Neuromuscular mechanisms and neural strategies in the control of time-varying muscle contractions

Erimaki, Sophia; Agapaki, Orsalia M.; Christakos, Constantinos N.

doi:10.1152/jn.00835.2012

Cited by 8 publications

(6 citation statements)

References 56 publications

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“…On average, maximum and minimum EMD were approximately 312 ms and 139 ms, respectively. In contrast to earlier findings, these values are greater than what is obtained with standard EMD computation [2], [3], [4], [5], but more similar to [11]. These differences may partly be explained by the methodology we employed to compute the MU/Force delay but also by the nature of the performed movement, as continuous time-varying movements differ in the way motor units are recruited.…”

Section: Resultscontrasting

confidence: 89%

“…This technique may achieve a more precise estimation of the portion of EMD due to MU recruitment while preserving the portion related to the mechanical properties of the muscle and the tendon. A similar approach was used previously [11] but was limited to the cross-correlation of the activity of only individual MUs with force. Conversely, here we estimate the neural drive as the cumulative set of discharges of several MUs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Úbeda

Vecchio

Sartori

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

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We evaluated the electromechanical delay (EMD) for the tibialis anterior (TA) muscle during the performance of time-varying ankle dorsiflexions. Subjects were asked to track a sinusoidal trajectory, for a range of amplitudes and frequencies. Motor unit (MU) action potential trains were identified from surface electromyography (EMG) decomposition and summed to generate the cumulative spike train (CST). CST and the exerted force were cross-correlated to identify the delay between the CST and force, which was considered as an estimate of the EMD. The results showed that the EMD decreased logarithmically with the increase in the slope of the force produced.

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Section: Resultscontrasting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Úbeda

Vecchio

Sartori

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

View full text Add to dashboard Cite

show abstract

“…Although the accuracy of surface EMG decomposition is still debatable (Farina and Enoka, 2011; De Luca et al, 2015a), it is beyond the scope of the present setup to resolve pre-existing disputes over surface EMG decomposition. In the least, the motor unit behaviors during cyclic contraction observed in this study were largely consistent with those of previous reports (Iyer et al, 1994; Knight and Kamen, 2007; Erimaki et al, 2013). On the other hand, one technical merit of using surface EMG decomposition is that it can detect more active MUs than intramuscular EMG can, which helps to characterize error-dependent discharges in a small portion of the HP MUs.…”

Section: Discussionsupporting

confidence: 93%

“…Examinations of motor unit (MU) control have also revealed that the brain can mediate input excitation to motoneurons based on the perceived error and thereby maintain force at the target level via rate modulation and/or motor unit recruitment in various visual conditions (Kamen and Du, 1999; Contessa and De Luca, 2013). MUs discharge rhythmically during cyclic force-tracking (Iyer et al, 1994; Knight and Kamen, 2007; Erimaki et al, 2013). Fast cyclic force-tracking favors the use of predictive control, and MUs discharge more coherently at the target rate during faster force-tracking than during slower force-tracking (Sosnoff et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Paradigm Shifts in Voluntary Force Control and Motor Unit Behaviors with the Manipulated Size of Visual Error Perception

et al. 2017

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The detection of error information is an essential prerequisite of a feedback-based movement. This study investigated the differential behavior and neurophysiological mechanisms of a cyclic force-tracking task using error-reducing and error-enhancing feedback. The discharge patterns of a relatively large number of motor units (MUs) were assessed with custom-designed multi-channel surface electromyography following mathematical decomposition of the experimentally-measured signals. Force characteristics, force-discharge relation, and phase-locking cortical activities in the contralateral motor cortex to individual MUs were contrasted among the low (LSF), normal (NSF), and high scaling factor (HSF) conditions, in which the sizes of online execution errors were displayed with various amplification ratios. Along with a spectral shift of the force output toward a lower band, force output with a more phase-lead became less irregular, and tracking accuracy was worse in the LSF condition than in the HSF condition. The coherent discharge of high phasic (HP) MUs with the target signal was greater, and inter-spike intervals were larger, in the LSF condition than in the HSF condition. Force-tracking in the LSF condition manifested with stronger phase-locked EEG activity in the contralateral motor cortex to discharge of the (HP) MUs (LSF > NSF, HSF). The coherent discharge of the (HP) MUs during the cyclic force-tracking predominated the force-discharge relation, which increased inversely to the error scaling factor. In conclusion, the size of visualized error gates motor unit discharge, force-discharge relation, and the relative influences of the feedback and feedforward processes on force control. A smaller visualized error size favors voluntary force control using a feedforward process, in relation to a selective central modulation that enhance the coherent discharge of (HP) MUs.

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“…Again, it should be noted that all analyses are based on the temporal dynamics of the force produced by the participants and not on the displayed target. This renders any positive or negative tracking lags irrelevant [although they would be minimal given the highly feed-forward nature of this type of task (Erimaki et al, 2013 )]. To examine the relationship between tracking phase and force variability, we first converted each band-pass filtered force signal into an instantaneous amplitude signal by rectification and smoothing with a 200 ms Gaussian window.…”

Section: Methodsmentioning

confidence: 99%

The Dynamics of Voluntary Force Production in Afferented Muscle Influence Involuntary Tremor

Laine

Nagamori

Valero-Cuevas

2016

Front. Comput. Neurosci.

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Voluntary control of force is always marked by some degree of error and unsteadiness. Both neural and mechanical factors contribute to these fluctuations, but how they interact to produce them is poorly understood. In this study, we identify and characterize a previously undescribed neuromechanical interaction where the dynamics of voluntary force production suffice to generate involuntary tremor. Specifically, participants were asked to produce isometric force with the index finger and use visual feedback to track a sinusoidal target spanning 5–9% of each individual's maximal voluntary force level. Force fluctuations and EMG activity over the flexor digitorum superficialis (FDS) muscle were recorded and their frequency content was analyzed as a function of target phase. Force variability in either the 1–5 or 6–15 Hz frequency ranges tended to be largest at the peaks and valleys of the target sinusoid. In those same periods, FDS EMG activity was synchronized with force fluctuations. We then constructed a physiologically-realistic computer simulation in which a muscle-tendon complex was set inside of a feedback-driven control loop. Surprisingly, the model sufficed to produce phase-dependent modulation of tremor similar to that observed in humans. Further, the gain of afferent feedback from muscle spindles was critical for appropriately amplifying and shaping this tremor. We suggest that the experimentally-induced tremor may represent the response of a viscoelastic muscle-tendon system to dynamic drive, and therefore does not fall into known categories of tremor generation, such as tremorogenic descending drive, stretch-reflex loop oscillations, motor unit behavior, or mechanical resonance. Our findings motivate future efforts to understand tremor from a perspective that considers neuromechanical coupling within the context of closed-loop control. The strategy of combining experimental recordings with physiologically-sound simulations will enable thorough exploration of neural and mechanical contributions to force control in health and disease.

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Neuromuscular mechanisms and neural strategies in the control of time-varying muscle contractions

Abstract: Erimaki S, Agapaki OM, Christakos CN. Neuromuscular mechanisms and neural strategies in the control of time-varying muscle contractions.

Cited by 8 publications

References 56 publications

Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Paradigm Shifts in Voluntary Force Control and Motor Unit Behaviors with the Manipulated Size of Visual Error Perception

The Dynamics of Voluntary Force Production in Afferented Muscle Influence Involuntary Tremor

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