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.

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.

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.

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.

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.

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.

Atypical Auditory Perception Caused by Environmental Stimuli in Autism Spectrum Disorder: A Systematic Approach to the Evaluation of Self-Reports.

Recent studies have revealed that atypical sensory perception is common in individuals with autism spectrum disorder (ASD) and is considered a potential cause of social difficulties. Self-reports by individuals with ASD have provided great insights into atypical perception from the first-person point of view and indicated its dependence on the environment. This study aimed to investigate the patterns and environmental causes of atypical auditory perception in individuals with ASD. Qualitative data from subject reports are inappropriate for statistical analysis, and reporting subjective sensory experiences is not easy for every individual. To cope with such challenges, we employed audio signal processing methods to simulate the potential patterns of atypical auditory perception. The participants in our experiment were able to select and adjust the strength of the processing methods to manipulate the sounds in the videos to match their experiences. Thus, the strength of atypical perception was recorded quantitatively and then analyzed to assess its correlation with the audio-visual stimuli contained in the videos the participants observed. In total, 22 participants with ASD and 22 typically developed (TD) participants were recruited for the experiment. The results revealed several common patterns of atypical auditory perception: Louder sounds perceived in a quiet environment, noise perception induced by intense and unsteady audio-visual stimuli, and echo perception correlated with movement and variation in sound level. The ASD group reported atypical perceptions more frequently than the control group. However, similar environmental causes were shared by the ASD and TD groups. The results help us infer the potential neural and physiological mechanisms of sensory processing in ASD.

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.

Bimanual digit training improves right-hand dexterity in older adults by reactivating declined ipsilateral motor-cortical inhibition.

Improving deteriorated sensorimotor functions in older individuals is a social necessity in a super-aging society. Previous studies suggested that the declined interhemispheric sensorimotor inhibition observed in older adults is associated with their deteriorated hand/finger dexterity. Here, we examined whether bimanual digit exercises, which can train the interhemispheric inhibitory system, improve deteriorated hand/finger dexterity in older adults. Forty-eight healthy, right-handed, older adults (65-78 years old) were divided into two groups, i.e., the bimanual (BM) digit training and right-hand (RH) training groups, and intensive daily training was performed for 2 months. Before and after the training, we evaluated individual right hand/finger dexterity using a peg task, and the individual state of interhemispheric sensorimotor inhibition by analyzing ipsilateral sensorimotor deactivation via functional magnetic resonance imaging when participants experienced a kinesthetic illusory movement of the right-hand without performing any motor tasks. Before training, the degree of reduction/loss of ipsilateral motor-cortical deactivation was associated with dexterity deterioration. After training, the dexterity improved only in the BM group, and the dexterity improvement was correlated with reduction in ipsilateral motor-cortical activity. The capability of the brain to inhibit ipsilateral motor-cortical activity during a simple right-hand sensory-motor task is tightly related to right-hand dexterity in older adults.

Existence of Interhemispheric Inhibition between Foot Sections of Human Primary Motor Cortices: Evidence from Negative Blood Oxygenation-Level Dependent Signal.

Interhemispheric inhibition (IHI) between the left and right primary motor cortices (M1) plays an important role when people perform an isolated unilateral limb movement. Moreover, negative blood oxygenation-level dependent signal (deactivation) obtained from the M1 ipsilateral to the limb could be a surrogate IHI marker. Studies have reported deactivation in the hand section of the ipsilateral M1 during simple unilateral hand movement. However, deactivation in the foot section during unilateral foot movement has not been reported. Therefore, IHI between the foot sections of the bilateral M1s has been considered very weak or absent. Thirty-seven healthy adults performed active control of the right foot and also passively received vibration to the tendon of the tibialis anterior muscle of the right foot, which activates the foot section of the contralateral M1, with brain activity being examined through functional magnetic resonance imaging. The vibration and active tasks significantly and non-significantly, respectively, deactivated the foot section of the ipsilateral M1, with a corresponding 86% and 60% of the participants showing decreased activity. Thus, there could be IHI between the foot sections of the bilateral M1s. Further, our findings demonstrate between-task differences and similarities in cross-somatotopic deactivation.

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.

Gray-Matter Expansion of Social Brain Networks in Individuals High in Public Self-Consciousness.

Self-consciousness is a personality trait associated with an individual's concern regarding observable (public) and unobservable (private) aspects of self. Prompted by previous functional magnetic resonance imaging (MRI) studies, we examined possible gray-matter expansions in emotion-related and default mode networks in individuals with higher public or private self-consciousness. One hundred healthy young adults answered the Japanese version of the Self-Consciousness Scale (SCS) questionnaire and underwent structural MRI. A voxel-based morphometry analysis revealed that individuals scoring higher on the public SCS showed expansions of gray matter in the emotion-related regions of the cingulate and insular cortices and in the default mode network of the precuneus and medial prefrontal cortex. In addition, these gray-matter expansions were particularly related to the trait of "concern about being evaluated by others", which was one of the subfactors constituting public self-consciousness. Conversely, no relationship was observed between gray-matter volume in any brain regions and the private SCS scores. This is the first study showing that the personal trait of concern regarding public aspects of the self may cause long-term substantial structural changes in social brain networks.

Comparison Between the Facial Flow Lines of Androids and Humans.

The behavior of an android robot face is difficult to predict because of the complicated interactions between many and various attributes (size, weight, and shape) of system components. Therefore, the system behavior should be analyzed after these components are assembled to improve their performance. In this study, the three-dimensional displacement distributions for the facial surfaces of two android robots were measured for the analysis. The faces of three adult males were also analyzed for comparison. The visualized displacement distributions indicated that the androids lacked two main deformation features observed in the human upper face: curved flow lines and surface undulation, where the upstream areas of the flow lines elevate. These features potentially characterize the human-likeness. These findings suggest that innovative composite motion mechanisms to control both the flow lines and surface undulations are required to develop advanced androids capable of exhibiting more realistic facial expressions. Our comparative approach between androids and humans will improve androids' impressions in future real-life application scenes, e.g., receptionists in hotels and banks, and clerks in shops.

Measurement of the concentration of serum soluble interleukin-2 receptor alpha chain in dogs with lymphoma.

Soluble interleukin-2 receptor (sIL-2r) is released directly from the surface of lymphocytes expressing interleukin-2 receptor alpha chain (CD25), and its serum concentration has been found to reflect the prognosis of human lymphoproliferative malignancies. In this study, we demonstrated the presence of sIL-2r in canine serum and developed a specific sandwich enzyme-linked immunosorbent assay (ELISA) to quantify the concentration of canine serum sIL-2r. In the immunoprecipitation (IP) assay, CD25 protein weighing approximately 45 kDa was detected in canine serum, smaller than the membrane-bound CD25 (approximately 55 kDa). To measure the concentration of serum sIL-2r in dogs, an ELISA system was developed. Serum sIL-2r levels were significantly higher in dogs with multicentric high-grade B-cell lymphoma before therapy than that in healthy dogs. Serum sIL-2r concentration was also found to be elevated in a proportion of dogs with other types of lymphoma. Changes in serum sIL-2r levels generally paralleled the changes in mass and lymph node size in dogs with high-grade B-cell lymphoma. This study demonstrated that serum sIL-2r level would be a marker to monitor tumour growth and regression in canine lymphoma.

Importance of the Primary Motor Cortex in Development of Human Hand/Finger Dexterity.

Hand/finger dexterity is well-developed in humans, and the primary motor cortex (M1) is believed to play a particularly important role in it. Here, we show that efficient recruitment of the contralateral M1 and neuronal inhibition of the ipsilateral M1 identified by simple hand motor and proprioceptive tasks are related to hand/finger dexterity and its ontogenetic development. We recruited healthy, right-handed children (n = 21, aged 8-11 years) and adults (n = 23, aged 20-26 years) and measured their brain activity using functional magnetic resonance imaging during active and passive right-hand extension-flexion tasks. We calculated individual active control-related activity (active-passive) to evaluate efficient brain activity recruitment and individual task-related deactivation (neuronal inhibition) during both tasks. Outside the scanner, participants performed 2 right-hand dexterous motor tasks, and we calculated the hand/finger dexterity index (HDI) based on their individual performance. Participants with a higher HDI exhibited less active control-related activity in the contralateral M1 defined by the active and passive tasks, independent of age. Only children with a higher HDI exhibited greater ipsilateral M1 deactivation identified by these tasks. The results imply that hand/finger dexterity can be predicted by recruitment and inhibition styles of the M1 during simple hand sensory-motor tasks.

Right-hemispheric Dominance in Self-body Recognition is Altered in Left-handed Individuals.

Visual self-face and proprioceptive postural recognition predominantly activate the right inferior frontoparietal cortices in human right-handers at the population level. In the present study, prompted by the finding that left-handedness may alter lateralized cortical organization for language, sensory-motor, and cognitive functions observed in right-handers, we investigated individual variations in right-dominant use of the cortices in 50 right-handers and 50 left-handers during self-body recognition (self-face and proprioceptive) tasks. We also investigated possible between-tasks differences in this right-dominant use, and possible atypical left-right reversed lateralization (right-dominance for language and left-dominance for self-body recognition) in left-handers. We measured brain activity using functional magnetic resonance imaging while participants performed a proprioceptive postural recognition task (experiencing illusory movements of the left and the right hands), a visual self-face recognition (self-other distinction) task, and a language (verb generation) task. To evaluate hemispheric dominance, we computed individual lateralization indices for the inferior frontoparietal activities in these tasks. Left-handedness altered the right-hemispheric dominance that was observed in the majority of right-handed participants in both self-body recognition tasks. In the left-handed group, during proprioceptive recognition, participants with right-lateralization, bilaterality, or left-lateralization were equally distributed, and during self-face recognition, right-lateralization was still observed, though the number of participants who demonstrated left-lateralization increased. Atypical left-right reversed lateralization was only observed in left-handed participants, but during both self-body recognition tasks. The present study provides novel and valuable knowledge about right-hemispheric dominance in self-body recognition affected by left-handedness. We discuss how functional lateralization of self-body recognition is shaped in human brain, in terms of handedness, language lateralization, and development.

Early pregnancy loss in 15-hydroxyprostaglandin dehydrogenase knockout (15-HPGD-/-) mice due to requirement for embryo 15-HPGD activity.

Prostaglandins (PGs) have critical signaling functions in a variety of processes including the establishment and maintenance of pregnancy, and the initiation of labor. Most PGs are non-enzymatically degraded, however, the two PGs most prominently implicated in the termination of pregnancy, including the initiation of labor, prostaglandin E2 (PGE2) and prostaglandin F2α (PGF2α), are enzymatically degraded by 15-hydroxyprostaglandin dehydrogenase (15-HPGD). The role of PG metabolism by 15-HPGD in the maintenance of pregnancy remains largely unknown, as direct functional studies are lacking. To test the hypothesis that 15-PGDH-mediated PG metabolism is essential for pregnancy maintenance and normal labor timing, we generated and analyzed pregnancy in 15-HPGD knockout mice (Hpgd-/-). We report here that pregnancies resulting from matings between 15-HPGD KO mice (Hpgd-/- X Hpgd-/-KO mating) are terminated at mid gestation due to a requirement for embryo derived 15-HPGD. Aside from altered implantation site spacing, pregnancies from KO matings look grossly and histologically normal at days post coitum (dpc) 6.5 and 7.5 of pregnancy. However, virtually all of these pregnancies are resorbed by dpc 8.5. This resorption is preceded by elevation of PGF2∝ but is not preceded by a decrease in circulating progesterone, suggesting that pregnancy loss is a local inflammatory phenomenon rather than a centrally mediated phenomena. This pregnancy loss can be temporarily deferred by indomethacin treatment, but treated pregnancies are not maintained to term and indomethacin treatment increases maternal mortality. We conclude that PG metabolism to inactive products by embryo derived 15-HPGD is essential for pregnancy maintenance in mice, and may serve a similar function during human pregnancy.

Developmental Changes in Task-Induced Brain Deactivation in Humans Revealed by a Motor Task.

Performing tasks activates relevant brain regions in adults while deactivating task-irrelevant regions. Here, using a well-controlled motor task, we explored how deactivation is shaped during typical human development and whether deactivation is related to task performance. Healthy right-handed children (8-11 years), adolescents (12-15 years), and young adults (20-24 years; 20 per group) underwent functional magnetic resonance imaging with their eyes closed while performing a repetitive button-press task with their right index finger in synchronization with a 1-Hz sound. Deactivation in the ipsilateral sensorimotor cortex (SM1), bilateral visual and auditory (cross-modal) areas, and bilateral default mode network (DMN) progressed with development. Specifically, ipsilateral SM1 and lateral occipital deactivation progressed prominently between childhood and adolescence, while medial occipital (including primary visual) and DMN deactivation progressed from adolescence to adulthood. In adults, greater cross-modal deactivation in the bilateral primary visual cortices was associated with higher button-press timing accuracy relative to the sound. The region-specific deactivation progression in a developmental period may underlie the gradual promotion of sensorimotor function segregation required in the task. Task-induced deactivation might have physiological significance regarding suppressed activity in task-irrelevant regions. Furthermore, cross-modal deactivation develops to benefit some aspects of task performance in adults.