Influence of central and peripheral motor unit properties on isometric muscle force entropy: A computer simulation study

Abstract

Approximate entropy of isometric force is a popular measure to characterize behavioral changes across muscle contraction conditions. The degree to which force entropy characterizes the randomness of the motor control strategy, however, is not known. In this study, we used a computational model to investigate the correlation between approximate entropy of the synaptic input to a motor neuron pool, the neural drive to muscle (cumulative spike train; CST), and the force. This comparison was made across several simulation conditions, that included different synaptic command signal bandwidths, motor neuron pool sizes, and muscle contractile properties. The results indicated that although force entropy to some degree reflects the entropy of the synaptic command to motor neurons, it is biased by changes in motor unit properties. As a consequence, there was a low correlation between approximate entropy of force and the motor neuron input signal across all simulation conditions (r² = 0.13). Therefore, force entropy should only be used to compare motor control strategies across conditions where motor neuron properties can be assumed to be maintained. Instead, we recommend that the entropy of the descending motor commands should be estimated from CSTs comprising spike trains of multiple motor units.

Keywords: Approximate entropy; Computational model; Force variability; Neural drive.