Two distinct ipsilateral cortical representations for individuated finger movements.

Movements of the upper limb are controlled mostly through the contralateral hemisphere. Although overall activity changes in the ipsilateral motor cortex have been reported, their functional significance remains unclear. Using human functional imaging, we analyzed neural finger representations by studying differences in fine-grained activation patterns for single isometric finger presses. We demonstrate that cortical motor areas encode ipsilateral movements in 2 fundamentally different ways. During unimanual ipsilateral finger presses, primary sensory and motor cortices show, underneath global suppression, finger-specific activity patterns that are nearly identical to those elicited by contralateral mirror-symmetric action. This component vanishes when both motor cortices are functionally engaged during bimanual actions. We suggest that the ipsilateral representation present during unimanual presses arises because otherwise functionally idle circuits are driven by input from the opposite hemisphere. A second type of representation becomes evident in caudal premotor and anterior parietal cortices during bimanual actions. In these regions, ipsilateral actions are represented as nonlinear modulation of activity patterns related to contralateral actions, an encoding scheme that may provide the neural substrate for coordinating bimanual movements. We conclude that ipsilateral cortical representations change their informational content and functional role, depending on the behavioral context

Error Correction, Sensory Prediction, and Adaptation in Motor Control

Motor control is the study of how organisms make accurate goal-directed movements. There are two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.Engineering and Applied Science

Rethinking Motor Learning and Savings in Adaptation Paradigms: Model-Free Memory for Successful Actions Combines with Internal Models

SummaryAlthough motor learning is likely to involve multiple processes, phenomena observed in error-based motor learning paradigms tend to be conceptualized in terms of only a single process: adaptation, which occurs through updating an internal model. Here we argue that fundamental phenomena like movement direction biases, savings (faster relearning), and interference do not relate to adaptation but instead are attributable to two additional learning processes that can be characterized as model-free: use-dependent plasticity and operant reinforcement. Although usually “hidden” behind adaptation, we demonstrate, with modified visuomotor rotation paradigms, that these distinct model-based and model-free processes combine to learn an error-based motor task. (1) Adaptation of an internal model channels movements toward successful error reduction in visual space. (2) Repetition of the newly adapted movement induces directional biases toward the repeated movement. (3) Operant reinforcement through association of the adapted movement with successful error reduction is responsible for savings

Human sensorimotor learning: adaptation, skill, and beyond.

Recent studies of upper limb movements have provided insights into the computations, mechanisms, and taxonomy of human sensorimotor learning. Motor tasks differ with respect to how they weight different learning processes. These include adaptation, an internal-model based process that reduces sensory-prediction errors in order to return performance to preperturbation levels, use-dependent plasticity, and operant reinforcement. Visuomotor rotation and force-field tasks impose systematic errors and thereby emphasize adaptation. In skill learning tasks, which for the most part do not involve a perturbation, improved performance is manifest as reduced motor variability and probably depends less on adaptation and more on success-based exploration. Explicit awareness and declarative memory contribute, to varying degrees, to motor learning. The modularity of motor learning processes maps, at least to some extent, onto distinct brain structures. Introduction Sensorimotor learning refers to improvement, through practice, in the performance of sensory-guided motor behavior. Here we will focus primarily on learning studies of the hand and arm in humans. Based on our own scientific leanings and limited space for this review, we chose to neglect learning with eyes and legs. It is worthwhile to admit to this effector chauvinism as it raises a question that almost never gets explicitly mentioned in the field of motor learning: how to choose which animal, body part, or task to study? Reductionism applies in motor control as much as in as the rest of science; we need reduced systems in order to build up from the simple to the complex. Sherrington's studies of reflexes across single joints in cats and dogs were predicated on just this kind of reasoning Psychophysical studies, in which learning is recorded through quantitative movement analysis, reveal regularities and performance patterns at the behavioral level, which suggest organizational principles for learning. Computational modeling offers normative principles, such as optimal Bayesian estimation and minimization of costs, to explain and predict behavioral data. Lesions in patients and stimulation techniques, such as transcranial magnetic (TMS) and direct current stimulation (tDCS), can be used to test the causal role of anatomical structures. This review, necessarily selective, will describe recent noteworthy studies of goal-directed arm movements, and is organized around the principles of modularity and hierarchy. The text is structured on the premise that motor learning (as a blanket term) consists of multiple component processes, each of which has been studied with particular experimental paradigms. We have divided the sections into what we view as roughly separable components of learning. The order of the sections proceeds from adaptation, to skills, and then to the role of explicit cognitive processes. Adaptation Learning rates Error-based paradigms (prisms, rotations, force fields) have been used extensively to investigate motor learning Smith et al. originally introduced a two-rate state-space model description of force-field adaptation, which posits that adaptation is driven by a fast error reduction process with poor retention and a slow process with good retention Skill and the motor cortex: What is the structural anatomical basis for a high level of motor skill? One approach has been to use transcranial magnetic stimuation (TMS) to probe the variety of finger postures elicited by stimulation at various scalp positions over the motor cortex. Gentner and Classen [54] initially demonstrated that TMS elicits a range of finger postures that can be summarized by a small number of mathematical building blocks (principal components), and that these building blocks can be used to accurately reconstruct finger postures assumed by the hand during normal grasping movements. These findings suggested that finger postures are embedded in a modular fashion in the circuitry of the motor cortex. In a subsequent study [55 ], the authors then compared this cortical organization between musicians and non-musicians. The hypothesis was that the motor cortex of musicians may have elicitable representations specialized for playing their particular instrument. Musicians' and non-musicians' finger postures were recorded while they played a musical instrument. While the musicians' principal components, obtained from stimulation, could be used to accurately reconstruct these skilled movements, those of non-musicians could not. In other words, the musicians' ability to play the instrument was reflected in an instrument-specific specialized motor cortical organization that was not present in nonmusicians. That motor cortex maintains representations of motor skills was brought one step further in a study [29 ] that used tDCS over the motor cortex of healthy subjects while they practiced a difficult visuomotor task. Task performance was limited by a speedaccuracy trade-off function (SAF), and motor skill learning was measured as daily improvements in the SAF. Motor cortex stimulation enhanced skill learning through an effect on overnight retention, consistent with the idea that motor cortex can store task-specific representations of motor skill. Current Opinion in Neurobiology 2011, 21:1-9 www.sciencedirect.com The 2-state, and other LTI models, cannot, however, explain all the phenomena observed in adaptation experiments Wei and Kording [15] also found a reduction in learning rate with increased sensory noise but not for increased output noise, and no increase when they increased variability in the motor-to-sensory mapping. To remain within the Bayesian framework it could be conjectured that some parameters are fixed (hard-wired) or that changes in, for example, state noise cannot be detected in the short time frame of a single experiment. More damaging, however, are examples where changes in rate occur when they are not predicted by a Kalman filter; for example, increases in learning rate after a period of baseline trials with normal feedback (washout) Representation of adapted mappings Several recent studies have probed the constraints on adaptation to test hypotheses about neural representations of new sensorimotor mappings. Sing et al. What determines how much adaptation to a particular perturbation generalizes across the workspace? For example, adaptation to visuomotor rotation shows limited generalization whereas adaptation of gain (amplitude scaling) generalizes broadly More insight into the possible reasons underlying differences in rotation and gain generalization was provided by Liu et al. [22 ] The cursor position depended on experimenter-determined hand configurations recorded with a cyberglove, that is, the mapping from hand-space to cursor space was completely arbitrary and highly nonlinear. The idea behind the cyberglove paradigm is that the transformations are being experienced by a naïve system and thus priors about gain (scaling) and rotation would presumably not apply. Perturbations of scaling and rotation were learned quite differently. Specifically, subjects explored and acquired a new finger coordination pattern for rotation adaptation but scaled their baseline coordination pattern for gain adaptation, thus supporting the idea that these two kinds of adaptation are computationally distinct [20] studies together. Perhaps the two-gain condition is analogous to the cyberglove: when the task space becomes complex and unfamiliar, generalization narrows to an upper, presumably neural, bound. If this is correct then it suggests an additional top-down mechanism that 'surveys' the task space and applies a general rule if the rule is easily applied to a familiar effector. Thus rotation might not generalize even for the arm because although the effector is familiar the rotation rule is not easily applied top-down. Note that this interpretation is speculative and quite different from the 'mixture of experts' argument given by Pearson et al. [20] and the argument provided by Liu et al. [22 ]. The idea of a hierarchy whereby lower level building blocks generalize narrowly and higher levels generalize broadly bears conceptual familiarities to the notion of reverse hierarchies in perceptual learning [23]. Beyond adaptation in error-based paradigms Recent studies suggest that other learning processes are active, in addition to adaptation itself, in error-based paradigms. This is important to appreciate -the whole brain is taking part in the experiment, not just the cerebellum updating a forward model. Use-dependent plasticity It has recently been shown that repetition of a particular reaching direction leads future movements to be biased towards that direction [24 ]. The term that has been used to for these repetition-induced biases is use-dependent plasticity. Diedrichsen et al. [25 ] used a redundant task design to show that use-dependent plasticity and adaptation can occur simultaneously and in opposing directions in the task-irrelevant dimension of an adaptation task. Subjects were required to make a reach of specified amplitude but, unbeknownst to them, their arm was incrementally displaced laterally by a force channel applied by the manipulandum. Interestingly, even though the lateral movement was irrelevant to task completion, subjects nevertheless biased their movements laterally when the manipulandum was no longer applying a lateral force and short-lived adaptation after-effects had washed out. Huang et al. [13 ] used a modified visuomotor rotation paradigm to show that adaptation itself can act as a channel to induce directional biases in the direction of the adapted movement. Interestingly, the biases were larger in the setting of adaptation than those observed in the study by Verstynen and Sabes [24 ], which suggests that use-dependent plasticity can be modified by the implicit reward of successful error reduction. Success-based learning Can a systematic perturbation be learned using scalar reward rather than vector error? Izawa and Shadmehr [26 ] found that the answer is yes for visuomotor rotation, albeit under very specific circumstances. A rotation was introduced in 18 increments every 40 trials until it reached 88. One group received full cursor feedback and explicit reward when they hit the target; the other group only received explicit reward. Both groups updated their commands by a similar amount and had a comparable amount of total learning. The authors argue that the two groups achieved the same performance in two qualitatively distinct ways based on two findings: only the group that received error feedback showed evidence for a change in the perceived position of their hand following a motor command (adaptation of a forward model) and showed broad generalization across directions. The group that only received scalar reward as feedback used a trial-anderror exploratory strategy, a strategy made possible by the gradual nature of the perturbation so that the required changes in movements largely occurred within the range of baseline variability. Although it is unlikely that large step perturbations could be learned with reward alone, the study by Izawa and Shadmehr shows that reinforcement learning, in some circumstances, can substitute for adaptation when there is uncertainty about, or no, sensory prediction error. Huang et al. have recently suggested that even putatively pure adaptation paradigms are in fact made up of multiple distinct learning processes Structural learning Adaptation paradigms have also been used to provide evidence for a new framework, albeit consistent with the idea of modifiable priors, to explain learning-to-learn (meta-learning) phenomena. Braun et al. [27 ,28] pointed out the important distinction between parametric learning and structural learning. Parametric learning describes the adaptation processes we described in the first section: countering a perturbation through error-driven updates of a parameterized model. Structural learning can be considered learning the covariance structure among these parameters. For example, rotations and shears of a cursor's x,y position with respect to hand position can both be represented by 2 by 2 matrices. Knowing exactly how the entries of this matrix covary within each family of perturbations simplifies the decision of how to update the entries of the matrix following a given observed error; this dimensionality reduction allows an increase in learning rate. From the Bayesian point of view this would correspond to learning a new prior distribution on the parameters of the perturbation. In support of the structural learning hypothesis, Braun et al. [27 ] found that learning a particular rotation is facilitated after experiencing a lead-in period of random rotations, which suggests that the invariant feature during the lead-in period (the fact that all perturbations were rotations) was successfully extracted. Optimization and skill We recently defined skill change operationally as a shift the speed-accuracy trade-off function (SAF) for a task when no systematic perturbation is present [29 ]. Adaptation to a perturbation, by contrast, is not a skill because subjects are knocked off their baseline SAF but at best only return to it -their performance is not better than baseline performance. The question is how is skill, that is, improved performance captured as a shift in the SAF, accomplished when there is no systematic change in the relationship between commands and their sensory consequences? Behavioral performance could be improved through better state estimation (improved forward models, or improved processing of sensory feedback), and/or through better motor execution (improved signal-to-noise ratio in motor output). Which of these processes is the rate-limiting step in skill learning is unknown. It is interesting to note that if skill could be attributed to improved state estimation by a forward model, then this would suggest that systematic changes in internal models occur much faster than improvements in the precision of these models, given that skill learning takes much longer than adaptation. Optimal feedback control (OFC) [30] has proven a comprehensive theory of motor coordination in redundant systems [31][32][33]34 ]. A cost function made up of effort and accuracy terms can be optimized, assuming that unbiased estimates of a number of crucial parameters, including the parameters of a forward dynamic model, are available a priori, to derive a feedback control policy for a given task goal. How does OFC relate to skill learning? Nagengast et al. [35 ] addressed how we learn to control complex objects with internal degrees of freedom. For such objects, there is no simple one-to-one correspondence between the state of the hand and the state of the object. For example, how does a cowboy, or Wonder Woman, learn to control a lasso? In the study, subjects learned to control 6 simulated objects with complex dynamics. They were trained with these strange virtual objects and improved at meeting an accuracy criterion even though they had to move progressively faster. The main result was that at the end of learning the hand paths that subjects adopted for each object were predicted by OFC using a simple cost function. Thus the assumption was that the lead-in training comprised adaptation -subjects first learn the complex object dynamics and then model-based optimization of a cost function occurs. But the lead-in training phase was marked by an improvement in both speed and accuracy, and therefore amounted to a shift of the SAF for this task. Although this lead-in phase was not the focus of the study, the data suggest that one of two other processes must also have been occurring in the training period to lead to better performance: convergence on the optimal policy, or improved execution of the control policy itself, perhaps through an increased signal-tonoise ratio via expanded neural representations. Either of these possibilities could be the explanation for shifts in the SAF [29 ] and reductions in variability described in motor skill learning studies [36,37]. We would suggest that optimal behavior is converged upon not only through model-based mechanisms, but also through model-free processes. Interaction between implicit and explicit processes during motor learning Sensorimotor learning, in the form of mirror writing (a form of adaptation), served as the prototypical instantiation of procedural or implicit learning when it was shown to be intact in the amnesic patient HM [38,39]. Having lost explicit memory, HM did not recall having practiced the motor task before but nevertheless showed motor improvement over days. This very famous result has led, however, to oversimplifications and misunderstandings. That HM could not explicitly recall having done the task does not imply that explicit processes were not used each time he performed the task, that is, explicit memory and explicit control processes are not synonymous. Adaptation can indeed proceed entirely implicitly [3] but this does not preclude the possibility that it could benefit from explicit processes. Finally, as stated in the introduction, adaptation should not stand in for all of motor learning; what is true for adaptation may not be true for other forms of motor learning. It is very unlikely that explicit and implicit processes do not interact during motor learning; it is hard to imagine that the prefrontal cortex would just stand by as motor areas do the learning. Two ways can be envisaged for how explicit cognitive processes could help motor learning. One is the idea that an alternative explicit strategy might be found to solve the learning task. The second, hitherto less considered possibility, is that explicit cognitive processes could augment implicit processes themselves. Adaptation A recent study by Taylor and Ivry [40 ] further pursued the finding that, at least initially, implicit rotation adaptation cannot be bypassed by an explicit strategy Keisler and Shadmehr [41 ] used an interesting approach to examine whether declarative memory contributes to force field adaptation. Subjects began by adapting to force field A, then were briefly exposed to a counter force field B, and then, after a 3-min interval, were exposed to a force channel. Movements in a force channel reveal the lateral forces that subjects have learned through adaptation. This is the same paradigm as originally used to posit the existence of a fast and a slow adaptation component Skilled sequential movements Sequence learning tasks have also been used extensively to study motor learning. The most popular task is the Serial Reaction Time Task (SRTT) [42]. This task has been used to argue that sequence order can be learned implicitly because onset times (reaction time (RT) plus movement time (MT)) are gradually reduced when subjects make sequential movements without explicit awareness that a sequence is present. In a recent innovative study, Moisello et al. took a critical look at the SRTT using a reaching task that allowed them to break the onset time measure into RT and MT [43 ]. Their main surprising finding was that there was no evidence for implicit learning of sequence order once one took account of explicit awareness of sequence fragments and the nonspecific effect of practice on MT. This is a somewhat heretical result but adds to already existing skepticism as to whether purely implicit learning of sequence order is at all possible [44]. Can explicit awareness of sequence order and declarative memory enhance execution of sequence elements, that is, is there a way in which knowing what you have to do at the global task level improves the precision of component movements that are already practiced to a high level? Two recent studies suggest that the answer to this question is yes. Ghilardi et al. [45] showed that spatial accuracy was higher to an explicitly known wellpracticed target in an array of 8 targets when the order of the other 7 targets was also known, compared to when the order of these remaining targets still had to be learned. Crump and Logan [46 ] found that alreadyskilled typists, using a familiar keyboard, showed a difference on a sequence execution measure (interkey stroke interval) if they were given a word that they had recently seen before versus a new word. These results are interesting because they go against the idea that as tasks become well practiced and automatic, they break free of explicit control. The possibility that explicit cognitive processes can always enhance overlearned skills suggests an interesting difference between skill learning and adaptation and raises the question as to whether HM could have learned to type if he had never done so before. Conclusions Motor learning is a general term that covers multiple model-free and model-based learning processes that are likely to be differentially weighted across tasks and implemented by multiple functional and anatomical brain modules 6 Sensory and Motor Systems CONEUR-954; NO. OF PAGES 9 Please cite this article in press as: Krakauer JW, Mazzoni P. Human sensorimotor learning: adaptation, skill, and beyond, Curr Opin Neurobiol (2011)

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

A Comparison of Two Methods for MRI Classification of At-Risk Tissue and Core Infarction

Objective: To compare how at-risk tissue and core infarction were defined in two major trials that tested the use of MRI in selecting acute stroke patients for endovascular recanalization therapy.Methods: MRIs from 12 patients evaluated for possible endovascular therapy were processed using the methods published from two major trials, MR RESCUE and DEFUSE 2. Specifically, volumes of at-risk tissue and core infarction were generated from each patient’s MRI. MRIs were then classified as to whether or not they met criteria for salvageable tissue: penumbral pattern for MR RESCUE and/or target profile for DEFUSE 2) as defined by each trial.Results: Volumes of at-risk tissue by the two definitions were correlated (p=0.017) while the volumes of core infarct were not (p=0.059). The volume of at-risk tissue was consistently larger when defined by the penumbral pattern than the target profile while the volume of core infarct was consistently larger when defined by the target profile than the penumbral pattern. When these volumes were used to classify the MRI scans, nine out of 12 patients (75%) were classified as having a penumbral pattern, while only 4 out of 12 patients (33%) were classified as having a target profile. Of the 9 patients classified as penumbral pattern, 5 (55%) were classified differently by the target profile.Interpretation: Our analysis found that the MR RESCUE trial defined salvageable tissue in a way which made it more likely for patients be labeled as favorable for treatment. For the cohort of patients examined in this study, had they been enrolled in both trials, most of the patients identified as having salvageable tissue by the MR RESCUE trial would not have been considered to have salvageable tissue in the DEFUSE 2 trial. Caution should be taken in concluding that MRI selection for endovascular therapy is not effective as imaging selection criteria were substantially different between trials

State-of-the-art clinical assessment of hand function

We have assembled a multi-disciplinary team of engineers, surgeons, clinicians and neuroscientists from Johns Hopkins School of Medicine and Western University to develop a new device for assessing hand function. It will be capable of sensitively measuring fingertip forces across all five fingers and along all movement directions. Then we can use this device to develop and validate a clinical hand assessment for patients with brain injuries.https://ir.lib.uwo.ca/brainscanprojectsummaries/1005/thumbnail.jp

A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis

In neuroimaging studies of human cognitive abilities, brain activation patterns that include regions that are strongly interactive in response to experimental task demands are of particular interest. Among the existing network analyses, partial least squares (PLS; McIntosh, 1999; McIntosh, Bookstein, Haxby, & Grady, 1996) has been highly successful, particularly in identifying group differences in regional functional connectivity, including differences as diverse as those associated with states of awareness and normal aging. However, we address the need for a within-group model that identifies patterns of regional functional connectivity that exhibit sustained activity across graduated changes in task parameters. For example, predictions of sustained connectivity are commonplace in studies of cognition that involve a series of tasks over which task difficulty increases (Baddeley, 2003). We designed ordinal trend analysis (OrT) to identify activation patterns that increase monotonically in their expression as the experimental task parameter increases, while the correlative relationships between brain regions remain constant. Of specific interest are patterns that express positive ordinal trends on a subject-by-subject basis. A unique feature of OrT is that it recovers information about functional connectivity based solely on experimental design variables. In particular, there is no requirement by OrT to provide either a quantitative model of the uncertain relationship between functional brain circuitry and subject variables (e.g., task performance and IQ) or partial information about the regions that are functionally connected. In this letter, we provide a step-by-step recipe of the computations performed in the new OrT analysis, including a description of the inferential statistical methods applied. Second, we describe applications of OrT to an event-related fMRI study of verbal working memory and H2 15 O-PET study of visuomotor learning. In sum, OrT has potential applications to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease