The ability to modulate digit forces during grasping relies on the coordination of multiple hand muscles. Because many muscles innervate each digit, the CNS can potentially choose from a large number of muscle coordination patterns to generate a given digit force. Studies of single-digit force production tasks have revealed that the electromyographic (EMG) activity scales uniformly across all muscles as a function of digit force. However, the extent to which this finding applies to the coordination of forces across multiple digits is unknown. We addressed this question by asking subjects (n = 8) to exert isometric forces using a three-digit grip (thumb, index, and middle fingers) that allowed for the quantification of hand muscle coordination within and across digits as a function of grasp force (5, 20, 40, 60, and 80% maximal voluntary force). We recorded EMG from 12 muscles (6 extrinsic and 6 intrinsic) of the three digits. Hand muscle coordination patterns were quantified in the amplitude and frequency domains (EMG-EMG coherence). EMG amplitude scaled uniformly across all hand muscles as a function of grasp force (muscle x force interaction: P = 0.997; cosines of angle between muscle activation pattern vector pairs: 0.897-0.997). Similarly, EMG-EMG coherence was not significantly affected by force (P = 0.324). However, coherence was stronger across extrinsic than that across intrinsic muscle pairs (P = 0.0039). These findings indicate that the distribution of neural drive to multiple hand muscles is force independent and may reflect the anatomical properties or functional roles of hand muscle groups.
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.
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