Developmental Cognitive Neuroscience

Hispanic Americans face persistent health disparities rooted in complex sociocultural and bioecodevelopmental dynamics. Identifying early neurodevelopmental trajectories that confer risk or resilience during adolescence is critical for developing targeted health equity interventions. Grounded in the NIMHD Research Framework and bioecodevelopmental theory, data from 12- to 14-year-old Hispanic adolescents in the Adolescent Brain Cognitive Development (ABCD) Study were analyzed (n=236). Partial least squares correlation (PLSC) was used to evaluate multivariate associations between sociocultural measures, spanning individual, family, and community levels, and resting-state functional connectivity (rsFC) networks. Post hoc linear mixed-effects models examined how derived sociocultural-rsFC patterns predicted measures of physical and mental health, linking bioecodevelopmental patterns to adolescent well-being. PLSC revealed dual community- and family-level neural pathways of biological embedding, diverging from traditional nested bioecodevelopmental assumptions. The community pathway linked child opportunity and neighborhood disadvantage to connectivity across Cingulo-Opercular, Default, Dorsal Attention, Visual Networks. This community-rsFC profile was associated with lower respiratory functioning, reflecting an allostatic load marker of environmental stress. The family pathway linked caregiver ethnic identity and heritage acculturation to Default, Ventral Attention, Sensorimotor-Mouth, and Visual Networks connectivity, suggesting that the family system shapes neural systems underlying social attunement and identity formation. Structural community factors and familial culture impose distinct neurobiological demands on the adolescent connectome among Hispanic youth. Findings highlight respiratory health as a biological marker of environmental adversity and underscore the necessity of structural and culturally sustaining interventions to promote health equity among Hispanic youth.

Importance: Pediatric obesity affects 1 in 5 US children and has been linked to brain structural differences. Most evidence is cross-sectional and models body mass index (BMI) linearly, with metrics that compress severe obesity. Objective: To examine associations between brain structure and BMI for age across the weight spectrum and Centers for Disease Control and Prevention (CDC) weight status categories, distinguishing between and within person associations, and their age and sex moderation. Design, Setting, and Participants: Longitudinal cohort study of youth aged 8 to 10 years at baseline in the US community based Adolescent Brain Cognitive Development Study (4 waves, 2016 to 2025; ages 8 to 18 years). Exposures: Extended CDC BMI-for-age z-score, grouped by wave-mean z-score: underweight (<5th percentile, n=297), healthy weight (5th to <85th, n=7,452), overweight (85th to <95th, n=1,919), and obesity ([&ge;]95th, n=1,842). Main Outcomes and Measures: Cortical thickness (12 regions) and subcortical volume (7 regions) from T1 weighted magnetic resonance imaging (MRI), defined a priori. Results: Among 11,510 participants contributing 32,212 observations (mean [SD] age, 12.6 [2.5] years; 52.2% male), higher between person BMI for age z was associated with lower frontal and temporal thickness (6 regions) and larger subcortical volume (5 regions; all q < .001), but associations were nonlinear (17 of 19 regions). Youth with obesity had lower thickness than healthy weight youth in 8 frontal and temporal regions (largest, -0.21 SD; q < .001), with dorsal frontal differences decreasing across adolescence; underweight youth had smaller subcortical volumes. Higher BMI for age z was associated with larger subcortical volume (all 7 regions) within the healthy weight category and lower cortical thickness (11 of 12 regions; fusiform: {beta} = -0.27; q < .001) within the obesity category, steeper than in healthy weight youth in 10 regions, but not in overweight or underweight youth. Within person associations did not reproduce the obesity associations, reversed from negative at age 10 to positive at age 16, and were more negative in female than male participants. Conclusions and Relevance: Youth with obesity had lower cortical thickness than healthy weight peers, but associations between brain structure and BMI for age differed between CDC weight status groups, suggesting a complex relationship between adiposity and brain structure.

Childhood adversity is robustly linked to differences in amygdala and hippocampal volume. However, findings across studies have been inconsistent and contradictory. This inconsistency stems partly from the many reasonable but arbitrary choices that researchers make when operationalizing brain regions, stress exposure, and covariates. To deal with this variability, we applied a multiverse analysis approach using adolescent data from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study to evaluate how multiple analytic choices influence the relationships between stress and hippocampal/amygdala volume. Our primary analysis crossed three region-of-interest operationalizations (left, right, total), four intracranial volume scaling methods, and multiple sociodemographic covariates. Greater lifetime stress severity was associated with smaller amygdala volume across 72% of specifications, and with smaller hippocampal volume across 52% of specification models. Statistical significance was strongly influenced by the region of interest. Our secondary multiverse analysis tested six operationalizations of stress exposure (total/acute/chronic x count/severity). This analysis found that the laterality pattern in the primary analysis was not strongly influenced by stress operationalization. These findings demonstrate the robustness of the stress-medial temporal lobe association and identify specific analytic choices (i.e. hemisphere operationalization) that most strongly drive the variability observed in previous literature. Our study is one of the first that utilized multiverse analysis for adversity-neuroimaging research, and provides a strong framework for evaluating and interpreting inconsistent findings.

Structural connectomics provides a means of characterising white matter (WM) network organisation using diffusion MRI (dMRI), but reconstructing neonatal structural connectomes remains challenging due to the immature and heterogeneous nature of the developing brain. Although recent neonatal studies have increasingly adopted approaches beyond conventional diffusion tensor imaging (DTI), there remains scope to incorporate more advanced tractography and streamline post-processing approaches within neonatal pipelines. Here, we propose a neonatal structural connectome pipeline integrating surface-based anatomical constraints, ensemble tractography, hierarchical anatomical refinement of pathways (HARP) filtering, and spherical-deconvolution informed filtering (SIFT2) reweighting. We acquired 3T MRI data from 300 neonates (185 preterm, 115 term-born) to assess the impact of pipeline configuration on network organisation. Mean gestational age (GA) at birth was 29.31 (23.43-32.86) weeks in preterm infants and 39.52 (37.00-42.43) weeks in term infants. At scan, mean GA was 40.85 (36.29-49.00) weeks in preterm infants and 42.06 (38.29-48.00) weeks in term infants. Four connectome variants were generated: raw surfaced-based tractograms, HARP-filtered, SIFT2-weighted, and combined HARP+SIFT2. Pipeline performance was primarily evaluated using an existing brain age prediction model: connectome-based predictive model (CPM) and our derived connectomes to estimate GA at scan in term and preterm infants. In addition, global graph theoretical metric analyses were conducted to provide a complementary assessment of pipeline configuration differences in network organisation. In total, seven graph theoretical metrics were calculated and evaluated: global efficiency, local efficiency, transitivity, modularity, assortativity, degree variance and statistical complexity. Across all configurations, connectomes supported accurate brain age prediction, with mean absolute errors (MAE) of 0.76-0.79 weeks in term infants and 1.00-1.06 weeks in preterm infants. Prediction error and brain age gap variability were greater in preterm than term infants across configurations, with HARP+SIFT2 achieving the lowest preterm MAE (1.00 weeks) and brain age gap (BAG) variability (SD = 1.38 weeks). However, differences in prediction performance across configurations were minimal, suggesting limited influence of pipeline choice on brain age prediction. However, analysis of global network metrics demonstrated that pipeline choice significantly influenced downstream measures of network organisation, with HARP+SIFT2 being the only configuration to identify significant preterm vs. term differences across all seven metrics examined. These findings demonstrate the importance of considering tractography and streamline-processing choices when constructing neonatal structural connectomes and provide a framework for more anatomically informed investigation of early brain development.

A central premise of developmental neuroscience is that mature behaviour emerges through reciprocal interactions between infants spontaneous actions, neural activity, and environmental experience. Yet, much human neuroimaging research relies on highly controlled, repetitive paradigms designed to minimize behavioural variability, obscuring the very brain-behaviour dynamics that drive developmental change. Here, we introduce a naturalistic functional near-infrared spectroscopy (fNIRS) paradigm that exploits, rather than suppresses, natural variability in infants behaviour to investigate cortical changes associated with behavioural development. Continuous video coding of spontaneous infant behaviour was combined with finite impulse response (FIR) modelling to estimate behaviour-specific hemodynamic responses associated with reach-to-grasp, hand-to-mouth, and vocalization behaviours while accounting for temporally overlapping hemodynamic responses by treating extraneous behaviours as nuisance regressors. From 6 to 13 months of age, reach-to-grasp and vocalizations increased in frequency, while all three behaviours shortened in duration. Critically, developmental changes in cortical activation were behaviour-specific. During reach-to-grasp movements, bilateral posterior parietal activity decreased with age. In contrast, during hand-to-mouth and vocalizations, somatomotor and premotor activity increased with age. Cortical activity was also related to individual variation in the frequency and duration of these behaviours, indicating that cortical recruitment remained coupled to behavioural variation independent of age. These findings reveal behaviour-specific patterns of cortical reorganisation during infant development and establish a methodological framework for linking spontaneous behaviour to cortical activity in freely behaving infants. More broadly, this approach enables direct investigation of cortical development as it unfolds through naturalistic interactions among behaviour, neural activity, and experience. Significance StatementContinuous self-generated interactions with the environment play a critical role in driving infant brain development, yet neuroimaging research often examines infants while sleeping or in highly constrained and repetitive tasks. Here, we show that spontaneous infant behaviour can instead be leveraged to provide the temporal variability needed to resolve behaviour-specific cortical responses in freely behaving infants. By combining continuous behavioural coding with data-driven finite impulse response (FIR) modelling of overlapping hemodynamic responses, we identified behaviour-specific developmental differences in cortical recruitment without tightly constraining the infants behaviour. This novel approach expands developmental neuroimaging beyond sleeping or trial-based paradigms and demonstrates how naturally occurring behavioural variability can become a useful feature of fNIRS experimental design. More broadly, it generates new opportunities to directly test theories of neurobehavioural development in more ecologically valid settings.

Working memory (WM), a core executive function that supports learning and reasoning, undergoes rapid development during early childhood years. Neural activation patterns underlying WM have been extensively studied in both adults and children, revealing increasing involvement of the frontoparietal network (FPN) with age and coordinated interactions with the default mode network (DMN). However, relatively little is known about how these network interactions develop in children, particularly within the visuospatial domain. The present study investigated the influence of age and task performance on effective connectivity within the FPN and its interactions with DMN hubs during a visuospatial WM task in early childhood. Functional MRI data were collected during a visual n-back task with two WM load levels (0-back and 2-back) in 120 typically developing children aged 5-8 years. Dynamic Causal Modeling (DCM) with parametric empirical Bayes was used to estimate effective connectivity among eight regions of interest spanning the FPN and DMN. Behaviorally, age-related gains were steepest under high-load for accuracy, and under low-load for reaction time, suggesting WM capacity is a demand-sensitive marker of development at this age. At the network level, high-load robustly engaged the FPN, whereas low-load was associated with relatively greater engagement of DMN and episodic memory-related regions. Baseline effective connectivity was dominated by parietal-to-frontal drive and strong self-inhibition in the anterior cingulate cortex. Age-related effects on this architecture were sparse, involving asymmetric interhemispheric frontal coupling and reduced parieto-cingulate coupling, whereas accuracy-related effects were larger in magnitude, particularly at self-connections. More accurate children showed greater excitability in frontal and parietal FPN nodes alongside increased self-inhibition in the precuneus, a DMN hub, consistent with a neural efficiency account in which better performance reflects more selective, rather than more global, network engagement. Task load further shaped connectivity in distinct ways: low-load elicited widespread inter-regional modulation, whereas high-load predominantly modulated self-connections, with age and accuracy effects on this modulation dissociating by load. These findings suggest that, in primary school children, current task performance was associated with more pronounced differences in frontoparietal effective connectivity than was age, and that WM-related network engagement becomes increasingly selective and load-dependent across this developmental window.

Objective: To provide national norms, percentiles, and results of factor analyses for the ADHD symptoms and performance items on the parent-report and self-report versions of the Vanderbilt Assessment Scale (VAS) for youth ages 6 to 17. We hypothesized that symptom items would demonstrate separate inattention and hyperactivity/impulsivity factors, performance items would demonstrate interpretable latent dimensions, measurement invariance would be supported across age and sex, and symptom ratings would vary systematically by age and sex. Method: In a nationally stratified survey conducted in July 2025-March 2026, 2,230 parents of youth aged 6-17 years and 996 adolescents aged 12-17 years completed Vanderbilt Assessment Scale items. Both versions asked about the frequency of 18 ADHD symptoms and performance in 8 domains. To evaluate the psychometric properties and age-related normative curves of the VAS across parent- and youth-report forms, we conducted exploratory factor analysis, confirmatory factor analysis, and measurement invariance testing. We also generated age and sex-specific norms. Results: Factor analyses produced the expected two symptom factors and two performance factors. Developmental and sex-specific patterns of ratings of inattention and hyperactivity/impulsivity symptoms indicated that after increasing slightly during the preadolescent years, parent ratings of inattention symptoms declined and remained stable. In contrast, ratings of hyperactivity/impulsivity declined steadily with age. Normative data were generated using GAMLSS estimated centiles from fitted probability distributions. Conclusions: This study provides norms for the parent-report and adolescent self-report VAS. Findings underscore the importance of using sex- and age-based norms to better inform estimates of severity and diagnostic decision making.

We evaluated whether participant-separated within-cohort discrimination was retained when electroencephalography (EEG) classifiers for attention-deficit/hyperactivity disorder (ADHD) were applied to a different population and recording task. This exploratory reanalysis included 121 children performing a visual task and 79 adults recorded during eyes-open rest after basic channel screening. Both cohorts used Cz/F4, 128 Hz, and the first fifteen non-overlapping 2-second epochs per participant. Fixed logistic regression and random forest classifiers used spectral, amplitude-derived, and combined features. Internal evaluation used five repetitions of five-fold participant-grouped cross-validation; bidirectional transfer fitted transformations and classifiers only on the source cohort. Adult spectral balanced accuracy (BA) was 0.9519-0.9546, compared with 0.5504-0.5664 in children. Adult spectral logistic regression exceeded all 999 internal label permutations (Monte Carlo p=0.001; within-family Holm-adjusted p=0.002). Paired spectral-minus-amplitude conditional intervals excluded zero in adults but spanned zero in children. Cross-cohort spectral BA was 0.3584-0.4675, with area under the receiver operating characteristic curve (AUC) of 0.0309-0.3857 in the unchanged score direction. Increasing pediatric source-epoch budgets moved random-forest AUC toward 0.5, whereas logistic-regression AUC remained below 0.1. Strong adult internal discrimination thus coexisted with reverse cross-cohort ranking. These findings characterize the evaluated pipelines, not clinical diagnostic utility or a unique mechanism: age, task, and acquisition differences were not separated, target results had previously been inspected, and conditional intervals excluded model-refitting uncertainty.

Family environment is a robust predictor of adolescent internalizing symptoms, yet its strength varies across adolescents. We tested whether this variation is partly captured by how similar an adolescent's cortical structure is to their parent's and whether different forms of this similarity matter in different cortical systems. In 115 parent-child dyads from two cohorts, structural MRI quantified similarity across 400 cortical parcels in two forms: phenotypic similarity in regional morphometry and architectural similarity in interregional morphometric organization. Parent-child similarity was not directly associated with symptoms, whereas its interactions with family environment were jointly significant (chi-square(4) = 14.92, P = 0.005). Phenotypic similarity in the sensory-limbic system and architectural similarity in the association system each moderated the association between family environment and symptoms (both beta = 0.22). In both cases, the association was strongest among less similar dyads. The phenotypic interaction was distributed across sensory-limbic networks and was not reproduced by similarity to unrelated adults or by the child's cortical atypicality. The architectural interaction was concentrated in the dorsal attention and default mode networks and was distinguishable from unrelated-adult similarity but not from child atypicality. Together, these findings raise the possibility that markers of environmental sensitivity need not reside in the child alone. Parent-child cortical similarity may index a dyadic component of this sensitivity, one not adequately represented by a single similarity score but distributed across forms of similarity and cortical systems.

Intracranial volume (ICV) is often estimated as part of automated neuroimaging processing pipelines and commonly used to account for and characterize cranial size. FreeSurfer's estimated intracranial volume (eTIV) produces implausible longitudinal estimates in developmental samples when using versions prior to FreeSurfer 8. Across two different adolescent datasets, we document eTIV changes of up to 39% between 1.5 - 2 year sessions for FreeSurfer 7, with errors more pronounced on Siemens scanners and in female participants. These errors are markedly reduced in FreeSurfer 8 and AssemblyNet. However, the widespread use of FreeSurfer 7 raises concern about developmental neuroimaging studies that utilize eTIV without quality control.

Brain network development differs between neurodivergent (ND) and neurotypical (NT) youth, but its developmental trajectory, and relationship to clinical outcomes, remains unclear. We examined age-related changes in functional network connectivity and clinical symptoms in 861 participants (5-21 years) from the Healthy Brain Network, including NT youth and those with Neurodevelopmental Disorders (NDDs) and generalized anxiety disorder (GAD). Functional connectivity during movie-watching was modeled using generalized additive models across eight large-scale networks to characterize nonlinear developmental trajectories. Divergence in connectivity profiles between ND and NT groups first emerged during late childhood and were largest during adolescence. Importantly, divergence in connectivity patterns followed a similar trajectory path, but preceded, divergence in clinical outcomes. These findings identify adolescence as a sensitive period when differences in patterns of brain activity and clinical differences become increasingly pronounced, highlighting a potential window for targeted intervention.

Background and objective. The WHO growth reference judges a child's height against the population at one age, without using the child's own earlier growth. This study built and validated a quantile regression based conditional growth reference predicting a child's future height for age distribution from early growth status. Methods. Using deidentified longitudinal annual school children health check data from three major cities in Vietnam, this study linked a baseline height and BMI for age Z score near age 6 to outcome visits near age 12 or 15 in 4,857 children. A linear quantile regression and a pooled quantile generalized additive model, fit by sex, were compared against a linear benchmark and a naive persistence model on a held out test set, using pinball loss, interval coverage, and width against the WHO 90% band. This study also tested adding growth phenotype and checked generalization across the three cities. Results. The pooled model outperformed the discrete quantile regression in every group. Every individualized 90% interval was narrower than the WHO band, by 34.6 to 54.5%. Growth phenotype improved accuracy in most groups. The model generalized well to a held out Hanoi or Haiphong but poorly to a held out Ho Chi Minh City, where coverage fell to between 0.35 and 0.52, though the all-city model stayed well calibrated for Ho Chi Minh City itself. Conclusion. Conditional quantile modeling gives individualized growth predictions markedly narrower than a fixed population reference and gains further from growth phenotype, but needs local validation before use at an unrepresented site.

Theta oscillations are pivotal for attention and memory, with developmental shifts in theta activity and connectivity tracking memory ability across the lifespan. Building on initial evidence that the theta band separates into slow and fast sub-bands across development within the medial temporal lobe (MTL) and the prefrontal cortex (PFC), this preregistered study (https://osf.io/gsru7) characterized the frequencies of slow (~1.5-4.5 Hz) and fast (~4.5-9.0 Hz) theta oscillations across the brain in a large developmental cohort. We analyzed intracranial EEG (iEEG) recordings from widespread brain regions in 101 children and adults (5.93-54.00 years, 63 males; n electrodes = 5691) using task-based (attention to to-be-remembered visual stimuli) and task-free (resting-state) data. We reveal distinct slow and fast theta oscillations in all regions and identify a spatial gradient in both frequency ranges, with slow theta oscillations speeding up and fast theta oscillations slowing down from posterior to anterior regions. The dissociation between slow and fast theta frequencies peaks during young adulthood, mirroring the developmental trajectory of attention and memory. In the hippocampus and PFC, attentional state (task-based, task-free) modulated age effects, and individual differences in task-based slow theta frequencies predicted individual differences in memory performance, linking the development of slow theta oscillations to the development of memory. Gray matter volume was not associated with age- or task-related differences in theta frequencies, suggesting that the development of theta oscillations is independent of regional brain structure. This study establishes developmental trajectories of slow and fast theta oscillations in localized brain regions and links the maturation of hippocampal and PFC slow theta oscillations to the development of attention and memory.

Background: ADHD symptoms follow heterogeneous trajectories across childhood and adolescence, with important implications for later functioning. Early-life factors, including perinatal health, family environment, cognition, socioeconomic conditions, and community exposures, may contribute to this variability. The present study examined (1) early-life predictors of ADHD symptom trajectories, and (2) ADHD symptom trajectories as predictors of later substance use, suicidality, and new-onset psychiatric disorders. Methods: Growth models were used to fit longitudinal trajectories of inattention, hyperactivity, and irritability measures in two independent samples: the Oregon ADHD-1000 cohort (N=1,215; 49.17% ADHD; 39.9% female, ages 7-23) and a subsample of the Adolescent Brain Cognitive Development (ABCD) study (N=1,252; 33.3% ADHD; 31.6% female, ages 9-16). Regression analyses examined multiple risk factors as predictors of symptom growth parameters as well as associations between symptom trajectories and later adverse outcomes. Results: In both cohorts, growth models fit well (CFI > .96, RMSEA < .04), with all symptoms decreasing over time (ps < .001) and hyperactivity declining significantly faster than inattention. ADHD polygenic risk, family conflict, parenting, and cognition were associated with symptom intercepts in both cohorts (ps < .001). In Oregon ADHD-1000, poorer baseline working memory and processing speed predicted slower declines in inattention (ps < .001), but these effects did not replicate in ABCD; no other early-life factors predicted symptom trajectories. Increasing irritability trajectories predicted new disorder onset in both cohorts (ORs = 1.73 - 2.09, ps < .001), while more positive inattention slopes predicted new disorder onset in ABCD (OR = 3.07, p < .001) and showed a weaker association in Oregon ADHD-1000 (OR = 1.50, p = .007). Conclusion: While several early risk factors were associated with ADHD symptoms initially, none robustly predicted symptom trajectories over time. However, increasing irritability and inattention were robustly associated with increased risk for new disorder onsets later in adolescence.

Early-life adversity is associated with highly variable developmental outcomes, but identifying sources of this heterogeneity is difficult because variation in outcome is typically accompanied by variation in exposure. Animal models provide experimental leverage by standardizing features of the early environment while allowing variation in developmental experience and outcome to remain observable. Using a graded limited bedding and nesting model spanning normative rearing, moderate restriction, and severe scarcity, we asked what distinguishes social outcomes among animals exposed to the same resource restriction. Severe scarcity increased adverse maternal handling while leaving organized nurturing behaviors intact. Following the P8-P12 rearing manipulation, pups were tested outside the home cage in a P13 social approach assay. Severe scarcity reduced the proportion of pups that approached the dam, but outcomes varied substantially among scarcity litters. Greater typical maternal pup transport predicted greater approach, whereas a greater proportion of rough transport predicted poorer approach. Transport composition did not differ across rearing conditions, but its relationship with social behavior did. Amygdala intercalated-cell recruitment also covaried with social approach under severe scarcity despite limited condition-level differences in regional c-Fos. Plasma corticosterone differed across rearing conditions but did not covary detectably with the principal caregiving, behavioral, or neural measures. Maternal transport and intercalated-cell recruitment were not robustly associated, identifying separable experiential and neural correlates of behavioral variation. These findings show that standardizing an environmental constraint does not produce uniform developmental experience or outcome, and that caregiving experiences preserved across rearing conditions can distinguish social outcomes under severe scarcity.

Inhibitory control deficits have long been proposed as a central neurocognitive mechanism underlying childhood Attention Deficit/Hyperactivity Disorder (ADHD). However, neuroimaging findings remain heterogeneous, partly due to methodological variability across developmental samples and experimental paradigms. The present study aimed to characterize the neural architecture of motor response inhibition in typically developing (TD) children and identify reproducible functional abnormalities associated with pediatric ADHD using stringent, developmentally specific inclusion criteria. We conducted two coordinate-based meta-analyses using Activation Likelihood Estimation (ALE). Following a systematic literature search (up to August 2026), we identified 23 task-based fMRI studies reporting inhibitory-control-related activation in TD children (237 foci) and 17 studies comparing children with ADHD and TD peers (114 foci) during the Stop-Signal Task or Go/No-Go Task. All participants were younger than 18 years of age. Statistical significance was determined using cluster-level family-wise error (FWE) correction. In TD children, ALE revealed a robust inhibitory-control network encompassing the bilateral anterior insula (AI), inferior frontal gyrus (IFG), and the midline pre-supplementary motor area (preSMA) and anterior cingulate cortex (ACC). This spatial organization closely resembled the canonical inhibitory-control network consistently observed in adults, suggesting that the core neural architecture supporting motor inhibition is already detectably organized during development. In the between-group analysis, children with ADHD exhibited significant hypoactivation primarily within the preSMA. Notably, this region demonstrated striking spatial overlap with the preSMA cluster reliably recruited in TD children during motor inhibition. These findings provide convergent support for inhibitory-control models of ADHD and identify altered preSMA recruitment as one of the most consistent functional abnormalities associated with pediatric ADHD. While the neural infrastructure supporting inhibitory control is broadly established during childhood, children with ADHD exhibit reduced recruitment of a key cortical node within this network during motor inhibition demands.

The development of speech in human children is a protracted process that depends on social experience. From early life, young children produce vocalizations in the presence of others, which affords unique opportunities for feedback about how to communicate successfully. However, for feedback to happen, young childrens speech needs to effect change in the brains of their caregivers. Using dual-brain functional near-infrared spectroscopy (fNIRS) to measure real-time communication between caregivers and their 2- to 3-year-old children (n = 56), we found that children's vocalizations selectively modulated caregivers' brain activity. Parents' brains showed a rapid response to children's production of speech that was communicatively advanced (multiword utterances) vs. less advanced (babble). The findings imply that if part of learning to talk is figuring out how to get your communication partner to listen, then early conversations likely present continuous opportunities for feedback that guide children toward advances in spoken language.

Background: Infants with extremely low birth weight (ELBW) are at high risk for neurodevelopmental impairment, but the relationship between cerebellar structure and later developmental outcome remains incompletely defined. Methods: In this retrospective single-center study, we examined total cerebellar volume on clinically acquired brain magnetic resonance imaging (MRI) in ELBW infants while accounting for postmenstrual age (PMA) at imaging. Results: Of 65 infants screened, 42 were included. Mean gestational age at birth was 25.54 {+/-} 1.68 weeks, mean birth weight was 733.2 {+/-} 118.7 g, and mean PMA at MRI was 43.02 {+/-} 6.53 weeks (range, 31.57--68.00). Mean cerebellar volume was 22.17 {+/-} 9.63 cm. Independently adjudicated developmental outcomes comprised normal development in 16 infants, isolated or specific impairment in 12, and moderate/severe impairment in 14. Unadjusted cerebellar volumes did not differ across developmental groups (P = 0.490) or qualitative MRI categories (P = 0.875). However, the PMA-by-developmental-category interaction was significant (P = 0.002), with estimated cross-sectional slopes of 1.72, 1.56, and 0.39 cm per week, respectively. The interaction remained significant within 36--46 weeks PMA (n = 29; P = 0.011) and after adjustment for small-for-gestational-age (SGA) status. Sensitivity analyses indicated uncertainty in the stability of the interaction. Conclusions: These findings suggest that age-informed cerebellar volumetric patterns may provide developmental information not evident from average volumes alone and support prospective validation with standardized imaging and developmental assessments. As a result, cerebellar volumes obtained early in life may hold prognostic information for ELBW infants.

The existence and mechanistic bases of sex effects on human subcortical anatomy remain unclear. To address these gaps, we combined structural neuroimaging of sex differences in subcortical volume and shape across three independent cohorts (combined N=2,886) with neuroimaging maps of sex chromosomal and endocrine effects from three clinical cohorts (combined N=313, including sex chromosome aneuploidies and isolated gonadotropin-releasing hormone deficiency). We found that human subcortical anatomy shows focal sex differences of moderate-strong effect size during young adulthood (male-biased surface area overlying lateral/ventral putamen and pallidum, nucleus accumbens, ventral posterior complex of thalamus, anterior hippocampus, and medial and central nuclei of amygdala; female-biased surface area overlying caudate head, posterior/dorsal pallidum, mediodorsal, lateral dorsal, lateral geniculate, and medial geniculate nuclei of thalamus, posterior hippocampus, and basolateral and lateral nuclei of amygdala). These regional sex differences show strong within-sample reproducibility at modest sample sizes (N[&ge;]200) and are replicable in a fully independent mid-adulthood cohort. Adult subcortical sex differences are already evident in nascent form by pre/early puberty (age 9 years), and the same holds for cortical sex differences. Comparison with neuroimaging data from rare disorder cohorts reveals a statistically significant spatial and directional congruence of subcortical sex differences with the anatomical effects of X-chromosome dosage in the striatum and effects of testicular hormones in the amygdala. Our findings provide a reference map for the fine-grained spatial patterning of sex differences in adult subcortical shape, demonstrating its replicability, establishment before pubertal progression, and spatial concordance with potentially causal sex chromosomal and gonadal effects.

Age of first alcohol use is associated with alterations in brain development and an increased risk of problematic substance use later in life. However, it remains unclear if neurobiological alterations associated with first-use age are present in young adulthood and how these alterations may be associated with different patterns of adult substance use. In this study, we employed SuperBigFLICA, a semi-supervised multimodal imaging data fusion approach, to identify joint structural morphometric and functional MRI (fMRI) brain profiles present in adulthood that are most predictive of age of alcohol first-use in the Human Connectome Project-Young Adult cohort (n=622). Our model achieved moderate performance (r=0.22 in external validation), identifying multimodal structure-function profiles predictive of first-use largely involving frontal regions. We then utilized Canonical Correlation Analysis to determine whether these multimodal brain profiles were differentially linked to patterns of adult substance use behaviors. We identified three significant brain modes (Mode 1: r = 0.2750, p = 0.014; Mode 2: r = 0.2249, p = 0.005; Mode 3: r = 0.1568, p = 0.029) associated with distinct profiles of alcohol and cannabis use. Together, we identified multimodal brain patterns in young adults that predict age of first use of alcohol and demonstrated that resulting multimodal structure-function brain profiles capture distinct multivariate patterns of adult substance use, providing a more integrated view of substance use neurobiology.