Alterations in Neural Control of Constant Isometric Contraction with the Size of Error Feedback

Abstract

Discharge patterns from a population of motor units (MUs) were estimated with multi-channel surface electromyogram and signal processing techniques to investigate parametric differences in low-frequency force fluctuations, MU discharges, and force-discharge relation during static force-tracking with varying sizes of execution error presented via visual feedback. Fourteen healthy adults produced isometric force at 10% of maximal voluntary contraction through index abduction under three visual conditions that scaled execution errors with different amplification factors. Error-augmentation feedback that used a high amplification factor (HAF) to potentiate visualized error size resulted in higher sample entropy, mean frequency, ratio of high-frequency components, and spectral dispersion of force fluctuations than those of error-reducing feedback using a low amplification factor (LAF). In the HAF condition, MUs with relatively high recruitment thresholds in the dorsal interosseous muscle exhibited a larger coefficient of variation for inter-spike intervals and a greater spectral peak of the pooled MU coherence at 13–35 Hz than did those in the LAF condition. Manipulation of the size of error feedback altered the force-discharge relation, which was characterized with non-linear approaches such as mutual information and cross sample entropy. The association of force fluctuations and global discharge trace decreased with increasing error amplification factor. Our findings provide direct neurophysiological evidence that favors motor training using error-augmentation feedback. Amplification of the visualized error size of visual feedback could enrich force gradation strategies during static force-tracking, pertaining to selective increases in the discharge variability of higher-threshold MUs that receive greater common oscillatory inputs in the β-band.

Variations in Static Force Control and Motor Unit Behavior with Error Amplification Feedback in the Elderly

“…The β-band motor unit coherence reflects the interaction between cortical and spinal circuits, and its magnitude varies with the demands for sensorimotor integration. The β-band motor unit coherence has been showed to increase proportionally to greater amounts of visuospatial (Laine et al, 2014 ) and error information (Hwang et al, 2017 ) for young adults during a force-tracking task. Coincidentally, enhanced β-band electroencephalography (EEG)-EMG coherence is linked to steady-state force control with a higher degree of attentional focus (Kristeva et al, 2007 ; Witte et al, 2007 ).…”

“…The time window of interest was the 7th to 37th seconds of the experimental trial, during which force output was presumed to be relatively stable. The participants were guided with perceived errors in the control and EA conditions (Chen et al, 2017 ; Hwang et al, 2017 ). In the EA condition, the visualized force (VF) output was mathematically transformed so that the size of the visualized error (VE) was virtually augmented by 100% as compared to real error (RE) (VE = 2*RE) (Figure 1 ).…”

“…Following the z transformation, we subtracted the mean coherence between 100 Hz and 500 Hz from each coherence profile because the mean coherence contained no significant coherence (Witte et al, 2007 ; Nawab et al, 2010 ). A pooled motor unit coherence, which reflects common drive to the motoneuronal pool in the spectral domain, contained two prominent spectral peaks in the range of 0–4 Hz (ZC 0–4 Hz ) and 13–35 Hz (ZC 13–35 Hz ; Hwang et al, 2017 ). All of the force variables and discharge variables of the three experimental trials in the control and EA conditions were calculated and averaged subject-by-subject.…”

“…Visual EA is thought to inflate response conflicts and facilitate attentional focus on the motor task via error-monitoring networks (Calhoun et al, 2002 ). The EA feedback has been shown to improve point-to-point visuomotor tasks for healthy elderly (Bouchard et al, 2015 ) and neurological patients (Abdollahi et al, 2014 ), as well as a continuous static force-tracking task for young adults (Hwang et al, 2017 ). For young adults during static isometric contraction, visual EA could better stabilize force output with the enhanced complexity of force fluctuations, corollary to a larger coefficient of variation for inter-spike intervals and a greater motor unit coherence at 13–35 Hz (Hwang et al, 2017 ).…”

“…The EA feedback has been shown to improve point-to-point visuomotor tasks for healthy elderly (Bouchard et al, 2015 ) and neurological patients (Abdollahi et al, 2014 ), as well as a continuous static force-tracking task for young adults (Hwang et al, 2017 ). For young adults during static isometric contraction, visual EA could better stabilize force output with the enhanced complexity of force fluctuations, corollary to a larger coefficient of variation for inter-spike intervals and a greater motor unit coherence at 13–35 Hz (Hwang et al, 2017 ). In addition, EA produces a strategic feedback-feedforward mode shift during sinusoidal force tracking (Chen et al, 2017 ).…”