With age, adolescents increasingly demonstrate the ability to forgo immediate, smaller rewards in favor of larger delayed rewards, indicating reduced delay discounting. Adolescence is also a time of social reorientation, where decisions not only involve weighing immediate against future outcomes, but also consequences for self versus those for others. In this functional Magnetic Resonance Imaging study, we examined the neural correlates of immediate and delayed reward choices where the delayed outcomes could benefit self, friends, or unknown others. A total of 196 adolescent twins aged 14-17 completed a social delay discounting task, with fMRI data acquired from 174 participants. Out of these, 156 adolescents had valid fMRI data, and 138 adolescents had observations in every condition. Adolescents more often chose the immediate reward when it was larger, and when the delay was longer. Area-under-the-curve (AUC) comparisons revealed that behavior differed across delay-beneficiaries, with AUC being highest for the self, followed by friends, and lowest for unknown others. This suggests that adolescents are more willing to wait for rewards for self. Neuroimaging analyses showed increased activity in the midline areas medial prefrontal cortex (MPFC) and precuneus, as well as bilateral temporal parietal junction (TPJ) when considering delayed reward for unknown others and friends compared to self. A whole-brain interaction with choice showed that the bilateral insula and right dorsolateral prefrontal cortex (DLPFC) were more active for delayed choices for unknown others and for immediate choices for friends and self. This underscores that the neuro-cognitive processing of how delays reduce the value of rewards depends on closeness of the beneficiary.

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features, including artificial neural networks. With the notable exception of temporal response function models that are applied to electroencephalography, most prior studies have focused on adult subjects, making it difficult to capture how brain representations change with learning and development. Here, we argue that future developmental cognitive neuroscience studies would benefit from approaches relying on encoding models. We provide an overview of encoding models used in adult functional magnetic resonance imaging research. This research has notably used data with a small number of subjects, but with a large number of samples per subject. Studies using encoding models also generally require task-based neuroimaging data. Though these represent challenges for developmental studies, we argue that these challenges may be overcome by using functional alignment techniques and naturalistic paradigms. These methods would facilitate encoding model analysis in developmental neuroimaging research, which may lead to important theoretical advances.

Children's socioeconomic status (SES) is related to patterns of intrinsic resting-state brain function that subserve relevant cognitive processes over the course of development. Although infant research has demonstrated the association between children's environments, cognitive outcomes, and resting-state electroencephalography (rsEEG), it remains unknown how these aspects of their environment, tied to SES, impact neural and cognitive development throughout the school years. To address this gap, we applied a multivariate pattern analysis (MVPA) to rsEEG data to identify which neural frequencies at rest are differentially associated with unique aspects of socioeconomic status (SES; income and maternal education) and cognitive (vocabulary, working memory) outcomes among school-aged children (8-15 years). We find that the alpha frequency is associated with both income and maternal education, while lower gamma and theta fluctuations are tied to dissociable aspects of SES and cognitive outcomes. Specifically, changes in the gamma frequency are predictive of both maternal education and vocabulary outcome, while changes in the theta frequency are related to both income and working memory ability. The current findings extend our understanding of unique pathways by which SES influences cognitive and neural development in school-aged children.

Enhancing the generalizability of neuroimaging studies requires actively engaging participants from under-represented communities. This paper leverages qualitative data to outline participant-driven recommendations for incorporating under-represented populations in neuroimaging protocols. Thirty-one participants, who had participated in neuroimaging research or could be eligible for one as part of an ongoing longitudinal study, engaged in semi-structured one-on-one interviews (84 % under-represented ethnic-racial identities and low-income backgrounds). Through thematic analysis, we identified nine relevant research practices from participants' reports, highlighting aspects of their experience that they appreciated and suggestions for improvement: (1) forming a diverse research team comprising members with whom participants can interact as equals; (2) increasing accessibility to research by providing transportation and flexible scheduling; (3) providing family-oriented spaces; (4) enriching the campus visits to include optional on-campus activities to connect with the University; (5) developing safe strategies to accommodate participants with tattoos during the MRI; (6) incorporating engaging and interactive tasks during neuroimaging sessions; (7) providing small gifts, such as a picture of one's brain, in addition to financial compensation; (8) sharing research findings with the research participants; and (9) fostering long-term bidirectional relationships. The findings may be used to develop best practices for enhancing participant diversity in future neuroimaging studies.

Parental mental health is associated with children's emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children's functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings' connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = -0.05-0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (β = -0.05; β = -0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.

Puberty and associated changes in pubertal hormones influence structural brain development. Hormones such as dehydroepiandrosterone (DHEA) and progesterone remain understudied, and it remains unclear how these aspects of puberty contribute uniquely to structural brain development. We used the Human Connectome Project in Development cross-sectional sample of 1304 youth (aged 5-21 years) to investigate unique contributions of sex, age, pubertal stage, DHEA, testosterone, estradiol, and progesterone to cortical thickness, surface area, and subcortical volume development within functionally-relevant networks. Sex and age explain the most unique variance in all three aspects of structural development. Pubertal stage and pubertal hormones uniquely contribute more to cortical surface area, compared to thickness. Among the pubertal hormones, progesterone contributed unique variance to surface area in the default mode network, as well as to thickness in the orbito-affective network. Pubertal mechanisms also contributed unique variance to subcortical volumes. This demonstrates unique relations of understudied pubertal hormones to brain structure development and may help understand risk for psychopathology.

Combining existing datasets to investigate key questions in developmental cognitive neuroscience brings exciting opportunities and unique challenges. However, many data pooling methods require identical or harmonized methodologies that are often not feasible. We propose Integrative Data Analysis (IDA) as a promising framework to advance developmental cognitive neuroscience with secondary data analysis. IDA serves to test hypotheses by combining data of the same construct from commensurate (but not identical) measures. To overcome idiosyncrasies of neuroimaging data, IDA explicitly evaluates if measures across studies assess the same construct. Moreover, IDA allows investigators to examine meaningful individual variability by de-confounding source-specific differences. To demonstrate IDA's potential, we explain foundational concepts, outline necessary steps, and apply IDA to volumetric measures of hippocampal subfields from 443 4- to 17-year-olds across three independent studies. We identified commensurate measures of Cornu Ammonis (CA) 1, dentate gyrus (DG)/CA3, and Subiculum (Sub). Model testing supported use of IDA to create IDA factor scores. We found age-related differences in DG/CA3, not but CA1 and Sub volume in the integrated dataset. By successfully demonstrating IDA, our hope is that future innovations come from the combination of existing neuroimaging data to create representative integrated samples when testing critical developmental questions.

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. Central to its mission of reducing health disparities is the establishment of the Spanish Language and Culture Committee (SLCC) within the HBCD framework, a significant step towards demographic representation and inclusivity in research. By addressing linguistic and sociocultural barriers and embracing the diverse identities of Hispanic/Latine individuals nationwide, the SLCC aims to promote inclusion, equity, and representation of all Hispanic/Latine subgroups, a population that has been historically misrepresented in health research. In this paper we describe the role of the SLCC in advocating for Hispanic/Latine families within the study, ensuring their inclusion from inception. This report also provides an overview of the SLCC organization, workflow, challenges and lessons learned thus far to reduce stigma and improve study outcomes, highlighting recruitment and retention strategies for the Hispanic/Latine population, and expanding outreach to promote inclusion across diverse Hispanic/Latine subgroups in the United States.

Early life adversity (ELA) has shown to have negative impacts on mental health. One possible mechanism is through alterations in neural emotion processing. We sought to characterize how multiple indices of ELA were related to naturalistic neural socio-emotional processing.

Although prior studies have revealed alterations in gray matter volume (GMV) among individuals with internet gaming disorder (IGD). The brain's multifaceted functions hinge crucially on the intricate connections and communication among distinct regions. However, the intricate interaction of information between brain regions with altered GMV and other regions, and how they synchronize with various neurotransmitter systems, remains enigmatic. Therefore, we aimed to integrate structural, functional and molecular data to explore the GMV-based Granger causal connectivity abnormalities and their correlated neurotransmitter systems in IGD adolescents. Voxel-based morphometry (VBM) analysis was firstly performed to investigate GMV differences between 37 IGD adolescents and 35 matched controls. Brain regions with altered GMV were selected as seeds for further Granger causality analysis (GCA). Two-sample t tests were performed using the SPM12 toolkit to compare the GMV and Granger causal connectivity between IGD and control groups (GRF corrected, P<0.005, P<0.05). Then, GMV-based Granger causal connectivity was spatially correlated with PET- and SPECT-derived maps covering multifarious neurotransmitter systems. Multiple comparison correction was performed using false discovery rate (FDR). Compared with controls, IGD adolescents showed higher GMV in the caudate nucleus and lingual gyrus. For the GCA, IGD adolescents showed higher Granger causal connectivity from insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to the caudate nucleus, and lower Granger causal connectivity from superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the lingual gyrus. Besides, GMV-based Granger causal connectivity of IGD adolescents were associated with the dopaminergic, serotonergic, GABAergic and noradrenaline systems. This study revealed that the caudate nucleus and lingual gyrus may be the key sites of neuroanatomical changes in IGD adolescents, and whole-brain Granger causal connectivity abnormalities based on altered GMV involved large brain networks including reward, cognitive control, and visual attention networks, and these abnormalities are associated with a variety of neurotransmitter systems, which may be associated with higher reward sensitivity, cognitive control, and attention control dysfunction.

There is increasing interest in examining the development of frontal EEG power in relation to self-regulation in early childhood. However, the majority of previous studies solely focuses on the brain's alpha rhythm and little is known about the differences between young boys and girls. The aim of the current study was therefore to gain more insight into the neural mechanisms involved in the emergence of self-regulation. The sample consisted of 442 children and data were collected at approximately 5 months, 10 months, and around 3 years of age. Latent growth curve models indicated that,while the neurobiological foundations of self-regulation are established during infancy,it is the maturation of the frontal alpha rhythm that contributes to variations in both observed and parent-reported self-regulation. In addition, it appears that boys might have a greater reliance on external regulation than girls during early childhood, as evident by higher scores of girls on both measures of self-regulation. More insight into the role of external regulators in brain maturation can help to implement interventions aimed at establishing bottom-up self-regulatory skills early in life, in order to provide the necessary foundations for the emergence of top-down self-regulatory skills in the preschool period.

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 acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Cognitive control processes likely influence the extent to which adolescents can successfully regulate their emotions. This study examined whether individual differences in affective inhibition and heart rate variability (HRV), as a peripheral index of cognitive control, moderated the association between momentary emotion regulation and negative affect (NA). Ecological Momentary Assessments (EMA) over 14 days were obtained in 235 adolescents (M = 13.48 years; 106 females). At each assessment, participants reported their current NA and the extent to which they used cognitive reappraisal and rumination. Moreover, at three time points (approximately 1 year, 6 months, and just before the EMA), affective inhibition was assessed using an affective go/no-go task and HRV was recorded at rest. Results indicate that adolescents with lower affective inhibition reported lower average levels of daily rumination. However, affective inhibition did not moderate the association between either daily cognitive reappraisal or rumination and momentary NA. Consistent with hypotheses, the association between momentary rumination and NA was weaker in adolescents showing higher levels of resting HRV. Overall, findings may underscore the importance of interventions targeting HRV as a malleable factor for enhancing adolescents' affective well-being.

Based on findings from a simulation study, Parsons and McCormick (2024) argued that growth models with exactly two time points are poorly-suited to model individual differences in linear slopes in developmental studies. Their argument is based on an empirical investigation of the increase in precision to measure individual differences in linear slopes if studies are progressively extended by adding an extra measurement occasion after one unit of time (e.g., year) has passed. They concluded that two-time point models are inadequate to reliably model change at the individual level and that these models should focus on group-level effects. Here, we show that these limitations can be addressed by deconfounding the influence of study duration and the influence of adding an extra measurement occasion on precision to estimate individual differences in linear slopes. We use asymptotic results to gauge and compare precision of linear change models representing different study designs, and show that it is primarily the longer time span that increases precision, not the extra waves. Further, we show how the asymptotic results can be used to also consider irregularly spaced intervals as well as planned and unplanned missing data. In conclusion, we like to stress that true linear change can indeed be captured well with only two time points if careful study design planning is applied before running a study.

Previous research suggests that episodic memory relies on functional neural networks,which are present even in the absence of an explicit task. The regions that integrate.these networks and the developmental changes in intrinsic functional connectivity.remain elusive. In the present study, we outlined an intrinsic episodic memory network.(iEMN) based on a systematic selection of functional connectivity studies, and.inspected network differences in resting-state fMRI between adolescents (13-17 years.old) and adults (23-27 years old) from the publicly available NKI-Rockland Sample.Through a review of brain regions commonly associated with episodic memory.networks, we identified a potential iEMN composed by 14 bilateral ROIs, distributed.across temporal, frontal and parietal lobes. Within this network, we found an increase.in resting-state connectivity from adolescents to adults between the right temporal pole.and two regions in the right lateral prefrontal cortex. We argue that the coordination of.these brain regions, connecting areas of semantic processing and areas of controlled.retrieval, arises as an important feature towards the full maturation of the episodic.memory system. The findings add to evidence suggesting that adolescence is a key.period in memory development and highlights the role of intrinsic functional.connectivity in such development.

We investigate whether neural, cognitive, and psychopathology phenotypes that are more strongly related to genetic differences are less strongly associated with family- and state-level economic contexts (N = 5374 individuals with 1KG-EUR-like genotypes with 870 twins, from the Adolescent Behavior and Cognitive Development study). We estimated the twin- and SNP-based heritability of each phenotype, as well as its association with an educational attainment polygenic index (EA PGI). We further examined associations with family socioeconomic status (SES) and tested whether SES-related differences were moderated by state cost of living and social safety net programs (Medicaid expansion and cash assistance). SES was broadly associated with cognition, psychopathology, brain volumes, and cortical surface areas, even after controlling for the EA PGI. Brain phenotypes that were more heritable or more strongly associated with the EA PGI were not, overall, less related to SES, nor were SES-related differences in these phenotypes less moderated by macroeconomic context and policy. Informing a long-running theoretical debate, and contra to widespread lay beliefs, results suggest that aspects of child brain development that are more strongly related to genetic differences are not, in general, less associated with socioeconomic contexts and policies.

Impulsivity and cognitive function are essential for understanding behavioral regulation, particularly in relation to health-risk behaviors like substance use, physical activity, and academic performance. This study examined the factor structure underlying executive function in adolescents using the UPPS-P Impulsive Behavior Scale and NIH Toolbox Cognition Battery. We explored how parental monitoring moderates, and peer network health and perceived stress mediate, relationships between cognitive function and outcomes such as BMI, physical activity, and academic performance. Exploratory factor analysis (EFA) on 2228 observations identified a four-factor model (BIC = -97.92, RMSEA = 0.040, TLI = 0.936), validated by confirmatory factor analysis (CFA) (CFI = 0.961, RMSEA = 0.055). Structural equation modeling (SEM) on 5902 observations showed that parental monitoring moderated Factor 1 (adaptive impulsivity) in relation to physical activity and academic performance, while peer network health mediated Factor 2 (emotional impulsivity) effects on BMI and physical activity. This model underscores the influence of peer relationships, parental involvement, and stress on cognitive, health, and academic outcomes, suggesting that interventions enhancing peer support, reducing stress, and promoting healthy behaviors may improve adolescent well-being.

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 study plans enrolling over 7000 families across 27 sites. This manuscript presents the measures from the Neurocognition and Language Workgroup. Constructs were selected for their importance in normative development, evidence for altered trajectories associated with environmental influences, and predictive validity for child outcomes. Evaluation of measures considered psychometric properties, brevity, and developmental and cultural appropriateness. Both performance measures and caregiver report were used wherever possible. A balance of norm-referenced global measures of development (e.g., Bayley Scales of Infant Development-4) and more specific laboratory measures (e.g., deferred imitation) are included in the HBCD study battery. Domains of assessment include sensory processing, visual-spatial reasoning, expressive and receptive language, executive function, memory, numeracy, adaptive behavior, and neuromotor. Strategies for staff training and quality control procedures, as well as anticipated measures to be added as the cohort ages, are reviewed. The HBCD study presents a unique opportunity to examine early brain and neurodevelopment in young children through a lens that accounts for prenatal exposures, health and socio-economic disparities.

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 HBCD study has faced several ethical and legal challenges due to its goal of enrolling pregnant people (including those with substance use disorder) and their newborns. Challenges not fully anticipated at the outset emerged from the rapidly changing legal landscape around reproductive rights in the United States. By embedding scholars in bioethics and law within research teams and engaging them in conversation with each other and other study personnel, we were able to address many challenges proactively and respond promptly to unanticipated challenges. In this paper, we highlight several important ethical and legal challenges that arose from the first phase of funding through the beginning of participant enrollment. We explain the methods used to address these challenges, the ethical and legal tradeoffs that arose, and the resolution of challenges through the design of the study. Based on this experience, we provide recommendations for research teams, sponsors, and reviewers to address legal risks and promote the ethical conduct of studies with pregnant people and caregivers. We highlight the importance of collaboration with bioethics and legal scholars in studies involving complex and evolving legal risks, as well as the necessity of designing robust approaches to informed consent and maintaining participant trust while navigating ethical challenges in research.

Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.

Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.e., dataset shifts). Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized developmental samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. Through advanced methodological approaches, we demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features. Results indicate the potential of functional connectome-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings.

Recent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-behavior relationships relies on the application of purpose-built analysis methods to counter the biases that otherwise preclude causal inference from observational data. Here we introduce these approaches (i.e., propensity score-based methods, the 'G-methods', targeted maximum likelihood estimation, and causal mediation analysis) and conduct a review to determine the extent to which they have been applied thus far in the field of developmental cognitive neuroscience. We identify just eight relevant studies, most of which employ propensity score-based methods. Many approaches are entirely absent from the literature, particularly those that promote causal inference in settings with complex, multi-wave data and repeated neuroimaging assessments. Causality is central to an etiological understanding of the relationship between the brain and behavior, as well as for identifying targets for prevention and intervention. Careful application of methods for causal inference may help the field of developmental cognitive neuroscience approach these goals.

EEG studies play a crucial role in enhancing our understanding of brain development across the lifespan. The increasing clinical and policy implications of EEG research underscore the importance of utilizing reliable EEG measures and increasing the reproducibility of EEG studies. However, important data characteristics like reliability, effect sizes, and data quality metrics are often underreported in pediatric EEG studies. This gap in reporting could stem from the lack of accessible computational tools for quantifying these metrics for EEG data. To help address the lack of reporting, we developed a toolbox that facilitates the estimation of internal consistency reliability, effect size, and standardized measurement error with user-friendly software that facilitates both computing and interpreting these measures. In addition, our tool provides subsampled reliability and effect size in increasing numbers of trials. These estimates offer insights into the number of trials needed for detecting significant effects and reliable measures, informing the minimum number of trial thresholds for the inclusion of participants in individual difference analyses and the optimal trial number for future study designs. Importantly, our toolbox is integrated into commonly used preprocessing pipelines to increase the estimation and reporting of data quality metrics in developmental neuroscience.

Dopaminergic projections from the ventral tegmental area (VTA) to limbic regions play a key role in the initiation and maintenance of substance use; however, the relationship between mesolimbic resting-state functional connectivity (RSFC) and alcohol use during development remains unclear. We examined the associations between alcohol use and VTA RSFC to subcortical structures in 796 participants (12-21 years old at baseline, 51 % female) across 9 waves of longitudinal data from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Linear mixed effects models included interactions between age, sex, and alcohol use, and best fitting models were selected using log-likelihood ratio tests. Results demonstrated a positive association between alcohol use and VTA RSFC to the nucleus accumbens. Age was associated with VTA RSFC to the amygdala and hippocampus, and an age-by-alcohol use interaction on VTA-globus pallidus connectivity was driven by a positive association between alcohol and VTA-globus pallidus RSFC in adolescence, but not adulthood. On average, male participants exhibited greater VTA RSFC to the amygdala, nucleus accumbens, caudate, hippocampus, globus pallidus, and thalamus. Differences in VTA RSFC related to age, sex, and alcohol, may inform our understanding of neurobiological risk and resilience for alcohol use and other psychiatric disorders.

Children with prenatal alcohol exposure (PAE) may develop a range of neurological and behavioral deficits, including reading and language disorders. Studying the brain's structural connectivity and its relationship to pre-reading/reading skills in young children with PAE can help understand the roots of reading deficits associated with PAE. 363 diffusion MRI scans from 135 children (114 scans from 53 children with PAE) were collected between ages 3-7 years. Children completed NEPSY-II Phonological Processing and Speeded Naming to assess pre-reading skills at each scan. Structural brain network properties were assessed in 16 regions from both hemispheres using graph theory. Linear mixed models were used to account for repeated measures within participants. Children with PAE had significantly lower pre-reading scores than unexposed children, and significantly lower graph theory metrics across bilateral reading networks. Moreover, PAE significantly moderated the associations between Phonological Processing and global efficiency and nodal degree in the bilateral and left hemisphere reading networks, such that children with PAE had stronger associations than unexposed controls. No significant associations were found for Speeded Naming. Our results suggest that brain alterations may underlie early pre-reading difficulties in children with PAE.

Prenatal listening experience reportedly modulates how humans process speech at birth, but little is known about how speech perception develops throughout the perinatal period. The present experiment assessed the neural event-related potentials (ERP) and mismatch responses (MMR) to native vowels in 99 neonates born between 32 and 42 weeks of gestation. The vowels elicited reliable ERPs in newborns whose gestational age at time of experiment was at least 36 weeks and 1 day (36 + 1). The ERPs reflected spectral distinctions between vowel onsets from age 36 weeks + 6 days and durational distinctions at vowel offsets from age 37 weeks + 6 days. Starting at age 40 + 4, there was evidence of neural discrimination of vowel length, indexed by a negative MMR response. The present findings extend our understanding of the earliest stages of speech perception development in that they pinpoint the ages at which the cortex reliably responds to the phonetic characteristics of individual speech sounds and discriminates a native phoneme contrast. The age at which the brain reliably differentiates vowel onsets coincides with what is considered term age in many countries (37 weeks + 0 days of gestational age). Future studies should investigate to what extent the perinatal maturation of the cortical responses to speech sounds is modulated by the ambient language.

Early threat-associated cortical thinning may be interpreted as accelerated cortical development. However, non-adaptive processes may show similar macrostructural changes. Examining cortical thickness (CT) together with grey/white-matter contrast (GWC), a proxy for intracortical myelination, may enhance the interpretation of CT findings. In this prospective study, we examined associations between early life family-related threat (harsh parenting, family conflict, and neighborhood safety) and CT and GWC development from late childhood to middle adolescence. MRI was acquired from 4200 children (2069 boys) from the Generation R study at ages 8, 10 and 14 years (in total 6114 scans), of whom 1697 children had >1 scans. Linear mixed effect models were used to examine family factor-by-age interactions on amygdala volume, caudal and rostral anterior cingulate (ACC) and medial orbitofrontal cortex (mOFC) CT and GWC. A neighborhood safety-by-age-interaction was found for rostral ACC GWC, suggesting less developmental change in children from unsafe neighborhoods. Moreover, after more stringent correction for motion, family conflict was associated with greater developmental change in CT but less developmental change in GWC. Results suggest that early threat may blunt ACC GWC development. Our results, therefore, do not provide evidence for accelerated threat-associated structural development of the amygdala-mPFC circuit between ages 8-14 years.

Adolescence is a period of profound biopsychosocial development, with pubertally-driven neural reorganization as social demands increase in peer contexts. The explosive increase in social media access has fundamentally changed peer interactions among youth, creating an urgent need to understand its impact on neurobiological development and mental health. Extant literature indicates that using social media promotes social comparison and feedback seeking (SCFS) behaviors in youth, which portend increased risk for mental health disorders, but little is known about its impact on neurobiological development. We assessed social media behaviors, mental health symptoms, and spontaneous cortical activity using magnetoencephalography (MEG) in 80 typically developing youth (8-16 years) and tested how self-reported pubertal stage moderates their relationship. More mature adolescents who engaged in more SCFS showed weaker fusiform/parahippocampal alpha and medial prefrontal beta activity, and increased symptoms of anxiety and attention problems. Engaging in SCFS on social media during adolescence may thus relate to developmental differences in brain regions that undergo considerable development during puberty. These results are consistent with works indicating altered neurodevelopmental trajectories within association cortices surrounding the onset of many mental health disorders. Importantly, later pubertal stages may be most sensitive to the detrimental effects of social media use.

This editorial focuses on the issue of data misuse which is increasingly evidenced in social media as well as some premiere scientific journals. This issue is of critical importance to open science projects in general, and ABCD in particular, given the broad array of biological, behavioral and environmental information collected on this American sample of 12.000 youth and parents. ABCD data are already widely used with over 1000 publications and twice as many citations per year as expected (relative citation index based on year, field and journal). However, the adverse consequences of misuse of data, and inaccurate interpretation of emergent findings from this precedent setting study may have profound impact on disadvantaged populations and perpetuate biases and societal injustices.