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Increasing rates of overdose among U.S. adolescents and young adults, along with rising rates of emotional distress in these groups, are renewing the urgency for developmentally targeted and personalized substance use and other mental health prevention interventions. Most prevention programs recognize the unique vulnerability of childhood and adolescence and target parents and youths, addressing modifiable environmental risk and protective factors that affect behavior during periods when the brain is most susceptible to change. Until recently, a scarcity of comprehensive studies has limited a full understanding of the complexity of factors that may affect neurodevelopment, including substance exposure in pregnancy and/or subsequent substance use in adolescence, alongside their dynamic interactions with environmental factors and genetics. Two large longitudinal cohort studies funded by National Institutes of Health—the Adolescent Brain Cognitive Development (ABCD) Study and the HEALthy Brain and Child Development (HBCD) Study—are collecting data on neurodevelopment and a wide range of environmental and biological factors across the first two decades of life to build databases that will allow researchers to study how individual neurodevelopmental trajectories are influenced by drugs, adverse childhood experiences, and genetics, among other factors. These studies are already deepening the understanding of risk and resilience factors that prevention programs could target and will identify critical windows where interventions can have the most impact on an individual’s neurodevelopmental trajectory. This article describes what is being learned from ABCD and expected from HBCD and how these studies might inform prevention as these children grow and more data are gathered.

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The research team, prospective participants, and written materials all influence the success of the informed consent process. As digital health research becomes more prevalent, new challenges for successful informed consent are introduced. This exploratory research utilized a human centered design process in which 19 people were enrolled to participate in one of four online focus-groups. Participants discussed their experiences with informed consent, preferences for receiving study information and ideas about alternative consent approaches. Data were analyzed using qualitative methods. Six major themes and sixteen sub-themes were identified that included study information that prospective participants would like to receive, preferences for accessing information and a desire to connect with research team members. Specific to digital health, participants expressed a need to understand how the technologies worked and how the volume of granular personal information would be collected, stored, and shared.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study’s EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.

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Experiencing multiple adverse childhood experiences (ACEs) is associated with alcohol use in female adolescents and emerging adults. Protective and compensatory experiences (PACEs) have been theorized to off-set the health and behavioral consequences from the accumulation of ACEs throughout childhood. This study examines the association between protective experiences and subsequent alcohol and binge alcohol use frequency over one month among female adolescent and emerging adults reporting high and low levels of two ACE dimensions (household dysfunction and emotional abuse/neglect).

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Genetic testing of patients with neurodevelopmental disabilities (NDDs) is critical for diagnosis, medical management, and access to precision therapies. Because genetic testing approaches evolve rapidly, professional society practice guidelines serve an essential role in guiding clinical care; however, several challenges exist regarding the creation and equitable implementation of these guidelines. In this scoping review, we assessed the current state of United States professional societies’ guidelines pertaining to genetic testing for unexplained global developmental delay, intellectual disability, autism spectrum disorder, and cerebral palsy. We describe several identified shortcomings and argue the need for a unified, frequently updated, and easily-accessible cross-specialty society guideline. ANN NEUROL 2024;96:900-913.

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Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

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The fundamental organization of the human brain is established before birth, with rapid growth continuing over the first postnatal years. Children exposed before or after birth to various biological (e.g., substance exposure) or psychosocial hazards (e.g., maltreatment) are at elevated likelihood of deviating from a typical developmental trajectory, which in turn can be associated with psychological, behavioral, and physical health sequelae. In the HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, brain, physical, biological, cognitive, behavioral, social, and emotional development is being examined starting in pregnancy and planned through age 10 (data are sampled at varying degrees of granularity depending on age, with more dense sampling earlier in life). HBCD aims to determine the short- and long-term impacts of a variety of both harmful and protective factors, including prenatal substance use, on developmental trajectories through early childhood. Organized as a nationwide consortium across 27 sites, the HBCD Study will collect multimodal data that will be made publicly available on a yearly basis, through a data use application and approval process. Here we provide an overview of the HBCD Study design, sampling, protocol development, and data management. Data collected through HBCD will be fundamental to informing future prenatal and early childhood interventions and policies to promote wellbeing and resilience in all children.

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While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration () is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors.

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Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; < .001) and PFS (HR = 1.04 per milliliter; < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Segmentation, Glioblastoma, Multishell Diffusion MRI © RSNA, 2024.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The charge of the HBCD Social and Environmental Determinants (SED) working group is to develop and implement a battery of assessments to broadly characterize the social and physical environment during the prenatal period and early life to characterize risk and resilience exposures that can impact child growth and development. The SED battery consists largely of measures that will be repeated across the course of the HBCD Study with appropriate modifications for the age of the child and include participant demographics, indicators of socioeconomic status, stress and economic hardship, bias and discrimination (e.g., racism), acculturation, neighborhood safety, child and maternal exposures to adversity, environmental toxicants, social support, and other protective factors. Special considerations were paid to reducing participant burden, promoting diversity, equity, and inclusion, and adopting trauma-informed practices for the collection of sensitive information such as domestic violence exposure and adverse childhood experiences. Overall, the SED battery will provide essential data to advance understanding of child development and approaches to advance health equity across infant and child development.

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Preterm birth poses a major public health challenge, with significant and heterogeneous developmental impacts. Latent profile analysis was applied to the National Institutes of Health Toolbox performance of 1891 healthy prematurely born children from the Adolescent Brain and Cognitive Development study (970 boys, 921 girls; 10.00 ± 0.61 years; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 57.0% White, 10.4% Other). Three distinct neurocognitive profiles emerged: consistently performing above the norm (19.7%), mixed scores (41.0%), and consistently performing below the norm (39.3%). These profiles were associated with lasting cognitive, neural, behavioral, and academic differences. These findings underscore the importance of recognizing diverse developmental trajectories in prematurely born children, advocating for personalized diagnosis and intervention to enhance care strategies and long-term outcomes for this heterogeneous population.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. This article outlines methodological considerations and the decision-making process for measurement selection for child behavior, parenting/caregiver-child interactions, and the family/home environment for HBCD. The decision-making process is detailed, including formation of a national workgroup (WG-BEH) that focused on developmentally appropriate measures that take a rigorous and equitable approach and aligned with HBCD objectives. Multi-level-observational and caregiver-report measures were deemed necessary for capturing the desired constructs across multiple contexts while balancing the nuance of observational data with pragmatic considerations. WG-BEH prioritized developmentally sensitive, validated assessments with psychometrics supporting use in diverse populations and focused on mechanistic linkages and prediction of desired constructs. Other considerations included participant burden and retention, staff training needs, and cultural sensitivity. Innovation was permitted when it was grounded in evidence and filled key gaps. Finally, this article describes the rationale for the selected constructs (e.g., temperament, social-emotional development, parenting behaviors, family organization) and corresponding measures chosen for HBCD visits from early infancy through 17 months of age.

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The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7,000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individual’s genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Given its aim to examine the impact of adversity and protective factors on children’s outcomes, the recruitment and retention of families who have a wide diversity in experiences are essential. However, the unfortunate history of inequitable treatment of underrepresented families in research and the risks with which some participants will contend (e.g., substance use) makes their recruitment and retention in social science and neuroscience research particularly challenging. This article explores strategies that the HBCD Study has developed to recruit and retain participants, including marginalized, underserved, and hard-to-reach populations, capitalizing on the extant literature and the researchers’ own experiences. In this paper, we address strategies to recruit and retain families within HBCD, including: 1) creating experiences that engender trust and promote relationships; 2) maintaining connections with participants over time; 3) ensuring appropriate compensation and supports; 4) considerations for study materials and procedures; and 5) community engagement. The implementation of these strategies may increase representation and inclusiveness, as well as improve the quality of the resulting data.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. From the outset, the HBCD Study integrated diversity, equity, and inclusion (DEI) into its core mission, with key elements like an Associate Director for DEI, a DEI values statement, and establishing a DEI committee to help ensure sustainable progress for the future. The DEI Coordinating Committee supports DEI efforts impacting members of the HBCD consortium, study participants, and the analysis and dissemination of HBCD data. Committee members include representatives from every study site and workgroup which contributes to DEI integration throughout the study. Committee activities include reviewing all measures and protocols, creating accountability metrics, and supporting training opportunities for consortium members. Several successes and lessons have been learned through the Committee’s activities like implementing consortium-wide unconscious bias training and changing the DEI leadership and committee structure. This article presents an overview of HBCD’s DEI components within HBCD and provides examples of collaborative efforts between the DEI Coordinating Committee and other workgroups. The article concludes with plans for future activities and recommendations for other large consortia considering formal DEI structures.

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Preterm birth (birth at <37 completed weeks gestation) is a significant public heatlh concern worldwide. Important health, and developmental consequences of preterm birth include altered temperament development, with greater dysregulation and distress proneness.

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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social and emotional development beginning prenatally and planned through early childhood. Many prenatal and early childhood exposures impact both later physical health and development. Moreover, early deficits in physical health, such as growth and vision, are associated with differences in brain development, language and cognitive functioning. For these reasons, the HBCD Study includes measures of early childhood physical health, many of which have clinical relevance, and are applicable for use as both predictors and outcomes. Study measures assess a broad range of physical health domains and include both objective measurement of child growth and health and subjective caregiver report of behaviors and attitudes about constructs known to influence growth and physical development. Lastly, we obtain caregiver report of the child’s routine medical care as well as acute and chronic medical issues. We anticipate that these data will contextualize the impact of child physical growth and health on child brain development and function. In this report we present the rationale for each domain and an overview of the physical health measures included in the current HBCD Study protocol.

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Prenatal substance exposure (PSE) can lead to various harmful outcomes for the developing fetus and is linked to many emotional, behavioral, and cognitive difficulties later in life. Therefore, examination of the relationship between the development of associated brain structures and PSE is important for the development of more specific or new preventative methods.

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Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

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While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

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Although the link between alcohol involvement and behavioral phenotypes (e.g. impulsivity, negative affect, executive function [EF]) is well-established, the directionality of these associations, specificity to stages of alcohol involvement, and extent of shared genetic liability remain unclear. We estimate longitudinal associations between transitions among alcohol milestones, behavioral phenotypes, and indices of genetic risk.

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The brain’s white matter connections are thought to provide the structural basis for its functional connections between distant brain regions but how our brain selects the best structural routes for effective functional communications remains poorly understood. In this study, we propose a Unified Structural and Functional Connectivity (USFC) model and use an “economical assumption” to create the brain’s first “traffic map” reflecting how frequently each structural connection segment of the brain is used to achieve the global functional communication system. The resulting USFC map highlights regions in the subcortical, default-mode, and salience networks as the most heavily traversed nodes and a midline frontal-caudate-thalamus-posterior cingulate-visual cortex corridor as the backbone of the whole brain connectivity system. Our results further revealed a striking negative association between structural and functional connectivity strengths in routes supporting negative functional connections as well as much higher efficiency metrics in the USFC connectome when compared to structural and functional ones alone. Overall, the proposed USFC model opens up a new window for effective brain connectome modeling and provides a considerable leap forward in brain mapping efforts for a better understanding of the brain’s fundamental communication mechanisms.

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Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

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A central goal in the field of developmental psychopathology is to evaluate the complex, dynamic transactions occurring among biological, psychological, and broader social-cultural contexts that predict adaptive and maladaptive outcomes across ontogeny. Here, I briefly review research on the effects of a history of childhood maltreatment on parental, child, and dyadic functioning, along with more recent studies on the intergenerational transmission of trauma. Because the experience and sequelae of child maltreatment and the intergenerational transmission of trauma are embedded in complex biopsychosocial contexts, this research is best conceptualized in a developmental psychopathology framework. Moreover, there is a pressing need for investigators in this area of study to adopt dynamic, multi-level perspectives as well as using developmentally guided, sophisticated research methods. Other directions for research in this field are suggested, including the implementation of collaborative interdisciplinary team science approaches, as well as community-based participatory research, to increase representation, inclusion, and equity of community stakeholders. A greater focus on cultural and global perspectives is also recommended.

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Prenatal cannabis use is associated with adverse offspring neurodevelopmental outcomes, however the underlying mechanisms are relatively unknown. We sought to determine the impact of chronic delta-9-tetrahydrocannabinol (THC) exposure on fetal neurodevelopment in a rhesus macaque model using advanced imaging combined with molecular and tissue studies. Animals were divided into two groups, control (n = 5) and THC-exposed (n = 5), which received a daily THC edible pre-conception and throughout pregnancy. Fetal T2-weighted MRI was performed at gestational days 85 (G85), G110, G135 and G155 to assess volumetric brain development. At G155, animals underwent cesarean delivery with collection of fetal cerebrospinal fluid (CSF) for microRNA (miRNA) studies and fetal tissue for histologic analysis. THC exposure was associated with significant age by sex interactions in brain growth, and differences in fetal brain histology suggestive of brain dysregulation. Two extracellular vesicle associated-miRNAs were identified in THC-exposed fetal CSF; pathway analysis suggests that these miRNAs are associated with dysregulated axonal guidance and netrin signaling. This data is indicative of subtle molecular changes consistent with the observed histological data, suggesting a potential role for fetal miRNA regulation by THC. Further studies are needed to determine whether these adverse findings correlate with long-term offspring neurodevelopmental health.

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The extent to which neuroanatomical variability associated with substance involvement reflects pre-existing risk and/or consequences of substance exposure remains poorly understood. In the Adolescent Brain Cognitive Development (ABCD) Study, we identify associations between global and regional differences in brain structure and early substance use initiation (i.e., occurring <15 years of age; ns=6,556-9,804), with evidence that associations precede initiation. Neurodevelopmental variability in brain structure may confer risk for substance involvement.

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Food insecurity during pregnancy is associated with various adverse pregnancy outcomes for the mother and infant, but less is known about the role of periconception food insecurity and its links to maternal and child wellbeing in the postpartum period. In a sample of 115 diverse (41% white) and predominately low-income mothers, results of hierarchical regression analyses showed that periconception food insecurity was positively associated with parenting stress at 2 months postpartum. A negative association between food insecurity and maternal-infant bonding at 6 months postpartum was mediated after controlling for prenatal depression, social support, and demographic factors. Findings highlight the need for maternal linkage to effective food security programs, such as United States-based Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), for women during their childbearing years due to the critical importance of food security for maternal and infant well-being.

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Participant recruitment and retention (R&R) are well-documented challenges in longitudinal studies, especially those involving populations historically underrepresented in research and vulnerable groups (e.g., pregnant people or young children and their families), as is the focus of the HEALthy Brain and Child Development (HBCD) birth cohort study. Subpar access to transportation, overnight lodging, childcare, or meals can compromise R&R; yet, guidance on how to overcome these “logistical barriers” is sparse. This study’s goal was to learn about the HBCD sites’ plans and develop best practice recommendations for the HBCD consortium for addressing these logistical barriers.

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Wearable devices permit the continuous, unobtrusive collection of data from children in their natural environments and can transform our understanding of child development. Although the use of wearable devices has begun to emerge in research involving children, few studies have considered families’ experiences and perspectives of participating in research of this kind.

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While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques.

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Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, n /n  = 110/106). The outputs of FreeSurfer’s hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; R  = 65% vs. R  = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R  = 25%; p < 10 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.

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The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

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Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, = 16) and infants (Study 2, = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.

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To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T and T mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling.

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We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal. Such 6D data naturally arises in diffusion MRI (dMRI), a medical imaging modality widely used to measure microstructure and structural connectivity. As each dMRI voxel is typically a mixture of various overlapping structures, there is a need for blind deconvolution to recover crossing anatomical structures such as white matter tracts. Existing dMRI work takes either an iterative or deep learning approach to sparse spherical deconvolution, yet it typically does not account for relationships between neighboring measurements. This work constructs equivariant deep learning layers which respect to symmetries of spatial rotations, reflections, and translations, alongside the symmetries of voxelwise spherical rotations. As a result, RT-ESD improves on previous work across several tasks including fiber recovery on the DiSCo dataset, deconvolution-derived partial volume estimation on real-world human brain dMRI, and improved downstream reconstruction of fiber tractograms on the Tractometer dataset. Our implementation is available at https://github.com/AxelElaldi/e3so3_conv.

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In light of a history of categorical exclusion, it is critical that pregnant people are included in research to help improve the knowledge base and interventions needed to address public health. Yet the volatile legal landscape around reproductive rights in the United States threatens to undue recent progress made toward the greater inclusion of pregnant people in research. We offer ethical and practical guidance for researchers, sponsors, and institutional review boards to take specific steps to minimize legal risks and ensure the ethical conduct of research with pregnant people in an evolving legal environment.

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Grandparents play a critical role in child rearing across the globe. Yet, there is a shortage of neurobiological research examining the relationship between grandparents and their grandchildren. We employ multi-brain neurocomputational models to simulate how changes in neurophysiological processes in both development and healthy aging affect multigenerational inter-brain coupling – a neural marker that has been linked to a range of socio-emotional and cognitive outcomes. The simulations suggest that grandparent-child interactions may be paired with higher inter-brain coupling than parent-child interactions, raising the possibility that the former may be more advantageous under certain conditions. Critically, this enhancement of inter-brain coupling for grandparent-child interactions is more pronounced in tri-generational interactions that also include a parent, which may speak to findings that grandparent involvement in childrearing is most beneficial if the parent is also an active household member. Together, these findings underscore that a better understanding of the neurobiological basis of cross-generational interactions is vital, and that such knowledge can be helpful in guiding interventions that consider the whole family. We advocate for a community neuroscience approach in developmental social neuroscience to capture the diversity of child-caregiver relationships in real-world settings.

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Studies of prenatal substance exposure often rely on self-report, urine drug screens, and/or analyses of blood or meconium biomarkers. Accuracy of these measures is limited when assessing exposure over many weeks or months of gestation. Nails are increasingly being considered as a matrix from which to assess substance exposure. This systematic review synthesizes data on the validity of detecting alcohol, nicotine, cannabis, and opioid from nail clippings, with an emphasis on prenatal exposure assessment.

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The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.

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Brain iron is vital for core neurodevelopmental processes including myelination and neurotransmitter synthesis and, accordingly, iron accumulates in the brain with age. However, little is known about the association between brain iron and neural functioning and how they evolve with age in early infancy. This study investigated brain iron in 48 healthy infants (22 females) aged 64.00 ± 33.28 days by estimating R2 * relaxometry from multi-echo functional MRI (fMRI). Linked independent component analysis was performed to examine the association between iron deposition and spontaneous neural activity, as measured by the amplitude of low frequency fluctuations (ALFF) by interrogating shared component loadings across modalities. Further, findings were validated in an independent dataset (n = 45, 24 females, 77.93 ± 26.18 days). The analysis revealed developmental coupling between the global R2 * and ALFF within the default mode network (DMN). Furthermore, we observed that this coupling effect significantly increased with age (r = 0.78, p = 9.2e-11). Our results highlight the importance of iron-neural coupling during early development and suggest that the neural maturation of the DMN may correspond to growth in distributed brain iron.

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Prenatal cannabis exposure (PCE) is associated with mental health problems, but the neurobiological mechanisms remain unknown. We find that PCE is associated with localized differences across neuroimaging metrics that longitudinally mediate associations with mental health in adolescence (n=9,322-10,186). Differences in brain development may contribute to PCE-related variability in adolescent mental health.

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Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

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Previous studies have shown that brain volume is negatively associated with cigarette smoking, but there is an ongoing debate about whether smoking causes lowered brain volume or a lower brain volume is a risk factor for smoking. We address this debate through multiple methods that evaluate directionality: Bradford Hill’s criteria, which are commonly used to understand a causal relationship in epidemiological studies, and mediation analysis.

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The COVID-19 pandemic dramatically altered the psychosocial environment of pregnant women and new mothers. In addition, prenatal infection is a known risk factor for altered fetal development. Here we examine joint effects of maternal psychosocial stress and COVID-19 infection during pregnancy on infant attention at 6 months postpartum.

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Volumetric, high-resolution, quantitative mapping of brain-tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time-efficient wave-controlled aliasing in parallel imaging (wave-CAIPI) readouts with the 3D quantification using an interleaved Look-Locker acquisition sequence with a T preparation pulse (3D-QALAS), enabling full-brain quantitative T , T , and proton density (PD) maps at 1.15-mm isotropic voxels in 3 min.

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The COVID-19 pandemic has led to escalations in substance use, including alcohol consumption. Of particular concern are the potential impacts during the postpartum period, a time of heightened vulnerability to stress and potential transmission of the negative sequelae of substance use to offspring. However, postpartum alcohol consumption during the COVID-19 pandemic has not been well characterized.

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Cognitive gerontology research requires consideration of performance as well as perceptions of performance. While subjective memory is positively associated with memory performance, these correlations typically are modest in magnitude, leading to the need to consider whether certain people may show weaker or stronger linkages between performance and perceptions. The current study leveraged personality (NEO Big Five), memory performance (i.e., word recall), and perceptions of memory ability (i.e., metamemory in adulthood and memory decline) data from the St. Louis Personality and Aging Network (SPAN) study ( = 774, mean age: 71.52 years). Extraversion and conscientiousness held the most consistent associations with the cognitive variables of interest, as both traits were positively associated with metamemory and word recall, but negatively associated with subjective decline. Moreover, extraversion moderated associations between word recall and both memory capacity and complaints, insofar that objective-subjective associations were weaker for those adults higher in extraversion. These findings highlight the need to understand how personality influences the sources of information employed for subjective cognitive beliefs.

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Alcohol use is a growing global health concern and economic burden. Alcohol involvement (i.e., initiation, use, problematic use, alcohol use disorder) has been reliably associated with broad spectrum grey matter differences in cross-sectional studies. These findings have been largely interpreted as reflecting alcohol-induced atrophy. However, emerging data suggest that brain structure differences also represent pre-existing vulnerability factors for alcohol involvement. Here, we review evidence from human studies with designs (i.e., family-based, genomic, longitudinal) that allow them to assess the plausibility that these correlates reflect predispositional risk factors and/or causal consequences of alcohol involvement. These studies provide convergent evidence that grey matter correlates of alcohol involvement largely reflect predisposing risk factors, with some evidence for potential alcohol-induced atrophy. These conclusions highlight the importance of study designs that can provide causal clues to cross-sectional observations. An integrative model may best account for these data, in which predisposition to alcohol use affects brain development, effects which may then be compounded by the neurotoxic consequences of heavy alcohol use.

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Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.

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The COVID-19 pandemic and resulting public health directives led to many changes in families’ social and material environments. Prior research suggests that these changes are likely to impact composition of the gut microbiome, particularly during early childhood when the gut microbiome is developing most rapidly. Importantly, disruption to the gut microbiome during this sensitive period can have potentially long-lasting impacts on health and development. In the current study, we compare gut microbiome composition among a socioeconomically and racially diverse group of 12-month old infants living in New York City who provided stool samples before the pandemic (N = 34) to a group who provided samples during the first 9-months of the pandemic (March-December 2020; N = 20). We found that infants sampled during the pandemic had lower alpha diversity of the microbiome, lower abundance of Pasteurellaceae and Haemophilus, and significantly different beta diversity based on unweighted Unifrac distance than infants sampled before the pandemic. Exploratory analyses suggest that gut microbiome changes due to the pandemic occurred relatively quickly after the start of the pandemic and were sustained. Our results provide evidence that pandemic-related environmental disruptions had an impact on community-level taxonomic diversity of the developing gut microbiome, as well as abundance of specific members of the gut bacterial community.

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The underlying mechanisms leading to altered cognitive, behavioral, and vision outcomes in children with prenatal opioid exposure are yet to be fully understood. Some studies suggest WM alterations in infants and children with prenatal opioid exposure; however, the time course of WM changes is unknown. We aimed to evaluate differences in diffusion tensor imaging MRI parameters in the brain between opioid exposed fetuses and normal controls.

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To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE).

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Maternal stress has negative consequences on infant behavioral development, and COVID-19 presented uniquely stressful situations to mothers of infants born during the pandemic. We hypothesized that mothers with higher levels of perceived stress during the pandemic would report higher levels of infant regulatory problems including crying and interrupted sleep patterns.

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Rodent models suggest that exposure to under and overnutrition programs offspring physical activity (PA) behaviors. Such nexus has not been established in humans. This study evaluated the association of early pregnancy maternal adiposity with offspring PA at age 2 years (2-yo-PA) taking into consideration prenatal and postnatal factors. Women (n=153) were enrolled early in pregnancy (<10 weeks). At enrollment, maternal adiposity [air displacement plethysmography, fat mass index (FMI, kg/m )] and PA (accelerometers, activity counts) were measured, and age, race, and education self-reported. Gestational weight gain was measured at the research facility. Offspring birthweight and sex were self-reported. At age 2 years, parental feeding practices (child feeding questionnaire) were assessed, whereas anthropometrics (length and weight) and physical activity (accelerometers) were objectively measured. Offspring body mass index z-scores were calculated. Generalized linear regression analysis modeled the association of maternal FMI and 2-yo-PA [average activity counts (AC) /day]. There was an interaction between maternal FMI and offspring sex in association with 2-yo-PA (β= -1.03, p= 0.030). Specifically, 2-yo-PA was lower in girls compared to boys when maternal FMI was ≥7 kg/m . Maternal PA early in pregnancy positively associated with 2-yo-PA (β= 0.21, p= 0.005). In addition, children born to women with college education tended to be more active compared to children born to women without college education (β= 3.46, p= 0.059). Sexual dimorphism was observed in the associations of maternal adiposity with 2-yo-PA, with girls being less active compared to boys only when maternal FMI was ≥7 kg/m .

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The overarching goal of this paper is to examine the efficacy of early intervention when viewed through the lens of developmental neuroscience. We begin by briefly summarizing neural development from conception through the first few postnatal years. We emphasize the role of experience during the postnatal period, and consistent with decades of research on critical periods, we argue that experience can represent both a period of opportunity and a period of vulnerability. Because plasticity is at the heart of early intervention, we next turn our attention to the efficacy of early intervention drawing from two distinct literatures: early intervention services for children growing up in disadvantaged environments, and children at elevated likelihood of developing a neurodevelopmental delay or disorder. In the case of the former, we single out interventions that target caregiving and in the case of the latter, we highlight recent work on autism. A consistent theme throughout our review is a discussion of how early intervention is embedded in the developing brain. We conclude our article by discussing the implications our review has for policy, and we then offer recommendations for future research.

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Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

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To develop and evaluate a method for rapid estimation of multiparametric T , T , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary.

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With the growing availability of cannabis and the popularization of additional routes of cannabis use beyond smoking, including edibles, the prevalence of cannabis use in pregnancy is rapidly increasing. However, the potential effects of prenatal cannabis use on fetal developmental programming remain unknown.

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For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels).

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Myelination is a key developmental process that promotes rapid and efficient information transfer. Myelin also stabilizes existing brain networks and thus may constrain neuroplasticity, defined here as the brain’s potential to change in response to experiences rather than the canonical definition as the process of change. Characterizing individual differences in neuroplasticity may shed light on mechanisms by which early experiences shape learning, brain and body development, and response to interventions. The T1-weighted/T2-weighted (T1w/T2w) MRI signal ratio is a proxy measure of cortical microstructure and thus neuroplasticity. Here, in pre-registered analyses, we investigated individual differences in T1w/T2w ratios in children (ages 4-10, n = 157). T1w/T2w ratios were positively associated with age within early-developing sensorimotor and attention regions. We also tested whether socioeconomic status, cognition (crystallized knowledge or fluid reasoning), and biological age (as measured with molar eruption) were related to T1w/T2w signal but found no significant effects. Associations among T1w/T2w ratios, early experiences, and cognition may emerge later in adolescence and may not be strong enough to detect in moderate sample sizes.

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Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn’t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

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Given the potential respiratory health risks, the association of COVID infection and the use of combustible cigarettes, electronic nicotine delivery systems (ENDS), and concurrent dual use is a priority for public health. Many published reports have not accounted for known covarying factors. This study sought to calculate adjusted odds ratios for self-reported COVID infection and disease severity as a function of smoking and ENDS use, while accounting for factors known to influence COVID infection and disease severity (i.e., age, sex, race and ethnicity, socioeconomic status and educational attainment, rural or urban environment, self-reported diabetes, COPD, coronary heart disease, and obesity status). Data from the 2021 U.S. National Health Interview Survey, a cross-sectional questionnaire design, were used to calculate both unadjusted and adjusted odds ratios for self-reported COVID infection and severity of symptoms. Results indicate that combustible cigarette use is associated with a lower likelihood of self-reported COVID infection relative to non-use of tobacco products (AOR = .64; 95% CI [.55, .74]), whereas ENDS use is associated with a higher likelihood of self-reported COVID infection (AOR = 1.30; 95% CI [1.04, 1.63]). There was no significant difference in COVID infection among dual users (ENDS and combustible use) when compared with non-users. Adjusting for covarying factors did not substantially change the results. There were no significant differences in COVID disease severity between those of varying smoking status. Future research should examine the relationship between smoking status and COVID infection and disease severity utilizing longitudinal study designs and non-self-report measures of smoking status (e.g., the biomarker cotinine), COVID infection (e.g., positive tests), and disease severity (e.g., hospitalizations, ventilator assistance, mortality, and ongoing symptoms of long COVID).

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Childhood maltreatment (CM) is a potent risk factor for developing psychopathology later in life. Accumulating research suggests that the influence is not limited to the exposed individual but may also be transmitted across generations. In this study, we examine the effect of CM in pregnant women on fetal amygdala-cortical function, prior to postnatal influences.

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The impact of involving peers on research engagement is largely unknown. The purpose of this pilot study, a part of a larger research, was to evaluate the impact of recovery peer involvement as a study team member on recruitment/retention of persons with lived experience of SUD during pregnancy and to assess participant perceptions about factors impacting engagement of this population and their children in research, especially brain magnetic resonance imaging (MRI).

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Parents with substance use disorders are highly stigmatized by multiple systems (e.g., healthcare, education, legal, social). As a result, they are more likely to experience discrimination and health inequities [1, 2]. Children of parents with substance use disorders often do not fare any better, as they frequently experience stigma and poorer outcomes by association [3, 4]. Calls to action for person-centered language for alcohol and other drug problems have led to improved terminology [5-8]. Despite a long history of stigmatizing, offensive labels such as “children of alcoholics” and “crack babies,” children have been left out of person-centered language initiatives. Children of parents with substance use disorders can feel invisible, shameful, isolated, and forgotten-particularly in treatment settings when programming is centered on the parent [9, 10]. Person-centered language is shown to improve treatment outcomes and reduce stigma [11, 12]. Therefore, we need to adhere to consistent, non-stigmatizing terminology when referencing children of parents with substance use disorders. Most importantly, we must center the voices and preferences of those with lived experience to enact meaningful change and effective resource allocation.

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Converging evidence suggests that elevated inflammation may contribute to depression. Yet, the link between peripheral inflammation and neuroinflammation in depression is unclear. Here, using data from the UK Biobank, we estimated associations among depression, C-reactive protein (CRP) as a measure of peripheral inflammation, and neuroinflammation as indexed by diffusion basis spectral imaging-based restricted fraction (DBSI-RF).

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Adolescent substance use, externalizing and attention problems, and early life stress (ELS) commonly co-occur. These psychopathologies show overlapping neural dysfunction in the form of reduced recruitment of reward processing neuro-circuitries. However, it is unclear to what extent these psychopathologies show common different neural dysfunctions as a function of symptom profiles, as no studies have directly compared neural dysfunctions associated with each of these psychopathologies to each other.

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Cannabis use among individuals of reproductive age has increased with cannabis legalization and heightened stress during the COVID-19 pandemic. Our study provides data on preconception cannabis use and cannabis use disorder (CUD) during the pandemic and models the association between preconception cannabis use and depression and anxiety during pregnancy.

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The use and misuse of opioids, as well as opioid use disorder (OUD) have increased remarkably among reproductive-aged and pregnant women. As many as 25% of pregnant women who report non-medical opioid use in the past month also report concurrent alcohol use. While teratogenic effects of alcohol are well established, there are limited studies evaluating fetal intracranial effects associated with medications for OUD (MOUD) and concurrent use of MOUD and alcohol during pregnancy. The objective of this study was to determine the effect of MOUD, with and without concomitant alcohol use, on fetal intracranial measurements. The type of maternal MOUD therapy (methadone vs. buprenorphine) was also examined.

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Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.

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Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.

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The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.

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Animal studies of neurodevelopment have shown that recordings of intrinsic cortical activity evolve from synchronized and high amplitude to sparse and low amplitude as plasticity declines and the cortex matures. Leveraging resting-state functional MRI (fMRI) data from 1,033 youths (ages 8-23 years), we find that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental change. Declines in the amplitude of intrinsic fMRI activity were initiated heterochronously across regions and were coupled to the maturation of intracortical myelin, a developmental plasticity regulator. Spatiotemporal variability in regional developmental trajectories was organized along a hierarchical, sensorimotor-association cortical axis from ages 8 to 18. The sensorimotor-association axis furthermore captured variation in associations between youths’ neighborhood environments and intrinsic fMRI activity; associations suggest that the effects of environmental disadvantage on the maturing brain diverge most across this axis during midadolescence. These results uncover a hierarchical neurodevelopmental axis and offer insight into the progression of cortical plasticity in humans.

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Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of individuals of European descent and African descent. Nineteen independent SNPs were genome-wide significant ( < 5e-8) for the general addiction risk factor (), which showed high polygenicity. Across ancestries, was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

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Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.

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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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