Biomedical subjects

Reza Shadmehr

Publications and source records attributed to Reza Shadmehr.

At least 19 recordsLinked to original sources

PubMed2006

Interacting adaptive processes with different timescales underlie short-term motor learning.

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.

Adaptation, Physiological↗

PubMed2006

Why does the brain predict sensory consequences of oculomotor commands? Optimal integration of the predicted and the actual sensory feedback.

When the brain initiates a saccade, it uses a copy of the oculomotor commands to predict the visual consequences: for example, if one fixates a reach target, a peripheral saccade will produce an internal estimate of the new retinal location of the target, a process called remapping. In natural settings, the target likely remains visible after the saccade. So why should the brain predict the sensory consequence of the saccade when after its completion, the image of the target remains visible? We hypothesized that in the post-saccadic period, the brain integrates target position information from two sources: one based on remapping and another based on the peripheral view of the target. The integration of information from these two sources could produce a less variable target estimate than is possible from either source alone. Here, we show that reaching toward targets that were initially foveated and remapped had significantly less variance than reaches relying on peripheral target information. Furthermore, in a more natural setting where both sources of information were available simultaneously, variance of the reaches was further reduced as predicted by integration. This integration occurred in a statistically optimal manner, as demonstrated by the change in integration weights when we manipulated the uncertainty of the post-saccadic target estimate by varying exposure time. Therefore, the brain predicts the sensory consequences of motor commands because it integrates its prediction with the actual sensory information to produce an estimate of sensory space that is better than possible from either source alone.

Adult↗

PubMed2006

Dissociable effects of the implicit and explicit memory systems on learning control of reaching.

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.

Adaptation, Psychological↗

PubMed2006

Generalization of motor learning depends on the history of prior action.

Generalization of motor learning refers to our ability to apply what has been learned in one context to other contexts. When generalization is beneficial, it is termed transfer, and when it is detrimental, it is termed interference. Insight into the mechanism of generalization may be acquired from understanding why training transfers in some contexts but not others. However, identifying relevant contextual cues has proven surprisingly difficult, perhaps because the search has mainly been for cues that are explicit. We hypothesized instead that a relevant contextual cue is an implicit memory of action with a particular body part. To test this hypothesis we considered a task in which participants learned to control motion of a cursor under visuomotor rotation in two contexts: by moving their hand through motion of their shoulder and elbow, or through motion of their wrist. Use of these contextual cues led to three observations: First, in naive participants, learning in the wrist context was much faster than in the arm context. Second, generalization was asymmetric so that arm training benefited subsequent wrist training, but not vice versa. Third, in people who had prior wrist training, generalization from the arm to the wrist was blocked. That is, prior wrist training appeared to prevent both the interference and transfer that subsequent arm training should have caused. To explain the data, we posited that the learner collected statistics of contextual history: all upper arm movements also move the hand, but occasionally we move our hands without moving the upper arm. In a Bayesian framework, history of limb segment use strongly affects parameter uncertainty, which is a measure of the covariance of the contextual cues. This simple Bayesian prior dictated a generalization pattern that largely reproduced all three findings. For motor learning, generalization depends on context, which is determined by the statistics of how we have previously used the various parts of our limbs.

Adult↗

PubMed2005

Effects of human cerebellar thalamus disruption on adaptive control of reaching.

Lesion or degeneration of the cerebellum can profoundly impair adaptive control of reaching in humans. Computational models have proposed that internal models that help control movements form in the cerebellum and influence planned motor output through the cerebello-thalamo-cortical pathway. However, lesion studies of the cerebellar thalamus have not consistently found impairment in reaching or adaptation of reaching. To elucidate the role of the cerebellar thalamus in humans, we studied a group of essential tremor (ET) patients with deep brain stimulation (DBS) electrodes placed in the cerebellar thalamus. The stimulation can be turned on or off remotely and is thought to reduce tremor by blocking the spread of the pathological output from the cerebellum. We studied the effect of thalamic DBS on the ability to adapt arm movements to novel force fields. Although thalamic DBS resulted in a dramatic and significant reduction of tremor in ET, it also impaired motor adaptation: the larger the stimulation voltage, the greater the reduction in rates of adaptation. We next examined ET patients that had undergone unilateral thalamotomy in the cerebellar thalamus and found that adaptation with the contralateral arm was impaired compared with the ipsilateral arm. Therefore, although both lesion and electrical stimulation of the cerebellar thalamus are highly effective in reducing tremor, they significantly impair the ability of the brain to form internal models of action. Adaptive control of reaching appears to depend on the integrity of the cerebello-thalamo-cortical pathway.

Adaptation, Physiological↗

PubMed2005

Adaptation and generalization in acceleration-dependent force fields.

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.

Acceleration↗

PubMed2005

Consolidation of motor memory.

An issue of great recent interest is whether motor memory consolidates in a manner analogous to declarative memory--that is, with the formation of a memory that progresses over time from a fragile state, which is susceptible to interference by a lesion or a conflicting motor task, to a stabilized state, which is resistant to such interference. Here, we first review studies that examine the anatomical basis for motor consolidation. Evidence implicates cerebellar circuitry in two types of associative motor learning--eyelid conditioning and vestibulo-ocular reflex adaptation--and implicates primary motor cortex in skilled finger movements. We also review evidence for and against a consolidation process for adaptation of arm movements. We propose that contradictions have arisen because consolidation can be masked by inhibition of memory retrieval.

Adaptation, Physiological↗

PubMed2005

Neural correlates of reach errors.

Reach errors may be broadly classified into errors arising from unpredictable changes in target location, called target errors, and errors arising from miscalibration of internal models (e.g., when prisms alter visual feedback or a force field alters limb dynamics), called execution errors. Execution errors may be caused by miscalibration of dynamics (e.g., when a force field alters limb dynamics) or by miscalibration of kinematics (e.g., when prisms alter visual feedback). Although all types of errors lead to similar on-line corrections, we found that the motor system showed strong trial-by-trial adaptation in response to random execution errors but not in response to random target errors. We used functional magnetic resonance imaging and a compatible robot to study brain regions involved in processing each kind of error. Both kinematic and dynamic execution errors activated regions along the central and the postcentral sulci and in lobules V, VI, and VIII of the cerebellum, making these areas possible sites of plastic changes in internal models for reaching. Only activity related to kinematic errors extended into parietal area 5. These results are inconsistent with the idea that kinematics and dynamics of reaching are computed in separate neural entities. In contrast, only target errors caused increased activity in the striatum and the posterior superior parietal lobule. The cerebellum and motor cortex were as strongly activated as with execution errors. These findings indicate a neural and behavioral dissociation between errors that lead to switching of behavioral goals and errors that lead to adaptation of internal models of limb dynamics and kinematics.

Adolescent↗

PubMed2005

Internal models of limb dynamics and the encoding of limb state.

Studies of reaching suggest that humans adapt to novel arm dynamics by building internal models that transform planned sensory states of the limb, e.g., desired limb position and its derivatives, into motor commands, e.g., joint torques. Earlier work modeled this computation via a population of basis elements and used system identification techniques to estimate the tuning properties of the bases from the patterns of generalization. Here we hypothesized that the neural representation of planned sensory states in the internal model might resemble the signals from the peripheral sensors. These sensors normally encode the limb's actual sensory state in which movement errors occurred. We developed a set of equations based on properties of muscle spindles that estimated spindle discharge as a function of the limb's state during reaching and drawing of circles. We then implemented a simulation of a two-link arm that learned to move in various force fields using these spindle-like bases. The system produced a pattern of adaptation and generalization that accounted for a wide range of previously reported behavioral results. In particular, the bases showed gain-field interactions between encoding of limb position and velocity, very similar to the gain fields inferred from behavioral studies. The poor sensitivity of the bases to limb acceleration predicted behavioral results that were confirmed by experiment. We suggest that the internal model of limb dynamics is computed by the brain with neurons that encode the state of the limb in a manner similar to that expected of muscle spindle afferents.

Arm↗

PubMed2005

Detecting and adjusting for artifacts in fMRI time series data.

We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or arm movements can increase noise in a spatially global pattern throughout an image, leading to a non-stationary noise process. We derive a restricted maximum likelihood (ReML) algorithm that estimates the variance of the noise for each image in the time series. These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model. We apply this approach to a typical fMRI experiment with a block design and show that the noise estimates strongly vary across different images and that our method detects and appropriately weights images that are affected by artifacts. Furthermore, we show that the noise process has a global spatial distribution and that the variance increase is multiplicative rather than additive. The new algorithm results in significantly increased sensitivity in the ability to detect regions of activation. The new method may be particularly useful for studies that involve special populations (e.g., children or elderly) where sporadic, artifact-generating events are more likely.

Algorithms↗

PubMed2004

Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration.

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.

Adult↗

PubMed2004

Internal models and contextual cues: encoding serial order and direction of movement.

During reaching, the brain may rely on internal models to transform desired sensory outcomes into motor commands. This transformation depends on both the state of the limb and the cues that can identify the context of the movement. How are contextual cues and information about state of the limb combined in the computations of internal models? We considered a reaching task where forces on the hand depended on both the direction of movement (state of the limb) and order of that movement in a predefined sequence (contextual cue). When the cue was available, the motor system formed an internal model that used both serial order and target direction to program motor commands. Assuming that the internal model was formed by a population code through a combination of unknown basis elements, the sensitivity of the bases with respect to state of the limb and contextual cue should dictate how error in one type of movement affected all other movement types. Using a state-space theory, we estimated this generalization function and identified the adaptive system from trial-by-trial changes in performance. The results implied that the basis elements were tuned to direction of movement but output of each basis at its preferred direction was multiplicatively modulated by a weak tuning with respect to the contextual cue. Activity fields that multiplicatively encode diverse sources of information may serve as a general mechanism for a single network to produce context-dependent motor output.

Cues↗

PubMed2004

Generalization as a behavioral window to the neural mechanisms of learning internal models.

In generating motor commands, the brain seems to rely on internal models that predict physical dynamics of the limb and the external world. How does the brain compute an internal model? Which neural structures are involved? We consider a task where a force field is applied to the hand, altering the physical dynamics of reaching. Behavioral measures suggest that as the brain adapts to the field, it maps desired sensory states of the arm into estimates of force. If this neural computation is performed via a population code, i.e., via a set of bases, then activity fields of the bases dictate a generalization function that uses errors experienced in a given state to influence performance in any other state. The patterns of generalization suggest that the bases have activity fields that are directionally tuned, but directional tuning may be bimodal. Limb positions as well as contextual cues multiplicatively modulate the gain of tuning. These properties are consistent with the activity fields of cells in the motor cortex and the cerebellum. We suggest that activity fields of cells in these motor regions dictate the way we represent internal models of limb dynamics.

Biomechanical Phenomena↗

PubMed2003

A gain-field encoding of limb position and velocity in the internal model of arm dynamics.

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.

Adult↗

PubMed2003

Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control.

During reaching movements, the brain's internal models map desired limb motion into predicted forces. When the forces in the task change, these models adapt. Adaptation is guided by generalization: errors in one movement influence prediction in other types of movement. If the mapping is accomplished with population coding, combining basis elements that encode different regions of movement space, then generalization can reveal the encoding of the basis elements. We present a theory that relates encoding to generalization using trial-by-trial changes in behavior during adaptation. We consider adaptation during reaching movements in various velocity-dependent force fields and quantify how errors generalize across direction. We find that the measurement of error is critical to the theory. A typical assumption in motor control is that error is the difference between a current trajectory and a desired trajectory (DJ) that does not change during adaptation. Under this assumption, in all force fields that we examined, including one in which force randomly changes from trial to trial, we found a bimodal generalization pattern, perhaps reflecting basis elements that encode direction bimodally. If the DJ was allowed to vary, bimodality was reduced or eliminated, but the generalization function accounted for nearly twice as much variance. We suggest, therefore, that basis elements representing the internal model of dynamics are sensitive to limb velocity with bimodal tuning; however, it is also possible that during adaptation the error metric itself adapts, which affects the implied shape of the basis elements.

Adaptation, Physiological↗

PubMed2003

Saccade adaptation in response to altered arm dynamics.

The delays in sensorimotor pathways pose a formidable challenge to the implementation of stable error feedback control, and yet the intact brain has little trouble maintaining limb stability. How is this achieved? One idea is that feedback control depends not only on delayed proprioceptive feedback but also on internal models of limb dynamics. In theory, an internal model allows the brain to predict limb position. Earlier we had found that during reaching, the brain estimates hand position in real-time in a coordinate system that can be used for generating saccades. Here we tested the idea that the estimate of hand position, as expressed through saccades, depends on an internal model that adapts to dynamics of the arm. We focused on the behavior of the eyes as perturbations were applied to the unseen hand. We found that when the hand was perturbed from stable posture with a 100-ms force pulse of random direction and magnitude, a saccade was generated on average at 182 ms postpulse onset to a position that was an unbiased estimate of real-time hand position. To test whether planning of saccades depended on an internal model of arm dynamics, arm dynamics were altered either predictably or unpredictably during the postpulse period. When arm dynamics were predictable, saccade amplitudes changed to reflect the change in the arm's behavior. We suggest that proprioceptive feedback from the arm is integrated into an adaptable internal model that computes an estimate of current hand position in eye-centered coordinates.

Adaptation, Physiological↗

PubMed2003

Learned dynamics of reaching movements generalize from dominant to nondominant arm.

Accurate performance of reaching movements depends on adaptable neural circuitry that learns to predict forces and compensate for limb dynamics. In earlier experiments, we quantified generalization from training at one arm position to another position. The generalization patterns suggested that neural elements learning to predict forces coded a limb's state in an intrinsic, muscle-like coordinate system. Here, we test the sensitivity of these elements to the other arm by quantifying inter-arm generalization. We considered two possible coordinate systems: an intrinsic (joint) representation should generalize with mirror symmetry reflecting the joint's symmetry and an extrinsic representation should preserve the task's structure in extrinsic coordinates. Both coordinate systems of generalization were compared with a naïve control group. We tested transfer in right-handed subjects both from dominant to nondominant arm (D-->ND) and vice versa (ND-->D). This led to a 2 x 3 experimental design matrix: transfer direction (D-->ND/ND-->D) by coordinate system (extrinsic, intrinsic, control). Generalization occurred only from dominant to nondominant arm and only in extrinsic coordinates. To assess the dependence of generalization on callosal inter-hemispheric communication, we tested commissurotomy patient JW. JW showed generalization from dominant to nondominant arm in extrinsic coordinates. The results suggest that when the dominant right arm is used in learning dynamics, the information could be represented in the left hemisphere with neural elements tuned to both the right arm and the left arm. In contrast, learning with the nondominant arm seems to rely on the elements in the nondominant hemisphere tuned only to movements of that arm.

Adolescent↗

PubMed2002

A real-time state predictor in motor control: study of saccadic eye movements during unseen reaching movements.

Theoretical motor control predicts that because of delays in sensorimotor pathways, a neural system should exist in the brain that uses efferent copy of commands to the arm, sensory feedback, and an internal model of the dynamics of the arm to predict the future state of the hand (i.e., a forward model). We tested this theory under the hypothesis that saccadic eye movements, tracking an unseen reaching movement, would reflect the output of this state predictor. We found that in unperturbed reaching movements, saccade occurrence at any time t consistently provided an unbiased estimate of hand position at t + 196 msec. To investigate the behavior of this predictor during feedback error control, we applied 50 msec random-force perturbations to the moving hand. Saccades showed a sharp inhibition at 100 msec after perturbation. At approximately 170 msec, there was a sharp increase in saccade probabilities. These postperturbation saccades were an unbiased estimator of hand position at saccade time t + 150 msec. The ability of the brain to guide saccades to the future position of the hand failed when a force field unexpectedly changed the dynamics of the hand immediately after perturbation. The behavior of the eyes suggested that during reaching movements, the brain computes an estimate of future hand position based on an internal model that relies on real-time proprioceptive feedback. When an error occurs in reaching movements, the estimate of future hand position is recomputed. The saccade inhibition period that follows the hand perturbation may indicate the length of time it takes for this computation to take place.

Arm↗