BOLD coherence reveals segregated functional neural interactions when adapting to distinct torque perturbations

Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography

The ability to control online motor corrections is key to dealing with unexpected changes arising in the environment with which we interact. How the CNS controls online motor corrections is poorly understood, but evidence has accumulated in favor of a submovement-based model in which apparently continuous movement is segmented into distinct submovements. Although most studies have focused on submovements' kinematic features, direct links with the underlying neural dynamics have not been extensively explored. This study sought to identify an electroencephalographic signature of submovements. We elicited kinematic submovements using a double-step displacement paradigm. Participants moved their wrist toward a target whose direction could shift mid-movement with a 50% probability. Movement kinematics and cortical activity were concurrently recorded with a low-friction robotic device and high-density electroencephalography. Analysis of spatiotemporal dynamics of brain activation and its correlation with movement kinematics showed that the production of each kinematic submovement was accompanied by (1) stereotyped topographic scalp maps and (2) frontoparietal ERPs time-locked to submovements. Positive ERP peaks from frontocentral areas contralateral to the moving wrist preceded kinematic submovement peaks by 220-250 msec and were followed by positive ERP peaks from contralateral parietal areas (140-250 msec latency, 0-80 msec before submovement peaks). Moreover, individual subject variability in the latency of frontoparietal ERP components following the target shift significantly predicted variability in the latency of the corrective submovement. Our results are in concordance with evidence for the intermittent nature of continuous movement and elucidate the timing and role of frontoparietal activations in the generation and control of corrective submovements.

Neural correlates of adaptation to gradual and to sudden visuomotor distortions in humans

This study aimed at scrutinizing the neural correlates of sensorimotor adaptation. Subjects were exposed either to a gradually (group G) or to a suddenly introduced perturbation (group S) followed by a test of aftereffects. They were also exposed to a control condition equated for their movement errors during the adaptation condition. We registered subjects' brain activity by functional magnetic resonance imaging. Behavioral data revealed no difference between aftereffects in G and S, while imaging data suggest different neural correlates. Direct comparison between groups showed more adaptation-related activation in left cingulate and inferior frontal as well as right caudate and temporal areas in S than in G. In contrast, no neural activity was related more to G than to S and no common activations were found for both groups. Within-group analyses further revealed right inferior parietal lobe, cerebellar and cingulate cortex activity in group S and activation of frontal lobe and left cerebellum in group G for a contrast between adaptation condition and baseline. Less brain activity was observed when controlled for movement errors: the contrast between adaptation and control condition yielded left occipital lobe activity in group S, and left posterior dentate nucleus and brainstem activity in group G. The present data confirm an involvement of the cerebellar cortex in error processing during sudden adaptation, since this activation was found for the contrast 'adaptation-baseline' but not for 'adaptation-control.' In addition, our data suggest an involvement of deep cerebellar nuclei in the adaptation to gradually introduced distortions.

The extrinsic and intrinsic functional architectures of the human brain are not equivalent

The brain's intrinsic functional architecture, revealed in correlated spontaneous activity, appears to constitute a faithful representation of its repertoire of evoked, extrinsic functional interactions. Here, using broad task contrasts to probe evoked patterns of coactivation, we demonstrate tight coupling between the brain's intrinsic and extrinsic functional architectures for default and task-positive regions, but not for subcortical and limbic regions or for primary sensory and motor cortices. While strong correspondence likely reflects persistent or recurrent patterns of evoked coactivation, weak correspondence may exist for regions whose patterns of evoked functional interactions are more adaptive and context dependent. These findings were independent of task. For tight task contrasts (e.g., incongruent vs. congruent trials), evoked patterns of coactivation were unrelated to the intrinsic functional architecture, suggesting that high-level task demands are accommodated by context-specific modulations of functional interactions. We conclude that intrinsic approaches provide only a partial understanding of the brain's functional architecture. Appreciating the full repertoire of dynamic neural responses will continue to require task-based functional magnetic resonance imaging approaches.

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

Human basal ganglia and the dynamic control of force during on-line corrections

Natural movements are corrected in part by the generation of submovements, occurring early in a movement such that they amend an ongoing action. Submovements are associated with activity of the basal ganglia, implying a role for the structures in error correction. In parallel, the basal ganglia are linked to the generation and control of force amplitude, change, and duration. Here, we tested whether activity in human basal ganglia is associated with submovements generally, or was specific to a condition where the submovements only occurred in the face of unexpected proprioceptive error. Submovements were induced by introducing unexpected and variable viscous loads (augmenting the need for trial-specific grip forces) or by reducing target size (augmenting the need for visually guided on-line control) in a one-dimensional target-capture task. In both cases, subjects compensated for the increased task difficulty by generating corrective submovements, which were closely matched in frequency and type. Activity in the internal segment of the globus pallidus and subthalamic nucleus correlated strongly with the number of submovements during the viscous challenge but not with the target challenge. The effects could not be explained by kinematic differences, i.e., movement amplitude or average number of submovements. The results support a specific role for the basal ganglia in error correction under conditions of variable load where there is a need for the dynamic control of force within an ongoing movement.

Error correction, sensory prediction, and adaptation in motor control

Motor control is the study of how organisms make accurate goal-directed movements. Here we consider 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.

Model-based attenuation of movement artifacts in fMRI

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.

Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling

We dissociated the contributions to learning of four corticostriatal "loops" (interacting striatal and cortical regions): motor (putamen and motor cortex), visual (posterior caudate and visual cortex), executive (anterior caudate and prefrontal cortex), and motivational (ventral striatum and ventromedial frontal cortex). Subjects learned to categorize individual repeated images into one of two arbitrary categories via trial and error. We identified (1) regions sensitive to correct categorization, categorization learning, and feedback valence; (2) regions sensitive to prediction error (violation of feedback expectancy) and reward prediction (expected feedback associated with category response) using reinforcement learning modeling; and (3) directed influences between regions using Granger causality modeling. Each loop showed a unique pattern of sensitivity to each of these factors. Both the motor and visual loops were involved in acquisition of categorization ability: activity during correct categorization increased across learning and was sensitive to reward prediction. However, the posterior caudate received directed influence from visual cortex, whereas the putamen exerted directed influence on motor cortex. The motivational and executive loops were involved in feedback processing: both regions were sensitive to feedback valence, which interacted with learning across scans. However, the motivational loop activity reflected prediction error, whereas executive loop activity reflected reward prediction, consistent with the executive loop role in integrating reward and action. Granger causality modeling found directed influences between striatal and cortical regions within each loop. Across loops, the motor loop exerted directed influence on the executive loop which is consistent with the role of the executive loop in integrating feedback with stimulus-response history.

Basal ganglia contribution to the initiation of corrective submovements

We investigated the neural processes, with a focus on subcortical circuits, which govern corrective submovements in visually targeted action. During event-related fMRI, subjects moved a cursor to capture targets presented at varying movement amplitudes. Movements were performed in a rehearsed null and a novel viscous (25% random trials) torque field. Movement error feedback was provided after each trial. The viscous field invoked a significantly larger error at the end of the primary movement. Subjects compensated by producing more corrections than they had in the null condition. Corrective submovements were appropriately scaled such that terminal error was similar between the two conditions. Parametric analysis identified two regions where the BOLD signal correlated with the number of submovements per trial: a cerebellar region similar to the one noted in the task contrast and the contralateral dorsal putamen. A separate parametric analysis identified brain regions where activity correlated with movement amplitude. This identified the same cerebellar region as above, bilateral parietal cortex, and left motor and premotor cortex. Our data indicate that the basal ganglia and cerebellum play complementary roles in regulating ongoing actions when precise updating is required. The basal ganglia have a key role in contextually-based motor decision-making, i.e. for deciding if and when to correct a given movement by initiating corrective submovements, and the cerebellum is more generally involved in amplifying and refining the command signals for movements of different amplitudes.

A virtual reality-based system integrated with fmri to study neural mechanisms of action observation-execution: a proof of concept study

PURPOSE

Emerging evidence shows that interactive virtual environments (VEs) may be a promising tool for studying sensorimotor processes and for rehabilitation. However, the potential of VEs to recruit action observation-execution neural networks is largely unknown. For the first time, a functional MRI-compatible virtual reality system (VR) has been developed to provide a window into studying brain-behavior interactions. This system is capable of measuring the complex span of hand-finger movements and simultaneously streaming this kinematic data to control the motion of representations of human hands in virtual reality.

METHODS

In a blocked fMRI design, thirteen healthy subjects observed, with the intent to imitate (OTI), finger sequences performed by the virtual hand avatar seen in 1st person perspective and animated by pre-recorded kinematic data. Following this, subjects imitated the observed sequence while viewing the virtual hand avatar animated by their own movement in real-time. These blocks were interleaved with rest periods during which subjects viewed static virtual hand avatars and control trials in which the avatars were replaced with moving non-anthropomorphic objects.

RESULTS

We show three main findings. First, both observation with intent to imitate and imitation with real-time virtual avatar feedback, were associated with activation in a distributed frontoparietal network typically recruited for observation and execution of real-world actions. Second, we noted a time-variant increase in activation in the left insular cortex for observation with intent to imitate actions performed by the virtual avatar. Third, imitation with virtual avatar feedback (relative to the control condition) was associated with a localized recruitment of the angular gyrus, precuneus, and extrastriate body area, regions which are (along with insular cortex) associated with the sense of agency.

CONCLUSIONS

Our data suggest that the virtual hand avatars may have served as disembodied training tools in the observation condition and as embodied "extensions" of the subject's own body (pseudo-tools) in the imitation. These data advance our understanding of the brain-behavior interactions when performing actions in VE and have implications in the development of observation- and imitation-based VR rehabilitation paradigms.

Virtual reality to maximize function for hand and arm rehabilitation: exploration of neural mechanisms

Stroke patients report hand function as the most disabling motor deficit. Current evidence shows that learning new motor skills is essential for inducing functional neuroplasticity and functional recovery. Adaptive training paradigms that continually and interactively move a motor outcome closer to the targeted skill are important to motor recovery. Computerized virtual reality simulations when interfaced with robots, movement tracking and sensing glove systems, are particularly adaptable, allowing for online and offline modifications of task based activities using the participant's current performance and success rate. We have developed a second generation system that can exercise the hand and the arm together or in isolation and provide for both unilateral and bilateral hand and arm activities in three-dimensional space. We demonstrate that by providing haptic assistance for the hand and arm and adaptive anti-gravity support, the system can accommodate patients with lower level impairments. We hypothesize that combining training in virtual environments (VE) with observation of motor actions can bring additional benefits. We present a proof of concept of a novel system that integrates interactive VE with functional neuroimaging to address this issue. Three components of this system are synchronized, the presentation of the visual display of the virtual hands, the collection of fMRI images and the collection of hand joint angles from the instrumented gloves. We show that interactive VEs can facilitate activation of brain areas during training by providing appropriately modified visual feedback. We predict that visual augmentation can become a tool to facilitate functional neuroplasticity.

Long-term retention explained by a model of short-term learning in the adaptive control of reaching

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

Subcortical processes of motor response inhibition during a stop signal task

Previous studies have delineated the neural processes of motor response inhibition during a stop signal task, with most reports focusing on the cortical mechanisms. A recent study highlighted the importance of subcortical processes during stop signal inhibition in 13 individuals and suggested that the subthalamic nucleus (STN) may play a role in blocking response execution (Aron and Poldrack, 2006. Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J Neurosci 26, 2424-2433). Here in a functional magnetic resonance imaging (fMRI) study we replicated the finding of greater activation in the STN during stop (success or error) trials, compared to go trials, in a larger sample of subjects (n=30). However, since a contrast between stop and go trials involved processes that could be distinguished from response inhibition, the role of subthalamic activity during stop signal inhibition remained to be specified. To this end we followed an alternative strategy to isolate the neural correlates of response inhibition (Li et al., 2006a. Imaging response inhibition in a stop signal task--neural correlates independent of signal monitoring and post-response processing. J Neurosci 26, 186-192). We compared individuals with short and long stop signal reaction time (SSRT) as computed by the horse race model. The two groups of subjects did not differ in any other aspects of stop signal performance. We showed greater activity in the short than the long SSRT group in the caudate head during stop successes, as compared to stop errors. Caudate activity was positively correlated with medial prefrontal activity previously shown to mediate stop signal inhibition. Conversely, bilateral thalamic nuclei and other parts of the basal ganglia, including the STN, showed greater activation in subjects with long than short SSRT. Thus, fMRI delineated contrasting roles of the prefrontal-caudate and striato-thalamic activities in mediating motor response inhibition.

Neural substrates of visuomotor learning based on improved feedback control and prediction

Motor skills emerge from learning feedforward commands as well as improvements in feedback control. These two components of learning were investigated in a compensatory visuomotor tracking task on a trial-by-trial basis. Between-trial learning was characterized with a state-space model to provide smoothed estimates of feedforward and feedback learning, separable from random fluctuations in motor performance and error. The resultant parameters were correlated with brain activity using magnetic resonance imaging. Learning related to the generation of a feedforward command correlated with activity in dorsal premotor cortex, inferior parietal lobule, supplementary motor area and cingulate motor area, supporting a role of these areas in retrieving and executing a predictive motor command. Modulation of feedback control was associated with activity in bilateral posterior superior parietal lobule as well as right ventral premotor cortex. Performance error correlated with activity in a widespread cortical and subcortical network including bilateral parietal, premotor and rostral anterior cingulate cortex as well as the cerebellar cortex. Finally, trial-by-trial changes of kinematics, as measured by mean absolute hand acceleration, correlated with activity in motor cortex and anterior cerebellum. The results demonstrate that incremental, learning-dependent changes can be modeled on a trial-by-trial basis and neural substrates for feedforward control of novel motor programs are localized to secondary motor areas.

Watching others' actions: mirror representations in the parietal cortex

An observation that neurons in the motor cortex of the monkey are active both when the monkey performs a specific action and when he watches an actor executing the same action led to the mirror-system hypothesis. This hypothesis suggests that primates perceive and interpret others' actions by generating an internal motor representation (e.g., simulation). Recent evidence suggests that humans have a similar mirror system. In this review, we focus on the essential congruence between the motor and visual properties of an action. We summarize behavioral and imaging studies in humans that show that observing others' actions can interfere with our own motor execution. We discuss a framework for understanding such an internal representation and suggest that the activity in the parietal cortex during observation of others' actions is based on the sensory-to-motor remapping properties of this region, which are necessary for fine control of our own actions.

Evidence for a distributed hierarchy of action representation in the brain

Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional-anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional-anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal-oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior.