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

Abstract

Objective.-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.Approach.-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 10

Persistent Inward Currents Increase With The Level Of Voluntary Drive In Plantar Flexor Low-Threshold Motor Units

“…Second, Kim et al, 24 and Afsharipour et al, 23 did not track the same motor units across contractions intensities. As previously reported 27 , there is a large between-motor unit variability in ΔFs, which might have reduced the sensitivity of the variable to detect differences among contractions intensities. Third, both studies compared ΔFs obtained from different populations of motor units.…”

“…Next, participants were familiarised with both trapezoidal and triangular ramped contractions. Both triangular and trapezoidal contractions have been used previously to calculate ΔFs using the paired motor unit analysis 26,27 . These ramp-shaped contractions were performed to 10, 20 and 30% of their maximal voluntary contraction torque.…”

“…ΔF was calculated as the change in discharge frequencies of the control motor unit from the moment of recruitment to the moment of de-recruitment of the test unit 16,20 . In order to pair motor units, the following criteria were adopted: 1) rate-to-rate correlations between the smoothed discharge rate polynomials of the test and control units was r ≥ 0.7; 2) test units were recruited at least 1.0 s after the control units; and 3) the control unit did not show discharge rate saturation after the moment of test unit recruitment (> 0.5 pps) 15,16,27,28,32 . Furthermore, ΔFs were calculated only for pairs identified at all three contraction intensities, therefore including only motor units recruited from 0 to 10% of MVC, which were averaged to obtain a single ΔF for each motor unit.…”

Adaptive Neural Decoder for Prosthetic Hand Control

“…The simulations showed that a decoder that captures the initial firing rate as well as estimates the relative change in firing gain between the secondary and tertiary spiking phases could decode the excitation level to MNs accurately. Importantly, these parameters could be easily and accurately estimated from pool or cellular spiking in humans (Farina et al, 2017;Afsharipour et al, 2020;Hassan et al, 2020). While decoding MN spiking activity for estimating the synaptic input (as a measure of motor intent) or for estimating grip force have been recently examined in animals (Thompson et al, 2018) and humans (Farina et al, 2017;Twardowski et al, 2019;Afsharipour et al, 2020;Hassan et al, 2020), only one decoder in the literature has shown real-time performance for prosthetic control (decoding time < 10 ms, Twardowski et al, 2019).…”

“…Importantly, these parameters could be easily and accurately estimated from pool or cellular spiking in humans (Farina et al, 2017;Afsharipour et al, 2020;Hassan et al, 2020). While decoding MN spiking activity for estimating the synaptic input (as a measure of motor intent) or for estimating grip force have been recently examined in animals (Thompson et al, 2018) and humans (Farina et al, 2017;Twardowski et al, 2019;Afsharipour et al, 2020;Hassan et al, 2020), only one decoder in the literature has shown real-time performance for prosthetic control (decoding time < 10 ms, Twardowski et al, 2019). When compared to conventional amplitudebased myoelectric decoders, MN firing activity-based decoders provide more responsive, smooth, and proportional control, supporting the high performance in Twardowski et al (2019) and even higher performance observed in our results (see Pearson's CC, RMSE, and NRMSE, next section).…”