Visuomotor adaptation in Parkinson's disease: effects of perturbation type and medication state.

To perform simple everyday tasks, we use visual feedback from our external environment to generate and guide movements. However, tasks like reaching for a cup may become extremely difficult in movement disorders such as Parkinson's disease (PD), and it is unknown whether PD patients use visual information to compensate for motor deficiencies. We tested adaptation to changes in visual feedback of the hand in three subject groups, PD patients on daily levodopa (l-dopa) therapy (PD ON), PD patients off l-dopa (PD OFF), and age-matched control subjects, to determine the effects of PD on the visual control of movement. Subjects were tested on two classes of visual perturbations, one that altered visual direction of movement and one that altered visual extent of movement, allowing us to test adaptive sensitivity to changes in both movement direction (visual rotations) and extent (visual gain). The PD OFF group displayed more complete adaptation to visuomotor rotations compared with control subjects but initial, transient difficulty with adaptation to visual gain perturbations. The PD ON group displayed feedback control more sensitive to visual error compared with control subjects but compared with the PD OFF group had mild impairments during adaptation to changes in visual extent. We conclude that PD subjects can adapt to changes in visual information but that l-dopa may impair visual-based motor adaptation.

State dependence of adaptation of force output following movement observation.

Humans readily learn to move through direct physical practice and by watching the movements of others. Some researchers have proposed that action observation can inform subsequent changes in control through the acquisition of a neural representation of the novel dynamics, but to date learning following observation has been described by kinematic metrics. Here we designed an experiment to consider the specificity of adaptation to novel dynamic perturbations at the level of force generation. We measured changes in temporal patterns of force output following either the performance or observation of movements perturbed by either position- or velocity-dependent dynamic environments to 1) establish whether previously described observational motor learning effects were attributable to changes in predictive limb control and 2) determine whether such adaptation reflected a learned dependence on limb states appropriate to the haptic environment. We found that subjects who observed perturbed movements produced significant compensatory changes in their lateral force output, despite never directly experiencing force perturbations firsthand while performing the motor task. The time series of observers' adapted force outputs suggested that the state dependence of observed dynamics shapes adaptation. We conclude that the brain can transform observation of kinematics into state-dependent adaptation of reach dynamics.

Weakly circadian cells improve resynchrony.

The mammalian suprachiasmatic nuclei (SCN) contain thousands of neurons capable of generating near 24-h rhythms. When isolated from their network, SCN neurons exhibit a range of oscillatory phenotypes: sustained or damping oscillations, or arrhythmic patterns. The implications of this variability are unknown. Experimentally, we found that cells within SCN explants recover from pharmacologically-induced desynchrony by re-establishing rhythmicity and synchrony in waves, independent of their intrinsic circadian period We therefore hypothesized that a cell's location within the network may also critically determine its resynchronization. To test this, we employed a deterministic, mechanistic model of circadian oscillators where we could independently control cell-intrinsic and network-connectivity parameters. We found that small changes in key parameters produced the full range of oscillatory phenotypes seen in biological cells, including similar distributions of period, amplitude and ability to cycle. The model also predicted that weaker oscillators could adjust their phase more readily than stronger oscillators. Using these model cells we explored potential biological consequences of their number and placement within the network. We found that the population synchronized to a higher degree when weak oscillators were at highly connected nodes within the network. A mathematically independent phase-amplitude model reproduced these findings. Thus, small differences in cell-intrinsic parameters contribute to large changes in the oscillatory ability of a cell, but the location of weak oscillators within the network also critically shapes the degree of synchronization for the population.

Brevity of haptic force perturbations induces heightened adaptive sensitivity.

We have exposed human participants to both full-movement and pulsatile viscous force perturbations to study the effect of force duration on the incremental transformation of sensation into adaptation. Traditional views of movement biomechanics could suggest that pulsatile forces would largely be attenuated as stiffness and viscosity act as a natural low-pass filter. Sensory transduction, however, tends to react to changes in stimuli and therefore could underlie heightened sensitivity to briefer, pulsatile forces. Here, participants adapted within perturbation duration conditions in a manner proportionate to sensed force and positional errors. Across perturbation conditions, we found participants had greater adaptive sensitivity when experiencing pulsatile forces rather than full-movement forces. In a follow-up experiment, we employed error-clamped, force channel trials to determine changes in predictive force generation. We found that while participants learned to closely compensate for the amplitude and breadth of full-movement forces, they exhibited a persistent mismatch in amplitude and breadth between adapted motor output and experienced pulsatile forces. This mismatch could generate higher salience of error signals that contribute to heightened sensitivity to pulsatile forces.

Beside the point: motor adaptation without feedback-based error correction in task-irrelevant conditions.

Adaptation of movement may be driven by the difference between planned and actual motor performance, or the difference between expected and actual sensory consequences of movement. To identify how the nervous system differentially uses these signals, we asked: does motor adaptation occur when movement errors are irrelevant to the task goal? Participants reached on a digitizing tablet from a fixed start location to one of three targets: a point, an arc, or a ray. For the arc, reaches could be in any direction, but to a specific extent. For the ray, reaches could be to any distance, but in a targeted direction. After baseline reaching to the point, the direction or extent of continuous visual feedback was perturbed during training with either a cursor rotation or gain, respectively, while reaching to either the ray (goal = direction) or the arc (goal = extent). The perturbation, therefore, was either relevant or irrelevant to the task goal, depending on target type. During interspersed catch trials, the perturbation was removed and the target switched back to the point, identical to baseline. Although the goal of baseline and catch trials was the same, significant aftereffects in catch trials indicated behavioral adaptation in response to the perturbation. Adaptation occurred regardless of whether the perturbation was relevant to the task, and it was independent of feedback control. The presence of adaptation orthogonal to task demands supports the hypothesis that the nervous system can rely on sensory prediction to drive motor learning that can generalize across tasks.

Environmental experience within and across testing days determines the strength of human visuomotor adaptation.

The use of vision allows us to guide and modify our movements by appropriately transforming external sensory information into proper motor commands. We investigated how people learned visuomotor transformations in different visual feedback environments. These environments presented perturbations of visual sense of movement direction. Across experiments and testing days, we altered the likelihood of visual perturbation occurrence and the distribution of sign and strength of visual perturbation angles. We then observed how transformation of sensed error into incremental adaptation depended on visual perturbation angle and on environmental experience. We found that environmental context affected adaptive responses within a day and across days. The across-day effect was profound enough that people exhibited very weak or very strong adaptive sensitivity to identical stimuli, dependent solely on prior days' experience. We conclude that trial-by-trial adaptation to visual feedback is not fixed, but dependent on environmental experiences on both short and long time scales.

Trial-by-trial motor adaptation: a window into elemental neural computation.

How does the brain compute? To address this question, mathematical modelers, neurophysiologists, and psychophysicists have sought behaviors that provide evidence of specific neural computations. Human motor behavior consists of several such computations [Shadmehr, R., Wise, S.P. (2005). MIT Press: Cambridge, MA], such as the transformation of a sensory input to a motor output. The motor system is also capable of learning new transformations to produce novel outputs; humans have the remarkable ability to alter their motor output to adapt to changes in their own bodies and the environment [Wolpert, D.M., Ghahramani, Z. (2000). Nat. Neurosci., 3: 1212-1217]. These changes can be long term, through growth and changing body proportions, or short term, through changes in the external environment. Here we focus on trial-by-trial adaptation, the transformation of individually sensed movements into incremental updates of adaptive control. These investigations have the promise of revealing important basic principles of motor control and ultimately guiding a new understanding of the neuronal correlates of motor behaviors.

Rapid reshaping of human motor generalization.

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

The advantage of flexible neuronal tunings in neural network models for motor learning.

Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies.

Motor adaptation scaled by the difficulty of a secondary cognitive task.

BACKGROUND: Motor learning requires evaluating performance in previous movements and modifying future movements. The executive system, generally involved in planning and decision-making, could monitor and modify behavior in response to changes in task difficulty or performance. Here we aim to identify the quantitative cognitive contribution to responsive and adaptive control to identify possible overlap between cognitive and motor processes. METHODOLOGY/PRINCIPAL FINDINGS: We developed a dual-task experiment that varied the trial-by-trial difficulty of a secondary cognitive task while participants performed a motor adaptation task. Subjects performed a difficulty-graded semantic categorization task while making reaching movements that were occasionally subjected to force perturbations. We find that motor adaptation was specifically impaired on the most difficult to categorize trials. CONCLUSIONS/SIGNIFICANCE: We suggest that the degree of decision-level difficulty of a particular categorization differentially burdens the executive system and subsequently results in a proportional degradation of adaptation. Our results suggest a specific quantitative contribution of executive control in motor adaptation.

Divided attention impairs human motor adaptation but not feedback control.

When humans experience externally induced errors in a movement, the motor system's feedback control compensates for those errors within the movement. The motor system's predictive control then uses information about those errors to inform future movements. The role of attention in these two distinct motor processes is unclear. Previous experiments have revealed a role for attention in motor learning over the course of many movements; however, these experimental paradigms do not determine how attention influences within-movement feedback control versus across-movement adaptation. Here we develop a dual-task paradigm, consisting of movement and audio tasks, which can differentiate and expose attention's role in these two processes of motor control. Over the course of several days, subjects performed horizontal reaching movements, with and without the audio task; movements were occasionally subjected to transient force perturbations. On movements with a force perturbation, subjects compensated for the force-induced movement errors, and on movements immediately after the force perturbation subjects exhibited adaptation. On every movement trial, subjects performed a two-tone frequency-discrimination task. The temporal specificity of the frequency-discrimination task allowed us to divide attention within and across movements. We find that divided attention did not impair the within-movement feedback control of the arm, but did reduce subsequent movement adaptation. We suggest that the secondary task interfered with the encoding and transformation of errors into changes in predictive control.

Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics.

Humans can rapidly change their motor output to make goal-directed reaching movements in a new environment. Theories that describe this adaptive process have long presumed that adaptive steps scale proportionally with error. Here we show that while performing a novel reaching task, participants did not adopt a fixed learning rule, but instead modified their adaptive response based on the statistical properties of the movement environment. We found that as the directional bias of the force distribution shifted from strongly biased to unbiased, participants transitioned from an adaptive process that scaled proportionally with error to one that adapted to the direction, but not magnitude, of error. Participants also modified their response as the likelihood of the perturbation changed; as the likelihood decreased from 80 to 20% of trials, participants adopted an increasingly disproportional strategy. We propose that people can rapidly switch between learning processes within minutes of experiencing a novel environment.

Influence of viscous loads on motor planning.

Here we computationally investigate how encumbering the hand could alter predictions made by the minimum torque change (MTC) and minimum endpoint variance hypotheses (MEPV) of movement planning. After minutes of training, people have made arm trajectories in a robot-generated viscous force field that were similar to previous baseline trajectories without the force field. We simulate the human arm interacting with this viscous load. We found that the viscous forces clearly differentiated MTC and MEPV predictions from both minimum-jerk predictions and from human behavior. We conclude that learned behavior in the viscous environment could arise from minimizing kinematic costs but could not arise from a minimization of either torque change or endpoint variance.

Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude.

Although previous experiments have identified that errors in movement induce adaptation, the precise manner in which errors determine subsequent control is poorly understood. Here we used transient pulses of force, distributed pseudo-randomly throughout a movement set, to study how the timing of feedback within a movement influenced subsequent predictive control. Human subjects generated a robust adaptive response in postpulse movements that opposed the pulse direction. Regardless of the location or magnitude of the pulse, all pulses yielded similar changes in predictive control. All current supervised and unsupervised theories of motor learning presume that adaptation is proportional to error. Current neural models that broadly encode movement velocity and adapt proportionally to motor error can mimic human insensitivity to pulse location, but cannot mimic human insensitivity to pulse magnitude. We conclude that single trial adaptation to force pulses reveals a categorical strategy that humans adopt to counter the direction, rather than the magnitude, of movement error.