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Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; allP

After traumatic events, simple cue-threat associative memories strengthen while episodic memories become fragmented. However, how the brain prioritizes cue associations over episodic coding of traumatic events remains unclear. Here, we developed a new episodic threat conditioning paradigm in which participants concurrently form two memory representations: cue associations and episodic cue sequence. We discovered that these two distinct memories compete for physiological fear expression, reorganizing overnight from an overgeneralized cue-based to a precise sequence-based expression. With multivariate fMRI, we track inter-area communication of the memory representations and demonstrate that a shift from hippocampal-dominant to prefrontal-dominant control of the fear regulatory circuit governs this memory maturation. Critically, this overnight reorganization is altered in individuals with heightened trait anxiety. Together, these findings suggest the brain prioritizes generalizable associative memories under recent traumatic stress, but resorts to selective episodic memories 24 hrs later. Time-dependent memory competition provides a unifying account for memory dysfunctions in posttraumatic stress disorders.

Background and HypothesisDynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.Study DesignWe applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.Study ResultsDCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.ConclusionsDCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Learning a predictive cue of a threat - threat conditioning - allows us to prevent direct contact with the threat. In animals, threat-conditioned cues induce fear-like body movements such as freezing. Interestingly, training animals with alternative bodily defenses to avoid harm could reduce conditioned fear-like responses, suggesting that the physical capacity to actively defend against a threat could help overcome passively induced fear. However, evidence for the role of body movements in acquisition and alleviation of human fear is scarce. In the present study, human participants showed a deviation of multidimensional body movement patterns as threat conditioning progressed in a naturalistic virtual setting. The subsequent training to exert defensive body movements to prevent harm weakened the conditioned body movements and resulted in a long-term (24 hrs) reduction of the physiological responses and subjective fear toward the conditioned cues. Unlike the passive extinction procedure, the training of defensive body movements was resistant to spontaneous recovery of a fear response. The alleviating effect of the active defense training was observed when participants physically defended themselves but not when they merely observed another person vicariously defending against a threat on their behalf. These results suggest the critical role of body movements in both acquisition and reduction of conditioned fear among humans. Further investigations may leverage more embodied procedures to advance mechanistic understandings and clinical interventions of human fear memories.

The sense of agency is defined as the subjective experience that “I” am the one who is causing the action. Theoretical studies postulate that this subjective experience is developed through multistep processes extending from the sensorimotor to the cognitive level. However, it remains unclear how the brain processes such different levels of information and constitutes the neural substrates for the sense of agency. To answer this question, we combined two strategies: an experimental paradigm, in which self-agency gradually evolves according to sensorimotor experience, and a multivoxel pattern analysis. The combined strategies revealed that the sensorimotor, posterior parietal, anterior insula, and higher visual cortices contained information on self-other attribution during movement. In addition, we investigated whether the found regions showed a preference for self-other attribution or for sensorimotor information. As a result, the right supramarginal gyrus, a portion of the inferior parietal lobe (IPL), was found to be the most sensitive to self-other attribution among the found regions, while the bilateral precentral gyri and left IPL dominantly reflected sensorimotor information. Our results demonstrate that multiple brain regions are involved in the development of the sense of agency and that these show specific preferences for different levels of information.

Agents must correctly attribute the source of sensory inputs to the self or other for successful interaction with the environment. Many previous research works have revealed that self-other attribution depends on the discrepancy between sensory prediction and actual sensory feedback (sensory prediction error). Self-other attribution is assumed to determine how agents reflect sensory feedback in their feedback control. However, the direct effect of self-other attribution on feedback control is still unclear. In the current study, participants’ self-other attribution was biased through induction training in the selfward or otherward direction at a cognitive-judgment level. Before and after the induction, participants controlled a morphed cursor between self and other movements and evaluated the self-other attribution of the cursor movement. We first confirmed that the induction biased their self-other attributions toward the intended direction (i.e., selfward or otherward). Next, we examined whether the respective bias changed the feedback control of the cursor. For the self-biased group, the more participants attributed their visual feedback to themselves, the more they tried to control the morphed cursor in a specific condition. In contrast, for the other-biased group, the more participants attributed their visual feedback to others, the less they tried to control the morphed visual feedback in a specific condition. These results suggest that self-other attribution is not simply a cognitive process reflecting the sensory feedback of motor control but also a factor affecting motor control in a top-down manner.

Mindfulness meditation is increasingly used for clinical treatment and to improve well-being. One of the most fundamental benefits of mindfulness meditation is now considered as enhanced attentional control. Mindfulness meditation is a complex technique but most of its variants consist of a combination of two types of basic meditation practice: focused attention meditation (FAM) and open monitoring meditation (OMM). Although many studies have examined the effect of relatively long-term meditation on attention, some recent studies have focused on the effect of a brief one-time meditation on cognitive processing, including attentional functions. Furthermore, it is necessary to discuss the relationship between the effect of mindfulness meditation on attentional functions and personality traits (especially traits related to mindfulness). This study investigated whether attentional control is improved by a single 30-min FAM or OMM and whether the degree of improvement in attentional functions – alerting, orienting, and conflict monitoring – induced by the meditation varies according to the participant’s trait scores related to mindfulness measured by the Five Facets Mindfulness Questionnaire. Participants were randomly assigned to one of three groups, i.e., FAM, OMM, and no-meditation (noM) groups, and given an Attentional Network Test before and after each 30-min meditation session. Compared with the noM group, there was no overall improvement in attentional functions with either type of meditation. However, there were associations between the change of the alerting function’s score and the personality traits: in the FAM group, alerting scores were negatively associated with the nonreactivity facet of the FFMQ, and in the OMM group, alerting scores were positively associated with describing facet scores of the FFMQ. The results indicate that the effects of meditation methods on attentional functions could depend on the individual’s traits related to mindfulness and that mindfulness meditation could sometimes appear to have no impact on attentional functions.

Adaptive motor learning refers to the ability to adjust to novel disturbances in the environment as a way of minimizing sensorimotor errors. It is known that such processes show large individual differences and are linked to multiple perceptual and cognitive processes. On the other hand, the sense of agency refers to the subjective feeling of control during voluntary motor control. Is the sense of agency just a by-product of the control outcome, or is it actually important for motor control and learning? To answer this question, this study takes an approach based on individual differences to investigate the relationship between the sense of agency and learnability in sensorimotor adaptation. Specifically, we use an adaptive motor learning task to measure individual differences in the efficiency of motor learning. Regarding the sense of agency, we measure the perceptual sensitivity of detecting an increase or a decrease in control when the actual level of control gradually increases or decreases, respectively. The results of structure equation modelling reveal a significant influence of perceptual sensitivity to increased control on motor learning efficiency. On the other hand, the link between perceptual sensitivity to decreased control and motor learning is nonsignificant. The results show that the sense of agency in detecting increased control is associated with the actual ability of sensorimotor adaptation: people who are more sensitive in detecting their control in the environment can also more quickly adjust their behaviors to novel disturbances to acquire better control, compared to people who have a less sensitive sense of agency. Finally, the results also reveal that the processes of increasing control and decreasing control may be partially independent.

Car driving is supported by motor skills trained through continuous daily practice. One of the skills unique to expert drivers is the ability to detect abrupt changes in the driving environment and then quickly adapt their operation mode to the changes. Previous functional neuroimaging studies on motor control investigated the mechanisms underlying behaviors adaptive to changes in control properties of simple experimental devices such as a computer mouse or a joystick. The switching of multiple internal models mainly engages adaptive behaviors and underlies the interplay between the cerebellum and frontoparietal network (FPN) regions as the neural process. However, it remains unclear whether the neural mechanisms identified through an experimental paradigm using such simple devices also underlie practical driving behaviors. In the current study, we measure functional magnetic resonance imaging (fMRI) activities while participants control a realistic driving simulator inside the MRI scanner. Here, the accelerator sensitivity of a virtual car is abruptly changed, requiring participants to respond to this change as quickly as possible. We first compare brain activities before and after the sensitivity change. As a result, sensorimotor areas, including the left cerebellum, increase their activities after the sensitivity change. Moreover, after the change, activity significantly increases in the inferior parietal lobe and dorsolateral prefrontal cortex, parts of the FPN regions. By contrast, the posterior cingulate cortex, a part of the default mode network, deactivates after the sensitivity change. Our results suggest that the neural bases found in previous experiments using the simpler devices can serve as the foundation of adaptive car driving. At the same time, this study also highlights the unique contribution of non-motor-related regions to addressing the high cognitive demands of driving.

Car driving is supported by perceptual, cognitive, and motor skills trained through continuous daily practice. One of the skills that characterize experienced drivers is to detect changes in the driving environment and then flexibly switch their driving modes in response to the changes. Previous functional neuroimaging studies on motor control investigated the mechanisms underlying behaviors adaptive to changes in control properties or parameters of experimental devices such as a computer mouse or a joystick. The switching of multiple internal models mainly engages adaptive behaviors and underlies the interplay between the cerebellum and frontoparietal network (FPN) regions as the neural process. However, it remains unclear whether the neural mechanisms identified in previous motor control studies also underlie practical driving behaviors. In the current study, we measure functional magnetic resonance imaging (fMRI) activities while participants control a realistic driving simulator inside the MRI scanner. Here, the accelerator sensitivity of a virtual car is abruptly changed, requiring participants to respond to this change flexibly to maintain stable driving. We first compare brain activities before and after the sensitivity change. As a result, sensorimotor areas, including the left cerebellum, increase their activities after the sensitivity change. Moreover, after the change, activity significantly increases in the inferior parietal lobe (IPL) and dorsolateral prefrontal cortex (DLPFC), parts of the FPN regions. By contrast, the posterior cingulate cortex, a part of the default mode network, deactivates after the sensitivity change. Our results suggest that the neural bases found in previous experimental studies can serve as the foundation of adaptive driving behaviors. At the same time, this study also highlights the unique contribution of non-motor regions to addressing the high cognitive demands of driving.

Improving human motor performance via human-robot collaboration is a major interest in society in many different contexts, such as rehabilitation, sports training, and so on. Despite the increasing demands, the potential and limitations have been controversially discussed. This study proposes a versatile method that can statistically elaborate on the relation between the performance improvements and the initial skill level. The procedure is explained by applying an experimental data of 20 healthy subjects interacting with a robot-assisted motor training system from our laboratory. The subjects are physically guided through an ideal motion by the haptic interface, which is a major approach in robotic rehabilitation to facilitate the motor functional recovery. Meanwhile, such haptic guidance training is conjectured to improve motor performance with lower skills. Although some studies show such a tendency, a method for defining the effective boundary level has not been proposed. Identifying the boundary promises positive training effects for target users of each task or type of robotic training. This study proposes an identification method to figure out the training effect's dependence on the initial skill level thorough modelling the skill level change. With the proposed statistical method, the initial skill's boundary level could be simultaneously derived as inferring the model parameters. The pre- and post-performance showed that the post-performance can be presumed depending on each subject's initial skill level.

The subjective experience of causing an action is known as the sense of agency. Dysfunction in the sense of agency has been suggested as a cause of auditory hallucinations (AHs), an important diagnostic criterion for schizophrenia. However, agency over speech has not been extensively characterized in previous empirical studies. Here, we examine both implicit and explicit measures of the sense of agency and reveal bottom-up and top-down components that constitute self-agency during speech. The first is action-outcome causality, which is perceived based on a low-level sensorimotor process when hearing their own voice following their speech. The second component is self-voice identity, which is embedded in the acoustic quality of voice and dominantly influences agency over speech at the cognitive judgment level. Our findings provide profound insight into the sense of agency over speech and present an informative perspective for understanding aberrant experience in AHs.

A sense of agency (SoA) is the experience of subjective awareness regarding the control of one’s actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects’ degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.

Visual Abstract, The self is a distinct entity from the rest of the world, and actions and sensory feedback are our channels of interaction with the external world. This study examined how the sense of control influences people’s perception of sensorimotor input under the framework of categorical perception. Twenty human participants (18 males, two females) took part in both experiments. Experiment 1 showed that the sensitivity (d′) of detecting a 20% change in control from no change was higher when the changes occurred at the control-category boundary than within each category. Experiment 2 showed that the control categories greatly affected early attention allocation, even when the judgment of control was unnecessary to the task. Taken together, these results showed that our perceptual and cognitive systems are highly sensitive to small changes in control that build up to a determinant change in the control category within a relatively narrow boundary zone between categories, compared with a continuous, gradual physical change in control.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable major depressive disorder (MDD) brain network markers which would distinguish patients from healthy controls (a classifier) or would predict symptom severity (a prediction model) based on resting state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD brain network markers. The classifier achieved 70% generalization accuracy, and the prediction model moderately well predicted symptom severity for an independent validation dataset with 449 participants from 4 different imaging sites. Finally, we found common 2 functional connections between those related to MDD diagnosis and those related to depression symptoms. The successful generalization to the perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

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.

In addition to mental disorders such as attention, emotion, delusions, hallucinations, and difficulties in social skills, the patients with schizophrenia exhibits significant abnormality in sensorimotor perception and control. To seek a neurobiological cause of the heterogeneous symptoms in schizophrenia, we focused on the impaired inference mechanism of the self-agency of the schizophrenia's brain where the sensory outcome generated by the self-initiated action was misattributed to the other agent's action. By developing a novel computational model of agency experience using a Bayesian decision making framework, we united the computational mechanisms of agency and motor control via internal model: a model for one to predict the sensory consequence of action. Our theory based on optimal feedback control with Kalman filtering successfully predicted a variety of schizophrenia's motor abnormalities assuming a deformed internal model. To discuss the plausibility of these model predictions, we reviewed literature that might support these predictions. We further proposed some experiments that potentially examine the proposed model of schizophrenia. Our approach in investigating a problem of mind by projecting it on the coordinates system of the embodiment effectively shed light on a central neuropathology of this disease that had been latent behind the observed behaviors.

Summary In everyday life, people control objects in the world around them to varying degrees. The processes people actively use to establish their control, while interacting with an environment containing large ambiguity, remain unknown. This study examines how people explore their control over the environment and how they detect small differences in control among objects. In the experimental task, participants moved three dots on a screen and identified one dot over which the level of control is different from that of the other two. The results support a two-step behavior mechanism underlying the sensing of control difference: People first explore their overall control in the environment, and then the results of the initial exploration are used to selectively tune the direction (i.e., either more or less) of the detected control difference, ensuring efficient and rapid detection of the type of control difference that is potentially important for further action selections., Graphical Abstract, Highlights • This study examines how people explore their control over multiple objects • The results support a two-step behavior mechanism of active sensing of control • People first sense their overall control over the environment at an early stage • The initial exploration tunes the sensitivity of detecting control difference, Neuroscience; Behavioral Neuroscience; Cognitive Neuroscience

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual’s predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases., 複数の精神疾患における記憶力を共通のモデルで予測することに成功 --疾患に共通する認知機能低下のメカニズム解明に大きく前進--. 京都大学プレスリリース. 2019-01-11.

Haptic guidance is a motor training procedure in which the subject is physically guided through an ideal motion by an haptic interface. It is especially valuable for repetitive training, and more effective when coupled with additional sensory feedback, e.g. the visual modality. The advantage of additional feedback modalities may stem from the learner's increased active participation and engagement. Here, we test this hypothesis by analyzing the learners' brain state during haptic training. Specifically, we focus on the sensorimotor rhythms (SMR), since they have been previously associated with the level of engagement directed towards a motor task. We conducted a circle-drawing haptic training, where subjects were asked to memorise the guided trajectory while their electroencephalogram (EEG) was recorded. During the experiment, only the haptic modality was maintained (i.e. unimodal) by keeping the task workspace visually hidden. Results show a clear trend: subjects who exhibited a performance improvement, were characterized by a stronger desynchronization of the beta rhythms over the contralateral hemisphere. This is in agreement with recent studies showing that contralateral beta rhythms changes are associated with motor skills retention. Moreover, under the assumption that SMR are indeed a marker of engagement, our results represent accumulating evidence that active participation is crucial for haptic training.

A number of studies have reported that the digit ratio 2D:4D (length of the second finger divided by length of the fourth finger) is smaller (longer fourth digit) in autism spectrum disorder (ASD) than in typically developed (TD) controls. Because form and function are closely related in biological systems, we hypothesized that the 4D dominance occurs in not only finger morphology but also physical performance in ASD. Individuals with ASD and TD controls participated in a multi-digit force-producing task. Individuals with ASD showed a significant 4D dependence compared to TD controls in the task. We found a significant correlation between 4D dependence and scores of the standard diagnostic instrument across individuals with ASD. Our analysis of functional connectivity in resting-state functional MRI suggests that connectivity between the visual cortex and the cerebellum contributes to the 4D dependence. Collectively, these results extend the 2D:4D ratio beyond being a morphological marker to being involved in motor functions in the form of 4D dependence in a multi-digit force task.

Examination of temporal patterns in adaptation and extinction in human motor learning suggests the presence of multiple motor memories with different timescales. In this study, a computational model derived from behavioral data was use to, explain the analysis of brain. The results indicated that each brain region activated during motor learning is related to different timescales in motor memory. They revealed dynamic processes in the brain where multiple regions participate in the acquisition of memory at different stages of learning even in a single motor task.

Advances in functional magnetic resonance imaging have made it possible to provide real-time feedback on brain activity. Neurofeedback has been applied to therapeutic interventions for psychiatric disorders. Since many studies have shown that most psychiatric disorders exhibit abnormal brain networks, a novel experimental paradigm named connectivity neurofeedback, which can directly modulate a brain network, has emerged as a promising approach to treat psychiatric disorders. Here, we investigated the hypothesis that connectivity neurofeedback can induce the aimed direction of change in functional connectivity, and the differential change in cognitive performance according to the direction of change in connectivity. We selected the connectivity between the left primary motor cortex and the left lateral parietal cortex as the target. Subjects were divided into 2 groups, in which only the direction of change (an increase or a decrease in correlation) in the experimentally manipulated connectivity differed between the groups. As a result, subjects successfully induced the expected connectivity changes in either of the 2 directions. Furthermore, cognitive performance significantly and differentially changed from preneurofeedback to postneurofeedback training between the 2 groups. These findings indicate that connectivity neurofeedback can induce the aimed direction of change in connectivity and also a differential change in cognitive performance.

Perceived temporal interval between voluntary action and its outcome is shorter than that between involuntary action and its outcome (i.e., intentional binding). Although the effect is robust and extensively employed as a marker of sense of agency, the nature of intentional binding in multiple actions and outcomes remains unclear. We examined intentional binding in alternated action-outcome dyads. Participants actively or passively pressed a key, followed by a tone, and they again pressed the same key immediately after the preceding tone; resulting in four keypress-tone dyads in a trial. Participants reproduced the duration of alternated keypress-tone dyads or the temporal interval between a dyad embedded in the alternations. The reproduced duration was shorter in the active than in the passive condition, suggesting the intentional binding in action-outcome alternations. In contrast, the reproduced interval between a dyad was longer in the active condition and did not correlate with the reproduced duration of the alternations. These results suggest that subjective time during actions may rely not only on a general internal clock; rather, it may also be modulated by postdictive biases that are flexibly switched based on what we recall.

While the cerebellum's role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellum's status as a "supervised learning machine" can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellum's compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum.

The Sense of Agency (SoA) is the subjective sense such that I am the causal factor of the own action and accompanying events in the outside world. We proposed that SoA corresponds to likelihood of the predictive distribution conditioned by own-action. Mathematically, there exist different varieties of online learning algorithm for the predictive distribution. The goal of this article is to clarify the learning algorithm that subjects employ under the specific behavioral experiment. Our result suggests that subjects employ Bayesian update rather than SGD in our experiment.

We grasp and dexterously manipulate an object through multi-1 digit synergy. In the framework of the uncontrolled manifold (UCM) hypothesis, multi-digit synergy is defined as the coordinated control mechanism of fingers to stabilize variable important for task success, e.g., total force. Previous studies reported anticipatory synergy adjustments (ASAs) that correspond to a drop of the synergy index before a quick change of the total force. The present study compared ASA’s properties with individual performances of feedforward force control to investigate a relationship of those. Subjects performed a total finger force production task that consisted of a phase in which subjects tracked target line with visual information and a phase in which subjects produced total force pulse without visual information. We quantified their multi-digit synergy through UCM analysis and observed significant ASAs before producing total force pulse. The time of the ASA initiation and the magnitude of the drop of the synergy index were significantly correlated with the error of force pulse, but not with the tracking error. Almost all subjects showed a significant increase of the variance that affected the total force. Our study directly showed that ASA reflects the individual performance of feedforward force control independently of target-tracking performance and suggests that the multi-digit synergy was weakened to adjust the multi-digit movements based on a prediction error so as to reduce the future error.

Humans have remarkable abilities in the dex- terous use of tools to extend their physical capabilities. Although previous neuropsychological and functional neuroimaging studies have mainly focused on the contribution of frontal-parietal cerebral networks to skills for tool use, dense anatomical and functional connections are known to exist between the frontal-parietal regions and the lateral cerebellum, suggesting that the cerebellum also supports the information processing necessary for the dexterous use of tools. In this article, we review functional and structural imaging studies reporting that the cerebellum is related to the learning acquisition of neural mechanisms representing input-output properties of controlled objects, including tools. These studies also suggest that such mechanisms are modularly organized in the cerebellum corresponding to the different properties of objects, such as kinematic or dynamic properties and types of tools, and that they enable humans to flexibly cope with discrete changes in objects and environments by reducing interference and combining acquired mod- ules. Based on these studies, we propose a hypothesis that the cerebellum contributes to the skillful use of tools by representing the input-output properties of tools and providing information on the prediction of the sensory consequences of manipulation with the parietal regions, which are related to multisensory processing, and information on the necessary control of tools with the premotor regions, which contribute to the control of hand movements.

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.

Humans can rapidly and smoothly control their own bodies and external objects, such as tools. Because sensorimotor feedback on movements is inevitably delayed by many factors, including a delay for transmission of motor commands from the brain to muscles and time for processing of sensory information, many studies have suggested the existence of mechanisms in the brain that can predict the precise motor commands necessary for realizing the intended motions and the sensorimotor feedback from the motor commands before movement. This article focuses on mechanisms for the prediction of feedback and summarizes how the predicted feedback can contribute to rapid, efficient and flexible control of movements and processing of sensory information. Possible neural correlates for the prediction mechanisms and their contribution to higher-order cognitive functions are discussed.

Online stabilization of human standing posture utilizes multisensory afferences (e.g., vision). Whereas visual feedback of spontaneous postural sway can stabilize postural control especially when observers concentrate on their body and intend to minimize postural sway, the effect of intentional control of visual feedback on postural sway itself remains unclear. This study assessed quiet standing posture in healthy adults voluntarily controlling or merely observing visual feedback. The visual feedback (moving square) had either low or high gain and was either horizontally flipped or not. Participants in the voluntary-control group were instructed to minimize their postural sway while voluntarily controlling visual feedback, whereas those in the observation group were instructed to minimize their postural sway while merely observing visual feedback. As a result, magnified and flipped visual feedback increased postural sway only in the voluntary-control group. Furthermore, regardless of the instructions and feedback manipulations, the experienced sense of control over visual feedback positively correlated with the magnitude of postural sway. We suggest that voluntarily controlled, but not merely observed, visual feedback is incorporated into the feedback control system for posture and begins to affect postural sway.

Functional MRI was used to test predictions from a theory of the origin of human language. The gradual theory suggests that human language and tool-use skills have a similar hierarchical structure, and proposes that tool-manipulation skills are related to the origin and evolution of human language. Our results show an overlap of brain activity for perceiving language and using tools in Broca's area. The location of this overlap suggests that language and tool use share computational principles for processing complex hierarchical structures common to these two abilities. The involvement of monkeys' homologous region during tool use suggests that neural processes for computation of complex hierarchical structures exist in primates without language, and could have been exapted to support human grammatical ability.

Humans can guide their actions toward the realization of their intentions. Flexible, rapid and precise realization of intentions and goals relies on the brain learning to control its actions on external objects and to predict the consequences of this control. Neural mechanisms that mimic the input-output properties of our own body and other objects can be used to support prediction and control, and such mechanisms are called internal models. We first summarize functional neuroimaging, behavioral and computational studies of the brain mechanisms related to acquisition, modular organization, and the predictive switching of internal models mainly for tool use. These mechanisms support predictive control and flexible switching of intentional actions. We then review recent studies demonstrating that internal models are crucial for the execution of not only immediate actions but also higher-order cognitive functions, including optimization of behaviors toward long-term goals, social interactions based on prediction of others' actions and mental states, and language processing. These studies suggest that a concept of internal models can consistently explain the neural mechanisms and computational principles needed for fundamental sensorimotor functions as well as higher-order cognitive functions.

Humans have a remarkable ability to flexibly control various objects such as tools. Much evidence suggests that the internal models acquired in the central nervous system (CNS) support flexible control. Internal models are neural mechanisms that mimic the input-output properties of controlled objects. In a series of functional magnetic resonance imaging (fMRI) studies, we demonstrate how the CNS acquires and switches internal models for dexterous use of many tools. In the first study, we investigated human cerebellar activity when human subjects learned how to use a novel tool (a rotated computer mouse, where the cursor appears in a rotated position) and found that activity reflecting an internal model of the novel tool increases in the lateral cerebellum after learning how to use the tool. In the second study, we investigated the internal-model activity after sufficient training in the use of two types of novel tools (the rotated mouse and a velocity mouse, where the cursor's velocity is proportional to mouse's position) and found that the cerebellar activities for the two tools were spatially segregated. In the third study, we investigated brain activity associated with the flexible switching of tools. We found that the activity related to switching internal models was in the prefrontal lobe (area 46 and the insula), the parietal lobe, and the cerebellum. These results suggest that internal models in the cerebellum represent input-output properties of the tools as modulators of continuous signals. The cerebellar abilities in adaptive modulation of signals can be used to enhance the control signals in communications between the brain and computers.

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.

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.

The Sense of Agency (SoA) is the subjective sense such that I am the causal factor of the own experience [1].

UTokyo Research掲載「運動前の脳活動から反応の速さを予測することに成功」 URI: http://www.u-tokyo.ac.jp/ja/utokyo-research/research-news/prediction-of-reaction-speed-from-pre-movement-brain-activity.html, UTokyo Research "Prediction of reaction speed from pre-movement brain activity" URI: http://www.u-tokyo.ac.jp/en/utokyo-research/research-news/prediction-of-reaction-speed-from-pre-movement-brain-activity.html

SummarySkilled object manipulation requires knowledge, or internal models, of object dynamics relating applied force to motion [1–4], and our ability to handle myriad objects [5–6] indicates that the brain maintains multiple models [7–9]. 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 [10–12]. 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.

The monkey parietal anterior intraparietal area (AIP) is part of the grasp planning and execution circuit which contains neurons that encode object features relevant for grasping, such as the width and the height. In this study we focus on the formation of AIP neurons during grasp development. We propose and implement a neural network structure and a learning mechanism that is driven by successful grasp experiences during early grasp development. The simulations show that learning leads to emergence of units that have similar response properties as the AIP visual-dominant neurons. The results may have certain implications for the function of AIP neurons and thus should stimulate new experiments that cannot only verify/falsify the model but also advance our understanding of the visuomotor learning mechanisms employed by the primate brain.