Biomedical subjects

Mitsuo Kawato

Publications and source records attributed to Mitsuo Kawato.

At least 19 recordsLinked to original sources

PubMed2006

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.

Adult↗

PubMed2006

Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning.

Recent studies have shown that multiple internal models are acquired in the cerebellum and that these can be switched under a given context of behavior. It has been proposed that long-term depression (LTD) of parallel fiber (PF)-Purkinje cell (PC) synapses forms the cellular basis of cerebellar learning, and that the presynaptically synthesized messenger nitric oxide (NO) is a crucial "gatekeeper" for LTD. Because NO diffuses freely to neighboring synapses, this volume learning is not input-specific and brings into question the biological significance of LTD as the basic mechanism for efficient supervised learning. To better characterize the role of NO in cerebellar learning, we simulated the sequence of electrophysiological and biochemical events in PF-PC LTD by combining established simulation models of the electrophysiology, calcium dynamics, and signaling pathways of the PC. The results demonstrate that the local NO concentration is critical for induction of LTD and for its input specificity. Pre- and postsynaptic coincident firing is not sufficient for a PF-PC synapse to undergo LTD, and LTD is induced only when a sufficient amount of NO is provided by activation of the surrounding PFs. On the other hand, above-adequate levels of activity in nearby PFs cause accumulation of NO, which also allows LTD in neighboring synapses that were not directly stimulated, ruining input specificity. These findings lead us to propose the hypothesis that NO represents the relevance of a given context and enables context-dependent selection of internal models to be updated. We also predict sparse PF activity in vivo because, otherwise, input specificity would be lost.

Action Potentials↗

PubMed2006

Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields.

It is controversial whether simple-spike activity of cerebellar Purkinje cells during arm movements encodes movement kinematics like velocity or dynamics like muscle activities. To examine this issue, we trained monkeys to flex or extend the elbow by 45 degrees in 400 ms under resistive and assistive force fields but without altering kinematics. During the task movements after training, simple-spike discharges were recorded in the intermediate part of the cerebellum in lobules V-VI, and electromyographic activity was recorded from arm muscles. Velocity profiles (kinematics) in the two force fields were almost identical to each other, whereas not only the electromyographic activities (dynamics) but also simple-spike activities in many Purkinje cells differed distinctly depending on the type of force field. Simple-spike activities encoded much larger mutual information with the type of force field than that with the residual small difference in the height of peak velocity. The difference in simple-spike activities averaged over the recorded Purkinje-cells increased approximately 40 ms before the appearance of the difference in electromyographic activities between the two force fields, suggesting that the difference of simple-spike activities could be the origin of the difference of muscle activities. Simple-spike activity of many Purkinje cells correlated with electromyographic activity with a lead of approximately 80 ms, and these neurons had little overlap with another group of neurons the simple-spike activity of which correlated with velocity profiles. These results show that simple-spike activity of at least a group of Purkinje cells in the intermediate part of cerebellar lobules V-VI encodes movement dynamics.

Animals↗

PubMed2006

Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning.

The brain's most difficult computation in decision-making learning is searching for essential information related to rewards among vast multimodal inputs and then integrating it into beneficial behaviors. Contextual cues consisting of limbic, cognitive, visual, auditory, somatosensory, and motor signals need to be associated with both rewards and actions by utilizing an internal representation such as reward prediction and reward prediction error. Previous studies have suggested that a suitable brain structure for such integration is the neural circuitry associated with multiple cortico-striatal loops. However, computational exploration still remains into how the information in and around these multiple closed loops can be shared and transferred. Here, we propose a "heterarchical reinforcement learning" model, where reward prediction made by more limbic and cognitive loops is propagated to motor loops by spiral projections between the striatum and substantia nigra, assisted by cortical projections to the pedunculopontine tegmental nucleus, which sends excitatory input to the substantia nigra. The model makes several fMRI-testable predictions of brain activity during stimulus-action-reward association learning. The caudate nucleus and the cognitive cortical areas are correlated with reward prediction error, while the putamen and motor-related areas are correlated with stimulus-action-dependent reward prediction. Furthermore, a heterogeneous activity pattern within the striatum is predicted depending on learning difficulty, i.e., the anterior medial caudate nucleus will be correlated more with reward prediction error when learning becomes difficult, while the posterior putamen will be correlated more with stimulus-action-dependent reward prediction in easy learning. Our fMRI results revealed that different cortico-striatal loops are operating, as suggested by the proposed model.

Association Learning↗

PubMed2006

Mirror neurons and imitation: a computationally guided review.

Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of 'mirror systems' (not individual neurons) in the human. Current conceptual models attribute high level functions such as action understanding, imitation, and language to mirror neurons. However, only the first of these three functions is well-developed in monkeys. We thus distinguish current opinions (conceptual models) on mirror neuron function from more detailed computational models. We assess the strengths and weaknesses of current computational models in addressing the data and speculations on mirror neurons (macaque) and mirror systems (human). In particular, our mirror neuron system (MNS), mental state inference (MSI) and modular selection and identification for control (MOSAIC) models are analyzed in more detail. Conceptual models often overlook the computational requirements for posited functions, while too many computational models adopt the erroneous hypothesis that mirror neurons are interchangeable with imitation ability. Our meta-analysis underlines the gap between conceptual and computational models and points out the research effort required from both sides to reduce this gap.

Animals↗

PubMed2005

Anterior and superior lateral occipito-temporal cortex responsible for target motion prediction during overt and covert visual pursuit.

In smooth-pursuit eye movements (SPEM) with gain close to one, SPEM should be controlled mainly by prediction of target motion because retinal slip is nearly zero. We investigated the neural mechanisms of visual-target prediction by the three fMRI experiments. (1) Overt pursuit task: subjects pursued a sinusoidally moving target which blinked (blink condition) or did not blink (continuous condition). (2) Covert pursuit task: subjects covertly pursued the same target with eyes gazed at fixation point. (3) Attend-to-stationary target task: subjects brought attention on a stationary target with eyes gazed at fixation point. In the overt pursuit task, the SPEM gain and the delay in the blink condition were not very different from the continuous condition, indicating good prediction of the blinking target motion. Activities in the dorsolateral prefrontal, precentral, medial superior frontal, intraparietal, and lateral occipito-temporal cortexes increased in the blink-continuous subtraction. The V1 activity decreased for this contrast. In the covert pursuit task, only the anterior/superior LOTC activity remained in the blink-continuous subtraction. In the attend-to-stationary target task, the blink-continuous subtraction elicited no activation. Consequently, the a/sLOTC activity is responsible for target prediction rather than motor commands for eye movements or just target blinking such as visual saliency.

Adult↗

PubMed2005

A model of the coupling between grip aperture and hand transport during human prehension.

It has been repeatedly demonstrated that the opening between the index finger and thumb (grasp component) during an object-directed reach-to-grasp movement achieves maximum aperture approximately two-thirds of the way through the duration of the reaching movement (transport component). Here we offer a quantitative model of the temporal coupling between grip aperture and wrist velocity which shows experimentally that the correlation between grip aperture and object size is a sigmoidal function of movement duration. When wrist velocity reaches its peak value, the correlation between the grip aperture and the size of the goal object has reached half of the correlation that is achieved by the end of the movement.

Hand↗

PubMed2005

Central representation of dynamics when manipulating handheld objects.

To explore the neural mechanisms related to representation of the manipulation dynamics of objects, we performed whole-brain fMRI while subjects balanced an object in stable and highly unstable states and while they balanced a rigid object and a flexible object in the same unstable state, in all cases without vision. In this way, we varied the extent to which an internal model of the manipulation dynamics was required in the moment-to-moment control of the object's orientation. We hypothesized that activity in primary motor cortex would reflect the amount of muscle activation under each condition. In contrast, we hypothesized that cerebellar activity would be more strongly related to the stability and complexity of the manipulation dynamics because the cerebellum has been implicated in internal model-based control. As hypothesized, the dynamics-related activation of the cerebellum was quite different from that of the primary motor cortex. Changes in cerebellar activity were much greater than would have been predicted from differences in muscle activation when the stability and complexity of the manipulation dynamics were contrasted. On the other hand, the activity of the primary motor cortex more closely resembled the mean motor output necessary to execute the task. We also discovered a small region near the anterior edge of the ipsilateral (right) inferior parietal lobule where activity was modulated with the complexity of the manipulation dynamics. We suggest that this is related to imagining the location and motion of an object with complex manipulation dynamics.

Brain Mapping↗

PubMed2005

Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex.

Neuronal operations associated with the top-down control process of shifting attention from one locus to another involve a network of cortical regions, and their influence is deemed fundamental to visual perception. However, the extent and nature of these operations within primary visual areas are unknown. In this paper, we used magnetoencephalography (MEG) in combination with magnetic resonance imaging (MRI) to determine whether, prior to the onset of a visual stimulus, neuronal activity within early visual cortex is affected by covert attentional shifts. Time/frequency analyses were used to identify the nature of this activity. Our results show that shifting attention towards an expected visual target results in a late-onset (600 ms postcue onset) depression of alpha activity which persists until the appearance of the target. Independent component analysis (ICA) and dipolar source modeling confirmed that the neuronal changes we observed originated from within the calcarine cortex. Our results further show that the amplitude changes in alpha activity were induced not evoked (i.e., not phase-locked to the cued attentional task). We argue that the decrease in alpha prior to the onset of the target may serve to prime the early visual cortex for incoming sensory information. We conclude that attentional shifts affect activity within the human calcarine cortex by altering the amplitude of spontaneous alpha rhythms and that subsequent modulation of visual input with attentional engagement follows as a consequence of these localized changes in oscillatory activity.

Adult↗

PubMed2005

Task-specific disruption of perceptual learning.

For more than a century, the process of stabilization has been a central issue in the research of learning and memory. Namely, after a skill or memory is acquired, it must be consolidated before it becomes resistant to disruption by subsequent learning. Although it is clear that there are many cases in which learning can be disrupted, it is unclear when learning something new disrupts what has already been learned. Herein, we provide two answers to this question with the demonstration that perceptual learning of a visual stimulus disrupts or interferes with the consolidation of a previously learned visual stimulus. In this study, we trained subjects on two different hyperacuity tasks and determined whether learning of the second task disrupted that of the first. We first show that disruption of learning occurs between visual stimuli presented at the same orientation in the same retinotopic location but not for the same stimuli presented at retinotopically disparate locations or different orientations at the same location. Second, we show that disruption from stimuli in the same retinotopic location is ameliorated if the subjects wait for 1 h before training on the second task. These results indicate that disruption, at least in visual learning, is specific to features of the tasks and that a temporal delay of 1 h can stabilize visual learning. This research shows that visual learning is susceptible to disruption and elucidates the processes by which the brain can consolidate learning and thus protect what is learned from being overwritten.

Adult↗

PubMed2005

Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning.

To select appropriate behaviors leading to rewards, the brain needs to learn associations among sensory stimuli, selected behaviors, and rewards. Recent imaging and neural-recording studies have revealed that the dorsal striatum plays an important role in learning such stimulus-action-reward associations. However, the putamen and caudate nucleus are embedded in distinct cortico-striatal loop circuits, predominantly connected to motor-related cerebral cortical areas and frontal association areas, respectively. This difference in their cortical connections suggests that the putamen and caudate nucleus are engaged in different functional aspects of stimulus-action-reward association learning. To determine whether this is the case, we conducted an event-related and computational model-based functional MRI (fMRI) study with a stochastic decision-making task in which a stimulus-action-reward association must be learned. A simple reinforcement learning model not only reproduced the subject's action selections reasonably well but also allowed us to quantitatively estimate each subject's temporal profiles of stimulus-action-reward association and reward-prediction error during learning trials. These two internal representations were used in the fMRI correlation analysis. The results revealed that neural correlates of the stimulus-action-reward association reside in the putamen, whereas a correlation with reward-prediction error was found largely in the caudate nucleus and ventral striatum. These nonuniform spatiotemporal distributions of neural correlates within the dorsal striatum were maintained consistently at various levels of task difficulty, suggesting a functional difference in the dorsal striatum between the putamen and caudate nucleus during stimulus-action-reward association learning.

Adult↗

PubMed2005

A model of smooth pursuit in primates based on learning the target dynamics.

While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

Animals↗

PubMed2005

Inositol 1,4,5-trisphosphate-dependent Ca2+ threshold dynamics detect spike timing in cerebellar Purkinje cells.

Large Ca2+ signals essential for cerebellar long-term depression (LTD) at parallel fiber (PF)-Purkinje cell synapses are known to be induced when PF activation precedes climbing fiber (CF) activation by 50-200 ms, consistent with cerebellar learning theories. However, large Ca2+ signals and/or LTD can also be induced by massive PF stimulation alone or by photolysis of caged Ca2+ or inositol 1,4,5-trisphosphate (IP3). To understand the spike-timing detection mechanisms in cerebellar LTD, we developed a kinetic model of Ca2+ dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of PF inputs, and the Ca2+ influx in response to the CF input triggered regenerative Ca2+-induced Ca2+ release from the internal stores via the IP3 receptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF-CF interval. The Ca2+ dynamics revealed a threshold for large Ca2+ release that decreased as IP3 increased, and it coherently explained the different forms of LTD. At 2.5 microM IP3, CF activation after PF activation was essential to reach the threshold for the regenerative Ca2+ release. At 10 microM IP3, the same as achieved experimentally by strong IP3 photolysis, the threshold was lower, and thus large Ca2+ release was generated even without CF stimulation. In contrast, the basal 0.1 microM IP3 level resulted in an extremely high Ca2+ threshold for regenerative Ca2+ release. Thus, the results demonstrated that Ca2+ dynamics can detect spike timing under physiological conditions, which supports cerebellar learning theories.

Action Potentials↗

PubMed2005

Mental state inference using visual control parameters.

Although we can often infer the mental states of others by observing their actions, there are currently no computational models of this remarkable ability. Here we develop a computational model of mental state inference that builds upon a generic visuomanual feedback controller, and implements mental simulation and mental state inference functions using circuitry that subserves sensorimotor control. Our goal is (1) to show that control mechanisms developed for manual manipulation are readily endowed with visual and predictive processing capabilities and thus allows a natural extension to the understanding of movements performed by others; and (2) to give an explanation on how cortical regions, in particular the parietal and premotor cortices, may be involved in such dual mechanism. To analyze the model, we simulate tasks in which an observer watches an actor performing either a reaching or a grasping movement. The observer's goal is to estimate the 'mental state' of the actor: the goal of the reaching movement or the intention of the agent performing the grasping movement. We show that the motor modules of the observer can be used in a 'simulation mode' to infer the mental state of the actor. The simulations with different grasping and non-straight line reaching strategies show that the mental state inference model is applicable to complex movements. Moreover, we simulate deceptive reaching, where an actor imposes false beliefs about his own mental state on an observer. The simulations show that computational elements developed for sensorimotor control are effective in inferring the mental states of others. The parallels between the model and cortical organization of movement suggest that primates might have developed a similar resource utilization strategy for action understanding, and thus lead to testable predictions about the brain mechanisms of mental state inference.

Cerebral Cortex↗

PubMed2004

Failure to consolidate the consolidation theory of learning for sensorimotor adaptation tasks.

An influential idea in human motor learning is that there is a consolidation period during which motor memories are transformed from a fragile to a permanent state, no longer susceptible to interference from new learning. The evidence supporting this idea comes from studies showing that the motor memory of a task (A) is lost when an opposing task (B) is experienced soon after, but not if sufficient time is allowed to pass (approximately 6 hr). We report results from three laboratories challenging this consolidation idea. We used an ABA paradigm in the context of a reaching task to assess the influence of experiencing B after A on the retention of A. In two experiments using visuomotor rotations, we found that B fully interferes with the retention of A even when B is experienced 24 hr after A. Contrary to previous reports, in four experiments on learning force fields, we also observed full interference between A and B when they are separated by 24 hr or even 1 week. This latter result holds for both position-dependent and velocity-dependent force fields. For both the visuomotor and force-field tasks, complete interference is still observed when the possible affects of anterograde interference are controlled through the use of washout trials. Our results fail to support the idea that motor memories become consolidated into a protected state. Rather, they are consistent with recent ideas of memory formation, which propose that memories can shift between active and inactive states.

Adaptation, Physiological↗

PubMed2004

Rhythmic arm movement is not discrete.

Rhythmic movements, such as walking, chewing or scratching, are phylogenetically old motor behaviors found in many organisms, ranging from insects to primates. In contrast, discrete movements, such as reaching, grasping or kicking, are behaviors that have reached sophistication primarily in younger species, particularly primates. Neurophysiological and computational research on arm motor control has focused almost exclusively on discrete movements, essentially assuming similar neural circuitry for rhythmic tasks. In contrast, many behavioral studies have focused on rhythmic models, subsuming discrete movement as a special case. Here, using a human functional neuroimaging experiment, we show that in addition to areas activated in rhythmic movement, discrete movement involves several higher cortical planning areas, even when both movement conditions are confined to the same single wrist joint. These results provide neuroscientific evidence that rhythmic arm movement cannot be part of a more general discrete movement system and may require separate neurophysiological and theoretical treatment.

Adult↗

PubMed2004

Impedance control balances stability with metabolically costly muscle activation.

Humans are able to stabilize their movements in environments with unstable dynamics by selectively modifying arm impedance independently of force and torque. We further investigated adaptation to unstable dynamics to determine whether the CNS maintains a constant overall level of stability as the instability of the environmental dynamics is varied. Subjects performed reaching movements in unstable force fields of varying strength, generated by a robotic manipulator. Although the force fields disrupted the initial movements, subjects were able to adapt to the novel dynamics and learned to produce straight trajectories. After adaptation, the endpoint stiffness of the arm was measured at the midpoint of the movement. The stiffness had been selectively modified in the direction of the instability. The stiffness in the stable direction was relatively unchanged from that measured during movements in a null force field prior to exposure to the unstable force field. This impedance modification was achieved without changes in force and torque. The overall stiffness of the arm and environment in the direction of instability was adapted to the force field strength such that it remained equivalent to that of the null force field. This suggests that the CNS attempts both to maintain a minimum level of stability and minimize energy expenditure.

Arm↗

PubMed2004

Optimal impedance control for task achievement in the presence of signal-dependent noise.

There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not theoretically obvious and thus was examined here. Subjects made goal-directed, single-joint elbow movements to either move naturally to different target sizes or voluntarily co-contract at different levels. Stiffness was estimated as the weighted summation of rectified EMG signals through the index of muscle co-contraction around the joint (IMCJ) proposed previously. When subjects made movements to targets of different sizes, IMCJ increased with the accuracy requirements, leading to reduced endpoint deviations. Therefore without the need for great accuracy, subjects accepted worse performance with lower co-contraction. When subjects were asked to increase co-contraction, the variability of EMG and torque both increased, suggesting that noise in the neuromotor command increased with muscle activation. In contrast, the final positional error was smallest for the highest IMCJ level. Although co-contraction increases the motor-command noise, the effect of this noise on the task performance is reduced. Subjects were able to regulate their impedance and control endpoint variance as the task requirements changed, and they did not voluntarily select the high impedance that generated the minimum endpoint error. These data contradict predictions of the SDN-based theory, which postulates minimization of only endpoint variance and thus require its revision.

Adult↗