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Rossion et al. (2020) review over a decade of work investigating the neural basis of unfamiliar face individuation (FI)—the brain's ability to distinguish unfamiliar face identity—using fast periodic visual stimulation (FPVS). Though FPVS measures rapid, automatic processing, its value for studying vision and face cognition could be increased by addressing three important aspects.

[Purpose] Hippotherapy is an unusual type of treatment and has been found to be effective for diseases of the musculoskeletal system and rehabilitation. Horseback riding simulator is used as a beneficial alternative to the real horse with utilizing an optical motion capture system and EEG. [Participants and Methods] The idea is to monitor body and brain behaviour of the professional rider and non-professional rider utilizing a horse simulator, using optical motion capture system to identify differences in pelvic region activity between professional and non-professional riders and EEG to investigate the brain effect of professional rider utilizing horseback riding simulator. [Results] For the monitoring body and brain behaviour of the professional rider and non-professional rider, two types of experiment were handled, the first experiment represents body behaviour and the second experiment represents brain behaviour. [Conclusion] The study shows, that inexperienced rider may make mistakes of pelvis movements that leads to the asymmetry in hip external rotation and back region. Also, the study of EEG provides that while horseback riding mostly frontal lobe is active, that refers to concentration, body movements and intelligence. Moreover, temporal and parietal lobes are highlighted that relates to sensor-motor cortex and moving which are needed during riding.

In the past, microorganisms were not considered to be particularly important in brain development and functioning. However, recent evidence shows the existence of a bidirectional, and possibly multidimensional relationship between the body microbiota and the brain. The microbiota influence brain behavior in health or disease, by utilizing endocrine, neurocrine and immunologic signaling pathways. Also, the chemical mediators involved range from known neurotransmitters to small peptide molecules. Here, we discuss the evidence that currently exists in experimental animals and/or humans in support of the existence of a relationship involving the skin/gut microbiome, the brain, and behavior; and the mechanisms involved in such interactions. The implications of such interactions for shifts in behaviors, and the pathogenesis of behavioral and neurodegenerative disorders are also discussed. Finally, the possible clinical applications of deliberate manipulations of the microbiota composition and density for the management or prevention of behavioral and neurodegenerative disorders is discussed.

PurposeUnderstanding the brain basis of language and cognitive outcomes is a major goal of aphasia research. Prior studies have not often considered the many ways that brain features can relate to behavioral outcomes or the mechanisms underlying these relationships. The purpose of this review article is to provide a new framework for understanding the ways that brain features may relate to language and cognitive outcomes from stroke.MethodBrain–behavior relationships that may be important for aphasia outcomes are organized into a taxonomy, including features of the lesion and features of brain tissue spared by the lesion. Features of spared brain tissue are categorized into those that change after stroke and those that do not. Features that change are further subdivided, and multiple mechanisms of brain change after stroke are discussed.ResultsFeatures of the stroke, including size, location, and white matter damage, relate to many behavioral outcomes and likely account for most of the variance in outcomes. Features of the spared brain tissue that are unchanged by stroke, such as prior ischemic disease in the white matter, contribute to outcomes. Many different neurobiological and behavioral mechanisms may drive changes in the brain after stroke in association with behavioral recovery. Changes primarily driven by neurobiology are likely to occur in brain regions with a systematic relationship to the stroke distribution. Changes primarily driven by behavior are likely to occur in brain networks related to the behavior driving the change.ConclusionsOrganizing the various hypothesized brain–behavior relationships according to this framework and considering the mechanisms that drive these relationships may help investigators develop specific experimental designs and more complete statistical models to explain language and cognitive abilities after stroke. Eight main recommendations for future research are provided.Presentation Videohttps://doi.org/10.23641/asha.10257578

Background Bullying has been shown to increase the risk of developing a psychotic disorder. To date, no studies have examined brain behavior relationships within the context of bullying victimization in clinical high-risk (CHR) youth, a group characterized by both gray and white matter abnormalities. The present study employed multimodal neuroimaging to examine possible neural mechanisms associated with bullying victimization. Methods CHR and healthy volunteers underwent clinical interviews, parent reports and MRI scans. Regions of interest (ROIs) were picked based on sensitivity to environmental stress, including hippocampal, amygdala, and orbitofrontal cortex (OFC) structural ROIs, and uncinate fasciculus white matter integrity. Results CHR individuals were more exposed to bullying victimization than healthy volunteers, and bullying was associated with depressive symptoms across the whole sample. CHR individuals exhibited smaller volumes in OFC, but not in other ROIs. Increased bullying exposure was associated with lower medial OFC volumes in CHR and HV groups independently. Results ought to be interpreted as preliminary, as they did not survive correction at the whole brain level. Discussion Bullying victimization may affect or be affected by volumetric OFC differences in both healthy and CHR individuals. However, given CHR showed greater exposure to bullying as well as underlying vulnerability (e.g. lower volumes), results also point to etiological clues and novel intervention targets, though future replication is needed in better powered samples.

Identifying the neural correlates of positive interactions between friendship dyads may provide insights into mechanisms associated with adolescent social development. Forty-eight 14- to 18-year old typically developing adolescents were video-recorded discussing a shared positive event with a close friend and subsequently viewed clips during an fMRI scan of that friend during the interaction and of an unfamiliar peer in a similar interaction. Adolescents also reported on their positive affect in daily life while with friends using ecological momentary assessment. We used multivariate repeated measures models to evaluate how positive affect with friends in the laboratory and in daily life was associated with neural response to friend and stranger positive and neutral clips. Adolescents who exhibited more positive affect when with friends in the laboratory showed less dorsolateral prefrontal cortex to friend positive clips. More positive affect when with friends in daily life was associated with less bilateral anterior insula response to friend positive clips, but greater left anterior insula response to stranger positive clips. Findings provide information on the role of lateral prefrontal cortex and anterior insula in enjoyment of friendships during adolescence.

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain–behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables and 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain–behavior provided by these approaches to the brain–behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain–behavior relationships, as well as the unique insight into brain–behavior relationships offered by this approach.

Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion-symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion-symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. However, very little work has been done to empirically compare the advantages and disadvantages of these two different methods. In the current study, we provide a needed systematic comparison of 5 ULSM and 8 MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested power and spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method’s spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.

Purpose: Studies of reactive and proactive modes of inhibitory control tend to show age-related declines and are accompanied by abnormalities in the prefrontal cortex. We explored which mode of inhibitory control would be more amenable to change and accrue greater benefits following engagement in a 4-week theater acting intervention in older adults. These gains were evaluated by performance on the AX-CPT task. We hypothesized that an increase in proactive control would relate to an increase in AY errors and a decrease in BX errors. In contrast, an increase in reactive control would be associated with a decrease in AY errors, no change in AY reaction time, and an increase in BX response time. Further, we posited that an increase in behavioral proactive control would accompany greater cue versus probe activity for previously identified regions in the prefrontal cortex. In contrast, an increase in behavioral reactive control would be accompanied by greater probe activation in these identified brain areas.Materials and Methods: The participants were 179 community-dwelling adults aged 60–89 years who were on average, college-educated. Participants were pseudo-randomly assigned to either an active-experiencing acting intervention condition (n = 93) or the active control condition (n = 86); participant assignment was subject to time of enrollment. Participants in both groups were trained by theater-actor researchers with expertise in acting interventions. In contrast to the active control participants who attended a course on theater acting, the acting-intervention group was required to consistently deploy proactive and reactive control mechanisms. Both groups met two times/week for 75-min for 4 weeks. Participant brain-behavioral performance on the AX-CPT task was evaluated prior to and after this four-week period.Results: No intervention effects were found in favor of proactive control. Behavioral evidence in favor of reactive control was weak. Brain-related benefits to reactive control was illustrated by greater probe-activation in Brodmann areas 6 and 8, relative to controls and pre-intervention.Conclusion: We found some evidence for improvements in reactive control via brain measures, attributed to engagement in the acting intervention.

Recently, various researches have revealed the importance of the investigations performed for evaluating mechanical properties and damages of the brain tissues while dealing with the production of surgical ligaments and helmets. Therefore, it is vital to study the structure of the brain both experimentally and numerically. By experimental tests, despite being costly, it is almost impossible to establish stress distribution in micro scale, which causes injury. Micromechanical predictions are effective ways to assess brain behavior. They can be applied to compensate for some experimental test limitations. In this work, a numerical study of the axonal injury in different heterogeneous porcine brain parts with different axon distributions under quasi-static loading is provided. In order to produce a heterogeneous structure, axons are distributed in regular, semi-regular, and irregular patterns inside the representative volume element. To accurately examine the brain tissue time-dependent behavior, a visco-hyperelastic constitutive model is developed. Also, axonal damage is studied under different conditions by applying different levels of load and rate. Because of geometrical complexities, a self-consistent method was applied to study the damage in higher volume fractions of the axon. The results reveal that the regions of the brain enjoying a regular axon distribution would have higher strength. In addition, among the two influential load and loading rate parameters, the brain tissue in all regions shows more sensitivity toward the applying load.

Epilepsy is a neurological disease that’s characterized by perennial seizures. In this neurological condition the transient electrical phenomenon within the brain occurs that produces an amendment in sensation, awareness, and behavior of an individuals that leads to risk. To understand the brain behavior Electroencephalogram (EEG) signals are used in six different sub-bands viz. Alpha (\(\alpha \)), Beta (\(\beta \)), Gamma1 (\(\gamma \)1), Gamma2 (\(\gamma \)2), Theta (\(\theta \)) and Delta (\(\delta \)). The Brainstorm software is used for visualizing, analyzing and filtration of EEG signals in each sub-band. This paper deals with the extraction of the various features in each sub-bands and different Machine Learning classifiers were used on these extracted features for comparative analysis in terms of Accuracy, prediction Speed and training time in MatLab. The various statistical and spectral methods are applied on EEG signals to obtained the distinct features in each sub-band. After compared these classifiers on the performance parameters.we have 8 best classifier trained Models that were utilized in checking effectiveness to clearly distinguish between Epileptic and Normal cases.

Increasing evidence demonstrates that optogenetics contributes to the regulation of brain behavior, cognition, and physiology, particularly during myelination, potentially allowing for the bidirectional modulation of specific cell lines with spatiotemporal accuracy. However, the type of cell to be targeted, namely, glia vs neurons, and the degree to which optogenetically induced cell activity can regulate myelination during the development of the peripheral nervous system (PNS) are still underexplored. Herein, we report the comparison of optogenetic stimulation (OS) of Schwann cells (SCs) and motor neurons (MNs) for activation of myelination in the PNS. Capitalizing on these optogenetic tools, we confirmed that the formation of the myelin sheath was initially promoted more by OS of calcium translocating channelrhodopsin (CatCh)-transfected SCs than by OS of transfected MNs at 7 days

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Functional outcomes (e.g., subjective percepts, emotions, memory retrievals, decisions, etc…) are partly determined by external stimuli and/or cues. But they may also be strongly influenced by (trial-by-trial) uncontrolled variations in brain responses to incoming information. In turn, this variability provides information regarding how stimuli and/or cues are processed by the brain to shape behavioral responses. This can be exploited by brain-behavior mediation analysis to make specific claims regarding the contribution of brain regions to functionally-relevant input-output transformations. In this work, we address four challenges of this type of approach, when applied in the context of mass-univariate fMRI data analysis: (i) we quantify the specificity and sensitivity profiles of different variants of mediation statistical tests, (ii) we evaluate their robustness to hemo-dynamic and other confounds, (iii) we identify the sorts of brain mediators that one can expect to detect, and (iv) we disclose possible interpretational issues and address them using complementary information-theoretic approaches. En passant, we propose a computationally efficient algorithmic implementation of the approach that is amenable to whole-brain exploratory analysis. We also demonstrate the strengths and weaknesses of brain-behavior mediation analysis in the context of an fMRI study of decision under risk. Finally, we discuss the limitations and possible extensions of the approach.

A decade ago, Religion, Brain & Behavior (RBB) was still a dream in the minds of its founding editors, neuroscientist Patrick McNamara, anthropologist Richard Sosis, and philosopher of religion Wes...

The human population is exposed to literally hundreds of industrial chemicals. Studies show that, on average, there are well over 100 industrial chemicals in cord blood samples taken from babies born in the United States. This observation may be disregarded if it were not for solid—and growing—evidence that many of these chemicals interfere with hormone systems that play important roles in human brain development. For example, thyroid hormone is known to be essential for normal brain development and the human brain is much more sensitive to thyroid hormone insufficiency than believed 50 years ago. Chemicals such as perchlorate, polychlorinated biphenyls, and polybrominated flame retardants are associated with cognitive deficits in humans, and the experimental literature reveals a complex interaction with the thyroid system that can account for these effects. Likewise, estrogenic chemicals like bisphenol A (BPA) can affect the development of many organ systems, including sexually dimorphic regions of the brain. There is now convincing evidence that even slight perturbations in hormonal systems caused by endocrine-disrupting chemicals can impact brain development, reducing cognitive function and gender-specific behaviors throughout the remainder of life. The consequences of such chronic exposures at low doses typical of human exposures had, until relatively recently, not been appreciated due to safety testing strategies that are designed to identify overt toxicities, not endocrine disruption and its consequences.

Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain–behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel–Berlin Risk Study (N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention.

The stroke patients perceive emotions differently with normal people due to emotional disturbances, the emotional impairment of the stroke patients can be effectively analyzed using the EEG signal. The EEG signal has been known as non-linear and the neuronal oscillation under different mental states can be observed by non-linear method. The non-linear analysis of different emotional states in the EEG signal was performed by using hurst exponent (HURST). In this study, the long-range temporal correlation (LRTC) was examined in the emotional EEG signal of stroke patients and normal control subjects. The estimation of the HURST was more statistically significant in normal group than the stroke groups. In this study, the statistical test on the HURST has shown a more significant different among the emotional states of normal subject compared to the stroke patients. Particularly, it was also found that the gamma frequency band in the emotional EEG has shown more statistically significant among the different emotional states.

Neurotransmitter sensing is essential in biomedical research to understand brain behavior, with Optical sensing useful for passive measurements. For instance, visible-light spectrophotometry can be used to detect and measure neurotransmitter concentrations in liquid. An important part of the process is the prediction of molecule concentration from the measured data. This work describes an FPGA-based system to measure neurotransmitter concentration in a multi-sensor platform, using a visible-light optical spectrometer and a simple regression predictor. The design required very few hardware resources and it was tested on dopamine data with excellent results.

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain–behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain–behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain–behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain–behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain–behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain–behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Perceptual decision making - the process of detecting and categorizing information - has been studied extensively over the last two decades. In this study, we aim to bridge the gap between neural and behavioral representations of the perceptual decision-making process. The neural characterization of decision-making was investigated by evaluating the duration and neural signature of the information processing stages. We further evaluated the processing stages of the decision-making process at the behavioral level by estimating the drift rate and non-decision time parameters. We asked whether the neural and behavioral characterizations of the decision-making process provided consistent results under different stimulus coherency levels and spatial attention. Our statistical analysis revealed that, at both representational levels, decision-making was affected more by the coherency factor. We further found that among different information processing stages, the decision stage had the highest role in the performance of the decision-making process. Such that, the shorter decision stage duration at the neural level and higher drift rate at the behavioral level lead to faster decision-making. Through our consistent neural and behavioral results, we have shown that the decision-making components at these two representational levels were significantly associated. Moreover, the neural signature of the processing stages gave information about the regions that contributed more to the decision-making process. Our overall results demonstrate that uncovering the cognitive processing stages provided more insights into the decision-making process.

Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. In the current study, we provide a much‐needed systematic comparison of several ULSM and MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested the spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method's spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results., In the current study, we conducted the first comprehensive, empirical comparison of several univariate and multivariate lesion symptom mapping (LSM) methods using both synthetic and real behavioral data to identify the potential strengths and weaknesses of both approaches. Cumulatively, our analyses indicated that both univariate and multivariate methods can be equally robust in locating brain–behavior relationships, depending on the design of the study, the research question being asked, and with proper spatial metrics. The results provide crucial insights into the accuracy of different LSM methods and their susceptibility to artifact, providing a first of its kind data‐driven navigational guide for users of LSM analyses.

The study of the evolution of brain-behavior relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although the brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g., mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of progresses in neuroimaging and cognitive neuroscience, we propose a twofold model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems as well as of behavioral repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e., players), while the local trajectory shapes the behavioral repertoires (i.e., stochastic payoffs).

The data presented in this article is related to the research article entitled “Age-related Differences in BOLD Modulation to Cognitive Control Costs in a Multitasking Paradigm: Global Switch, Local Switch, and Compatibility-Switch Costs” (Nashiro et al., 2018) [1] . This article describes age-related differences in accuracies for various cognitive costs incurred during task switching across three different age-cohorts: younger (18–35 years), younger-old (50–64 years) and older-old (65–80 years). The cognitive costs evaluated were global switch costs (GSC), local switch costs (LSC) and compatibility switch costs (CSC). Whole brain analyses were conducted to determine the brain regions sensitive to these cognitive costs, irrespective of age. Furthermore, age-related differences in brain-behavior relationships were evaluated by correlating activations from these regions with global switch costs, indexed by both response times and accuracies, for younger and older adults separately. Activations of age-sensitive regions during the task, where younger adults activated more than the combined groups of older adults, were also correlated with response times and accuracies to determine age-related differences in brain-behavior relationships of these under-recruited brain regions by older adults.

A great challenge in the field of neurocomputing is to mimic the brain behavior by implementing artificial synapses and neurons directly in hardware. This work shows that a Leaky Integrate and Fire (LIF) artificial neuron can be realized with a two-terminal device made of Mott insulator thin films. Polycrystalline thin films of the well-known Mott insulator oxide (V0.95Cr0.05)2O3 were deposited by magnetron sputtering and patterned with micron-scale TiN electrodes. These devices exhibit a volatile resistive switching and a remarkable LIF behavior under a train of pulses suggesting that LIF artificial neurons may be realized from (V0.95Cr0.05)2O3 thin films.

BackgroundChildhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD.MethodsWe first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the individual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype.ResultsAdults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain–symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD.ConclusionsOverall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain–symptom mediators.

Our goal for this paper is to address changes in motor patterns that occur early in life. To do this, we begin by sharing first a brief set of exemplar patterns of movement that emerge prenatally and during the first year postnatally. We couch these descriptions in the hypotheses proposed to explain what has been observed, and emphasize, as well, the context in which they appear. We follow with some experimental studies developmental scientists have used to test these explanations. Subsequently, we address the brain-behavior collaboration that unfolds and supports skill acquisition across early development. We provide data to show that recent advances in brain-imaging technology enable researchers to monitor cortical activity as infants explore and learn functional skills in real time and over developmental time. This opens a new frontier to the scientific study of the early development of neuromotor control and can enhance both our basic science knowledge and our efforts to optimize positive clinical outcomes.