Population Coding of Forelimb Joint Kinematics by Peripheral Afferents in Monkeys

Tatsuya Umeda,Tadashi Isa,Mitsuo Kawato,Yukio Nishimura,Masa-Aki Sato,Kazuhiko Seki,Paul L Gribble

doi:10.1371/journal.pone.0047749

Abstract

Various peripheral receptors provide information concerning position and movement to the central nervous system to achieve complex and dexterous movements of forelimbs in primates. The response properties of single afferent receptors to movements at a single joint have been examined in detail, but the population coding of peripheral afferents remains poorly defined. In this study, we obtained multichannel recordings from dorsal root ganglion (DRG) neurons in cervical segments of monkeys. We applied the sparse linear regression (SLiR) algorithm to the recordings, which selects useful input signals to reconstruct movement kinematics. Multichannel recordings of peripheral afferents were performed by inserting multi-electrode arrays into the DRGs of lower cervical segments in two anesthetized monkeys. A total of 112 and 92 units were responsive to the passive joint movements or the skin stimulation with a painting brush in Monkey 1 and Monkey 2, respectively. Using the SLiR algorithm, we reconstructed the temporal changes of joint angle, angular velocity, and acceleration at the elbow, wrist, and finger joints from temporal firing patterns of the DRG neurons. By automatically selecting a subset of recorded units, the SLiR achieved superior generalization performance compared with a regularized linear regression algorithm. The SLiR selected not only putative muscle units that were responsive to only the passive movements, but also a number of putative cutaneous units responsive to the skin stimulation. These results suggested that an ensemble of peripheral primary afferents that contains both putative muscle and cutaneous units encode forelimb joint kinematics of non-human primates.

Highlights

Peripheral inputs contribute to kinesthesia, the sense of joint position and movements, and blocking peripheral primary afferents impairs the perception of limb position and movements [1,2,3,4]
To examine whether neuronal ensembles in the dorsal root ganglion (DRG) convey rich information about joint kinematics, we applied the sparse linear regression (SLiR) model to the encoding of kinematic variables from the activities of all the single and multiple units
By adding the neuronal activity of the putative cutaneous units to those of the putative muscle units, the SLiR selected both the putative muscle and cutaneous units (Tables 1, 2 and 3) and the prediction accuracy in all the kinematics was significantly improved (paired Student’s t-test with Bonferroni correction (n = 3); p,0.05) (Fig. 11). These results suggest that, while a great deal of elbow joint kinematic information was conveyed by the putative muscle units, the putative cutaneous units provided the central nervous system with joint kinematic information that the putative muscle units may not code, such as subtle forelimb movements accompanied with skin deformation

Summary

Introduction

Peripheral inputs contribute to kinesthesia, the sense of joint position and movements, and blocking peripheral primary afferents impairs the perception of limb position and movements [1,2,3,4]. Positional information arising from inputs of peripheral afferents is critical to achieve accurate and dexterous movements of the hands and arms of primates. Microneurographical recordings from humans or single fiber recordings from animals have been conducted to examine the responses of peripheral afferents to mechanical stimuli [13,14,15,16]. Individual sensory receptors were found to respond to movements of the hand or arm [17,18]. Recording from a single afferent neuron has limitations for examining the sensory processing from an ensemble of peripheral receptors during the dynamic movements. To understand the neural processing of natural movements, it is requisite to simultaneously record the activity of a population of peripheral afferents and to investigate the computation required for those multiple receptors to represent the kinesthesia

Methods

Results

Conclusion