Impact of parameter selection on estimates of motoneuron excitability using paired motor unit analysis

Noninvasive estimation of motoneuron excitability in human motoneurons is achieved through a paired motor unit analysis (ΔF) that quantifies hysteresis in the instantaneous firing rates at motor unit recruitment and de-recruitment. The ΔF technique provides insight into the magnitude of neuromodulatory synaptic input and persistent inward currents (PICs). While the ΔF technique is commonly used for estimating motoneuron excitability during voluntary contractions, computational parameters used for the technique vary across studies. A systematic investigation into the relationship between these parameters and ΔF values is necessary.

We assessed the sensitivity of the ΔF technique with several criteria commonly used in selecting motor unit pairs for analysis and methods used for smoothing the instantaneous motor unit firing rates. Using high-density surface EMG and convolutive blind source separation, we obtained a large number of motor unit pairs (5409) from the triceps brachii of ten healthy individuals during triangular isometric contractions.

We found an exponential plateau relationship between ΔF and the recruitment time difference between the motor unit pairs and an exponential decay relationship between ΔF and the de-recruitment time difference between the motor unit pairs, with the plateaus occurring at approximately 1 s and 1.5 s, respectively. Reduction or removal of the minimum threshold for rate-rate correlation of the two units did not affect ΔF values or variance. Removing motor unit pairs in which the firing rate of the control unit was saturated had no significant effect on ΔF. Smoothing the filter selection had no substantial effect on ΔF values and ΔF variance; however, filter selection affected the minimum recruitment and de-recruitment time differences.

Our results offer recommendations for standardized parameters for the ΔF approach and facilitate the interpretation of findings from studies that implement the ΔF analysis but use different computational parameters.