Person:

Smith, Maurice

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.

The aim of this paper was to determine the correlation between calcium burden (expressed as a volume) and extent of stenosis of the origin of the internal carotid artery (ICA) by CT angiography (CTA). Previous studies have shown that calcification in the coronary arteries correlates with significant vessel stenosis, and severe calcification (measured by CT) in the carotid siphon correlates with significant (greater than 50% stenosis) as determined angiographically. Sixty-one patients (age range 50–85 years) underwent CT of the neck with intravenous administration of iodinated contrast for a variety of conditions. Images were obtained with a helical multidetector array CT scanner and reviewed on a three-dimensional workstation. A single observer manipulated window and level to segment calcified plaque from vascular enhancement in order to quantify vascular calcium volume (cc) in the region of the bifurcation of the common carotid artery/ICA origin, and to measure the extent of ICA stenosis near the origin. A total of 117 common carotid artery bifurcations were reviewed. A significant stenosis was defined arbitrarily as >40% (to detect lesions before they become hemodynamically significant) of luminal diameter on CTA using NASCET-like criteria. All significant stenoses (21 out of 117 carotid bifurcations) had measurable calcium. We found a relatively strong correlation between percent stenosis and the calcium volume (Pearsons r = 0.65, P<0.0001). We also found that there was an even stronger correlation between the square root of the calcium volume and the percent stenosis as measured by CTA (r= 0.77, P<0.0001). Calcium volumes of 0.01, 0.03, 0.06, 0.09 and 0.12 cc were used as thresholds to evaluate for a significant stenosis. A receiver operating characteristic (ROC) curve demonstrated that thresholds of 0.06 cc (sensitivity 88%, specificity 87%) and 0.03 cc (sensitivity 94%, specificity 76%) generated the best combinations of sensitivity and specificity. Hence, this preliminary study demonstrates a relatively strong relationship between volume of calcium at the carotid bifurcation in the neck (measured by CT) and percent stenosis of the ICA below the skull base (as measured by CTA). Use of calcium volume measurements as a threshold may be both sensitive and specific for the detection of significant ICA stenosis. The significance of the correlation between calcium volume and ICA stenosis is that potentially a score can be obtained that will identify those at risk for high grade carotid stenosis.

The motor commands required to control voluntary movements under various environmental conditions may be formed by adaptively combining a fixed set of motor primitives. Since this motor output must contend with state-dependent physical dynamics during movement, these primitives are thought to depend on the position and velocity of motion. Using a recently developed ‘‘error-clamp’’ technique, we measured the fine temporal structure of changes in motor output during adaptation. Interestingly, these measurements reveal that motor primitives echo a key feature of the neural coding of limb motion— correlated tuning to position and velocity. We show that this correlated tuning explains why initial stages of motor learning are often rapid and stereotyped, whereas later stages are slower and stimulus specific. With our new understanding of these primitives, we design dynamic environments that are intrinsically the easiest or most difficult to learn, suggesting a theoretical basis for the rational design of improved procedures for motor training and rehabilitation.

Any passive rigid inertial object that we hold in our hand, e.g., a tennis racquet, imposes a field of forces on the arm that depends on limb position, velocity, and acceleration. A fundamental characteristic of this field is that the forces due to acceleration and velocity are linearly separable in the intrinsic coordinates of the limb. In order to learn such dynamics with a collection of basis elements, a control system would generalize correctly and therefore perform optimally if the basis elements that were sensitive to limb velocity were not sensitive to acceleration, and vice versa. However, in the mammalian nervous system proprioceptive sensors like muscle spindles encode a nonlinear combination of all components of limb state, with sensitivity to velocity dominating sensitivity to acceleration. Therefore, limb state in the space of proprioception is not linearly separable despite the fact that this separation is a desirable property of control systems that form models of inertial objects. In building internal models of limb dynamics, does the brain use a representation that is optimal for control of inertial objects, or a representation that is closely tied to how peripheral sensors measure limb state? Here we show that in humans, patterns of generalization of reaching movements in acceleration-dependent fields are strongly inconsistent with basis elements that are optimized for control of inertial objects. Unlike a robot controller that models the dynamics of the natural world and represents velocity and acceleration independently, internal models of dynamics that people learn appear to be rooted in the properties of proprioception, nonlinearly responding to the pattern of muscle activation and representing velocity more strongly than acceleration.

Two different compensatory mechanisms are engaged when the nervous system senses errors during a reaching movement. First, on-line feedback control mechanisms produce in-flight corrections to reduce errors in the on-going movement. Second, these errors modify the internal model with which the motor plan is transformed into motor commands for the subsequent movements. What are the neural mechanisms of these compensatory systems? In a previous study, we reported that while on-line error correction was disturbed in patients with Huntington's disease (HD), it was largely intact in patients with cerebellar degeneration. Here we altered dynamics of reaching and studied the effect of error in one trial on the motor commands that initiated the subsequent trial. We observed that in patients with cerebellar degeneration, motor commands changed from trial-to-trial by an amount that was comparable to control subjects. However, these changes were random and were uninformed by the error in the preceding trial. In contrast, the change in motor commands of HD patients was strongly related to the error in the preceding trial. This error-dependent change had a sensitivity that was comparable to healthy controls. As a result, HD patients exhibited no significant deficits in adapting to novel arm dynamics, whereas cerebellar subjects were profoundly impaired. These results demonstrate a double dissociation between on-line and trial-to-trial error correction suggesting that these compensatory mechanisms have distinct neural bases that can be differentially affected by disease.

A steady progression of motor dysfunction takes place in Huntington's disease1 (HD). The origin of this disturbance with relation to the motor control process is not understood. Here we studied reaching movements in asymptomatic HD gene-carriers (AGCs) and subjects with manifest HD. We found that movement jerkiness, which characterizes the smoothness and efficiency of motion, was a sensitive indicator of presymptomatic HD progression. A large fraction of AGCs displayed elevated jerk even when more than seven years remained until predicted disease onset. Movement termination was disturbed much more than initiation and was highly variable from trial to trial. Analysis of this variability revealed that the sensitivity of end-movement jerk to subtle, self-generated early-movement errors was greater in HD subjects than in controls. Additionally, we found that HD corrective responses to externally-generated force pulses were greatly disturbed, indicating that HD subjects display aberrant responses to both external and self-generated errors. Because feedback corrections are driven by error and are delayed such that they predominantly affect movement termination, these findings suggest that a dysfunction in error correction characterizes the motor control deficit in early HD. This dysfunction may be observed years before clinical disease onset and grows worse as the disease progresses.

Extensive theoretical, psychophysical, and neurobiological work has focused on the mechanisms by which short-term learning develops into long-term memory. Better understanding of these mechanisms may lead to the ability to improve the efficiency of training procedures. A key phenomenon in the formation of long-term memory is the effect of over learning on retention—discovered by Ebbinghaus in 1885: when the initial training period in a task is prolonged even beyond what is necessary for good immediate recall, long-term retention improves. Although this over learning effect has received considerable attention as a phenomenon in psychology research, the mechanisms governing this process are not well understood, and the ability to predict the benefit conveyed by varying degrees of over learning does not yet exist. Here we studied the relationship between the duration of an initial training period and the amount of retention 24 h later for the adaptation of human reaching arm movements to a novel force environment. We show that in this motor adaptation task, the amount of long-term retention is predicted not by the overall performance level achieved during the training period but rather by the level of a specific component process in a multi-rate model of short-term memory formation. These findings indicate that while multiple learning processes determine the ability to learn a motor adaptation, only one provides a gateway to long-term memory formation. Understanding the dynamics of this key learning process may allow for the rational design of training and rehabilitation paradigms that maximize the long-term benefit of each session.

Adaptive control of reaching depends on internal models that associate states in which the limb experienced a force perturbation with motor commands that can compensate for it. Limb state can be sensed via both vision and proprioception. However, adaptation of reaching in novel dynamics results in generalization in the intrinsic coordinates of the limb, suggesting that the proprioceptive states in which the limb was perturbed dominate representation of limb state. To test this hypothesis, we considered a task where position of the hand during a reach was correlated with patterns of force perturbation. This correlation could be sensed via vision, proprioception, or both. As predicted, when the correlations could be sensed only via proprioception, learning was significantly better as compared to when the correlations could only be sensed through vision. We found that learning with visual correlations resulted in subjects who could verbally describe the patterns of perturbations but this awareness was never observed in subjects who learned the task with only proprioceptive correlations. We manipulated the relative values of the visual and proprioceptive parameters and found that the probability of becoming aware strongly depended on the correlations that subjects could visually observe. In all conditions, aware subjects demonstrated a small but significant advantage in their ability to adapt their motor commands. Proprioceptive correlations produced an internal model that strongly influenced reaching performance yet did not lead to awareness. Visual correlations strongly increased the probability of becoming aware, yet had a much smaller but still significant effect on reaching performance. Therefore, practice resulted in acquisition of both implicit and explicit internal models.