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. 2013 Sep 4;33(36):14301-6.
doi: 10.1523/JNEUROSCI.0784-13.2013.

Predictive modeling by the cerebellum improves proprioception

Affiliations

Predictive modeling by the cerebellum improves proprioception

Nasir H Bhanpuri et al. J Neurosci. .

Abstract

Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

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Figures

Figure 1.
Figure 1.
Task description and example trials. A, Overhead view of arm schematic denoting conventions used to report elbow angle (θe) and applied torque about the elbow (τe). The shoulder angle (θs) was fixed with a mechanical clamp at 30°. B, Task 1: Passive discrimination. Subjects were instructed to remain passive throughout the task. At the start of each trial, the robot moved the arm to the start position (θe = 75°). Next, the arm was moved a specified distance and briefly held at the end location. The arm was returned to the start position and then moved a different distance. Subjects were asked to report whether Movement 1 or Movement 2 (indicated by bars in top left corner) went farther. White arrows indicate passive, robot-driven movement. C, Tasks 2 and 3: Active discrimination. Similar to Task 1 with the exception that, during the movement phase, subjects actively moved until the robot halted the arm at a specified location with a virtual wall (brown line, which is illustrative only and was not shown to subjects). Black arrows indicate active, subject-driven movement. The yellow horizontal bar was shown only if angular velocity exceeded 15°/s. The dotted lines representing the arm are for illustrative purposes only, as subjects could not see their arms in either of the tasks. DE, Example 10° movement trials for control (D) and cerebellar (E) subjects. The elbow angle (top) and commanded robot torque (bottom) are shown for the passive task (first column), active-simple task (second column), and active-complex task (third column). Horizontal scale bar represents 5 s. F, Position-based torque command pattern for a 12.25° trial for the active-complex task. All active-complex trials imposed two consecutive rectified sine waves of resistive torque as a function of distance from the start position.
Figure 2.
Figure 2.
Comparison of WFs. Error bars indicate SEM. The only significant difference across groups for the three tasks was for the active-simple task (p < 0.031). Controls (n = 11) were significantly different at the active-simple task compared with the passive task (p < 0.005) and the active-complex task (p < 0.022), but they did not show a difference between the passive and active-complex tasks (p > 0.38). Among the patients (n = 11), there were no significant differences between tasks (all p > 0.28). *p < 0.05, **p < 0.005.
Figure 3.
Figure 3.
Behavioral comparison of controls and cerebellar patients. Possible differences in movement during tasks could account for differences in perception. Group average of average maximum robot torque (top row), average mean velocity (middle row), and average smoothness index (bottom row) for all tasks. Smoothness index was computed as the SD of the high-pass-filtered (second-order, 2 Hz cutoff) position data (low values indicate smooth movement). Error bars indicate SE. Controls and patients demonstrated similar behavior (all p > 0.18).

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