-threshold distribution or the MU-force output. As a consequence, force recordingsalone are not sufficient to estimate the recruitment threshold and maximum force distributions. However, the use of acomputer model in combination with additional recorded data such as (surface) electromygraphy (EMG) is expectedto show results from which estimating MU properties is possible.