Functional reorganization of upper-body movement after spinal cord injury

Abstract

Survivors of spinal cord injury need to reorganize their residual body movements for interacting with assistive devices and performing activities that used to be easy and natural. To investigate movement reorganization, we asked subjects with high-level spinal cord injury (SCI) and unimpaired subjects to control a cursor on a screen by performing upper-body motions. While this task would be normally accomplished by operating a computer mouse, here shoulder motions were mapped into the cursor position. Both the control and the SCI subjects were rapidly able to reorganize their movements and to successfully control the cursor. The majority of the subjects in both groups were successful in reducing the movements that were not effective at producing cursor motions. This is inconsistent with the hypothesis that the control system is merely concerned with the accurate acquisition of the targets and is unconcerned with motions that are not relevant to this goal. In contrast, our findings suggest that subjects can learn to reorganize coordination so as to increase the correspondence between the subspace of their upper-body motions with the plane in which the controlled cursor moves. This is effectively equivalent to constructing an inverse internal model of the map from body motions to cursor motions, established by the experiment. These results are relevant to the development of interfaces for assistive devices that optimize the use of residual voluntary control and enhance the learning process in disabled users, searching for an easily learnable map between their body motor space and control space of the device.

Fig. 1

Experimental set-up

Fig. 2

Left panel circles on the periphery are the six possible target locations for both T1–T6 and BL movement sets (1 cm in diameter). Only the target (black filled circle) and the starting position (gray filled circle) for the current trial were displayed. The initial position was slightly bigger than the target and was represented by a different color. The cursor was 0.4 cm in diameter (small dark gray circle). The right panel shows the three directions of reaching generalization (these directions do not correspond to those of the left panel), and here the cursor was not visible during movement (small dotted circle). In every movement set, the amplitude of the required movements (distance of the targets from the center) was 5 cm

Fig. 3

a Movement trajectories in early (left) and late (right) phases of learning, for a control subject and 4 SCI subjects. Calibration lines on bottom right corner of each panel: 1 cm on the computer screen. b Example of speed profiles in early (left) and late (right) phases of learning for a control (upper) and a SCI subject—SCI 3—(bottom)

Fig. 4

Time course of duration, jerk, and linearity index of controls (top—mean + SE) and SCI subjects (bottom)

Fig. 5

Time course of end-point error (mean + SE) in vision (black line) and blind trials (gray line) of controls

Fig. 6

Left panel results of principal component analysis on the first (gray) and last movement set (black) for control subjects (mean + SE). In the first movement set (gray), more than 95% of variance was explained by four principal components. At the end of the training session (black), unimpaired controls mainly tended to increase the variance associated with the second principal component. Right panel control subjects (mean + SE). Results of the projection of the data of the first (gray) and last movement set (black) over the space defined by the transformation i.e. the space defined by the eight eigenvectors provided by the PCA computed on the calibration data set. For most of the control subjects, the movement variance associated with the dimension a₃, a₄ decreased with training in favor of the variance associated with a₁, a₂, the two “task relevant” components

Fig. 7

Movement of subject no 3 projected on the space defined by a₁, a₂, a₃ in the early (left panel) and late (right panel) phases of learning. In the first movement set, there was a relevant movement variance in the third dimension of the space a₃ (null space). That component of the variance was strongly reduced in the last target set, and the movement’s space seemed to become more planar, with the majority of the movement variance accounted by the task space defined by the eigenvectors a₁, a₂