Dual-MEG interbrain synchronization during turn-taking verbal interactions between mothers and children.

Verbal interaction and imitation are essential for language learning and development in young children. However, it is unclear how mother-child dyads synchronize oscillatory neural activity at the cortical level in turn-based speech interactions. Our study investigated interbrain synchrony in mother-child pairs during a turn-taking paradigm of verbal imitation. A dual-MEG (magnetoencephalography) setup was used to measure brain activity from interactive mother-child pairs simultaneously. Interpersonal neural synchronization was compared between socially interactive and noninteractive tasks (passive listening to pure tones). Interbrain networks showed increased synchronization during the socially interactive compared to noninteractive conditions in the theta and alpha bands. Enhanced interpersonal brain synchrony was observed in the right angular gyrus, right triangular, and left opercular parts of the inferior frontal gyrus. Moreover, these parietal and frontal regions appear to be the cortical hubs exhibiting a high number of interbrain connections. These cortical areas could serve as a neural marker for the interactive component in verbal social communication. The present study is the first to investigate mother-child interbrain neural synchronization during verbal social interactions using a dual-MEG setup. Our results advance our understanding of turn-taking during verbal interaction between mother-child dyads and suggest a role for social "gating" in language learning.

Effects of familiarity on child brain networks when listening to a storybook reading: A magneto-encephalographic study.

Parent-child book reading is important for fostering the development of various lifelong cognitive and social abilities in young children. Despite numerous reports describing the effects of familiarity on shared reading for children, the exact neural basis of the functional network architecture remains unclear. We conducted Magnet-Encephalographic (MEG) experiments using graph theory to elucidate the role of familiarity in shared reading in a child's brain network and to measure the connectivity dynamics of a child while Listening to Storybook Reading (LSBR), which represents the daily activity of shared book reading between the child and caregiver. The LSBR task was performed with normally developing preschool- and school-age children (N = 15) under two conditions: reading by their own mother (familiar condition) vs. an experimenter (unfamiliar condition). We used the phase lag index (PLI), which captures synchronization of MEG signals, to estimate functional connectivity. For the whole brain network topology, an undirected weighted graph was produced using 68 brain regions as nodes and interregional PLI values as edges for five frequency bands. Behavioral data (i.e., the degree of attention and facial expressions) were evaluated from video images of the child's face during the two conditions. Our results showed enhanced widespread functional connectivity in the alpha band during the mother condition. In the mother condition, the whole brain network in the alpha band exhibited topographically high local segregation with high global integration, indicating an increased small-world property. Results of the behavioral analysis revealed that children were more attentive and showed more positive facial expressions in the mother condition than in the experimenter condition. Behavioral data were significantly correlated with graph metrics in the mother condition but not in the experimenter condition. In this study, we identified the neural correlates of a familiarity effect in children's brain connectivity dynamics during LSBR. Furthermore, these familiarity-related brain dynamics were closely linked to the child's behavior. Graph theory applied to MEG data may provide useful insight into the familiarity-related child brain response in a naturalistic setting and its relevance to child attitudes.

Children's scale errors are a natural consequence of learning to associate objects with actions: A computational model.

Young children sometimes attempt an action on an object, which is inappropriate because of the object size-they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual-route theory of action selection, in which actions on objects are selected via a direct (nonsemantic or visual) route or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object-action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions.

Self-Face Recognition Begins to Share Active Region in Right Inferior Parietal Lobule with Proprioceptive Illusion During Adolescence.

We recently reported that right-side dominance of the inferior parietal lobule (IPL) in self-body recognition (proprioceptive illusion) task emerges during adolescence in typical human development. Here, we extend this finding by demonstrating that functional lateralization to the right IPL also develops during adolescence in another self-body (specifically a self-face) recognition task. We collected functional magnetic resonance imaging (fMRI) data from 60 right-handed healthy children (8-11 years), adolescents (12-15 years), and adults (18-23 years; 20 per group) while they judged whether a presented face was their own (Self) or that of somebody else (Other). We also analyzed fMRI data collected while they performed proprioceptive illusion task. All participants performed self-face recognition with high accuracy. Among brain regions where self-face-related activity (Self vs. Other) developed, only right IPL activity developed predominantly for self-face processing, with no substantial involvement in other-face processing. Adult-like right-dominant use of IPL emerged during adolescence, but was not yet present in childhood. Adult-like common activation between the tasks also emerged during adolescence. Adolescents showing stronger right-lateralized IPL activity during illusion also showed this during self-face recognition. Our results suggest the importance of the right IPL in neuronal processing of information associated with one's own body in typically developing humans.

Macroscopic Cluster Organizations Change the Complexity of Neural Activity.

In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.

Development of Right-hemispheric Dominance of Inferior Parietal Lobule in Proprioceptive Illusion Task.

Functional lateralization can be an indicator of brain maturation. We have consistently shown that, in the adult brain, proprioceptive processing of muscle spindle afferents generating illusory movement of the right hand activates inferior frontoparietal cortical regions in a right-side dominant manner in addition to the cerebrocerebellar motor network. Here we provide novel evidence regarding the development of the right-dominant use of the inferior frontoparietal cortical regions in humans using this task. We studied brain activity using functional magnetic resonance imaging while 60 right-handed blindfolded healthy children (8-11 years), adolescents (12-15 years), and young adults (18-23 years) (20 per group) experienced the illusion. Adult-like right-dominant use of the inferior parietal lobule (IPL) was observed in adolescents, while children used the IPL bilaterally. In contrast, adult-like lateralized cerebrocerebellar motor activation patterns were already observable in children. The right-side dominance progresses during adolescence along with the suppression of the left-sided IPL activity that emerges during childhood. Therefore, the neuronal processing implemented in the adult's right IPL during the proprioceptive illusion task is likely mediated bilaterally during childhood, and then becomes right-lateralized during adolescence at a substantially later time than the lateralized use of the cerebrocerebellar motor system for kinesthetic processing.

Mexican-Hat-Like Response in a Flexible Tactile Sensor Using a Magnetorheological Elastomer.

A significant challenge in robotics is providing a sense of touch to robots. Even though several types of flexible tactile sensors have been proposed, they still have various technical issues such as a large amount of deformation that fractures the sensing elements, a poor maintainability and a deterioration in the sensitivity caused by the presence of a thick and soft covering. As one solution for these issues, we proposed a flexible tactile sensor composed of a magnet, magnetic transducer and dual-layer elastomer, which consists of a magnetorheological and nonmagnetic elastomer sheet. In this study, we first investigated the sensitivity of the sensor, which was found to be high (approximately 161 mV/N with a signal-to-noise ratio of 42.2 dB); however, the sensor has a speed-dependent hysteresis in its sensor response curve. Then, we investigated the spatial response and observed the following results: (1) the sensor response was a distorted Mexican-hat-like bipolar shape, namely a negative response area was observed around the positive response area; (2) the negative response area disappeared when we used a compressible sponge sheet instead of the incompressible nonmagnetic elastomer. We concluded that the characteristic negative response in the Mexican-hat-like response is derived from the incompressibility of the nonmagnetic elastomer.

Antipsychotics promote neural differentiation of human iPS cell-derived neural stem cells.

We investigated the effects of antipsychotics on human induced pluripotent stem cell (hiPSC)-derived neural stem cell (NSC) differentiation. Induction of NSCs from hiPSCs was performed using PSC neural induction medium. Induced NSCs were subsequently cultured in neural differentiation medium containing antipsychotics. Cultured cells were subjected to neural differentiation marker analysis. As previously shown in rodent cells, antipsychotics promoted neural differentiation compared with vehicle treatment. Atypical antipsychotics appear to possess more differentiation induction potential than typical ones. Most NSCs do not express dopamine D2 receptor; however, our in vitro study indicates the clinical potential of antipsychotics could include effects independent of monoamine receptor expression in NSCs. Our study shows NSCs derived from hiPSCs provide opportunity to investigate the underlying direct effect of antipsychotics treatment on NSCs.

Infant's action skill dynamically modulates parental action demonstration in the dyadic interaction.

When interacting with infants, human adults modify their behaviours in an exaggerated manner. Previous studies have demonstrated that infant-directed modification affects the infant's behaviour. However, little is known about how infant-directed modification is elicited during infant-parent interaction. We investigated whether and how the infant's behaviour affects the mother's action during an interaction. We recorded three-dimensional information of cup movements while mothers demonstrated a cup-nesting task during interaction with their infants aged 11 to 13 months. Analyses revealed that spatial characteristics of the mother's task demonstration clearly changed depending on the infant's object manipulation. In particular, the variance in the distance that the cup was moved decreased after the infant's cup nesting and increased after the infant's task-irrelevant manipulation (e.g. cup banging). This pattern was not observed for mothers with 6- to 8-month-olds, who do not have the fine motor skill to perform the action. These results indicate that the infant's action skill dynamically affects the infant-directed action and suggest that the mother is sensitive to the infant's potential to learn a novel action. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=VNS2IHwLIhg&feature=youtu.be.

Broader autism phenotype in mothers predicts social responsiveness in young children with autism spectrum disorders.

AIMS: The aim of this study was to identify phenotypes in mothers and fathers that are specifically associated with disturbances in reciprocal social interactions and communication in their young children with autism spectrum disorder (ASD) in a Japanese sample. METHODS: Autistic traits in parents were evaluated using the Autism-spectrum Quotient (AQ), the Empathy Quotient (EQ) and the Systemizing Quotient (SQ) in 88 parents (44 mothers and corresponding fathers) of children with ASD and in 60 parents (30 mothers and corresponding fathers) of typically developing (TD) children. For the measurement of autistic traits in children, we employed the Social Responsiveness Scale (SRS). RESULTS: In two of the five AQ subscales (social skills and communication), the parents of ASD children scored significantly higher than did the parents of TD children, regardless of whether the parent was a mother or a father. In addition, in mothers of ASD children, there were significant positive correlations between two of the five AQ subscales (attention-switching and communication) and the SRS T-score in their children. CONCLUSIONS: This is the first study to demonstrate that the social skills and communication subscales in the AQ are more sensitive as autism traits in a Japanese sample and to demonstrate that some autistic traits in mothers are specifically associated with disturbances in the social ability of their young children with ASD, as measured by the SRS score. Further study is necessary to determine whether these results were caused by genetic or environmental factors.

Unusual developmental pattern of brain lateralization in young boys with autism spectrum disorder: Power analysis with child-sized magnetoencephalography.

AIMS: Autism spectrum disorder (ASD) is often described as comprising an unusual brain growth pattern and aberrant brain lateralization. Although it is important to study the pathophysiology of the developing ASD cortex, examples of physiological brain lateralization in young children with ASD have yet to be well examined. METHODS: Thirty-eight boys with ASD (aged 3-7 years) and 38 typically developing (TD) boys (aged 3-8 years) concentrated on video programs and their brain activities were measured non-invasively. We employed a customized child-sized magnetoencephalography system in which the sensors were located as close to the brain as possible for optimal recording in young children. To produce a credible laterality index of the brain oscillations, we defined two clusters of sensors corresponding to the right and left hemispheres. We focused on the laterality index ([left - right]/[left+right]) of the relative power band in seven frequency bands. RESULTS: The TD group displayed significantly rightward lateralized brain oscillations in the theta-1 frequency bands compared to the ASD group. CONCLUSIONS: This is the first study to demonstrate unusual brain lateralization of brain oscillations measured by magnetoencephalography in young children with ASD.

Reservoir computing using self-sustained oscillations in a locally connected neural network.

Understanding how the structural organization of neural networks influences their computational capabilities is of great interest to both machine learning and neuroscience communities. In our previous work, we introduced a novel learning system, called the reservoir of basal dynamics (reBASICS), which features a modular neural architecture (small-sized random neural networks) capable of reducing chaoticity of neural activity and of producing stable self-sustained limit cycle activities. The integration of these limit cycles is achieved by linear summation of their weights, and arbitrary time series are learned by modulating these weights. Despite its excellent learning performance, interpreting a modular structure of isolated small networks as a brain network has posed a significant challenge. Here, we investigate how local connectivity, a well-known characteristic of brain networks, contributes to reducing neural system chaoticity and generates self-sustained limit cycles based on empirical experiments. Moreover, we present the learning performance of the locally connected reBASICS in two tasks: a motor timing task and a learning task of the Lorenz time series. Although its performance was inferior to that of modular reBASICS, locally connected reBASICS could learn a time series of tens of seconds while the time constant of neural units was ten milliseconds. This work indicates that the locality of connectivity in neural networks may contribute to generation of stable self-sustained oscillations to learn arbitrary long-term time series, as well as the economy of wiring cost.

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity.

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

Learning long-term motor timing/patterns on an orthogonal basis in random neural networks.

The ability of the brain to generate complex spatiotemporal patterns with specific timings is essential for motor learning and temporal processing. An approach that can model this function, using the spontaneous activity of a random neural network (RNN), is associated with orbital instability. We propose a simple system that learns an arbitrary time series as the linear sum of stable trajectories produced by several small network modules. New finding in computer experiments is that the trajectories of the module outputs are orthogonal to each other. They created a dynamic orthogonal basis acquiring a high representational capacity, which enabled the system to learn the timing of extremely long intervals, such as tens of seconds for a millisecond computation unit, and also the complex time series of Lorenz attractors. This self-sustained system satisfies the stability and orthogonality requirements and thus provides a new neurocomputing framework and perspective for the neural mechanisms of motor learning.

Anthropomorphism-based causal and responsibility attributions to robots.

People tend to expect mental capabilities in a robot based on anthropomorphism and often attribute the cause and responsibility for a failure in human-robot interactions to the robot. This study investigated the relationship between mind perception, a psychological scale of anthropomorphism, and attribution of the cause and responsibility in human-robot interactions. Participants played a repeated noncooperative game with a human, robot, or computer agent, where their monetary rewards depended on the outcome. They completed questionnaires on mind perception regarding the agent and whether the participant's own or the agent's decisions resulted in the unexpectedly small reward. We extracted two factors of Experience (capacity to sense and feel) and Agency (capacity to plan and act) from the mind perception scores. Then, correlation and structural equation modeling (SEM) approaches were used to analyze the data. The findings showed that mind perception influenced attribution processes differently for each agent type. In the human condition, decreased Agency score during the game led to greater causal attribution to the human agent, consequently also increasing the degree of responsibility attribution to the human agent. In the robot condition, the post-game Agency score decreased the degree of causal attribution to the robot, and the post-game Experience score increased the degree of responsibility to the robot. These relationships were not observed in the computer condition. The study highlights the importance of considering mind perception in designing appropriate causal and responsibility attribution in human-robot interactions and developing socially acceptable robots.

Reduced gamma oscillation during visual processing of the mother's face in children with autism spectrum disorder: A pilot study.

Aim: This study aimed to investigate gamma oscillations related to face processing of children with autism spectrum disorders and typically developed children using magnetoencephalography. Methods: We developed stimuli that included naturalistic real-time eye-gaze situations between participants and their mothers. Eighteen young children with autism spectrum disorders (62-97 months) and 24 typically developed children (61-79 months) were included. The magnetoencephalography data were analyzed in the bilateral banks of the superior temporal sulcus, fusiform gyrus, and pericalcarine cortex for frequency ranges 30-59 and 61-90 Hz. The gamma oscillation normalized values were calculated to compare the face condition (children gazing at mother's face) and control measurements (baseline) using the following formula: (face - control)/(face + control). Results: The results revealed significant differences in gamma oscillation normalized values in the low gamma band (30-59 Hz) in the right banks of the superior temporal sulcus, right fusiform gyrus, and right pericalcarine cortex between children with autism spectrum disorders and typically developed children. Furthermore, there were significant differences in gamma oscillation normalized values in the high gamma band (61-90 Hz) in the right banks of the superior temporal sulcus, bilateral fusiform gyrus, and bilateral pericalcarine cortex between the groups. Conclusion: This report is the first magnetoencephalography study revealing atypical face processing in young children with autism spectrum disorders using relevant stimuli between participants and their mothers. Our naturalistic paradigm provides a useful assessment of social communication traits and a valuable insight into the underlying neural mechanisms in children with autism spectrum disorders.

Generation of myometrium-specific Bmal1 knockout mice for parturition analysis.

Human and rodent studies indicate a role for circadian rhythmicity and associated clock gene expression in supporting normal parturition. The importance of clock gene expression in tissues besides the suprachiasmatic nucleus is emerging. Here, a Bmal1 conditional knockout mouse line and a novel Cre transgenic mouse line were used to examine the role of myometrial Bmal1 in parturition. Ninety-two percent (22/24) of control females but only 64% (14/22) of females with disrupted myometrial Bmal1 completed parturition during the expected time window of 5p.m. on Day 19 through to 9a.m. on Day 19.5 of gestation. However, neither serum progesterone levels nor uterine transcript expression of the contractile-associated proteins Connexin43 and Oxytocin receptor differed between females with disrupted myometrial Bmal1 and controls during late gestation. The data indicate a role for myometrial Bmal1 in maintaining normal time of day of parturition.

Compensated Integrated Gradients for Reliable Explanation of Electroencephalogram Signal Classification.

The integrated gradients (IG) method is widely used to evaluate the extent to which each input feature contributes to the classification using a deep learning model because it theoretically satisfies the desired properties to fairly attribute the contributions to the classification. However, this approach requires an appropriate baseline to do so. In this study, we propose a compensated IG method that does not require a baseline, which compensates the contributions calculated using the IG method at an arbitrary baseline by using an example of the Shapley sampling value. We prove that the proposed approach can compute the contributions to the classification results reliably if the processes of each input feature in a classifier are independent of one another and the parameterization of each process is identical, as in shared weights in convolutional neural networks. Using three datasets on electroencephalogram recordings, we experimentally demonstrate that the contributions obtained by the proposed compensated IG method are more reliable than those obtained using the original IG method and that its computational complexity is much lower than that of the Shapley sampling method.

Hyper-Adaptation in the Human Brain: Functional and Structural Changes in the Foot Section of the Primary Motor Cortex in a Top Wheelchair Racing Paralympian.

The human brain has the capacity to drastically alter its somatotopic representations in response to congenital or acquired limb deficiencies and dysfunctions. The main purpose of the present study was to elucidate such extreme adaptability in the brain of an active top wheelchair racing Paralympian (participant P1) who has congenital paraplegia (dysfunction of bilateral lower limbs). Participant P1 has undergone long-term wheelchair racing training using bilateral upper limbs and has won a total of 19 medals in six consecutive summer Paralympic games as of 2021. We examined the functional and structural changes in the foot section of the primary motor cortex (M1) in participant P1 as compared to able-bodied control participants. We also examined the functional and structural changes in three other individuals (participants P2, P3, and P4) with acquired paraplegia, who also had long-term non-use period of the lower limbs and had undergone long-term training for wheelchair sports (but not top athletes at the level of participant P1). We measured brain activity in all the participants using functional magnetic resonance imaging (MRI) when bimanual wrist extension-flexion movement was performed, and the structural MRI images were collected. Compared to 37 control participants, participant P1 showed significantly greater activity in the M1 foot section during the bimanual task, and significant local GM expansion in this section. Significantly greater activity in the M1 foot section was also observed in participant P4, but not in P2 and P3, and the significant local GM expansion was observed in participant P2, but not in P3 and P4. Thus, functional or structural change was observed in an acquired paraplegic participant, but was not observed in all the paraplegic participants. The functional and structural changes typically observed in participant P1 may represent extreme adaptability of the human brain. We discuss the results in terms of a new idea of hyper-adaptation.

Imitation and mirror systems in robots through Deep Modality Blending Networks.

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.