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Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework – the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework’s ability to reliably capture brain-behavior relationships across 3 cognitive scores – general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

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Adversity has been linked to accelerated maturation. Molar eruption is a simple and scalable way to identify early maturation, but its developmental correlates remain unexplored. Thus, we examined whether accelerated maturation as indexed by molar eruption is associated with children’s mental health or cognitive skills.

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Attention-deficit/hyperactivity disorder (ADHD) is a prevalent, impairing, and highly heritable condition typically diagnosed in middle childhood. However, it is now recognized that symptoms emerge much earlier in development. Research focused on understanding-using multiple units of analysis-the cascade of early-life (i.e., prenatal-infant-toddler) developmental changes that will later emerge as ADHD has the potential to transform early identification, prevention, and intervention. To this end, we introduce the recently established Early ADHD Consortium, an international network of investigators engaged in prospective, longitudinal studies of risk for ADHD beginning early in life, conducted within a developmental framework, and which incorporate multimethod approaches. This network seeks to harmonize measures and methodological approaches to increase the potential for data sharing and subsequent impact.

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The prevalence of opioid use disorder (OUD) during pregnancy has quadrupled in recent years and widely varies geographically in the US. However, few studies have examined which environmental factors are associated with OUD during pregnancy. We conducted an external exposome-wide association study (ExWAS) to investigate the associations between external environmental factors and OUD diagnosed during pregnancy. Data were obtained from a unique, statewide database in Florida comprising linked individual-level birth and electronic health records. A total of 255,228 pregnancies with conception dates between 2012 and 2016 were included. We examined 82 exposome measures characterizing seven aspects of the built and social environment and spatiotemporally linked them to each individual record. A two-phase procedure was utilized for the external ExWAS. In Phase 1, we randomly divided the data into a discovery set (50 %) and a replication set (50 %). Associations between exposome measures (normalized and standardized) and OUD initially diagnosed during pregnancy were examined using logistic regression. A total of 15 variables were significant in both the discovery and replication sets. In Phase 2, multivariable logistic regression was used to fit all variables selected from Phase 1. Measures of walkability (the national walkability index, OR: 1.23, 95 % CI: 1.17, 1.29), vacant land (the percent vacant land for 36 months or longer, OR: 1.06, 95 % CI: 1.00, 1.12) and food access (the percentage of low food access population that are seniors at 1/2 mile, OR: 1.47, 95 % CI: 1.38, 1.57) were each associated with diagnosis of OUD during pregnancy. This is the first external ExWAS of OUD during pregnancy, and the results suggest that low food access, high walkability, and high vacant land in under-resourced neighborhoods are associated with diagnosis of OUD during pregnancy. These findings could help develop complementary tools for universal screening for substance use and provide direction for future studies.

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Psychiatric disorders are complex, often emerging from multiple atypical processes within specified domains over the course of development. Characterizing the development of the neural circuits supporting these domains may help break down the components of complex disorders and reveal variations in functioning associated with psychiatric risk. This review highlights the current and potential role of infant task-based functional magnetic resonance imaging (fMRI) in elucidating the developmental neurobiology of psychiatric disorders. Task-fMRI measures evoked brain activity in response to specific stimuli through changes in the blood oxygen level-dependent signal. First, we review extant studies using task fMRI from birth through the first few years of life and synthesize current evidence for when, where, and how different neural computations are performed across the infant brain. Neural circuits for sensory perception, the perception of abstract categories, and the detection of statistical regularities have been characterized with task fMRI in infants, providing developmental context for identifying and interpreting variation in the functioning of neural circuits related to psychiatric risk. Next, we discuss studies that specifically examine variation in the functioning of these neural circuits during infancy in relation to risk for psychiatric disorders. These studies reveal when maturation of specific neural circuits diverges, the influence of environmental risk factors, and the potential utility for task fMRI to facilitate early treatment or prevention of later psychiatric problems. Finally, we provide considerations for future infant task-fMRI studies with the potential to advance understanding of both functioning of neural circuits during infancy and subsequent risk for psychiatric disorders.

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Prenatal cocaine exposure (PCE) has been linked to specific cognitive deficits and behavioral outcomes through early adolescence but there is little information on adult outcomes nor on the relationship of environmental interventions, such as foster/adoptive care, to outcomes.

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Despite concerns about negative neurocognitive effects of in utero substance exposure on child and brain development, research in this area is limited. This study gathered perspectives of persons with lived experience of substance use (eg, alcohol, prescription and illicit opioids, and other illicit substances) during a previous pregnancy to determine facilitators and barriers to research engagement in this vulnerable population. We conducted structured, in-depth, individual interviews and 2 focus groups of adult persons with lived experience of substance use during a previous pregnancy. Questions were developed by clinical, research, bioethics, and legal experts, with input from diverse stakeholders. They inquired about facilitators and barriers to research recruitment and retention, especially in long-term studies, with attention to bio-sample and neuroimaging data collection and legal issues. Interviews and focus groups were audio-recorded, transcribed, and analyzed using inductive coding qualitative analysis methods. Ten participants completed in-depth interviews and 7 participated in focus groups. Three main themes emerged as potential barriers to research engagement: shame of using drugs while pregnant, fear of punitive action, and mistrust of health care and research professionals. Facilitative factors included trustworthiness, compassion, and a nonjudgmental attitude among research personnel. Inclusion of gender-concordant recovery peer support specialists as research team members was the most frequently identified facilitator important for helping participants reduce fears and bolster trust in research personnel. In this qualitative study, persons with lived experience of substance use during a previous pregnancy identified factors critical for engaging this population in research, emphasizing the involvement of peer support specialists as research team members.

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The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

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Fetal motor behavior is an important clinical indicator of healthy development. However, our understanding of associations between fetal behavior and fetal brain development is limited. To fill this gap, this study introduced an approach to automatically and objectively classify long durations of fetal movement from a continuous four-dimensional functional magnetic resonance imaging (fMRI) data set, and paired behavior features with brain activity indicated by the fMRI time series. Twelve-minute fMRI scans were conducted in 120 normal fetuses. Postnatal motor function was evaluated at 7 and 36 months age. Fetal motor behavior was quantified by calculating the frame-wise displacement (FD) of fetal brains extracted by a deep-learning model along the whole time series. Analyzing only low motion data, we characterized the recurring coactivation patterns (CAPs) of the supplementary motor area (SMA). Results showed reduced motor activity with advancing gestational age (GA), likely due in part to loss of space (r = -.51, p < .001). Evaluation of individual variation in motor movement revealed a negative association between movement and the occurrence of coactivations within the left parietotemporal network, controlling for age and sex (p = .003). Further, we found that the occurrence of coactivations between the SMA to posterior brain regions, including visual cortex, was prospectively associated with postnatal motor function at 7 months (r = .43, p = .03). This is the first study to pair fetal movement and fMRI, highlighting potential for comparisons of fetal behavior and neural network development to enhance our understanding of fetal brain organization.

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Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

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Adverse childhood experiences (ACEs) negatively impact health outcomes later in life, in a dose-dependent relationship; however, little is known about the impact of the individual ACE categories and subjective cognitive decline (SCD) later in life.

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Differences in reward processing have been associated with numerous psychiatric disorders, including autism and attention-deficit/hyperactivity disorder (ADHD). Many attempts to understand reward processing characterize differences in clinical populations after disorder onset; however, divergence may begin much earlier. In fact, the typical developmental progression of reward processing in infancy and early childhood is poorly understood. We re-conceptualize classic infant developmental constructs such as preferential looking into a Six-Component Developmental Model of Reward Processing: an infant- and young child-focused framework to guide research and assessment of reward processing across development.

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Understanding of human brain development has advanced rapidly as the field of developmental cognitive neuroscience (DCN) has matured into an established scientific discipline. Despite substantial progress, DCN lags behind other related disciplines in terms of diverse representation, standardized reporting requirements for socio-demographic characteristics of participants in pediatric neuroimaging studies, and use of intentional sampling strategies to more accurately represent the socio-demographic, ethnic, and racial composition of the populations from which participants are sampled. Additional efforts are needed to shift DCN towards a more inclusive field that facilitates the study of individual differences across a variety of cultural and contextual experiences. In this commentary, we outline and discuss barriers within our current scientific practice (e.g., research methods) and beliefs (i.e., what constitutes good science, good scientists, and good research questions) that contribute to under-representation and limited diversity within pediatric neuroimaging studies and propose strategies to overcome those barriers. We discuss strategies to address barriers at intrapersonal, interpersonal, community, systemic, and structural levels. Highlighting strength-based models of inclusion and recognition of the value of diversity in DCN research, along with acknowledgement of the support needed to diversify the field is critical for advancing understanding of neurodevelopment and reducing health inequities.

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Maximizing vaccine uptake is critical for the optimal implementation of COVID-19 immunization programs. Indicators of socioeconomic status (SES) have been associated with variations in COVID-19 vaccine uptake in the United States. The present study investigates COVID-19 vaccination behavior in individuals with history of COVID-19 infection, with the specific goal of understanding whether experiences during illness explain socioeconomic disproportionalities in vaccine uptake. We leveraged a large sample of adults ( = 1584) infected with COVID-19 in NYC to examine this question, investigating whether specific experiences during illness explained the association between socioeconomic status and COVID-19 vaccine hesitancy. Data from this study were collected during February and March 2021. Principal component analysis was used to create three composite variables that measure distinct COVID-19 related experiences: infection-related health impacts, pandemic-related psychosocial disruption, and perceived quality of medical care during COVID-19 illness. Neither infection-related impacts nor psychosocial disruption were related to vaccine hesitancy after adjusting for related sociodemographic covariates. However, perceptions of higher quality care received during COVID-19 illness predicted decreased COVID-19 vaccine hesitancy. Furthermore, mediation analysis revealed that perceived care quality during COVID-19 illness mediate the relationship between objective socioeconomic risk and COVID-19 vaccine hesitancy. These findings highlight patient-reported care quality during illness as a novel target that may increase vaccine uptake among socioeconomically vulnerable populations.

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Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it. Anthropologists have called the transition from early to middle childhood the “age of reason”, when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.

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The opioid epidemic has profoundly affected infants born in the United States, as in utero opioid exposure increases the risk of cognitive and behavioral problems in childhood. Scarce literature has evaluated prenatal brain development in fetuses with opioid exposure in utero (hereafter opioid-exposed fetuses). The purpose of this study is to compare opioid-exposed fetuses and fetuses without opioid exposure (hereafter unexposed fetuses) in terms of 2D biometric measurements of the brain and additional pregnancy-related assessments on fetal MRI. This prospective case-control study included patients in the third trimester of pregnancy who underwent investigational fetal MRI at one of three U.S. academic medical centers from July 1, 2020, through December 31, 2021. Fetuses were classified as opioid exposed or unexposed in utero. Fourteen 2D biometric measurements of the fetal brain were manually assessed and used to derive four indexes. Measurements and indexes were compared between the two groups by use of multivariable linear regression models, which were adjusted for gestational age (GA), fetal sex, and nicotine exposure. Additional pregnancy-related findings on MRI were evaluated. The study included 65 women (mean age, 29.0 ± 5.5 [SD] years). A total of 28 fetuses (mean GA at the time of MRI, 32.2 ± 2.5 weeks) were opioid-exposed, and 37 fetuses (mean GA at the time of MRI, 31.9 ± 2.7 weeks) were unexposed. In the adjusted models, seven measurements were smaller ( < .05) in opioid-exposed fetuses than in unexposed fetuses: cerebral frontooccipital diameter (93.8 ± 7.4 vs 95.0 ± 8.6 mm), bone biparietal diameter (79.0 ± 6.0 vs 80.3 ± 7.1 mm), brain biparietal diameter (72.9 ± 7.7 vs 74.1 ± 8.6 mm), corpus callosum length (37.7 ± 4.0 vs 39.4 ± 3.7 mm), vermis height (18.2 ± 2.7 vs 18.8 ± 2.6 mm), anteroposterior pons measurement (11.6 ± 1.4 vs 12.1 ± 1.4 mm), and transverse cerebellar diameter (40.4 ± 5.1 vs 41.4 ± 6.0 mm). In addition, in the adjusted model, the frontoocccipital index was larger ( = .02) in opioid-exposed fetuses (0.04 ± 0.02) than in unexposed fetuses (0.04 ± 0.02). Remaining measures and indexes were not significantly different between the two groups ( > .05). Fetal motion, cervical length, and deepest vertical pocket of amniotic fluid were not significantly different ( > .05) between groups. Opioid-exposed fetuses, compared with unexposed fetuses, showed higher frequencies of both breech position (21% vs 3%, = .03) and increased amniotic fluid volume (29% vs 8%, = .04). Fetuses with opioid exposure in utero had a smaller brain size and altered fetal physiology. The findings provide insight into the impact of prenatal opioid exposure on fetal brain development.

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Aggression is a major public health concern that emerges early in development and lacks optimized treatment, highlighting need for improved mechanistic understanding regarding the etiology of aggression. The present study leveraged fetal resting-state functional magnetic resonance imaging to identify candidate neurocircuitry for the onset of aggressive behaviors before symptom emergence.

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Purposeful adults may experience greater cognitive resilience because sense of purpose may help buffer against the effects of depressive symptoms and loneliness. We also evaluated whether these associations differed by race.

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How do children’s experiences relate to their naturalistic emotional and social processing? Because children can struggle with tasks in the scanner, we collected fMRI data while 4-to-11-year-olds watched a short film with positive and negative emotional events, and rich parent-child interactions (n = 70). We captured broad, normative stressful experiences by examining socioeconomic status (SES) and stressful life events, as well as children’s more proximal experiences with their parents. For a sub-sample (n = 30), parenting behaviors were measured during a parent-child interaction, consisting of a picture book, a challenging puzzle, and free play with novel toys. We characterized positive parenting behaviors (e.g., warmth, praise) and negative parenting behaviors (e.g., harsh tone, physical control). We found that higher SES was related to greater activity in medial orbitofrontal cortex during parent-child interaction movie events. Negative parenting behaviors were associated with less activation of the ventral tegmental area and cerebellum during positive emotional events. In a region-of-interest analysis, we found that stressful life events and negative parenting behaviors were associated with less activation of the amygdala during positive emotional events. These exploratory results demonstrate the promise of using movie fMRI to study how early experiences may shape emotional, social, and motivational processes.

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Groundbreaking insights into the origins of the human mind have been garnered through the study of eye movements in preverbal subjects who are unable to explain their thought processes. Developmental research has largely relied on in-lab testing with trained experimenters. This constraint provides a narrow window into infant cognition and impedes large-scale data collection in families from diverse socioeconomic, geographic, and cultural backgrounds. Here we introduce a new open-source methodology for automatically analyzing infant eye-tracking data collected on personal devices in the home. Using algorithms from computer vision, machine learning, and ecological psychology, we develop an online webcam-linked eye tracker (OWLET) that provides robust estimation of infants’ point of gaze from smartphone and webcam recordings of infant assessments in the home. We validate OWLET in a large sample of 7-month-old infants (N = 127) tested remotely, using an established visual attention task. We show that this new method reliably estimates infants’ point-of-gaze across a variety of contexts, including testing on both computers and mobile devices, and exhibits excellent external validity with parental-report measures of attention. Our platform fills a significant gap in current tools available for rapid online data collection and large-scale assessments of cognitive processes in infants. Remote assessment addresses the need for greater diversity and accessibility in human studies and may support the ecological validity of behavioral experiments. This constitutes a critical and timely advance in a core domain of developmental research and in psychological science more broadly.

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Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: “cumulative relation,” “relative relation,” “maximal relation” and “minimal relation.” These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).

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Maternal-fetal attachment (MFA), a woman’s relationship with and affiliative behaviors toward her unborn child, has been linked to near-term infant physical and developmental outcomes. However, further longitudinal research is needed to understand whether the impact of MFA extends past the earliest years of life. The current study explored relationships between MFA and child socioemotional competence and behavior problems at age 3 and whether parenting stress mediated the association between MFA and child outcomes. Data were collected from 221 primarily Black/African-American mothers who completed a scale of MFA during pregnancy. Mothers reported on parenting stress at infant age 7 months and reported on child socioemotional competence and problem behaviors at child age 3 years. In path analyses, MFA was directly associated with child socioemotional competence at age 3 years, but an indirect association between MFA and socioemotional competence via parenting stress was not significant. We also observed a significant indirect association between lower MFA and child internalizing behavior problems via parenting stress that was related to maternal dissatisfaction regarding interactions with her child. Findings suggest that assessing MFA may serve as a means to identify dyads who would benefit from support to promote individual health outcomes.

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Our primary objective was to document COVID-19 induced changes to perinatal care across the USA and examine the implication of these changes for maternal mental health. We performed an observational cross-sectional study with convenience sampling using direct patient reports from 1918 postpartum and 3868 pregnant individuals collected between April 2020 and December 2020 from 10 states across the USA. We leverage a subgroup of these participants who gave birth prior to March 2020 to estimate the pre-pandemic prevalence of specific birthing practices as a comparison. Our primary analyses describe the prevalence and timing of perinatal care changes, compare perinatal care changes depending on when and where individuals gave birth, and assess the linkage between perinatal care alterations and maternal anxiety and depressive symptoms. Seventy-eight percent of pregnant participants and 63% of postpartum participants reported at least one change to their perinatal care between March and August 2020. However, the prevalence and nature of specific perinatal care changes occurred unevenly over time and across geographic locations. The separation of infants and mothers immediately after birth and the cancelation of prenatal visits were associated with worsened depression and anxiety symptoms in mothers after controlling for sociodemographic factors, mental health history, number of pregnancy complications, and general stress about the COVID-19 pandemic. Our analyses reveal widespread changes to perinatal care across the US that fluctuated depending on where and when individuals gave birth. Disruptions to perinatal care may also exacerbate mental health concerns, so focused treatments that can mitigate the negative psychiatric sequelae of interrupted care are warranted.

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With significant increases in opioid use/misuse and persistent high prevalence of prenatal alcohol exposure (PAE), identifying infants at risk for long-term developmental sequelae due to these exposures remains an urgent need. This study reports on developmental outcomes in young children from a prospective cohort, ENRICH-1, which recruited pregnant women and followed up maternal-infant pairs.

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The goal of this Special Section is to highlight the generativity of taking a developmental perspective toward the RDoC framework that considers developmental processes and principles and the environmental and contextual processes relevant at different ages and developmental stages. The 9 papers in this Special Section and 2 invited commentaries exemplify and highlight sophisticated efforts to integrate development and principles of developmental psychopathology into the RDoC framework. In so doing, the papers both demonstrate how a developmental perspective can bolster strengths of the RDoC approach and identify notable gaps and shortcomings in how the RDoC framework, assumptions, and constructs are currently conceptualized. There are critical tensions between conducting developmentally informed and informative RDoC research. Our measures and research designs are often outstripped by the challenge of testing our ambitious ideas. Examining the causal transactions between individual differences in RDoC dimensions and normative maturational tasks, supportive and hindering contexts, and the potential moderation of associations by developmental history will produce important information about the development, manifestation, and course of psychopathology. Addressing these gaps holds great potential for identifying preventive-intervention targets, impactful intervention settings, and environmental and contextual supports. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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Despite the numerous benefits that breastfeeding confers to those who breastfeed and their infants, the United States’ exclusive breastfeeding rates and any breastfeeding rates at 12 months remain low and inequitable. This public health crisis has been prioritized in the US Healthy People 2030 goals. Current evidence-based practices to support lactation have afforded limited progress, thus, achieving national breastfeeding goals requires innovative ideas in thinking, technology, and care. This article highlights potential innovative strategies in the field of lactation to improve outcomes and work toward achieving health equity, while underscoring the critical role that perinatal caregivers play in lactation support.

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Increasing reports of long-term symptoms following COVID-19 infection, even among mild cases, necessitate systematic investigation into the prevalence and type of lasting illness. Notably, there is limited data regarding the influence of social determinants of health, like perceived discrimination and economic stress, that may exacerbate COVID-19 health risks. Here, 1,584 recovered COVID-19 patients that experienced mild to severe forms of disease provided detailed medical and psychosocial information. Path analyses examined hypothesized associations between discrimination, illness severity, and lasting symptoms. Secondary analyses evaluated sex differences, timing of infection, and impact of prior mental health problems. Post hoc logistic regressions tested social determinants hypothesized to predict neurological, cognitive, or mood symptoms. 70.6% of patients reported presence of one or more lasting symptom after recovery. 19.4% and 25.1% of patients reported lasting mood or cognitive/memory problems. Perceived discrimination predicted increased illness severity and increased lasting symptom count, even when adjusting for sociodemographic factors and mental/physical health comorbidities. This effect was specific to stress related to discrimination, not to general stress levels. Further, patient perceptions regarding quality of medical care influenced these relationships. Finally, illness early in the pandemic is associated with more severe illness and more frequent lasting complaints. Lasting symptoms after recovery from COVID-19 are highly prevalent and neural systems are significantly impacted. Importantly, psychosocial factors (perceived discrimination and perceived SES) can exacerbate individual health risk. This study provides actionable directions for improved health outcomes by establishing that sociodemographic risk and medical care influence near and long-ranging health outcomes. All data from this study have been made publicly available.

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Examine maternal and paternal ADHD and ASD symptoms in relation to very preterm (VPT) and full-term (FT) children’s ADHD and ASD symptoms.

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Inequitable urban environments are associated with toxic stress and altered neural social stress processing that threatens the development of self-regulation. Some children in these environments struggle with early onset externalizing problems that are associated with a variety of negative long-term outcomes. While previous research has linked parenting daily hassles to child externalizing problems, the role of frontal alpha asymmetry (FAA) as a potential modifier of this relationship has scarcely been explored. The present study examined mother-child dyads, most of whom were living in low socioeconomic status households in an urban environment and self-identified as members of racial minority groups. Analyses focused on frustration task electroencephalography (EEG) data from 67 children (mean age = 59.0 months, SD = 2.6). Mothers reported the frequency of their daily parenting hassles and their child’s externalizing problems. Frustration task FAA moderated the relationship between parenting daily hassles and child externalizing problems, but resting FAA did not. More specifically, children with left frontal asymmetry had more externalizing problems as their mothers perceived more hassles in their parenting role, but parenting hassles and externalizing problems were not associated among children with right frontal asymmetry. These findings lend support to the motivational direction hypothesis and capability model of FAA. More generally, this study reveals how individual differences in lateralization of cortical activity in response to a stressor may confer differential susceptibility to child behavioral problems with approach motivation (i.e., left frontal asymmetry) predicting externalizing problems under conditions of parental stress.

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Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.

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Neuroplasticity, defined as the brain’s potential to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here, we investigated the plasticity of the frontoparietal system by asking whether VTA resting-state functional connectivity and myelin map values (T1w/T2w ratios) predicted learning after short-term training on the adaptive n-back (n = 46, ages 18-25). We found that stronger baseline connectivity between VTA and lateral prefrontal cortex predicted greater improvements in accuracy. Lower myelin map values predicted improvements in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

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Maternal stress can shape long-term child neurodevelopment beginning in utero. One mechanism by which stress is transmitted from mothers to their offspring is via alterations in maternal cortisol, which can cross the placenta and bind to glucocorticoid receptor-rich regions in the fetal brain, such as the hippocampus. Although prior studies have demonstrated associations between maternal prenatal stress and cortisol levels with child brain development, we lack information about the extent to which these associations originate prior to birth and prior to confounding postnatal influences. Pregnant mothers ( = 77) completed questionnaires about current perceived stress, depressive symptoms, and anxiety symptoms, provided three to four salivary cortisol samples, and completed a fetal resting-state functional MRI scan during their second or third trimester of pregnancy (mean gestational age = 32.8 weeks). Voxelwise seed-based connectivity analyses revealed that higher prenatal self-reported distress and higher maternal cortisol levels corresponded to dissociable differences in fetal hippocampal functional connectivity. Specifically, self-reported distress was correlated with increased positive functional coupling between the hippocampus and right posterior parietal association cortex, while higher maternal cortisol was associated with stronger positive hippocampal coupling with the dorsal anterior cingulate cortex and left medial prefrontal cortex. Moreover, the association between maternal distress, but not maternal cortisol, on fetal hippocampal connectivity was moderated by fetal sex. These results suggest that prenatal stress and peripheral cortisol levels may shape fetal hippocampal development through unique mechanisms.

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Temperament involves stable behavioral and emotional tendencies that differ between individuals, which can be first observed in infancy or early childhood and relate to behavior in many contexts and over many years. One of the most rigorously characterized temperament classifications relates to the tendency of individuals to avoid the unfamiliar and to withdraw from unfamiliar people, objects, and unexpected events. This temperament is referred to as behavioral inhibition or inhibited temperament (IT). IT is a moderately heritable trait that can be measured in multiple species. In humans, levels of IT can be quantified from the first year of life through direct behavioral observations or reports by caregivers or teachers. Similar approaches as well as self-report questionnaires on current and/or retrospective levels of IT can be used later in life.

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Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

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Altered functional connectivity has been reported in infants with prenatal exposure to opioids, which significantly interrupts and influences endogenous neurotransmitter/receptor signaling during fetal programming. Better birth outcomes and long-term developmental outcomes are associated with medication for opioid use disorder (MOUD) during pregnancy, but the neural mechanisms underlying these benefits are largely unknown. We aimed to characterize effects of prenatal opioid/other drug exposure (PODE) and the neural basis for the reported beneficial effects of MOUD by examining neonatal brain functional organization. A cohort of 109 human newborns, 42 PODE, 39 with prenatal exposure to drugs excluding opioids (PDE), 28 drug-free controls (males and females) underwent resting-state fMRI at 2 weeks of age. To examine neural effects of MOUD, PODE infants were separated into subgroups based on whether mothers received MOUD ( = 31) or no treatment ( = 11). A novel heatmap analysis was designed to characterize PODE-associated functional connectivity alterations and MOUD-related effects, and permutation testing identified regions of interest with significant effects. PODE neonates showed alterations beyond those associated with PDE, particularly in reward-related frontal-sensory connectivity. MOUD was associated with a significant reduction of PODE-related alterations in key regions of endogenous opioid pathways including limbic and frontal connections. However, significant residual effects in limbic and subcortical circuitry were observed. These findings confirm altered brain functional organization associated with PODE. Importantly, widespread normalization effects associated with MOUD reveal, for the first time, the potential brain basis of the beneficial effects of MOUD on the developing brain and underscore the importance of this treatment intervention for better developmental outcomes. This is the first study to reveal the potential neural mechanisms underlying the beneficial effects on the neonate brain associated with MOUD during pregnancy. We identified both normalization and residual effects of MOUD on brain functional architecture by directly comparing neonates prenatally exposed to opioids with MOUD and those exposed to opioids but without MOUD. Our findings confirm altered brain functional organization associated with prenatal opioid exposure and demonstrate that although significant residual effects remain in reward circuitry, MOUD confers significant normalization effects on functional connectivity of regions associated with socioemotional development and reward processing. Together, our results highlight the importance of MOUD intervention for better neurodevelopmental outcomes.

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This study examined product use among pregnant women and new mothers in New York City during the COVID-19 pandemic (July 2020-June 2021). Women reported use of personal care and household cleaning products within the previous month, changes in antibacterial product use, receipt of healthcare provider advice, and opinions on environmental chemicals (n = 320). On average, women used 15 personal care products and 7 household cleaning products. Non-Hispanic Black women used nearly two more personal care products; non-Hispanic Black women, those with a college degree, and essential workers used 1-3 more household cleaning products. Women who were Hispanic or reported their race and ethnicity as Other were two times more likely to use antibacterial personal care products. Non-Hispanic Black, Hispanic, and women who reported their race and ethnicity as Other were 1.5 times more likely to increase antibacterial product use during the pandemic. Nearly all women agreed that environmental chemicals pose health risks and are impossible to avoid, while less than one quarter received advice regarding product use. Product use is a modifiable source of chemical exposures. Results from this study suggest that women may have increased their product use during the pandemic. Healthcare providers may use the current focus on health hygiene to promote discussion and assessment of environmental chemical exposures with patients.

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Prenatal opioid exposure has been linked to adverse effects spanning multiple neurodevelopmental domains, including cognition, motor development, attention, and vision. However, the neural basis of these abnormalities is largely unknown. A total of 49 infants, including 21 opioid-exposed and 28 controls, were enrolled and underwent MRI (43 ± 6 days old) after birth, including resting state functional MRI. Edge-centric functional networks based on dynamic functional connections were constructed, and machine-learning methods were employed to identify neural features distinguishing opioid-exposed infants from unexposed controls. An accuracy of 73.6% (sensitivity 76.25% and specificity 69.33%) was achieved using 10 times 10-fold cross-validation, which substantially outperformed those obtained using conventional static functional connections (accuracy 56.9%). More importantly, we identified that prenatal opioid exposure preferentially affects inter- rather than intra-network dynamic functional connections, particularly with the visual, subcortical, and default mode networks. Consistent results at the brain regional and connection levels were also observed, where the brain regions and connections associated with visual and higher order cognitive functions played pivotal roles in distinguishing opioid-exposed infants from controls. Our findings support the clinical phenotype of infants exposed to opioids in utero and may potentially explain the higher rates of visual and emotional problems observed in this population. Finally, our findings suggested that edge-centric networks could better capture the neural differences between opioid-exposed infants and controls by abstracting the intrinsic co-fluctuation along edges, which may provide a promising tool for future studies focusing on investigating the effects of prenatal opioid exposure on neurodevelopment.

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Prenatal opioid exposure (POE) is commonly associated with neonatal opioid withdrawal syndrome (NOWS), which is characterized by a broad variability in symptoms and severity. Currently there are no diagnostic tools to reliably predict which infants will develop severe NOWS, while risk stratification would allow for proactive decisions about appropriate clinical monitoring and interventions. The aim of this prospective cohort study was to assess if extracellular microRNAs (miRNAs) in umbilical cord plasma of infants with POE could predict NOWS severity. Participants (n = 58) consisted of pregnant women receiving medications for opioid use disorder and their infants. NOWS severity was operationalized as the need for pharmacologic treatment and prolonged hospitalization (≥ 14 days). Cord blood miRNAs were assessed using semi-quantitative qRT-PCR arrays. Receiver operating characteristic curves and area under the curve (AUC) were estimated. The expression of three miRNAs (miR-128-3p, miR-30c-5p, miR-421) predicted need for pharmacologic treatment (AUC: 0.85) and prolonged hospitalization (AUC: 0.90). Predictive validity improved after two miRNAs (let-7d-5p, miR-584-5p) were added to the need for pharmacologic treatment model (AUC: 0.94) and another two miRNAs (let-7b-5p, miR-10-5p) to the prolonged hospitalization model (AUC: 0.99). Infant cord blood extracellular miRNAs can proactively identify opioid-exposed neonates at high-risk for developing severe NOWS.

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Evidence-based interventions for treating opioid use disorder (OUD) in youth are limited and little is known about specific and general mechanisms of OUD treatments and how they promote abstinence.

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In the 6 months following our estimates from March 1, 2020, to April 30, 2021, the proliferation of new coronavirus variants, updated mortality data, and disparities in vaccine access increased the amount of children experiencing COVID-19-associated orphanhood. To inform responses, we aimed to model the increases in numbers of children affected by COVID-19-associated orphanhood and caregiver death, as well as the cumulative orphanhood age-group distribution and circumstance (maternal or paternal orphanhood).

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The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy on the developing fetal brain is poorly understood. Other antenatal infections such as influenza have been associated with adverse neurodevelopmental outcomes in offspring. Although vertical transmission has been rarely observed in SARS-CoV-2 to date, given the potential for profound maternal immune activation (MIA), impact on the developing fetal brain is likely. Here we review evidence that SARS-CoV-2 and other viral infections during pregnancy can result in maternal, placental, and fetal immune activation, and ultimately in offspring neurodevelopmental morbidity. Finally, we highlight the need for cellular models of fetal brain development to better understand potential short- and long-term impacts of maternal SARS-CoV-2 infection on the next generation.

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Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.

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The impact of COVID-19-related stress on perinatal women is of heightened public health concern given the established intergenerational impact of maternal stress-exposure on infants and fetuses. There is urgent need to characterize the coping styles associated with adverse psychosocial outcomes in perinatal women during the COVID-19 pandemic to help mitigate the potential for lasting sequelae on both mothers and infants. This study uses a data-driven approach to identify the patterns of behavioral coping strategies that associate with maternal psychosocial distress during the COVID-19 pandemic in a large multicenter sample of pregnant women (N = 2876) and postpartum women (N = 1536). Data was collected from 9 states across the United States from March to October 2020. Women reported behaviors they were engaging in to manage pandemic-related stress, symptoms of depression, anxiety and global psychological distress, as well as changes in energy levels, sleep quality and stress levels. Using latent profile analysis, we identified four behavioral phenotypes of coping strategies. Critically, phenotypes with high levels of passive coping strategies (increased screen time, social media, and intake of comfort foods) were associated with elevated symptoms of depression, anxiety, and global psychological distress, as well as worsening stress and energy levels, relative to other coping phenotypes. In contrast, phenotypes with high levels of active coping strategies (social support, and self-care) were associated with greater resiliency relative to other phenotypes. The identification of these widespread coping phenotypes reveals novel behavioral patterns associated with risk and resiliency to pandemic-related stress in perinatal women. These findings may contribute to early identification of women at risk for poor long-term outcomes and indicate malleable targets for interventions aimed at mitigating lasting sequelae on women and children during the COVID-19 pandemic.

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Prenatal nutrition may significantly impact brain aging. Results from the Dutch Famine Birth Cohort indicated that prenatal undernutrition is negatively associated with cognition, brain volumes, perfusion and structural brain aging in late life, predominantly in men. This study investigates the association between prenatal undernutrition and late-life functional brain network connectivity. In an exploratory resting-state functional magnetic resonance imaging study of 112 participants from the Dutch Famine Birth Cohort, we investigated whether the within- and between-network functional connectivity of the default mode network, salience network and central executive network differ at age 68 in men (N = 49) and women (N = 63) either exposed or unexposed to undernutrition in early gestation. Additionally, we explored sex-specific effects. Compared to unexposed participants, exposed participants revealed multiple clusters of different functional connectivity within and between the three networks studied. Sex-specific analyses suggested a pattern of network desegregation fitting with brain aging in men and a more diffuse pattern of group differences in women. This study demonstrates that associations between prenatal undernutrition and brain network functional connectivity extend late into life.

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First-person accounts of COVID-19 illness and treatment can complement and enrich data derived from electronic medical or public health records. With patient-reported data, it is uniquely possible to ascertain in-depth contextual information as well as behavioral and emotional responses to illness. The Novel Coronavirus Illness Patient Report (NCIPR) dataset includes complete survey responses from 1,584 confirmed COVID-19 patients ages 18 to 98. NCIPR survey questions address symptoms, medical complications, home and hospital treatments, lasting effects, anxiety about illness, employment impacts, quarantine behaviors, vaccine-related behaviors and effects, and illness of other family/household members. Additional questions address financial security, perceived discrimination, pandemic impacts (relationship, social, stress, sleep), health history, and coping strategies. Detailed patient reports of illness, environment, and psychosocial impact, proximal to timing of infection and considerate of demographic variation, is meaningful for understanding pandemic-related public health from the perspective of those that contracted the disease.

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Infant and toddler MRI enables unprecedented insight into the developing brain. However, consensus about optimal data collection practices is lacking, which slows growth of the field and impedes replication efforts. The goal of this study was to collect systematic data across a large number of infant/toddler research laboratories to better understand preferred practices. Survey data addressed MRI acquisition strategies, scan success rates, visit preparations, scanning protocols, accommodations for families, study design, and policies regarding incidental findings. Respondents had on average 8 years’ experience in early life neuroimaging and represented more than fifty research laboratories. Areas of consensus across labs included higher success rates among newborns compared to older infants or toddlers, high rates of data loss across age groups, endorsement of multiple layers of hearing protection, and age-specific scan preparation and participant accommodation. Researchers remain divided on decisions in longitudinal study design and practices regarding incidental findings. This study summarizes practices honed over years of work by a large collection of scientists, which may serve as an important resource for those new to the field. The ability to reference data about best practices facilitates future harmonization, data sharing, and reproducibility, all of which advance this important frontier in developmental science.

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A quarter of pregnant women use alcohol, 6.5/1000 deliveries are affected by opioid use disorder (OUD), and the prevalence of cannabis use in pregnant women is increasing. However, marijuana co-exposure in polysubstance-using women is not well described.

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The NIH HEALthy Brain and Cognitive Development (HBCD) study aims to characterize the impact of in utero exposure to substances, and related environmental exposures on child neurodevelopment and health outcomes. A key focus of HBCD is opioid exposure, which has disproportionately affected rural areas. While most opioid use and neonatal abstinence syndrome has been reported outside of large cities, rural communities are often under-represented in large-scale clinical research studies that involve neuroimaging, in-person assessments, or bio-specimen collections. Thus, there exists a likely mismatch between the communities that are the focus of HBCD and those that can participate. Even geographically proximal participants, however, are likely to bias towards higher socioeconomic status given the anticipated study burden and visit frequency. Wearables, ‘nearables’, and other consumer biosensors, however, are increasingly capable of collecting continuous physiologic and environmental exposure data, facilitating remote assessment. We review the potential of these technologies for remote in situ data collection, and the ability to engage rural, affected communities. While not necessarily a replacement, these technologies offer a compelling complement to traditional ‘gold standard’ lab-based methods, with significant potential to expand the study’s reach and importance.

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The COVID-19 pandemic has impacted data collection for longitudinal studies in developmental sciences to an immeasurable extent. Restrictions on conducting in-person standardized assessments have led to disruptive innovation, in which novel methods are applied to increase participant engagement. Here, we focus on remote administration of behavioral assessment. We argue that these innovations in remote assessment should become part of the new standard protocol in developmental sciences to facilitate data collection in populations that may be hard to reach or engage due to burdensome requirements (e.g., multiple in-person assessments). We present a series of adaptations to developmental assessments (e.g., Mullen) and a detailed discussion of data analytic approaches to be applied in the less-than-ideal circumstances encountered during the pandemic-related shutdown (i.e., missing or messy data). Ultimately, these remote approaches actually strengthen the ability to gain insight into developmental populations and foster pragmatic innovation that should result in enduring change.

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Executive function (EF) is essential to child development, with associated skills beginning to emerge in the first few years of life and continuing to develop into adolescence and adulthood. The prefrontal cortex (PFC), which follows a neurodevelopmental timeline similar to EF, plays an important role in the development of EF. However, limited research has examined prefrontal function in young children due to limitations of currently available neuroimaging techniques such as functional resonance magnetic imaging (fMRI). The current study developed and applied a multimodal Go/NoGo task to examine the EF component of inhibitory control in children 4-10 years of age. Cortical activity was measured using a non-invasive and child-friendly neuroimaging technique – functional near-infrared spectroscopy (fNIRS). Children’s response accuracy and reaction times were captured during the fNIRS session and compared with responses obtained using the standardized assessments from NIH Toolbox cognition battery. Results showed significant correlations between the behavioral measures during the fNIRS session and the standardized EF assessments, in line with our expectations. Results from fNIRS measures demonstrated a significant, age-independent effect of inhibitory control (IC) in the right PFC (rPFC), and an age-dependent effect in the left orbitofrontal cortex (lOFC), consistent with results in previous studies using fNIRS and fMRI. Thus, the new task designed for fNIRS was suitable for examining IC in young children, and results showed that fNIRS measures can reveal prefrontal IC function.

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Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children’s brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9-11 years (48% female, replication sample n=544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.

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The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

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Children’s behavior changes from day to day, but the factors that contribute to its variability are understudied. We developed a novel repeated measures paradigm to study children’s persistence by capitalizing on a task that children complete every day: toothbrushing (N = 81; 48% female; 36-47 months; 80% white, 14% Multiracial, 10% Hispanic, 2% Asian, 1% Black; 1195 observations collected between January 2019 and March 2020). Children brushed longer on days when their parents used more praise (d = .23) and less instruction (d = -.22). Sensitivity to mood, sleep, and parent stress varied across children, suggesting that identifying the factors that shape an individual child’s persistence could lead to personalized interventions.

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We studied the T cell response to SARS-CoV-2 spike and non-spike peptide epitopes in eight convalescent pregnant women together with the immune monitoring that included innate tolerogenic dendritic cell populations important to maintain the immunological mother/fetus interface to address a potential risk for the antiviral cellular response in the outcome of pregnancy. Four subjects had pre-existing chronic inflammatory conditions that could have potentially affected the SARS-CoV-2-specific T cell response. Seven of eight subjects responded to SARS-CoV-2 peptides with differences within CD4+ T helper (Th) and CD8+ cytotoxic T cells (CTL). SARS-CoV-2-specific inducible regulatory T cells (iTreg) were numerous in circulation. CD4+ T cell memory included central memory T cells (T) and effector memory (T). As far as the CD8+ memory repertoire, T and T were very low or absent in eight of eight subjects and only effector cells that revert to CD45RA+, defined as T were measurable in circulation. T cells were in the normal range in all subjects regardless of pre-existing inflammatory conditions. The immune phenotype indicated the expansion and activation of tolerogenic myeloid dendritic cells including CD14+ cDC2 and CD4+ ILT-4+ tmDC. In summary, SARS-CoV-2 infection induced a physiological anti-viral T cell response in pregnant women that included SARS-CoV-2-specific iTreg with no negative effects on the tolerogenic innate dendritic cell repertoire relevant to the immune homeostasis of the maternal-fetal interface. All eight subjects studied delivered full-term, healthy infants.

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Few studies have examined whether maternal caregiving representations are associated with maternal reflective functioning (MRF), especially when MRF is evaluated longitudinally beginning in pregnancy. This study addresses this gap by evaluating whether prenatal and postnatal MRF are associated with mothers’ caregiving representations assessed at 7 months postpartum, and by exploring theoretically unexpected MRF scores in each of the representational categories. Forty-seven mothers were recruited during their last trimester of pregnancy from an obstetrics clinic at a university hospital located in a large mid-western city in the United States. During pregnancy, mothers completed the Pregnancy Interview, and at 7 months postpartum they completed the Parent Development Interview (PDI) and the Working Model of the Child Interview. Results indicate that higher prenatal and postnatal MRF increased the odds of being classified as balanced versus disengaged. At 7 months, MRF also increased the odds of being balanced vs. distorted. Ten mothers who were classified as balanced or distorted had unexpected prenatal MRF scores, and six mothers had unexpected MRF scores when representations were assessed concurrently. Mothers classified as balanced with low MRF scores tended to have a low level of education, whereas mothers classified as distorted with high MRF scores had responses that were hostile, helpless, and role-reversed.

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Heightened psychological stress during pregnancy has repeatedly been associated with increased risk for development of behavior problems and psychiatric disorders in offspring. This review covers a rapidly growing body of research with the potential to advance a mechanistic understanding of these associations grounded in knowledge about maternal-placental-fetal stress biology and fetal brain development. Specifically, we highlight research employing magnetic resonance imaging to examine the infant brain soon after birth in relation to maternal psychological stress during pregnancy. This approach increases capacity to identify specific alterations in brain structure and function and to differentiate between effects of pre- versus postnatal exposures. We then focus on the extensive preclinical literature and emerging research in humans that have found that heightened maternal inflammation during pregnancy as a mechanism through which maternal stress influences the developing fetal brain. We place these findings in the context of recent work identifying psychotherapeutic interventions that have been found to be effective for reducing psychological stress among pregnant individuals and that also show promise for reducing inflammation. We argue that a focus on inflammation, among other mechanistic pathways, may lead to a productive and necessary integration of research focused on the effects of maternal psychological stress on offspring brain development and on prevention and intervention studies aimed at reducing maternal psychological stress during pregnancy. In addition to increasing capacity for common measurements and understanding potential mechanisms of action relevant to maternal mental health and fetal neurodevelopment, this focus may inform and broaden thinking about prevention and intervention strategies.

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Environmental experiences early in life have strong and enduring consequences for cognitive, emotional, and neurobiological development and related physical and mental health trajectories. The powerful influence of early caregiver nurturance and stimulation on promoting positive neurodevelopmental outcomes has been demonstrated across species. These findings elucidate the environmental conditions known to facilitate healthy neurodevelopment and underscore the potential for modifiable psychosocial factors in the environment to be harnessed to inform early preventive interventions to promote health and adaptive development. A framework for early preventive interventions to enhance nurturing and responsive caregiving for implementation during early sensitive periods of brain development delivered within existing health or educational infrastructures is proposed. Emotional development during sensitive periods is an important, under-recognized, and abundantly modifiable predictor of mental and physical health outcomes that warrants investment of resources and integration of interventions into public health infrastructure for children worldwide. Future studies are needed to further clarify whether and when sensitive periods are present for key developmental domains to inform the optimal timing and targets of these interventions. Numerous available empirically supported early interventions may be modified and applied in briefer and more feasible modalities of delivery to broader populations of developing children. As well established in growth and development across species, essential environmental inputs that are particularly important at specified developmental periods facilitate optimal growth trajectories. Such principles hold great potential in application to early child neurodevelopment to facilitate a thriving and resilient human population.

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J-difference-edited spectroscopy is a valuable approach for the detection of low-concentration metabolites with magnetic resonance spectroscopy (MRS). Currently, few edited MRS studies are performed in neonates due to suboptimal signal-to-noise ratio, relatively long acquisition times, and vulnerability to motion artifacts. Nonetheless, the technique presents an exciting opportunity in pediatric imaging research to study rapid maturational changes of neurotransmitter systems and other metabolic systems in early postnatal life. Studying these metabolic processes is vital to understanding the widespread and rapid structural and functional changes that occur in the first years of life. The overarching goal of this review is to provide an introduction to edited MRS for neonates, including the current state-of-the-art in editing methods and editable metabolites, as well as to review the current literature applying edited MRS to the neonatal brain. Existing challenges and future opportunities, including the lack of age-specific reference data, are also discussed.

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Children with a fetal alcohol spectrum disorder (FASD) experience a range of cognitive and behavioral effects. Prior studies have demonstrated white matter changes in children with FASD relative to typically developing controls (TDC) and these changes relate to behavior. Our prior MEG study (Candelaria-Cook et al. 2020) demonstrated reduced alpha oscillations during rest in FASD relative to TDC and alpha power is correlated with behavior. However, little is known about how brain structure influences brain function. We hypothesized that alpha power was related to corticothalamic connectivity. Children 8-13 years of age (TDC: N = 25, FASD: N = 24) underwent rest MEG with eyes open or closed and MRI to collect structural and diffusion tensor imaging data. MEG spectral analysis was performed for sensor and source data. We estimated mean fractional anisotropy in regions of interest (ROIs) that included the corticothalamic tracts. The FASD group had reduced mean FA in three of the corticothalamic ROIs. FA in these tracts was significantly correlated with alpha power at the sensor and source level. The results support the hypothesis that integrity of the corticothalamic tracts influences cortical alpha power. Further research is needed to understand how brain structure and function influence behavior.

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A major challenge in prenatal drug exposure research concerns the balance of measurement quality with sample sizes necessary to address confounders. To inform the selection of optimal exposure measures for the HEALthy Brain and Child Development (HBCD) Study, we employed integrated analysis to determine how different methods used to characterize prenatal tobacco exposure influence the detection of exposure-related risk, as reflected in normal variations in birth weight.

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Our objectives were to assess in perinatal women: the most effective methods used to meet social support needs during COVID-19, the impact of COVID-19 on self-reported social support levels, and how perceived change in social support related to distress, depression, and mental health.

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Understanding of the effects of in utero opioid exposure on neurodevelopment is a priority given the recent dramatic increase in opioid use among pregnant individuals. However, opioid abuse does not occur in isolation-pregnant individuals abusing opioids often have a significant history of adverse experiences in childhood, among other co-occurring factors. Understanding the specific pathways in which these frequently co-occurring factors may interact and cumulatively influence offspring brain development in utero represents a priority for future research in this area. We highlight maternal history of childhood adversity (CA) as one such co-occurring factor that is more prevalent among individuals using opioids during pregnancy and which is increasingly shown to affect offspring neurodevelopment through mechanisms beginning in utero. Despite the high incidence of CA history in pregnant individuals using opioids, we understand very little about the effects of comorbid prenatal opioid exposure and maternal CA history on fetal brain development. Here, we first provide an overview of current knowledge regarding effects of opioid exposure and maternal CA on offspring neurodevelopment that may occur during gestation. We then outline potential mechanistic pathways through which these factors might have interactive and cumulative influences on offspring neurodevelopment as a foundation for future research in this area.

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To evaluate the severity of neonatal opioid withdrawal syndrome (NOWS) in infants prenatally exposed to medications for opioid use disorder (MOUD) and serotonin reuptake inhibitors (SRI).

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Neonates born to mothers taking opioids during pregnancy are at risk for neonatal opioid withdrawal syndrome (NOWS), for which there is no recognized standard approach to care. Nonpharmacologic treatment is typically used as a first-line approach for management, and pharmacologic treatment is added when clinical signs are not responding to nonpharmacologic measures alone. Although morphine and methadone are the most commonly used pharmacotherapies for NOWS, buprenorphine has emerged as a treatment option based on its pharmacologic profile and results from initial single site clinical trials. The objective of this report is to provide an overview of NOWS including a summary of ongoing work in the field and to review the state of the science, knowledge gaps, and practical considerations specific to the use of buprenorphine for the treatment of NOWS as discussed by a panel of experts during a virtual workshop hosted by the National Institutes of Health.

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In response to the COVID-19 pandemic, research institutions across the globe have modified their operations in ways that have limited or eliminated the amount of permissible in-person research interaction. In order to prevent the loss of important developmentally-timed data during the pandemic, researchers have quickly pivoted and developed innovative methods for remote assessment of research participants. In this manuscript, we describe methods developed for remote assessment of a parent child cohort with a focus on examining the perinatal environment, behavioral and biological indicators of child neurobehavioral development, parent-child interaction, as well as parent and child mental and physical health. We include recommendations relevant to adapting in-laboratory assessments for remote data collection and conclude with a description of the successful dissemination of the methods to eight research sites across the United States, each of whom are involved in Phase 1 of the HEALthy Brain and Child Development (HBCD) Study. These remote methods were born out of pandemic-related necessity; however, they have much wider applicability and may offer advantages over in-laboratory neurodevelopmental assessments.

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Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static -space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary -space sampling given commonly acquired structural images (e.g., B0, T1, T2). Our translation network linearly modulates its internal representations conditioned on continuous -space information, thus removing the need for fixed sampling schemes. Moreover, this approach enables downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Code is available at https://github.com/mengweiren/q-space-conditioned-dwi-synthesis.

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Alcohol and cannabis are commonly used by adolescents in the United States. Both alcohol use disorder (AUD) and cannabis use disorder (CUD) have been associated with reduced emotion expression recognition ability. However, this work has primarily occurred in adults and has not considered neuro-cognitive risk factors associated with conduct problems that commonly co-occur with, and precede, substance use. Yet, conduct problems are also associated with reduced emotion expression recognition ability. The current study investigated the extent of negative association between AUD and CUD symptom severity and expression recognition ability any association of expression recognition ability with conduct problems [conduct disorder (CD) diagnostic status]. In this study, 152 youths aged 12.5-18 years (56 female; 60 diagnosed with CD) completed a rapid presentation morphed intensity facial expression task to investigate the association between relative severity of AUD/CUD and expression recognition ability. Cannabis use disorder identification test (CUDIT) scores were negatively associated with recognition accuracy for higher intensity (particularly sad and fearful) expressions while CD diagnostic status was independently negatively associated with recognition of sad expressions. Alcohol use disorder identification test (AUDIT) scores were not significantly associated with expression recognition ability. These data indicate that relative severity of CUD and CD diagnostic status are statistically independently associated with reduced expression recognition ability. On the basis of these data, we speculate that increased cannabis use during adolescence may exacerbate a neuro-cognitive risk factor for the emergence of aggression and antisocial behavior.

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Brain and cognitive development is a burgeoning area of scientific inquiry, with tremendous potential to better the lives of children. Large scale longitudinal neuroimaging studies offer opportunities for significant scientific advances in our understanding of developing brain structure and function. The proposed manuscript will focus on the scientific potential of the HEALthy Brain and Cognitive Development (HBCD) Study, highlighting what questions these data can and what they cannot answer about child development. Specifically, we caution against the misuse of these data for advancing de-contextualized and scientifically questionable narratives about the development of children from marginalized communities. We will focus on building and organizing a framework for interpreting HBCD data through the lens of sampling, cultural context, measurement, and developmental science theory. Our goal is to thoughtfully offer the scientific community opportunities to use the large scale and collaborative nature of HBCD to collectively revise practices in developmental science that to-date have not carefully considered their own role in perpetuating narratives that support systemic injustice.

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The ability to map number words to their corresponding quantity representations is a gatekeeper for children’s future math success (Spaepen et al., 2018). Without number word knowledge at school entry, children are at greater risk for developing math learning difficulties (Chu et al., 2019). In the present study, we used functional magnetic resonance imaging (fMRI) to examine the neural basis for processing the meaning of spoken number words and its developmental trajectory in 4- to 10-year-old children, and in adults. In a number word-quantity mapping paradigm, participants listened to number words while simultaneously viewing quantities that were congruent or incongruent to the number word they heard. Whole brain analyses revealed that adults showed a neural congruity effect with greater neural activation for incongruent relative to congruent trials in anterior cingulate cortex (ACC) and left intraparietal sulcus (LIPS). In contrast, children did not show a significant neural congruity effect. However, a region of interest analysis in the child sample demonstrated age-related increases in the neural congruity effect, specifically in the LIPS. The positive correlation between neural congruity in LIPS and age was stronger in children who were already attending school, suggesting that developmental changes in LIPS function are experience-dependent.

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Opioid Use Disorder (OUD) affects approximately 1% of the population. Despite the prevalence of OUD, it remains a highly stigmatized disorder. Using person-centered language (PCL) – and thereby emphasizing the significance of the person rather than their diagnosis – is a potential strategy to reduce stigma in medical research related to addiction. Thus, we aimed to determine adherence to PCL in OUD-related publications according to the American Medical Association’s guidelines.

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Maternal smoking is a risk factor for offspring smoking. Lifetime maternal smoking vs. prenatal tobacco exposure (PTE) appears to act through different mechanisms. This study tested the hypothesis that maternal smoking measures’ effects on offspring smoking could be attributable to hereditary mechanisms: personality traits (novelty-seeking, impulsivity, neuroticism, and self-esteem) and initial subjective smoking experiences (pleasurable, unpleasurable, and dizziness).

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This study sought to advance understanding of the potential long-term consequences of the COVID-19 pandemic for child development by characterizing trajectories of maternal perinatal depression, a common and significant risk factor for adverse child outcomes. Data came from 393 women (86% White, 8% Latina; mean age = 33.51 years) recruited during pregnancy (n = 247; mean gestational age = 22.94 weeks) or during the first year postpartum (n = 146; mean child age = 4.50 months; 55% female). Rates of depression appear elevated, relative to published reports and to a pre-pandemic comparison group (N = 155). This study also provides evidence for subgroups of individuals who differ in their depressive symptom trajectories over the perinatal period. Subgroup membership was related to differences in maternal social support, but not to child birth outcomes.

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Classification of infant and parent vocalizations, particularly emotional vocalizations, is critical to understanding how infants learn to regulate emotions in social dyadic processes. This work is an experimental study of classifiers, features, and data augmentation strategies applied to the task of classifying infant and parent vocalization types. Our data were recorded both in the home and in the laboratory. Infant vocalizations were manually labeled as cry, fus (fuss), lau (laugh), bab (babble) or scr (screech), while parent (mostly mother) vocalizations were labeled as ids (infant-directed speech), ads (adult-directed speech), pla (playful), rhy (rhythmic speech or singing), lau (laugh) or whi (whisper). Linear discriminant analysis (LDA) was selected as a baseline classifier, because it gave the highest accuracy in a previously published study covering part of this corpus. LDA was compared to two neural network architectures: a two-layer fully-connected network (FCN), and a convolutional neural network with self-attention (CNSA). Baseline features extracted using the OpenSMILE toolkit were augmented by extra voice quality, phonetic, and prosodic features, each targeting perceptual features of one or more of the vocalization types. Three web data augmentation and transfer learning methods were tested: pre-training of network weights for a related task (adult emotion classification), augmentation of under-represented classes using data uniformly sampled from other corpora, and augmentation of under-represented classes using data selected by a minimum cross-corpus information difference criterion. Feature selection using Fisher scores and experiments of using weighted and unweighted samplers were also tested. Two datasets were evaluated: a benchmark dataset (CRIED) and our own corpus. In terms of unweighted-average recall of CRIED dataset, the CNSA achieved the best UAR compared with previous studies. In terms of classification accuracy, weighted F1, and macro F1 of our own dataset, the neural networks both significantly outperformed LDA; the FCN slightly (but not significantly) outperformed the CNSA. Cross-examining features selected by different feature selection algorithms permits a type of post-hoc feature analysis, in which the most important acoustic features for each binary type discrimination are listed. Examples of each vocalization type of overlapped features were selected, and their spectrograms are presented, and discussed with respect to the type-discriminative acoustic features selected by various algorithms. MFCC, log Mel Frequency Band Energy, LSP frequency, and F1 are found to be the most important spectral envelope features; F0 is found to be the most important prosodic feature.

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Increasing evidence supports a link between maternal prenatal cannabis use and altered neural and physiological development of the child. However, whether cannabis use relates to altered human brain development prior to birth, and specifically, whether maternal prenatal cannabis use relates to connectivity of fetal functional brain systems, remains an open question. The major objective of this study was to identify whether maternal prenatal cannabis exposure (PCE) is associated with variation in human brain hippocampal functional connectivity prior to birth. Prenatal drug toxicology and fetal fMRI data were available in a sample of 115 fetuses [43 % female; mean age 32.2 weeks (SD = 4.3)]. Voxelwise hippocampal connectivity analysis in a subset of age and sex-matched fetuses revealed that PCE was associated with alterations in fetal dorsolateral, medial and superior frontal, insula, anterior temporal, and posterior cingulate connectivity. Classification of group differences by age 5 outcomes suggest that compared to the non-PCE group, the PCE group is more likely to have increased connectivity to regions associated with less favorable outcomes and to have decreased connectivity to regions associated with more favorable outcomes. This is preliminary evidence that altered fetal neural connectome may contribute to neurobehavioral vulnerability observed in children exposed to cannabis in utero.

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A major challenge in designing large-scale, multi-site studies is developing a core, scalable protocol that retains the innovation of scientific advances while also lending itself to the variability in experience and resources across sites. In the development of a common Healthy Brain and Child Development (HBCD) protocol, one of the chief questions is “is fetal MRI ready for prime-time?” While there is agreement about the value of prenatal data obtained non-invasively through MRI, questions about practicality abound. There has been rapid progress over the past years in fetal and placental MRI methodology but there is uncertainty about whether the gains afforded outweigh the challenges in supporting fetal MRI protocols at scale. Here, we will define challenges inherent in building a common protocol across sites with variable expertise and will propose a tentative framework for evaluation of design decisions. We will compare and contrast various design considerations for both normative and high-risk populations, in the setting of the post-COVID era. We will conclude with articulation of the benefits of overcoming these challenges and would lend to the primary questions articulated in the HBCD initiative.

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A test-retest study of FreeSurfer derived cortical thickness, cortical surface area, and cortical volume, as well as quantitative R1 relaxometry assessed on the midpoint of the cortex, was performed on a cohort of pediatric subjects (6-12 years old) scanned without sedation using SNARE-MPnRAGE (self navigated retrospective motion corrected magnetization prepared with n rapid gradient echoes) imaging. Reliability was assessed with coefficients of variation (CoVs) and intraclass correlation coefficients (ICCs) and statistical tests were used to determine differences with and without SNARE motion correction. Comparison of the test-retest measures of SNARE-MPnRAGE with prospectively motion corrected PROMO MPRAGE were also performed. When SNARE motion correction was used all parameters had statistically significant improvements and demonstrated high reliability. Reliability varied depending on parameter, region, and measurement type (vertex or region of interest). For mean thickness/surface area/volume/mean R1 across the regions of FreeSurfer’s DK Atlas, the mean CoVs (% x100) were (1.2/1.6/1.9/0.9) and the mean ICCs were (0.88/0.96/0.94/0.83). When assessed on a per-vertex basis, the CoVs and ICCs for thickness/R1 had mean values of (2.9/1.9) and (0.82/0.68) across the regions of the DK Atlas. Retrospectively motion corrected MPnRAGE had significantly lower CoVs and higher ICCs for the morphological measures than PROMO MPRAGE. Motion correction effectively removed motion related biases in nearly all regions for R1 and morphometric measures.

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Prenatal opioid exposure has been linked to altered neurodevelopment and visual problems such as strabismus and nystagmus. The neural substrate underlying these alterations is unclear. Resting-state functional connectivity MRI (rsfMRI) is an advanced and well-established technique to evaluate brain networks. Few studies have examined the effects of prenatal opioid exposure on resting-state network connectivity in infancy. In this pilot study, we characterized network connectivity in opioid-exposed infants (n = 19) and controls (n = 20) between 4-8 weeks of age using both a whole-brain connectomic approach and a seed-based approach. Prenatal opioid exposure was associated with differences in distribution of betweenness centrality and connection length, with positive connections unique to each group significantly longer than common connections. The unique connections in the opioid-exposed group were more often inter-network connections while unique connections in controls and connections common to both groups were more often intra-network. The opioid-exposed group had smaller network volumes particularly in the primary visual network, but similar network strength as controls. Network topologies as determined by dice similarity index were different between groups, particularly in visual and executive control networks. These results may provide insight into the neural basis for the developmental and visual problems associated with prenatal opioid exposure.

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There is a dire need for research regarding the implications of opioid use during pregnancy on fetal and childhood development to better inform both medical practice and policy. The Healthy Brain and Child Development Study will examine brain and behavioral development from birth through the first decade of life. Due to large scope and anticipated complexity of this initiative, an 18-month planning phase was implemented across 28 sites across the nation. A core element of the Phase I initiative involved the development of Stakeholder Advisory Committees to inform the next phase of the initiative. Phase I stakeholder meetings were conducted at Oregon Health and Science University, New York University Langone Medical Center, the University of Pittsburgh, and the University of Vermont to better understand perspectives and inform upcoming research. Despite differences in the structure of the stakeholder meetings by site, the overarching goals for the meetings included establishing relationships, gathering input, and learning about research engagement. Documents from each meeting were reviewed for location, duration, attendees, common research themes, and pertinent suggestions for improving research approaches. All stakeholders had high levels of interest in research for pregnant people with substance use disorders and agreed on research priorities including collaboration, connection, communication, and support. Different stakeholders offered unique perspectives on various aspects of study design and themes that emerged through meetings. Overall, there was excitement about the research, especially the opportunity to include the voices of people with lived experience; collaboration between providers, peer support specialists, patients, and others; and excitement around contributing to research that could elucidate new and pertinent findings in the realm of addiction medicine and child development. Sites also found that there is mistrust between people with substance use disorder and the medical system, and this could be addressed by including people with lived experience on the research team, forming connections, communicating clearly, training the research team in implicit bias, and practicing trauma-informed care. In conclusion, these stakeholder meetings provided valuable information for structuring upcoming studies; however, researchers would have benefitted from more time and more opportunities for in-person connection.

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The COVID-19 pandemic priorities have focused on prevention, detection, and response. Beyond morbidity and mortality, pandemics carry secondary impacts, such as children orphaned or bereft of their caregivers. Such children often face adverse consequences, including poverty, abuse, and institutionalisation. We provide estimates for the magnitude of this problem resulting from COVID-19 and describe the need for resource allocation.

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Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

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Though electrophysiological measures (EEG and ERP) offer complementary information to MRI and a variety of advantages for studying infants and young children, these measures have not yet been included in large cohort studies of neurodevelopment. This review summarizes the types of EEG and ERP measures that could be used in the HEALthy Brain and Cognitive Development (HBCD) study, and the promises and challenges in doing so. First, we provide brief overview of the use of EEG/ERP for studying the developing brain and discuss exemplar findings, using resting or baseline EEG measures as well as the ERP mismatch negativity (MMN) as exemplars. We then discuss the promises of EEG/ERP such as feasibility, while balancing challenges such as ensuring good signal quality in diverse children with different hair types. We then describe an ongoing multi-site EEG data harmonization from our groups. We discuss the process of alignment and provide preliminary usability data for both resting state EEG data and auditory ERP MMN in diverse samples including over 300 infants and toddlers. Finally, we provide recommendations and considerations for the HBCD study and other studies of neurodevelopment.

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The number of children with prenatal polysubstance exposure is increasing. Supportive mother-child interaction is a protective factor, which can ameliorate adverse effects of prenatal polysubstance exposure on developmental outcomes.

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The National Institutes of Health announced the Healthy Brain and Child Development (HBCD) study to further understanding of infant brain development. This study examined perceptions and knowledge about research among the demographic groups to be studied in HBCD.

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Studies reporting significant associations between maternal prenatal stress and child outcomes are frequently confounded by correlates of prenatal stress that influence the postnatal rearing environment. The major objective of this study is to identify whether maternal prenatal stress is associated with variation in human brain functional connectivity prior to birth. We utilized fetal fMRI in 118 fetuses [48 female; mean age 32.9 weeks (SD = 3.87)] to evaluate this association and further addressed whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Community detection was used to empirically define networks and enrichment was used to isolate differential within- or between-network connectivity effects. Significance for χ enrichment was determined by randomly permuting the subject pairing of fetal brain connectivity and maternal stress values 10,000 times. Mixtures modelling was used to test whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Increased maternal prenatal negative affect/stress was associated with alterations in fetal frontoparietal, striatal, and temporoparietal connectivity (β = 0.82, p < 0.001). Follow-up analysis demonstrated that these associations were stronger in women with better health behaviors, more positive interpersonal support, and lower overall stress (β = 0.16, p = 0.02). Additionally, magnitude of stress-related differences in neural connectivity was marginally correlated with younger gestational age at delivery (β = -0.18, p = 0.05). This is the first evidence that negative affect/stress during pregnancy is reflected in functional network differences in the human brain in utero, and also provides information about how positive interpersonal and health behaviors could mitigate prenatal brain programming.

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Different functional networks exhibit distinct longitudinal trajectories throughout development, but the timeline of the dynamics of functional connectivity across the whole brain remains to be elucidated. Here we used resting-state fMRI to investigate the development of voxel-level changes in functional connectivity across the first six years of life. Globally, we found that developmental changes in functional connectivity are nonlinear with more changes during the first postnatal year than the second, followed by most significant changes from ages 2-4 and from ages 4-6. However, the overall global difference observed between the first and second year appears to have been driven by girls. Limbic and subcortical areas consistently demonstrated the most substantial changes, whereas primary sensory areas were the most stable. These patterns were consistent in full-term and preterm subgroups. Validation on randomly divided subsamples as well as in an independent cross-sectional sample revealed global patterns consistent with the main results. Overall, the derived developmental heatmaps reveal novel dynamics underlying functional circuit development during the first 6 years of life.

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Exposure to adversity can accelerate biological aging. However, existing biomarkers of early aging are either costly and difficult to collect, like epigenetic signatures, or cannot be detected until late childhood, like pubertal onset. We evaluated the hypothesis that early adversity is associated with earlier molar eruption, an easily assessed measure that has been used to track the length of childhood across primates. In a preregistered analysis ( = 117, ages 4 to 7 y), we demonstrate that lower family income and exposure to adverse childhood experiences (ACEs) are significantly associated with earlier eruption of the first permanent molars, as rated in T2-weighted magnetic resonance images (MRI). We replicate relationships between income and molar eruption in a population-representative dataset (National Health and Nutrition Examination Survey; = 1,973). These findings suggest that the impact of stress on the pace of biological development is evident in early childhood, and detectable in the timing of molar eruption.

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The study examined whether mother-child reciprocity across increasingly challenging contexts moderated the association between household chaos and early childhood behavior problems.

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Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

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Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, resolution, and noise. To this end, in the absence of paired data, variations of Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. Importantly, these methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. In this work, based on an underlying assumption that morphological shape is consistent across imaging sites, we propose a segmentation-renormalized image translation framework to reduce inter-scanner heterogeneity while preserving anatomical layout. We replace the affine transformations used in the normalization layers within generative networks with trainable scale and shift parameters conditioned on jointly learned anatomical segmentation embeddings to modulate features at every level of translation. We evaluate our methodologies against recent baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on datasets with and without lesions. Segmentation-renormalization for translation GANs yields superior image harmonization as quantified by Inception distances, demonstrates improved downstream utility via post-hoc segmentation accuracy, and improved robustness to translation perturbation and self-adversarial attacks.

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Recent studies indicate that sex-based differences exist in co-occurring psychiatric symptoms and disorders among individuals with opioid use disorders (OUD). Whether these associations are present in adolescent samples and change during OUD treatment is poorly understood.

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Understanding the impact of substance use during pregnancy on fetal development and child health is essential for designing effective approaches for reducing prenatal substance exposures and improving child outcomes. Research on the developmental impacts of prenatal substance exposure has been limited by legal, ethical, and practical challenges. This study examined approaches to engage substance-using (with an emphasis on opioids) pregnant persons in longitudinal research, from multi-stakeholder perspectives.

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We design a framework for studying prelinguistic child voice from 3 to 24 months based on state-of-the-art algorithms in diarization. Our system consists of a time-invariant feature extractor, a context-dependent embedding generator, and a classifier. We study the effect of swapping out different components of the system, as well as changing loss function, to find the best performance. We also present a multiple-instance learning technique that allows us to pre-train our parameters on larger datasets with coarser segment boundary labels. We found that our best system achieved 43.8% DER on test dataset, compared to 55.4% DER achieved by LENA software. We also found that using convolutional feature extractor instead of logmel features significantly increases the performance of neural diarization.

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Black Americans have vastly increased odds and earlier onsets of stress- and age-related disease compared to White Americans. However, what contributes to these racial health disparities remains poorly understood. Using a sample of 1577 older adults (32.7% Black; ages 55-65 at baseline), we examined whether stress, health behaviors, social isolation, and inflammation are associated with racial disparities in self-reported physical health. A latent cumulative stress factor and unique stress-domain specific factors were modeled by applying bifactor confirmatory analysis to assessments across the lifespan (i.e., childhood maltreatment, trauma exposure, discrimination, stressful life events, and indices of socioeconomic status). Physical health, health behavior, and social isolation were assessed using self-report. Interleukin-6 (IL-6) and C-reactive protein (CRP) were assayed from morning fasting serum samples; a z-scored inflammation index was formed across these 2 cytokines. A parallel serial mediational model tested whether race (i.e., Black/White) is indirectly associated with health through the following 3 independent pathways: (1) cumulative stress to preventative health behaviors (e.g., healthy eating) to inflammation, (2) cumulative stress to risky health behaviors (e.g., substance use) to inflammation; and (3) cumulative stress to social isolation to inflammation. There were significant indirect effects between race and self-reported physical health through cumulative stress, preventative health behaviors, and inflammation (B = -0.02, 95% CI: -0.05, -0.01). Specifically, Black Americans were exposed to greater cumulative stress, which was associated with reduced engagement in preventative health behaviors, which was, in turn, associated with greater inflammation and reduced physical health. A unique SES factor also indirectly linked race to physical health through preventative health behaviors. Cumulative stress exposure and unique aspects of socioeconomic status are indirectly associated with Black-White racial health disparities through behavioral (i.e., preventative health behavior) and biological (i.e., inflammation) factors. Culturally responsive evidence-based interventions that enhance engagement in preventative health behaviors are needed to directly confront health disparities. Ultimately, large scale anti-racist public policies that reduce cumulative stress burden (e.g., a living wage, universal healthcare) may best attenuate racial health disparities.

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