Force-Independent Distribution of Correlated Neural Inputs to Hand Muscles During Three-Digit Grasping

Abstract

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.

Is the Control of Applied Digital Forces During Natural Five-digit Grasping Affected by Carpal Tunnel Syndrome?

“…Furthermore, we found that the variability increased along with the applied force in the index, middle, and little fingers of the patients with CTS. Although this was observed only in three digits, this finding is similar to the results of previous studies which reported that the variability of pinch force correlated with the force level in healthy subjects [14,32]. Because The significant correlations were found only in the patients with CTS on the (B) index, (C) middle and (E) little fingers, but no correlation was seen in the control subjects.…”

“…The decreased correlations seen in the patients with CTS may be because the F r of the thumb and opposite finger were adjusted in a nonsynchronous manner, which was observed as the mismatched tendency in the changes in force. From a neurophysiologic perspective, the electromyographic coherence of pair muscles, which varies across digits and implies that neural inputs from the central controller drive the engaged digits by coordinating the related digital muscles, may be altered in specific digits in patients with CTS [12,32]. In addition, our patients with CTS showed few alterations of pair correlations with respect to the phase changes.…”

Robustness of muscle synergies underlying three-dimensional force generation at the hand in healthy humans

“…In single-digit and three-digit force generation, the amplitude of EMGs recorded from muscles that contributed to generate fingertip force scaled linearly across all hand muscles as a function of grasp force, suggesting that voluntary digit forces were produced by appropriately scaling the magnitude of underlying muscular coordination patterns (Poston et al 2010;Valero-Cuevas 2000). Furthermore, a study of three-digit force generation reported that the EMG-EMG coherence was not significantly affected by force, suggesting that the distribution of neural drive to multiple hand muscles is force independent (Poston et al 2010). Our study extended these findings to muscles of the elbow and shoulder.…”

The influence of visual information on multi-muscle control during quiet stance: a spectral analysis approach

“…Experimental and computational studies have shown that rectified EMG is optimal for assessing intermuscular coherence at low force levels (Boonstra and Breakspear 2012;Farina et al 2013;Ward et al 2013), as required during quiet standing. All analyzes were performed following similar procedures reported by Poston et al (2010). One important aspect of the experimental design was to ensure our investigation followed the functional relationship of the muscles recorded, more specifically those muscles forming both posterior and anterior M-modes.…”

“…Concatenation is a standard procedure used to increase the reliability of coherence estimation (Amjad et al 1997;Maris et al 2007;Poston et al 2010), and intermuscular coherence was estimated for pairs of EMG signals by normalizing the cross-spectrum of two EMG signals (fxy) squared by the product of the autospectrum of each signal (fxx and fyy) at each frequency (λ): Intermuscular coherence estimates were obtained from non-overlapping 1-s data segments (i.e., 1,200 samples per segment), resulting in a frequency resolution of 1 Hz. The frequency range analyzed in this study was bounded from 0 to 55 Hz and coherence estimates were considered statistically significant when they exceeded the confidence limit of the null distribution, as proposed by Rosenberg et al (1989).…”