On the Relationship Between Joint Angular Velocity and Motor Cortical Discharge During Reaching

Abstract

Single-unit activity in area M1 was recorded in awake, behaving monkeys during a three-dimensional (3D) reaching task performed in a virtual reality environment. This study compares motor cortical discharge rate to both the hand's velocity and the arm's joint angular velocities. Hand velocity is considered a parameter of extrinsic space because it is measured in the Cartesian coordinate system of the monkey's workspace. Joint angular velocity is considered a parameter of intrinsic space because it is measured relative to adjacent arm/body segments. In the initial analysis, velocity was measured as the difference in hand position or joint posture between the beginning and ending of the reach. Cortical discharge rate was taken as the mean activity between these two times. This discharge rate was compared through a regression analysis to either an extrinsic-coordinate model based on the three components of hand velocity or to an intrinsic-coordinate model based on seven joint angular velocities. The model showed that velocities about four degrees-of-freedom (elbow flexion/extension, shoulder flexion/extension, shoulder internal/external rotation, and shoulder adduction/abduction) were those best represented in the sampled population of recorded activity. Patterns of activity recorded across the cortical population at each point in time throughout the task were used in a second analysis to predict the temporal profiles of joint angular velocity and hand velocity. The population of cortical units from area M1 matched the hand velocity and three of the four major joint angular velocities. However, shoulder adduction/abduction could not be predicted even though individual cells showed good correlation to movement on this axis. This was also the only major degree-of-freedom not well correlated to hand velocity, suggesting that the other apparent relations between joint angular velocity and neuronal activity may be due to intrinsic-extrinsic correlations inherent in reaching movements.

Neurobiologically Inspired Distributed and Hierarchical System for Control and Learning

“…Neurophysiological studies propose that motor cortex and cerebellum contain much information in joint coordinates (Ajemian et al 2001;, Cartesian coordinates (Georgopoulos et al 1982,Ajemian et al 2001Poppele et al 2002, Roitman 2007. However other studies are consistent with the possibility that parietal and some motor cortical signals are in Cartesian or body-centered (Graziano 2001), shoulder-centered (Soechting & Flanders 1989) workspace coordinates, or a combination (Reina et al 2001). However, it would be highly likely that a coordinate at an area is selected to conveniently process control variables from high level command to low Level execution.…”

Functional network reorganization during learning in a brain-computer interface paradigm

“…For behavioral training, monkeys sat in a primate chair facing a mirror that reflects a 3D image from a stereoscopic computer monitor in a periscope-like design, allowing the space in front of the monkey to remain available for free arm movements. Before electrode implantation, the monkey was trained to move its hand, fitted with an optical marker, to proportionally move a spherical cursor from the center of an imaginary cube to targets appearing at one of its eight corners for a liquid reward (31). All procedures were performed in accordance with the guidelines of the Institutional Care and Use Committee of the University of Pittsburgh.…”

Motor Cortical Representation of Hand Translation and Rotation during Reaching

“…This analysis focused on neuronal firing rates averaged over reaction and movement time (i.e., from target onset to movement offset) calculated using the partial binning method (Richmond et al, 1987;Reina et al, 2001;Wang et al, 2007). For each neuron, a two-way ANOVA was performed using hand translation and hand rotation as two factors (eight translation directions and three rotation conditions).…”

“…A p value Ͻ0.05 indicates significant modulation. Furthermore, for all neurons modulated by hand translation, the preferred translational direction (PD) was calculated by regressing neuronal firing rates to hand translation (Georgopoulos et al, 1986;Reina et al, 2001).…”

“…For each trial, the movement time was divided into 40 equal time bins. Then, 30 premovement bins and 30 postmovement bins were defined with the same bin width (Reina et al, 2001;Wang et al, 2007), resulting in 100 time bins for each trial. Firing rates of individual neurons were calculated for each bin using the partial binning method to generate a time series of instantaneous neuronal firing rates (Richmond et al, 1987;Wang et al, 2007), which was then low-pass filtered with a cutoff frequency of 10 Hz.…”