The purpose of the study was to determine the practice-induced adjustments in the motor-output variability and the agonist-antagonist activity that accompanied improvements in endpoint accuracy of goal-directed isometric contractions in young and old adults. Young and old adults performed 100 trials that involved accurately matching the peak of a force trajectory (25% maximum) to a target force in 150 ms. Endpoint accuracy was quantified as the absolute difference between the target and the peak force and time-to-peak force. Motor-output variability was expressed as the SDs of the force trajectory, peak force, and time-to-peak force. The force and time errors differed between the two groups initially, but after 35 practice trials the errors were similar for the two groups. Reductions in force endpoint error were predicted by decreases in the variability of the force trajectory for both groups, adaptations in the agonist (first dorsal interosseus) and antagonist (second palmar interosseus) EMG for young adults, and adaptations only for the agonist EMG for old adults. Reductions in time endpoint error were predicted by increases in the SD of time-to-peak force and a longer delay to the peak EMG of the antagonist muscle for young adults, but by decreases in the SDs of time-to-peak force and force trajectory and a shorter delay to the peak EMG of the antagonist muscle for the old adults. The findings indicate that the neural adjustments underlying the improvement in endpoint accuracy with practice differed for young and old adults.

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.

To identify the underlying physiological mechanisms for the difference in the time to failure for two types of fatiguing contractions, 20 subjects performed force and position tasks with the elbow flexor muscles at a comparable net muscle torque for a similar duration. Prior to terminating each task, blood flow was occluded to estimate the relative amount of feedback transmitted by small-diameter afferents to the spinal cord. Mean arterial pressure at the conclusion of the fatiguing contraction increased similarly for the two tasks (force: 119% +/- 14%; position: 114% +/- 15%). However, the final values for the electromyographic activity for the elbow flexor muscles (26% +/- 14% and 21% +/- 11%, respectively; P < 0.05), and the increase in the fluctuations in acceleration and force (225% +/- 152% and 154% +/- 53%, respectively; P < 0.05) in the sagittal plane, were significantly greater during the position task compared with the force task. These results suggest a different balance in the excitatory and inhibitory inputs to the spinal motor neurons for the two tasks, which has implications for the design of work tasks and exercise prescription in rehabilitation.

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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.