Modeling of muscle motor unit innervation process correlation and common drive.
Concurrently active motor units (MUs) of a given muscle can exhibit a certain degree of synchronous firings, and a certain degree of common variation in their firing rates. The former property is referred to as motor unit synchrony in the literature, which is termed motor unit innervation process (MUIP) correlation in this study. The latter is referred to as motor unit common drive and can be quantified by the common drive coefficient, which is the correlation coefficient between the smoothed firing rates of the two MUs. Both properties have important roles and implications in the generation and resulting characteristics of the myoelectric signal and for the development of signal processing algorithms in myoelectric signal (MES) applications. In order to study these implications and characteristics, in this paper estimation procedures are developed to quantify the degree of MUIP correlation and common drive as functions of physiological parameters. Also, the interaction between MUIP correlation and motor unit common drive is studied in a physiologically realistic simulation model. Neurons modeled by Hodgkin-Huxley systems form the framework of the simulation model in which excitation and synaptic characteristics can be modified. MUIP correlation and common drive degree and interaction are studied through a number of simulations. To support the simulation results, experimental in vivo motor unit trains were collected at low levels of contraction from 11 subjects, and decomposed into the constituent unit trains giving 50 concurrently active motor unit pairs. The simulation demonstrated that the innervation process correlation coefficient is controlled primarily by the postsynaptic conductance, gsyn, and was less than 0.05 mS/cm2 for realistic values of gsyn. The common drive was found to be controlled by the exciting neuron input with no statistically significant interaction between it and the MUIP correlation. The experimental data gave results in close agreement with those of the simulation.
Jiang
Ning
N
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, Canada. ning.jiang@unb.ca
Parker
Philip A
PA
Englehart
Kevin B
KB
D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology*; D013223:Statistics as Topic