Disruption of somatosensory cortex impairs motor learning and retention.

This study tests for a function of the somatosensory cortex, that, in addition to its role in processing somatic afferent information, somatosensory cortex contributes both to motor learning and the stabilization of motor memory. Continuous theta-burst magnetic stimulation (cTBS) was applied, before force-field training to disrupt activity in either the primary somatosensory cortex, primary motor cortex, or a control zone over the occipital lobe. Tests for retention and relearning were conducted after a 24 h delay. Analysis of movement kinematic measures and force-channel trials found that cTBS to somatosensory cortex disrupted both learning and subsequent retention, whereas cTBS to motor cortex had little effect on learning but possibly impaired retention. Basic movement variables are unaffected by cTBS suggesting that the stimulation does not interfere with movement but instead disrupts changes in the cortex that are necessary for learning. In all experimental conditions, relearning in an abruptly introduced force field, which followed retention testing, showed extensive savings, which is consistent with previous work suggesting that more cognitive aspects of learning and retention are not dependent on either of the cortical zones under test. Taken together, the findings are consistent with the idea that motor learning is dependent on learning-related activity in the somatosensory cortex.NEW & NOTEWORTHY This study uses noninvasive transcranial magnetic stimulation to test the contribution of somatosensory and motor cortex to human motor learning and retention. Continuous theta-burst stimulation is applied before learning; participants return 24 h later to assess retention. Disruption of the somatosensory cortex is found to impair both learning and retention, whereas disruption of the motor cortex has no effect on learning. The findings are consistent with the idea that motor learning is dependent upon learning-related plasticity in somatosensory cortex.

Neural Basis of Sensorimotor Plasticity in Speech Motor Adaptation.

When we speak, we get correlated sensory feedback from speech sounds and from the muscles and soft tissues of the vocal tract. Here we dissociate the contributions of auditory and somatosensory feedback to identify brain networks that underlie the somatic contribution to speech motor learning. The technique uses a robotic device that selectively alters somatosensory inputs in combination with resting-state fMRI scans that reveal learning-related changes in functional connectivity. A partial correlation analysis is used to identify connectivity changes that are not explained by the time course of activity in any other learning-related areas. This analysis revealed changes related to behavioral improvements in movement and separately, to changes in auditory perception: Speech motor adaptation itself was associated with connectivity changes that were primarily in non-motor areas of brain, specifically, to a strengthening of connectivity between auditory and somatosensory cortex and between presupplementary motor area and the inferior parietal lobule. In contrast, connectively changes associated with alterations to auditory perception were restricted to speech motor areas, specifically, primary motor cortex and inferior frontal gyrus. Overall, our findings show that during adaptation, somatosensory inputs result in a broad range of changes in connectivity in areas associated with speech motor control and learning.

Cerebellum as a forward but not inverse model in visuomotor adaptation task: a tDCS-based and modeling study.

Despite several pieces of evidence, which suggest that the human brain employs internal models for motor control and learning, the location of these models in the brain is not yet clear. In this study, we used transcranial direct current stimulation (tDCS) to manipulate right cerebellar function, while subjects adapt to a visuomotor task. We investigated the effect of this manipulation on the internal forward and inverse models by measuring two kinds of behavior: generalization of training in one direction to neighboring directions (as a proxy for inverse models) and localization of the hand position after movement without visual feedback (as a proxy for forward model). The experimental results showed no effect of cerebellar tDCS on generalization, but significant effect on localization. These observations support the idea that the cerebellum is a possible brain region for internal forward, but not inverse model formation. We also used a realistic human head model to calculate current density distribution in the brain. The result of this model confirmed the passage of current through the cerebellum. Moreover, to further explain some observed experimental results, we modeled the visuomotor adaptation process with the help of a biologically inspired method known as population coding. The effect of tDCS was also incorporated in the model. The results of this modeling study closely match our experimental data and provide further evidence in line with the idea that tDCS manipulates FM's function in the cerebellum.

Somatosensory Contribution to the Initial Stages of Human Motor Learning.

The early stages of motor skill acquisition are often marked by uncertainty about the sensory and motor goals of the task, as is the case in learning to speak or learning the feel of a good tennis serve. Here we present an experimental model of this early learning process, in which targets are acquired by exploration and reinforcement rather than sensory error. We use this model to investigate the relative contribution of motor and sensory factors to human motor learning. Participants make active reaching movements or matched passive movements to an unseen target using a robot arm. We find that learning through passive movements paired with reinforcement is comparable with learning associated with active movement, both in terms of magnitude and durability, with improvements due to training still observable at a 1 week retest. Motor learning is also accompanied by changes in somatosensory perceptual acuity. No stable changes in motor performance are observed for participants that train, actively or passively, in the absence of reinforcement, or for participants who are given explicit information about target position in the absence of somatosensory experience. These findings indicate that the somatosensory system dominates learning in the early stages of motor skill acquisition. SIGNIFICANCE STATEMENT: The research focuses on the initial stages of human motor learning, introducing a new experimental model that closely approximates the key features of motor learning outside of the laboratory. The finding indicates that it is the somatosensory system rather than the motor system that dominates learning in the early stages of motor skill acquisition. This is important given that most of our computational models of motor learning are based on the idea that learning is motoric in origin. This is also a valuable finding for rehabilitation of patients with limited mobility as it shows that reinforcement in conjunction with passive movement results in benefits to motor learning that are as great as those observed for active movement training.

The role of internal forward models and proprioception in hand position estimation.

Our ability to properly move and react in different situations is largely dependent on our perception of our limbs' position. At least three sources - vision, proprioception, and internal forward models (FMs) - seem to contribute to this perception. To the best of our knowledge, the effect of each source has not been studied individually. Specifically, role of FM has been ignored in some previous studies. We hypothesized that FM has a critical role in subjects' perception which needs to be considered in the relevant studies to obtain more reliable results. Therefore, we designed an experiment with the goal of investigating FM and proprioception role in subjects' perception of their hand's position. Three groups of subjects were recruited in the study. Based on the experiment design, it was supposed that subjects in different groups relied on proprioception, FM, and both of them for estimating their unseen hand's position. Comparing the results of three groups revealed significant difference between their estimation' errors. FM provided minimum estimation error, while proprioception had a bias error in the tested region. Integrating proprioception with FM decreased this error. Integration of two Gaussian functions, fitted to the error distribution of FM and proprioception groups, was simulated and created a mean error value almost similar to the experimental observation. These results suggest that FM role needs to be considered when studying the perceived position of the limbs. This can lead to gain better insights into the mechanisms underlying the perception of our limbs' position which might have potential clinical and rehabilitation applications, e.g., in the postural control of elderly which are at high risk of falls and injury because of deterioration of their perception with age.

Structure of plasticity in human sensory and motor networks due to perceptual learning.

As we begin to acquire a new motor skill, we face the dual challenge of determining and refining the somatosensory goals of our movements and establishing the best motor commands to achieve our ends. The two typically proceed in parallel, and accordingly it is unclear how much of skill acquisition is a reflection of changes in sensory systems and how much reflects changes in the brain's motor areas. Here we have intentionally separated perceptual and motor learning in time so that we can assess functional changes to human sensory and motor networks as a result of perceptual learning. Our subjects underwent fMRI scans of the resting brain before and after a somatosensory discrimination task. We identified changes in functional connectivity that were due to the effects of perceptual learning on movement. For this purpose, we used a neural model of the transmission of sensory signals from perceptual decision making through to motor action. We used this model in combination with a partial correlation technique to parcel out those changes in connectivity observed in motor systems that could be attributed to activity in sensory brain regions. We found that, after removing effects that are linearly correlated with somatosensory activity, perceptual learning results in changes to frontal motor areas that are related to the effects of this training on motor behavior and learning. This suggests that perceptual learning produces changes to frontal motor areas of the brain and may thus contribute directly to motor learning.

Perceptual learning in sensorimotor adaptation.

Motor learning often involves situations in which the somatosensory targets of movement are, at least initially, poorly defined, as for example, in learning to speak or learning the feel of a proper tennis serve. Under these conditions, motor skill acquisition presumably requires perceptual as well as motor learning. That is, it engages both the progressive shaping of sensory targets and associated changes in motor performance. In the present study, we test the idea that perceptual learning alters somatosensory function and in so doing produces changes to human motor performance and sensorimotor adaptation. Subjects in these experiments undergo perceptual training in which a robotic device passively moves the subject's arm on one of a set of fan-shaped trajectories. Subjects are required to indicate whether the robot moved the limb to the right or the left and feedback is provided. Over the course of training both the perceptual boundary and acuity are altered. The perceptual learning is observed to improve both the rate and extent of learning in a subsequent sensorimotor adaptation task and the benefits persist for at least 24 h. The improvement in the present studies varies systematically with changes in perceptual acuity and is obtained regardless of whether the perceptual boundary shift serves to systematically increase or decrease error on subsequent movements. The beneficial effects of perceptual training are found to be substantially dependent on reinforced decision-making in the sensory domain. Passive-movement training on its own is less able to alter subsequent learning in the motor system. Overall, this study suggests perceptual learning plays an integral role in motor learning.

Computational model of motor learning and perceptual change.

Motor learning in the context of arm reaching movements has been frequently investigated using the paradigm of force-field learning. It has been recently shown that changes to somatosensory perception are likewise associated with motor learning. Changes in perceptual function may be the reason that when the perturbation is removed following motor learning, the hand trajectory does not return to a straight line path even after several dozen trials. To explain the computational mechanisms that produce these characteristics, we propose a motor control and learning scheme using a simplified two-link system in the horizontal plane: We represent learning as the adjustment of desired joint-angular trajectories so as to achieve the reference trajectory of the hand. The convergence of the actual hand movement to the reference trajectory is proved by using a Lyapunov-like lemma, and the result is confirmed using computer simulations. The model assumes that changes in the desired hand trajectory influence the perception of hand position and this in turn affects movement control. Our computer simulations support the idea that perceptual change may come as a result of adjustments to movement planning with motor learning.

Observing motor learning produces somatosensory change.

Observing the actions of others has been shown to affect motor learning, but does it have effects on sensory systems as well? It has been recently shown that motor learning that involves actual physical practice is also associated with plasticity in the somatosensory system. Here, we assessed the idea that observational learning likewise changes somatosensory function. We evaluated changes in somatosensory function after human subjects watched videos depicting motor learning. Subjects first observed video recordings of reaching movements either in a clockwise or counterclockwise force field. They were then trained in an actual force-field task that involved a counterclockwise load. Measures of somatosensory function were obtained before and after visual observation and also following force-field learning. Consistent with previous reports, video observation promoted motor learning. We also found that somatosensory function was altered following observational learning, both in direction and in magnitude, in a manner similar to that which occurs when motor learning is achieved through actual physical practice. Observation of the same sequence of movements in a randomized order did not result in somatosensory perceptual change. Observational learning and real physical practice appear to tap into the same capacity for sensory change in that subjects that showed a greater change following observational learning showed a reliably smaller change following physical motor learning. We conclude that effects of observing motor learning extend beyond the boundaries of traditional motor circuits, to include somatosensory representations.

Sensorimotor adaptation changes the neural coding of somatosensory stimuli.

Motor learning is reflected in changes to the brain's functional organization as a result of experience. We show here that these changes are not limited to motor areas of the brain and indeed that motor learning also changes sensory systems. We test for plasticity in sensory systems using somatosensory evoked potentials (SEPs). A robotic device is used to elicit somatosensory inputs by displacing the arm in the direction of applied force during learning. We observe that following learning there are short latency changes to the response in somatosensory areas of the brain that are reliably correlated with the magnitude of motor learning: subjects who learn more show greater changes in SEP magnitude. The effects we observe are tied to motor learning. When the limb is displaced passively, such that subjects experience similar movements but without experiencing learning, no changes in the evoked response are observed. Sensorimotor adaptation thus alters the neural coding of somatosensory stimuli.

Motor learning and its sensory effects: time course of perceptual change and its presence with gradual introduction of load.

A complex interplay has been demonstrated between motor and sensory systems. We showed recently that motor learning leads to changes in the sensed position of the limb (Ostry DJ, Darainy M, Mattar AA, Wong J, Gribble PL. J Neurosci 30: 5384-5393, 2010). Here, we document further the links between motor learning and changes in somatosensory perception. To study motor learning, we used a force field paradigm in which subjects learn to compensate for forces applied to the hand by a robotic device. We used a task in which subjects judge lateral displacements of the hand to study somatosensory perception. In a first experiment, we divided the motor learning task into incremental phases and tracked sensory perception throughout. We found that changes in perception occurred at a slower rate than changes in motor performance. A second experiment tested whether awareness of the motor learning process is necessary for perceptual change. In this experiment, subjects were exposed to a force field that grew gradually in strength. We found that the shift in sensory perception occurred even when awareness of motor learning was reduced. These experiments argue for a link between motor learning and changes in somatosensory perception, and they are consistent with the idea that motor learning drives sensory change.

Functionally specific changes in resting-state sensorimotor networks after motor learning.

Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well? We examined in humans the effects of motor learning using fMRI measures of functional connectivity under resting conditions and found persistent changes in networks involving both motor and somatosensory areas of the brain. We developed a technique that allows us to distinguish changes in functional connectivity that can be attributed to motor learning from those that are related to perceptual changes that occur in conjunction with learning. Using this technique, we identified a new network in motor learning involving second somatosensory cortex, ventral premotor cortex, and supplementary motor cortex whose activation is specifically related to perceptual changes that occur in conjunction with motor learning. We also found changes in a network comprising cerebellar cortex, primary motor cortex, and dorsal premotor cortex that were linked to the motor aspects of learning. In each network, we observed highly reliable linear relationships between neuroplastic changes and behavioral measures of either motor learning or perceptual function. Motor learning thus results in functionally specific changes to distinct resting-state networks in the brain.

Sensory change following motor learning.

Here we describe two studies linking perceptual change with motor learning. In the first, we document persistent changes in somatosensory perception that occur following force field learning. Subjects learned to control a robotic device that applied forces to the hand during arm movements. This led to a change in the sensed position of the limb that lasted at least 24 h. Control experiments revealed that the sensory change depended on motor learning. In the second study, we describe changes in the perception of speech sounds that occur following speech motor learning. Subjects adapted control of speech movements to compensate for loads applied to the jaw by a robot. Perception of speech sounds was measured before and after motor learning. Adapted subjects showed a consistent shift in perception. In contrast, no consistent shift was seen in control subjects and subjects that did not adapt to the load. These studies suggest that motor learning changes both sensory and motor function.

Somatosensory plasticity and motor learning.

Motor learning is dependent upon plasticity in motor areas of the brain, but does it occur in isolation, or does it also result in changes to sensory systems? We examined changes to somatosensory function that occur in conjunction with motor learning. We found that even after periods of training as brief as 10 min, sensed limb position was altered and the perceptual change persisted for 24 h. The perceptual change was reflected in subsequent movements; limb movements following learning deviated from the prelearning trajectory by an amount that was not different in magnitude and in the same direction as the perceptual shift. Crucially, the perceptual change was dependent upon motor learning. When the limb was displaced passively such that subjects experienced similar kinematics but without learning, no sensory change was observed. The findings indicate that motor learning affects not only motor areas of the brain but changes sensory function as well.

Effects of human arm impedance on dynamics learning and generalization.

Previous studies have demonstrated anisotropic patterns of hand impedance under static conditions and during movement. Here we show that the pattern of kinematic error observed in studies of dynamics learning is associated with this anisotropic impedance pattern. We also show that the magnitude of kinematic error associated with this anisotropy dictates the amount of motor learning and, consequently, the extent to which dynamics learning generalizes. Subjects were trained to reach to visual targets while holding a robotic device that applied forces during movement. On infrequent trials, the load was removed and the resulting kinematic error was measured. We found a strong correlation between the pattern of kinematic error and the anisotropic pattern of hand stiffness. In a second experiment subjects were trained under force-field conditions to move in two directions: one in which the dynamic perturbation was in the direction of maximum arm impedance and the associated kinematic error was low and another in which the perturbation was in the direction of low impedance where kinematic error was high. Generalization of learning was assessed in a reference direction that lay intermediate to the two training directions. We found that transfer of learning was greater when training occurred in the direction associated with the larger kinematic error. This suggests that the anisotropic patterns of impedance and kinematic error determine the magnitude of dynamics learning and the extent to which it generalizes.

Muscle cocontraction following dynamics learning.

Coactivation of antagonist muscles is readily observed early in motor learning, in interactions with unstable mechanical environments and in motor system pathologies. Here we present evidence that the nervous system uses coactivation control far more extensively and that patterns of cocontraction during movement are closely tied to the specific requirements of the task. We have examined the changes in cocontraction that follow dynamics learning in tasks that are thought to involve finely sculpted feedforward adjustments to motor commands. We find that, even following substantial training, cocontraction varies in a systematic way that depends on both movement direction and the strength of the external load. The proportion of total activity that is due to cocontraction nevertheless remains remarkably constant. Moreover, long after indices of motor learning and electromyographic measures have reached asymptotic levels, cocontraction still accounts for a significant proportion of total muscle activity in all phases of movement and in all load conditions. These results show that even following dynamics learning in predictable and stable environments, cocontraction forms a central part of the means by which the nervous system regulates movement.

Control of hand impedance under static conditions and during reaching movement.

It is known that humans can modify the impedance of the musculoskeletal periphery, but the extent of this modification is uncertain. Previous studies on impedance control under static conditions indicate a limited ability to modify impedance, whereas studies of impedance control during reaching in unstable environments suggest a greater range of impedance modification. As a first step in accounting for this difference, we quantified the extent to which stiffness changes from posture to movement even when there are no destabilizing forces. Hand stiffness was estimated under static conditions and at the same position during both longitudinal (near to far) and lateral movements using a position-servo technique. A new method was developed to predict the hand "reference" trajectory for purposes of estimating stiffness. For movements in a longitudinal direction, there was considerable counterclockwise rotation of the hand stiffness ellipse relative to stiffness under static conditions. In contrast, a small counterclockwise rotation was observed during lateral movement. In the modeling studies, even when we used the same modeled cocontraction level during posture and movement, we found that there was a substantial difference in the orientation of the stiffness ellipse, comparable with that observed empirically. Indeed, the main determinant of the orientation of the ellipse in our modeling studies was the movement direction and the muscle activation associated with movement. Changes in the cocontraction level and the balance of cocontraction had smaller effects. Thus even when there is no environmental instability, the orientation of stiffness ellipse changes during movement in a manner that varies with movement direction.

Transfer and durability of acquired patterns of human arm stiffness.

Previous studies have shown that the nervous system can produce anticipatory adjustments that alter the mechanical behavior of the arm in order to resist environmental disturbances. In the present paper, we focus on the ability of subjects to transfer acquired stiffness patterns to other parts of the workspace and on the durability of stiffness adaptations. To explore the transfer of stiffness control, subjects were trained at the left of the workspace to resist the effects of a single-axis disturbance that was applied by a robotic device. Following training, they were tested for transfer at the right. One group of subjects experienced similar torques at the left and right of the workspace, whereas the other group of subjects experienced similar forces at the hand. Following the initial training at the left, the observed orientation of the hand-stiffness ellipse rotated in the direction of the disturbance. In tests at the right, transfer was observed only when the direction of disturbance resulted in torques that were similar to those experienced during training. The results thus suggest that under the conditions of this experiment stiffness control is acquired and transfers in a joint- or muscle-based system of coordinates. A second experiment assessed the durability of an acquired stiffness pattern. Subjects were trained on 2 consecutive days to resist a single-axis disturbance. On a third day, the direction of the disturbance was switched by 90 degrees . Substantial interference with the new adaptation was observed. This suggests that stiffness training results in durable changes to the neural signals that underlie stiffness control.

Learning to control arm stiffness under static conditions.

We used a robotic device to test the idea that impedance control involves a process of learning or adaptation that is acquired over time and permits the voluntary control of the pattern of stiffness at the hand. The tests were conducted in statics. Subjects were trained over the course of 3 successive days to resist the effects of one of three different kinds of mechanical loads: single axis loads acting in the lateral direction, single axis loads acting in the forward/backward direction, and isotropic loads that perturbed the limb in eight directions about a circle. We found that subjects in contact with single axis loads voluntarily modified their hand stiffness orientation such that changes to the direction of maximum stiffness mirrored the direction of applied load. In the case of isotropic loads, a uniform increase in endpoint stiffness was observed. Using a physiologically realistic model of two-joint arm movement, the experimentally determined pattern of impedance change could be replicated by assuming that coactivation of elbow and double joint muscles was independent of coactivation of muscles at the shoulder. Moreover, using this pattern of coactivation control we were able to replicate an asymmetric pattern of rotation of the stiffness ellipse that was observed empirically. These findings are consistent with the idea that arm stiffness is controlled through the use of at least two independent co-contraction commands.