Influence of Fatigue on Hand Muscle Coordination and EMG-EMG Coherence During Three-Digit Grasping

Abstract

Fingertip force control requires fine coordination of multiple hand muscles within and across the digits. While the modulation of neural drive to hand muscles as a function of force has been extensively studied, much less is known about the effects of fatigue on the coordination of simultaneously active hand muscles. We asked eight subjects to perform a fatiguing contraction by gripping a manipulandum with thumb, index, and middle fingers while matching an isometric target force (40% maximal voluntary force) for as long as possible. The coordination of 12 hand muscles was quantified as electromyographic (EMG) muscle activation pattern (MAP) vector and EMG-EMG coherence. We hypothesized that muscle fatigue would cause uniform changes in EMG amplitude across all muscles and an increase in EMG-EMG coherence in the higher frequency bands but with an invariant heterogeneous distribution across muscles. Muscle fatigue caused a 12.5% drop in the maximum voluntary contraction force (P < 0.05) at task failure and an increase in the SD of force (P < 0.01). Although EMG amplitude of all muscles increased during the fatiguing contraction (P < 0.001), the MAP vector orientation did not change, indicating that a similar muscle coordination pattern was used throughout the fatiguing contraction. Last, EMG-EMG coherence (0-35 Hz) was significantly greater at the end than at the beginning of the fatiguing contraction (P < 0.01) but was heterogeneously distributed across hand muscles. These findings suggest that similar mechanisms are involved for modulating and sustaining digit forces in nonfatiguing and fatiguing contractions, respectively.

Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies

“…In nearly all cases, findings of decreased beta-band CMC can be interpreted as reflecting either (1) a departure from steady-state control of a particular muscle, or (2) the cortical “unbinding” of muscles when individuated, rather than synergistic, activation is called for. Given that beta-band cortical activity has been suggested as a “binding” signal for many years (Gray, 1994; Santello, 2014), and that such binding would naturally favor synergistic rather than individuated control of the fingers (Boonstra et al, 2009b; Danna-Dos Santos et al, 2010; Kattla and Lowery, 2010; Aumann and Prut, 2015), our overall hypothesis was that beta-band corticomotor drive should be reduced or eliminated when the degree of individuated muscle control is increased.…”

High-Frequency Intermuscular Coherence between Arm Muscles during Robot-Mediated Motor Adaptation

“…Moreover, BSS using the JADE algorithm was also applied to minimize crosstalk (see Supplementary Figures 1, 2) and maximize the source content of each muscle signal (Cardoso and Souloumiac, 1993; Kilner et al, 2002). An effect of muscle fatigue on IMC (i.e., increase) was also unlikely as peak forces produced and peak EMG amplitudes during motor adaptation were small fractions of maximal values in this group of subjects (Danna-Dos Santos et al, 2010; Kattla and Lowery, 2010; Beck et al, 2014). Moreover, an additional investigation of EMG median frequency changes reported in Supplementary Material didn't show any effects of fatigue (see Supplementary Table 2).…”

“…IMC has been used for the evaluation of coherent activation in muscle pairs during isometric contraction tasks (Baker et al, 1999; Kilner et al, 1999; Poston et al, 2010; Semmler et al, 2013; Jesunathadas et al, 2013), tremor (Halliday et al, 2000; van Rootselaar et al, 2006; van der Stouwe et al, 2015), and more recently in rhythmic movement such as pedaling (De Marchis et al, 2015) and stepping (Chang et al, 2012). Changes in different frequency bands of coherence may confer information on the changes in descending neural signals (grip task, IMC in 0–35 Hz range, Danna-Dos Santos et al, 2010; precision grip and ankle dorsiflexion task, IMC in 15–30 Hz range Fisher et al, 2012; precision grip tasks during sustained extension/flexion of elbow joint, IMC in 13–25 Hz range, Lee et al, 2014), on the status of functional recovery of neural structures after injury (force-tracking precision grip task, IMC in 30–46 Hz range, Nishimura et al, 2009) or of impaired motor skills (reaching, IMC in 0–11 Hz range, Kisiel-Sajewicz et al, 2011). IMC has been also recently shown to increase between muscles pairs that are more strongly coordinated during specific motor tasks (bimanual coordination, de Vries et al, 2016; upper-limb isometric contractions to control a myoelectric cursor, Nazarpour et al, 2012), experimentally supporting the hypothesis that multiple muscles coordination may be the result of a neural synchronization strategy of cortical origins (Farmer, 1998).…”

A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction

“…The data are from a published report [17], which investigated the influence of fatigue on EMG–EMG coherence across hand muscles.…”

“…The needle was then removed and the wire electrode remained in the muscle for the duration of the experiment. The muscles recorded and analyzed included six intrinsic and six extrinsic hand muscles (for details, see [17]).…”