Spike-triggered averaging provides inaccurate estimates of motor unit twitch properties under optimal conditions

Abstract

The authors confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.ABSTRACT Spike-triggered averaging is a commonly used technique for the estimation of motor unit twitches during voluntary contractions, although the obtained twitch estimates are known to be inaccurate in several conditions. Nevertheless, it is commonly assumed that a careful selection of the triggers may reduce the inaccuracy. This study aimed to analyze the impact of trigger selection criteria and thereby to identify the minimum estimation errors using a computational neuromuscular model.Force signals of five-minute duration were simulated at 10 contraction levels between 1 and 30% of the maximal voluntary contraction level (MVC) for motor unit pools of varying size (100, 300, and 800 motor units). Triggers were selected based on the inter-spike intervals (minimal value: 90-175 ms) and the number of triggers (minimal value: 100-800). The simulation results indicated that a minimum of 400 triggers with inter-spike intervals >125 ms are needed to achieve the most accurate twitch estimates. Even under these conditions, however, a substantial estimation error remained (11.8-31.2% for different twitch parameters for simulations with 100 motor units). The error increased with the innervation number. The study demonstrates the fundamental inaccuracy of twitch estimates from spike-triggered averaging, which has important implications for our understanding of muscular adaptations.

Mathematical relationships between spinal motoneuron properties

“…Sag experiments are irrelevant in humans ( Buchthal and Schmalbruch, 1970 ; Thomas et al, 1991 ; Bakels and Kernell, 1993 ; Macefield et al, 1996 ; Bigland-Ritchie et al, 1998 ; Fuglevand et al, 1999 ) and lack consistency with other identification methods ( Nordstrom and Miles, 1990 ). MU-type identification by twitch contraction time measurements is limited by the strong sources of inaccuracy involved in the transcutaneous stimulation, intramuscular microstimulation, intraneural stimulation, and spike-triggered averaging techniques ( Taylor et al, 2002 ; Keen and Fuglevand, 2004 ; McNulty and Macefield, 2005 ; Negro et al, 2014 ; Dideriksen and Negro, 2018 ). Finally, as muscle fibres show a continuous distribution of contractile properties among the MU pool, some MUs fail to be categorized in discrete MU types in some animal studies by histochemical approaches ( Reinking et al, 1975 ; Tötösy de Zepetnek et al, 1992 ).…”

Mathematical Relationships between Spinal Motoneuron Properties

“…Sag experiments are irrelevant in humans (Buchthal & Schmalbruch, 1970;Thomas et al, 1991;Bakels & Kernell, 1993;Macefield et al, 1996;Bigland-Ritchie et al, 1998;Fuglevand et al, 1999), and lack consistency with other identification methods (Nordstrom & Miles, 1990). MU type identification by twitch contraction time measurements is limited by the strong sources of inaccuracy involved in the transcutaneous stimulation, intramuscular microstimulation, intraneural stimulation, and spiketriggered averaging techniques (Taylor et al, 2002;Keen & Fuglevand, 2004;McNulty & Macefield, 2005;Negro et al, 2014;Dideriksen & Negro, 2018). Finally, as muscle fibres show a continuous distribution of contractile properties among the MU pool, some MUs fail to be categorized in discrete MU types in some animal studies by histochemical approaches (Reinking et al, 1975;Totosy de Zepetnek et al, 1992).…”

Neural filtering of physiological tremor oscillations to spinal motor neurons mediates short-term acquisition of a skill learning task

“…In this model, the population of motor neurons received common and independent inputs in varying relative proportions (Negro and Farina, 2012). Detailed descriptions of the modelling approach can be found in previous studies (Negro and Farina, 2011b; Farina et al, 2014; Negro et al, 2016a; Dideriksen and Negro, 2018). The motor neuron parameters were consistent with those used by Cisi and Kohn (2008) and were selected according to an exponential distribution over the pool of motor neurons (Fuglevand et al, 1993).…”

“…Similarly, we created two models to represent pre- and post-skill acquisition, but in this case, we simulated an increase in presynaptic inhibition of Ia afferent feedback in the post-skill acquisition model by decreasing the gain of the Ia afferent input to the motor neuron pool. In both scenarios, each model ( pre- and post-skill acquisition) was repeated ten times as has been done in previous stimulation studies (Dideriksen and Negro, 2018). Following the same approach on the experimental data, we calculated the simulated force output power spectrum; the ratio ( post/pre ) of the area under the curve of motor unit z-coherence within delta (1-5 Hz), alpha (5-15 Hz) and beta (15-35 Hz) bands; and the area under the curve ratio ( post/pre ) of z-coherence between simulated force/CST and the target template.…”