Persistent inward currents (PICs) are responsible for amplifying motoneuronal synaptic inputs and contribute to generating normal motoneuron activation. Delta-F (ΔF) is a well-established method that estimates PICs in humans indirectly from firing patterns of individual motor units. Traditionally, motor unit firing patterns are obtained by manually decomposing electromyography (EMG) signals recorded through intramuscular electrodes (iEMG). A previous iEMG study has shown that in humans the elbow extensors have higher ΔF than the elbow flexors. In this study, EMG signals were collected from the ankle extensors and flexors using high-density surface array electrodes during isometric sitting and standing at 10-30% maximum voluntary contraction. The signals were then decomposed into individual motor unit firings. We hypothesized that comparable to the upper limb, the lower limb extensor muscles (soleus) would have higher ΔF than the lower limb flexor muscles [tibialis anterior (TA)]. Contrary to our expectations, ΔF was higher in the TA than the soleus during sitting and standing despite the difference in cohort of participants and body positions. The TA also had significantly higher maximum discharge rate than the soleus while there was no difference in rate increase. When only the unit pairs with similar maximum discharge rates were compared, ∆F was still higher in the TA than the soleus. Future studies will focus on investigating the functional significance of the findings.NEW & NOTEWORTHY With the use of high-density surface array electrodes and convolutive blind source separation algorithm, thousands of motor units were decomposed from the soleus and tibialis anterior muscles. Persistent inward currents were estimated under seated and standing conditions via delta-F (∆F) calculation, and the results showed that unlike the upper limb, the flexor has higher ∆F than the extensor in the lower limb. Future studies will focus on functional significance of the findings.
Keywords: EMG; PICs; delta-F; motor control; motor neuron.