Neural Control of Stopping and Stabilizing the Arm

We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.

A central problem in motor control is understanding how the many biomechanical degrees of freedom are coordinated to achieve a common goal. An especially puzzling aspect of coordination is that behavioral goals are achieved reliably and repeatedly with movements rarely reproducible in their detail. Existing theoretical frameworks emphasize either goal achievement or the richness of motor variability, but fail to reconcile the two. Here we propose an alternative theory based on stochastic optimal feedback control. We show that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions. This strategy does not enforce a desired trajectory, but uses feedback more intelligently, correcting only those deviations that interfere with task goals. From this framework, task-constrained variability, goal-directed corrections, motor synergies, controlled parameters, simplifying rules and discrete coordination modes emerge naturally. We present experimental results from a range of motor tasks to support this theory.

Conjunction analysis methods were used in functional magnetic resonance imaging to investigate brain regions commonly activated in subjects performing different versions of go/no-go and stop tasks, differing in probability of inhibitory signals and/or contrast conditions. Generic brain activation maps highlighted brain regions commonly activated in (a) two different go/no-go task versions, (b) three different stop task versions, and (c) all 5 inhibition task versions. Comparison between the generic activation maps of stop and go/no-go task versions revealed inhibitory mechanisms specific to go/no-go or stop task performance in 15 healthy, right-handed, male adults. In the go/no-go task a motor response had to be selectively executed or inhibited in either 50% or 30% of trials. In the stop task, the motor response to a go-stimulus had to be retracted on either 50 or 30% of trials, indicated by a stop signal, shortly (250 ms) following the go-stimulus. The shared "inhibitory" neurocognitive network by all inhibition tasks comprised mesial, medial, and inferior frontal and parietal cortices. Generic activation of the go/no-go task versions identified bilateral, but more predominantly left hemispheric mesial, medial, and inferior frontal and parietal cortices. Common activation to all stop task versions was in predominantly right hemispheric anterior cingulate, supplementary motor area, inferior prefrontal, and parietal cortices. On direct comparison between generic stop and go/no-go activation maps increased BOLD signal was observed in left hemispheric dorsolateral prefrontal, medial, and parietal cortices during the go/no-go task, presumably reflecting a left frontoparietal specialization for response selection. Copyright 2001 Academic Press.