Temporal recalibration of motor and visual potentials in lag adaptation in voluntary movement

Adaptively recalibrating motor-sensory asynchrony is critical for animals to perceive self-produced action consequences. It is controversial whether motor- or sensory-related neural circuits recalibrate this asynchrony. By combining magnetoencephalography (MEG) and functional MRI (fMRI), we investigate the temporal changes in brain activities caused by repeated exposure to a 150-ms delay inserted between a button-press action and a subsequent flash. We found that readiness potentials significantly shift later in the motor system, especially in parietal regions (average: 219.9 ms), while visually evoked potentials significantly shift earlier in occipital regions (average: 49.7 ms) in the delay condition compared to the no-delay condition. Moreover, the shift in readiness potentials, but not in visually evoked potentials, was significantly correlated with the psychophysical measure of motor-sensory adaptation. These results suggest that although both motor and sensory processes contribute to the recalibration, the motor process plays the major role, given the magnitudes of shift and the correlation with the psychophysical measure.

Empirical Evaluation of Voluntarily Activatable Muscle Synergies

The muscle synergy hypothesis assumes that individual muscle synergies are independent of each other and voluntarily controllable. However, this assumption has not been empirically tested. This study tested if human subjects can voluntarily activate individual muscle synergies extracted by non-negative matrix factorization (NMF), the standard mathematical method for synergy extraction. We defined the activation of a single muscle synergy as the generation of a muscle activity pattern vector parallel to the single muscle synergy vector. Subjects performed an isometric force production task with their right hand, and the 13 muscle activity patterns associated with their elbow and shoulder movements were measured. We extracted muscle synergies during the task using electromyogram (EMG) data and the NMF method with varied numbers of muscle synergies. The number (N) of muscle synergies was determined by using the variability accounted for (VAF, N_VAF) and the coefficient of determination (CD, N_CD). An additional muscle synergy model with N_AD was also considered. We defined a conventional muscle synergy as the muscle synergy extracted by the N_VAF, N_CD, and N_AD. We also defined an extended muscle synergy as the muscle synergy extracted by the N_EX> N_AD. To examine whether the individual muscle synergy was voluntarily activatable or not, we calculated the index of independent activation, which reflects similarities between a selected single muscle synergy and the current muscle activation pattern of the subject. Subjects were visually feed-backed the index of independent activation, then instructed to generate muscle activity patterns similar to the conventional and extended muscle synergies. As a result, an average of 90.8% of the muscle synergy extracted by the N_VAF was independently activated. However, the proportion of activatable muscle synergies extracted by N_CD and N_AD was lower. These results partly support the assumption of the muscle synergy hypothesis, i.e., that the conventional method can extract voluntarily and independently activatable muscle synergies by using the appropriate index of reconstruction. Moreover, an average of 25.5% of the extended muscle synergy was significantly activatable. This result suggests that the CNS can use extended muscle synergies to perform voluntary movements.

Control strategy of hand movement depends on target redundancy

Reaching toward a point target has been intensively studied in human motor control. However, little is known about reaching toward a redundant target, such as grasping a bar, in which the grasping point is irrelevant to the achievement of a task. We examined whether humans could solve the target-redundancy and control problems in a serial fashion or control their body without solving the target-redundancy problem. We equalized the target ranges between two reaching tasks: a point-to-point reaching task without target-redundancy and a point-to-bar reaching task with target-redundancy. In the both tasks, we measured hand viscoelasticity at movement end as parameters that reflect the adopted control strategy. As a result, the hand viscoelasticity in the point-to-bar reaching task was smaller than that in the point-to-point reaching task, even under the same kinematics. These results indicate that the hand viscoelasticity was modulated depending on the target-redundancy. Moreover, it is suggested that a human reaches toward a redundant target by effectively utilizing information of target redundancy rather than explicitly solving the target-redundancy problem.

Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation

Recent computational and behavioral studies suggest that motor adaptation results from the update of multiple memories with different timescales. Here, we designed a model-based functional magnetic resonance imaging (fMRI) experiment in which subjects adapted to two opposing visuomotor rotations. A computational model of motor adaptation with multiple memories was fitted to the behavioral data to generate time-varying regressors of brain activity. We identified regional specificity to timescales: in particular, the activity in the inferior parietal region and in the anterior-medial cerebellum was associated with memories for intermediate and long timescales, respectively. A sparse singular value decomposition analysis of variability in specificities to timescales over the brain identified four components, two fast, one middle, and one slow, each associated with different brain networks. Finally, a multivariate decoding analysis showed that activity patterns in the anterior-medial cerebellum progressively represented the two rotations. Our results support the existence of brain regions associated with multiple timescales in adaptation and a role of the cerebellum in storing multiple internal models.

Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD) signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e., temporal correlation between two regions) is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least 2 months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation.

Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns

Individual learning performance of cognitive function is related to functional connections within ‘task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80–90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R² = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

Imposed visual feedback delay of an action changes mass perception based on the sensory prediction error

While performing an action, the timing of when the sensory feedback is given can be used to establish the causal link between the action and its consequence. It has been shown that delaying the visual feedback while carrying an object makes people feel the mass of the object to be greater, suggesting that the feedback timing can also impact the perceived quality of an external object. In this study, we investigated the origin of the feedback timing information that influences the mass perception of the external object. Participants made a straight reaching movement while holding a manipulandum. The movement of the manipulandum was presented as a cursor movement on a monitor. In Experiment 1, various delays were imposed between the actual trajectory and the cursor movement. The participants' perceived mass of the manipulandum significantly increased as the delay increased to 400 ms, but this gain did not reach significance when the delay was 800 ms. This suggests the existence of a temporal tuning mechanism for incorporating the visual feedback into the perception of mass. In Experiment 2, we examined whether the increased mass perception during the visual delay was due to the prediction error of the visual consequence of an action or to the actual delay of the feedback itself. After the participants adapted to the feedback delay, the perceived mass of the object became lighter than before, indicating that updating the temporal prediction model for the visual consequence diminishes the overestimation of the object's mass. We propose that the misattribution of the visual delay into mass perception is induced by the sensorimotor prediction error, possibly when the amount of delay (error) is within the range that can reasonably include the consequence of an action.

Illusory Reversal of Causality between Touch and Vision has No Effect on Prism Adaptation Rate

Learning, according to Oxford Dictionary, is “to gain knowledge or skill by studying, from experience, from being taught, etc.” In order to learn from experience, the central nervous system has to decide what action leads to what consequence, and temporal perception plays a critical role in determining the causality between actions and consequences. In motor adaptation, causality between action and consequence is implicitly assumed so that a subject adapts to a new environment based on the consequence caused by her action. Adaptation to visual displacement induced by prisms is a prime example; the visual error signal associated with the motor output contributes to the recovery of accurate reaching, and a delayed feedback of visual error can decrease the adaptation rate. Subjective feeling of temporal order of action and consequence, however, can be modified or even reversed when her sense of simultaneity is manipulated with an artificially delayed feedback. Our previous study (Tanaka et al., 2011; Exp. Brain Res.) demonstrated that the rate of prism adaptation was unaffected when the subjective delay of visual feedback was shortened. This study asked whether subjects could adapt to prism adaptation and whether the rate of prism adaptation was affected when the subjective temporal order was illusory reversed. Adapting to additional 100 ms delay and its sudden removal caused a positive shift of point of simultaneity in a temporal order judgment experiment, indicating an illusory reversal of action and consequence. We found that, even in this case, the subjects were able to adapt to prism displacement with the learning rate that was statistically indistinguishable to that without temporal adaptation. This result provides further evidence to the dissociation between conscious temporal perception and motor adaptation.

Physical delay but not subjective delay determines learning rate in prism adaptation

Timing is critical in determining the causal relationship between two events. Motor adaptation relies on the timing of actions and their results for determining which preceding control signals were responsible for subsequent error in the resulting movements. An artificially induced temporal delay in error feedback as short as 50 ms has been found to slow the learning rate of prism adaptation. Recent studies have demonstrated that our sense of simultaneity is flexibly adaptive when a persistent delay is inserted into visual feedback timing of one's own action. Therefore, judgments of "subjective simultaneity" (i.e. whether two events are simultaneous on a subjective basis) do not necessarily correspond to the actual simultaneity of physical events. We evaluated the effects of adaptation to a temporal shift of subjective simultaneity on prism adaptation by examining whether prism adaptation depends on physical timing or subjective timing. We found that after persistently experiencing an additional 100-ms delay in a pointing experiment, psychometric curves of the timing of judgments about the temporal order of touching and visual feedback were shifted by 40 ms, indicating that subjective simultaneity adapted. Next, while maintaining temporal adaptation, participants adapted to spatial displacement caused by a prism with and without an additional temporal delay in feedback. Learning speed was reliably predicted by physical timing but not by subjective timing. These results indicate that prism adaptation occurs independently of awareness of subjective timing and may be processed in primary motor areas that are thought to have fidelity with temporal relations.

Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods

Reconstruction of movements from non-invasively recorded brain activity is a key technology for brain-machine interfaces (BMIs). However, electroencephalography (EEG) or magnetoencephalography (MEG) inevitably records a mixture of signals originating from many cortical regions, and thus it is not only less effective than invasive methods but also poses more difficulty for incorporating neuroscience knowledge. We combined two sparse Bayesian methods to overcome this difficulty. First, thousands of cortical currents were estimated on the order of millimeters and milliseconds by a hierarchical Bayesian MEG inverse method, and then a sparse regression method automatically selected only relevant cortical currents in accurate reconstruction of movements by a linear weighted sum of their time series. Using the combined methods, we reconstructed two-dimensional trajectories of the index fingertip during pointing movements to various directions by moving the wrist joint. A good generalization (reconstruction) performance was observed for test datasets: mean error between the predicted and actual positions was 15 mm, which was 7% of the path length of the required movement. The reconstruction accuracy of the proposed method was significantly higher than directly using MEG sensor signals. Moreover, spatial distribution and temporal characteristics of weight values revealed that the primary sensorimotor, higher motor, and parietal regions mainly contributed to the reconstruction with expected time courses. These results suggest that the combined sparse Bayesian methods provide effective means to predict movement trajectory from non-invasive brain activity directly related to sensorimotor control.

Central control of grasp: Manipulation of objects with complex and simple dynamics

We performed whole-brain fMRI to explore the neural mechanisms that contribute to the ability to manipulate an object with complex dynamics. Subjects grasped a weighted flexible ruler and balanced it in an unstable equilibrium position as an archetype of grasping an object with complex dynamics. This was contrasted with squeezing a soft foam ball as an archetype of grasping an object with simple dynamics. We hypothesized that changes in activity in primary motor cortex (MI) would be similar under the two conditions, since muscle activation was matched, which was confirmed. We hypothesized further that the cerebellum would be selectively activated when manipulating the flexible ruler because the ability to make the adjustments necessary to balance the ruler would require an internal dynamics model, represented in the cerebellum. As predicted, the ipsilateral cerebellum was strongly activated when balancing the weighted ruler whereas only moderate activation was found when squeezing the foam ball. We also found evidence for selective activation of areas, previously implicated in tactile object recognition, when holding the flexible ruler. We speculate that these areas, which include secondary somatosensory cortex (SII), Brodmann area 40 and insula, integrate tactile and proprioceptive information in the context of controlling the orientation of the flexible ruler and provide appropriate feedback to MI. We speculate that the failure to find activation of these areas when squeezing the ball was due to the fact that tactile stimulation was entirely self-produced, resulting in the attenuation of cortical sensory activity (Blakemore, S.-J., Wolpert, D.M., Frith, C.D., 1998. Central cancellation of self-produced tickle sensation. Nat. Neurosci. 1, 635-640, Blakemore, S.-J., Frith, C.D., Wolpert, D.M., 2001. The cerebellum is involved in predicting the sensory consequences of action. NeuroReport 12, 1879-1884).

Explicit contextual information selectively contributes to predictive switching of internal models

Many evidences suggest that the central nervous system (CNS) acquires and switches internal models for adaptive control in various environments. However, little is known about the neural mechanisms responsible for the switching. A recent computational model for simultaneous learning and switching of internal models proposes two separate switching mechanisms: a predictive mechanism purely based on contextual information and a postdictive mechanism based on the difference between actual and predicted sensorimotor feedbacks. This model can switch internal models solely based on contextual information in a predictive fashion immediately after alteration of the environment. Here we show that when subjects simultaneously adapted to alternating blocks of opposing visuomotor rotations, explicit contextual information about the rotations improved the initial performance at block alternations and asymptotic levels of performance within each block but not readaptation speeds. Our simulations using separate switching mechanisms duplicated these effects of contextual information on subject performance and suggest that improvement of initial performance was caused by improved accuracy of the predictive switch while adaptation speed corresponds to a switch dependent on sensorimotor feedback. Simulations also suggested that a slow change in output signals from the switching mechanisms causes contamination of motor commands from an internal model used in the previous context (anterograde interference) and partial destruction of internal models (retrograde interference). Explicit contextual information prevents destruction and assists memory retention by improving the changes in output signals. Thus, the asymptotic levels of performance improved.

Neural correlates of internal-model loading

Skilled object manipulation requires knowledge, or internal models, of object dynamics relating applied force to motion , and our ability to handle myriad objects indicates that the brain maintains multiple models . Recent behavioral studies have shown that once learned, an internal model of an object with novel dynamics can be rapidly recruited and derecruited as the object is grasped and released . We used event-related fMRI to investigate neural activity linked to grasping an object with recently learned dynamics in preparation for moving it after a delay. Subjects also performed two control tasks in which they either moved without the object in hand or applied isometric forces to the object. In all trials, subjects received a cue indicating which task to perform in response to a go signal delivered 5-10 s later. We examined BOLD responses during the interval between the cue and go and assessed the conjunction of the two contrasts formed by comparing the primary task to each control. The analysis revealed significant activity in the ipsilateral cerebellum and the contralateral and supplementary motor areas. We propose that these regions are involved in internal-model recruitment in preparation for movement execution.

Multi-joint arm movements to investigate motor control with FMRI

Performing multi-joint arm movements in controllable dynamic environments during functional magnetic resonance imaging (fMRI) could provide important insights into the brain mechanisms involved in human motor control and related dysfunctions. In order to obtain useful data, these movements must be possible and comfortable for the subject within the narrow bore of the scanner and should not create any movement artifacts in the image. We found that commonly studied arm movements involving the shoulder create movement artifacts, and investigated alternative multijoint arm movements within a mock-up of an MR scanner. We selected movements involving the elbow and wrist joints, with an extension attached to the hand, and propose a dedicated kinematic structure using the MR compatible actuators we have previously developed.

Accurate Real-Time Feedback of Surface EMG During fMRI

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

Cerebellar Activity Evoked By Common Tool-Use Execution And Imagery Tasks: An Fmri Study

The purpose of this study is to identify the functional brain networks activated in relation to actual tool-use in humans. Although previous studies have identified brain activity related to tool-use gestures (Moll et al., 2000), they did not investigate the brain activity involved in such tool-use. We investigated brain activity using functional magnetic resonance imaging (fMRI) while human subjects mentally imagined using sixteen common tools and while they actually used them. Brain activity for both actual and imagined tool-use was found in the posterior part of the parietal cortex, in the supplementary motor area, and in the cerebellum. Under imagined tool-use conditions, we found brain activity in the premotor and right pars opercularis. Under actual tool-use conditions, we found it in the primary motor area, in the thalamus, and in the left pars opercularis. Our precise analysis in the cerebellum indicated that activity evoked by imagery was located significantly more lateral to that evoked by actual use. We found a relationship between activity in the tool imagery and execution conditions by comparing their t-value-weighted centroid of activation coordinates. Moreover, for half of the subjects the spatial distribution pattern for each tool was similar, suggesting that neural mechanisms contributing to skillful tool-use are modularly organized in the cerebellum.

Reorganization of Brain Activity for Multiple Internal Models After Short But Intensive Training

Internal models are neural mechanisms that can mimic the input-output properties of controlled objects. Our studies have shown that: 1) an internal model for a novel tool is acquired in the cerebellum (Imamizu et al., 2000); 2) internal models are modularly organized in the cerebellum (Imamizu et al., 2003); 3) their outputs are sent to the premotor regions after learning (Tamada et al., 1999); and 4) the prefrontal and parietal regions contribute to the blending of the outputs (Imamizu et al., 2004). Here, we investigated changes in global neural networks resulting from the acquisition of a new internal model. Human subjects manipulated three types of rotating joystick whose cursor appeared at a position rotated 60 degrees, 110 degrees, or 160 degrees around the screen's center. In a pre-test after long-term training (5 days) for the 60 degrees and 160 degrees joysticks, brain activation was scanned during manipulation of the three joysticks. The subjects were then trained for the 110 degrees for only 25 min. In a post-test, activation was scanned using the same method as the pre-test. Comparisons of the post-test to the pre-test revealed that the volume of activation decreased in most of the regions where activation for the three rotations was observed. However, there was an increase in volume at a marginally significant level (p < .08) only in the inferior-lateral cerebellum and only for the 110 degrees joystick. In the cerebral cortex, activation related to 110 degrees decreased in the prefrontal and parietal regions but increased in the premotor and supplementary motor area (SMA) regions. These results can be explained by a model in which outputs of the 60 degrees and 160 degrees internal models are blended by prefrontal and parietal regions to cope with the novel 110 degrees joystick before the 25-minute training; after the acquisition within the cerebellum of an internal model for the 110 degrees, output is directly sent to the premotor and SMA regions, and activation in these regions increases.