Developmental Cognitive Neuroscience

IntroductionBody mass index (BMI) is widely used to screen for weight-related health risks during adolescence. Prior neuroimaging studies have assumed a linear relationship between BMI and brain microstructure, potentially obscuring how this association varies across the BMI distribution. Using restriction spectrum imaging (RSI) in the Adolescent Brain Cognitive Development (ABCD) Study, previous work has identified positive linear associations between BMI and weight-related metrics and the restricted normalized isotropic (RNI) signal fraction in subcortical structures, but it remains unclear whether these associations are uniform across the full BMI spectrum or driven by particular portions of the distribution. MethodsWe examined the relationship between BMI percentile and voxelwise RNI in subcortical gray matter and white matter structures using data from the ABCD Study 6.1 release, which includes four imaging timepoints spanning ages 9-18 years (22,011 observations from 10,465 unique participants). Sex-stratified generalized additive mixed-effects models with smooth terms for BMI percentile, age, and pubertal development were used to model the shape of the BMI-microstructure association across the full percentile range, controlling for genetic principal components, household income, parental education, and MRI scanner/software version. ResultsThe association between BMI percentile and RNI was nonlinear in the bilateral nucleus accumbens, caudate, pallidum, putamen, thalamus, and forceps minor. A modest, positive association was present across most of the BMI range, but the rate of change accelerated markedly above the 80th percentile. This pattern was consistent across structures and sexes, though the overall magnitude of the partial effect was higher for males across most structures, while females showed steeper rates of change in most structures above the 80th percentile. Voxelwise analyses revealed spatial heterogeneity within structures, with stronger effects concentrated in specific subregions including the posterior forceps minor, dorsal pallidum, anterior putamen, and posterior thalamus. DiscussionThe relationship between BMI and subcortical brain microstructure during adolescence is not uniform but instead accelerates at the upper end of the BMI distribution, suggesting that prior linear estimates may reflect a blended average of a modest slope across most of the range and a steep slope above the 80th percentile. These findings extend the existing literature by capturing a wider developmental window, employing voxelwise rather than ROI-averaged analyses, identifying the forceps minor as a novel region of interest, and highlighting the advantages of nonlinear modeling in revealing dynamic associations.

BackgroundSubstance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information. MethodsWe conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes--alcohol, nicotine, cannabis, and any substance use--using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochrans Q and I2), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci. ResultsIn the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p-values: alcohol [~] 1 x 10-7, any [~] 1 x 10-7, cannabis [~] 5 x 10-8, nicotine [~] 1 x 10-8). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses (p < 5 x 10-8). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates. ConclusionsSurvival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.

Objective: To investigate overall and neighborhood socioeconomic deprivation moderated associations between glycemic control and brain structure in youth. Research Design and Methods: This was a cross-sectional study of 705 healthy 11-12-year-olds across 21 study sites in the United States. Data was obtained from the Adolescent Brain and Cognitive Development (ABCD) Study(R). Glycemic control was assessed using hemoglobin A1c (HbA1c), brain structure was evaluated via MRI, and neighborhood deprivation was measured with the Area Deprivation Index (ADI). Mixed effects models were used to examine relationships between HbA1c, brain structure and ADI controlling for sociodemographic covariates. Stratified analysis was performed by tertiles of ADI. Results: Higher HbA1c was associated with lower mean cortical thickness (CT) and smaller total cortical gray matter volume (GMV). One percent increase in HbA1c corresponded to a 0.024 mm reduction in mean CT and a 9,611 mm3 reduction in total cortical GMV. Regionally, higher HbA1c was associated with thinner cortex and smaller gray matter volumes primarily in the frontal, cingulate and occipital areas. There was a significant interaction of HbA1c and ADI on total GMV, which was driven by significant negative associations of HbA1c with total GMV in the high ADI group, and medium ADI group, but not the low ADI group. Conclusions: Mild elevations in HbA1c, even within the non-diabetic range, are linked to early brain structural changes, particularly in youth from neighborhoods with greater socioeconomic deprivation. These results highlight the interplay between metabolic health and neighborhood deprivation on shaping brain development in youth.

Previous research has demonstrated that children exhibit superior - as compared to adults - consolidation of newly acquired motor sequences across post-learning periods of wakefulness. Given that consolidation is thought to be supported by the reactivation of learning-related patterns of brain activity during the rest periods following active task practice, we hypothesized that the childhood advantage in offline consolidation may be linked to greater reactivation during post-learning wakefulness. Twenty-two children (7-11 years) and 23 adults (18-30 years) completed two sessions of a motor sequence learning task, separated by a 5-hour wake interval. Multivoxel analyses of task-related and resting-state functional magnetic resonance imaging data were employed to assess the persistence of learning-related patterns of neural activity into post-task rest epochs, reflective of reactivation processes. Behavioral results demonstrated the previously reported childhood advantage in offline consolidation over a post-learning wake interval. Imaging results revealed that children exhibited greater persistence of task-related hippocampal - but not putaminal - activity into post-learning rest as compared to adults. These findings suggest that the childhood advantage in awake motor memory consolidation may be supported, at least partially, by enhanced reactivation of task-dependent hippocampal activity patterns during offline epochs.

Reading is a complex skill with a well-characterized neural basis. Multivariate fMRI analyses have deepened our neuroscientific understanding of literacy by linking neural patterns to behavioral traits. Although task-based fMRI often outperforms resting-state fMRI in predicting cognitive traits, few studies have applied it to continuous measures of childrens reading ability. To identify neural markers of literacy, we compared predictive performance across multiple fMRI tasks and reading-related measures. In this data-driven study, we predicted literacy skills in school-aged children (6.7-10.3 years) from eleven behavioral scores grouped into Reading (fluency and comprehension), Verbal (vocabulary knowledge and verbal intelligence), and Naming (object naming speed). Predictive performance was examined across four fMRI tasks completed by subgroups of children (n = 73-97): two active tasks - phonological-lexical decisions (PhonLex) and audiovisual character learning (Learn) - and two passive tasks - word and face viewing (Localizer) and character processing (CharProc). Individual activation contrast maps, categorized as simple (single condition) or subtractive (condition contrasts), were analyzed using a machine learning model with whole-brain predictors derived from principal component analysis. Results showed the highest predictive performance for Reading and Naming with PhonLex > Learn > Localizer = CharProc, and for Verbal with PhonLex = Learn > Localizer = CharProc. Simple contrasts generally outperformed subtractive contrasts in predicting behavioral scores. Key neural predictors, identified through whole-brain and region-of-interest analyses, included the left inferior frontal gyrus, supramarginal gyrus, ventral occipitotemporal cortex, insula, and default mode network regions. Together, these findings indicate that, for predicting literacy traits in children, active tasks and tasks that engage brain systems involved in multisensory learning tend to outperform both passive paradigms and simple subtractive task contrasts. This study provides a methodological benchmark for brain-based prediction of reading ability and highlights the value of activation heterogeneity across distributed regions as a potential marker for tracking literacy development over time.

BackgroundHormonal transition phases represent windows of increased neuroplasticity across the female lifespan. In this study, we aim to investigate the brain anatomical architecture of hormonal transition phases by directly comparing menarche, as a period of rising levels of steroid hormones, and menopause, as a time of declining levels. MethodsWe fit linear models on cross-sectional and linear mixed-effect models on longitudinal magnetic resonance imaging (MRI) datasets, to explore the effects of menarche onset (ABCD study data, Ncross-sectional=1274, Nlongitudinal=611) and transition into menopause (UK Biobank data, Ncross-sectional=1614, Nlongitudinal=212) on 66 cortical and 135 subcortical brain volumes, and to identify brain structures with opposing but regional overlapping effects in both periods. Models were adjusted for age and corrected for multiple comparison (P <.05; FDR-corrected). ResultsCross-sectionally, using a between-subject design, 83 brain volumes showed effects of menarche-onset and 17 volumes showed effects of menopause-transition. Of these, seven brain volumes were significantly affected by both transitional periods, showing opposing directional volume changes. Longitudinally, using a within-subject design, 56 brain volumes exhibited menarche effects, of which 46 replicated cross-sectionally. No menopause effect survived correction for multiple comparison, likely due to limited longitudinal sample size. ConclusionOur findings confirm regionally overlapping brain structural alteration between the two hormonal phases - menarche and menopause - showing the hypothesized opposite effect directions. Additionally, our results show the robustness of menarche effects, which converged across cross-sectional and longitudinal study designs. Taken together, our results contribute to a better understanding of hormone related neuroplasticity, emphasizing the importance of not only understanding individual phases, but understanding the overarching patterns across the female reproductive lifespan.

Background: The Adolescent Brain Cognitive Development (ABCD) Study provides rich longitudinal data on environmental, genetic, and behavioral factors related to substance use initiation. Classical marginal structural models (MSMs) require selecting covariates for propensity models, which is challenging when there are many correlated predictors. Methods: We analyzed longitudinal panel data from 11,868 ABCD participants with repeated observations over time. Interval-level binary outcomes were defined for initiation of alcohol, nicotine, cannabis, and any substance, including only participants at risk before initiation. All predictors were constructed as lagged variables to preserve temporal ordering. We used a two-stage machine learning-based causal framework. First, we performed graph discovery using a Granger-inspired lagged predictive modeling approach with elastic-net logistic regression to identify relationships between past predictors and future outcomes. Stable candidate edges were selected using subject-level bootstrap stability selection. Second, we estimated adjusted effects for stable predictors using double machine learning (DML) with partialling-out and cross-fitting. For each predictor, the lagged variable was treated as the exposure and adjusted for high-dimensional lagged covariates. Cross-fitting with group-based splitting accounted for within-subject dependence. Nuisance functions were estimated using random forests, and cluster-robust standard errors were used for inference. Results: We identified stable predictors across multiple domains, including sleep patterns, family environment, peer relationships, behavioral traits, and genetic risk. Many predictors were shared across substance outcomes, while some were outcome-specific. Effect sizes were modest, typically ranging from -0.01 to 0.02 per standard deviation increase in the predictor. Both risk-increasing and protective associations were observed. Risk factors included sleep disturbance and behavioral risk indicators, while protective factors included parental monitoring and structured environments. Conclusions: This study presents a practical framework for analyzing high-dimensional longitudinal data and identifying time-varying predictors of substance use initiation. The approach combines machine learning for variable selection with causal inference for effect estimation. The results highlight both shared and outcome-specific risk factors and identify modifiable targets, such as family environment and sleep, that may inform prevention strategies.

ObjectiveSocial difficulties are transdiagnostic in childhood, but their heterogeneity is poorly characterised and rarely treated as a primary neurodevelopmental phenotype. This matters because childhood and adolescence are sensitive periods for peer relationships and brain development. We used data-driven modelling and non-linear mapping to derive social profiles and test their clinical, cognitive, and neural correlates. MethodsParticipants were 992 children aged 5-18 years from CALM (Mage = 9.6). Social items from the SDQ, CCC-2, and Conners-3 were modelled using a regularised partial correlation network to derive core social dimensions. A self-organising map captured graded social profiles. Simulated archetypes, SVM-based island identification, and permutation testing defined profile regions and centroid-distance scores. Profiles were related to referral, diagnosis, cognition, BRIEF indices, and T1-derived MIND network structure in an MRI subsample (n = 431). ResultsWe identified four profiles: social engagement, friendship difficulties, social withdrawal, and peer victimisation. Profile expression tracked variation in referral and diagnostic pathways. Social withdrawal showed the clearest disadvantage across cognitive domains, whereas social engagement was associated with fewer executive function difficulties across BRIEF indices. MIND strength components covaried with profile expression (a significant PLS latent variable, p = 0.02), with covariance strongest for social withdrawal and peer victimisation. ConclusionsChildhood social functioning organises graded signatures that relate to clinically relevant pathways, cognitive and executive outcomes, and brain structure. Profiling social signatures provides a scalable framework for identifying social need beyond diagnostic categories, motivating studies to test directionality and improve developmental outcomes.

Objective: Childhood poverty is a high-risk context that involves diverse adversities, making it difficult to understand how poverty confers later psychopathology risk and why some children remain resilient despite growing up in poverty. To address this heterogeneity, we quantified adversity-linked vulnerability as adversity-psychopathology coupling and tested whether childhood poverty amplifies this coupling and whether multilevel inhibitory-control profiles stratify vulnerability and resilience within poverty-exposed youth. Methods: We analyzed 10,112 youth (48.4% female; mean age = 9.92 years) from the Adolescent Brain Cognitive Development Study, linking baseline cumulative early-life adversity (ELA) to later behavioral problems across 4 waves. In the stop-signal task fMRI subsample of 7,401 youth, semi-supervised clustering of inhibitory-control activation identified neurofunctional subtypes within poverty-exposed youth. We also tested temperamental inhibitory control as an additional moderator. Results: Childhood poverty amplified the association between cumulative ELA and behavioral problems at baseline ({Delta}{beta} = 0.088; P < .001) and across follow-up waves. Two neurofunctional subtypes were identified within poverty-exposed youth: subtype-1 showed greater vulnerability than higher-income peers ({Delta}{beta} = 0.149; P < .001), whereas subtype-2 showed attenuated vulnerability and did not differ from higher-income peers ({Delta}{beta} = 0.049; P = .135); this pattern persisted longitudinally. Among poverty-exposed youth in subtype-2 with high temperamental inhibitory control, the association between cumulative ELA and later behavioral problems was no longer significant. Conclusions: Childhood poverty strengthened the translation of adversity burden into later behavioral problems, but inhibitory-control profiles differentiated higher- and lower-risk pathways within poverty, highlighting inhibitory control as a candidate target for prevention.

Early childhood mental health problems are common and difficult to detect due to reliance on caregiver reports of often unobservable symptoms. This study quantified threat response movement patterns during a 30-second laboratory threat induction task using wearable inertial sensors. Movement patterns were used to examine (1) changes in stimuli response across the task (task validity) and (2) associations with symptom severity (clinical validity). Sacral accelerometer and gyroscope data were analyzed from 91 children aged 4-8 years during the brief task, 48.4% of whom had a mental health diagnosis. Consistent with task validity, Turning Speed varied across task phases differing in potential threat intensity. Consistent with clinical validity, internalizing symptoms were associated with smaller Turning Angle, possibly indicating vigilance. This effect was moderated by comorbid externalizing symptoms, such that children with high internalizing and high externalizing symptoms exhibited larger Turning Angles, possibly indicating avoidance. Findings demonstrate that brief wearable-enabled tasks can capture subtle objective behavioral markers of threat responses and underscore the importance of considering comorbid symptom dimensions in early childhood mental health screening.

Dopaminergic (DA) function and basal ganglia neurobiology are central to reward learning, motivation, and cognitive control, and dysregulation of these systems contributes to neuropsychiatric conditions that emerge during development. Adolescence is marked by profound reorganization of DAergic basal ganglia circuitry, yet direct in vivo assessment of the DA system remains limited in youth. Brain tissue iron is a developmentally sensitive marker of DA-related neurobiology that can be measured non-invasively via magnetic resonance imaging (MRI). Iron is an essential co-factor for DA synthesis and a foundational metabolic resource that supports cellular metabolism, myelination, and energetic demands of the basal ganglia. T2*-weighted echo-planar imaging (EPI), collected during functional MRI (fMRI), is sensitive to magnetic susceptibility of non-heme brain iron. Leveraging this property, we demonstrate the validity and broad applicability of an iron-sensitive metric that can be derived from conventional single-echo fMRI: {Delta}R2*. In a longitudinal developmental dataset (N = 151; age range 12-31), {Delta}R2* showed high reliability, strong longitudinal stability, and validity via robust convergence with established quantitative relaxometry-based iron measures (R2* and R2). Critically, {Delta}R2* can be retrospectively estimated from extant fMRI data and derived in large-scale consortium data repositories, demonstrated here in the Adolescent Brain and Cognitive Development (ABCD) baseline cohort (N = 8,366; ages 9-11). We show that {Delta}R2* captures known age-related increases in basal ganglia iron, highlighting neurodevelopmental sensitivity at population-scale. Together, these findings establish {Delta}R2* as a reliable, widely accessible marker of basal ganglia iron, enabling scalable investigation of lifespan trajectories and neuropsychiatric risk in existing and future datasets.

BackgroundPhysical activity has been associated with better reading comprehension and reduces cognitive load (CL), but the role of brain volume in modulating this relationship remains unclear. Therefore, this study aims to determine whether the gray matter volume in key regions modulates the effects of different physical activity modalities on reading comprehension and associated CL. MethodsThirteen male adolescents (12-13 years). Adolescents with MRI data participated in a randomized cross-over trial comparing three conditions: 1) sedentary behavior (SC, emulating a school class), 2) moderate-intensity continuous training (MICT), and 3) cooperative high-intensity interval training (C-HIIT), with physical activity conditions duration adjusted to match SC energy expenditure. Gray matter volumes were measured in the bilateral hippocampus, left pars opercularis, and the brainstem. CL was assessed via pupil dilation during reading using eye-tracking. Reading comprehension was measured through seven-question multiple-choice tests with expert-validated items. ResultsC-HIIT demonstrated superior effects on both CL and reading comprehension compared to MICT and SC, with significant brain volume modulation effects across all examined regions. Brain volume interactions with physical activity modalities systematically modified the pattern of cognitive responses, with C-HIIT consistently benefiting from these modulations, whereas the effects of MICT were generally attenuated. ConclusionThis study suggests that selecting the appropriate physical activity modality may be relevant for cognitive outcomes during reading in adolescents. C-HIIT yielded lower CL and better reading comprehension, and these effects were not explained by brain volume alone but by its interaction with exercise modality.

Background: Early initiation of substance use is linked to later adverse outcomes, and risk factors come from multiple domains and are shared across substances. In our previous work, traditional time-to-event Cox models identified individual risk factors, but these models are not designed to jointly model multiple outcomes or capture complex non-linear relationships. Multi-task learning (MTL) can leverage shared structure across related outcomes to improve prediction and distinguish common versus substance-specific predictors. However, most MTL studies rely on baseline features and focus on single outcomes, which limits their ability to capture shared risk and temporal changes. Substance use initiation is a time-dependent process that unfolds during development and reflects changing exposures over time. Baseline-only models cannot capture these changes or represent risk dynamics. Discrete-time modeling provides a practical approach by estimating interval-level initiation risk and combining it into cumulative risk at the subject level. By integrating multi-task learning with dynamic modeling, it is possible to share information across outcomes while capturing how risk evolves over time, which may improve prediction performance. Methods: Using the Adolescent Brain Cognitive Development (ABCD) Study (release 5.1), we developed two complementary multi-task learning (MTL) frameworks to predict initiation of alcohol, nicotine, cannabis, and any substance use. A baseline MTL model predicted fixed- horizon (48-month) initiation using one record per participant, while a dynamic discrete-time MTL model incorporated longitudinal interval data to model time-varying risk. Both models used multi-domain environmental exposures, core covariates, and polygenic risk scores (PRS). Performance was evaluated on a held-out test set using AUROC, PR-AUC, and calibration metrics, and compared with single-task logistic regression (LR). Feature importance was assessed using permutation importance and compared with Cox proportional hazards models. Results: MTL showed comparable or improved performance relative to LR, with larger gains for low-prevalence outcomes (cannabis and nicotine). Incorporating longitudinal information led to consistent improvements across all outcomes. Dynamic models increased AUROC by +0.044 to +0.062 for MTL and +0.050 to +0.084 for LR, indicating that temporal information was the primary driver of performance gains. Feature importance analyses showed modest overlap across methods, with higher agreement between dynamic MTL and Cox models than static MTL. A small set of features, including externalizing behavior, parental monitoring, and developmental factors, were consistently identified across all approaches. Conclusions: Dynamic multi-task learning improves the prediction of substance use initiation by leveraging longitudinal structure and shared information across outcomes. While MTL provides additional gains, incorporating time-varying information is the dominant factor for improving performance. Combining baseline and dynamic frameworks offers a comprehensive strategy for identifying robust risk factors and modeling adolescent substance use initiation.

Understanding depressive symptoms dynamics and their determinants is crucial for designing effective mental health support initiatives. This study compared two methods for describing youth depressive symptoms trajectories and investigated associations of early-life factors (maternal education, maternal perinatal depression, domestic violence, physical, emotional, or sexual abuse, bullying victimisation, psychiatric disorder) with trajectory features. Prospective data from 8,264 mostly White European participants (54% female), including self-reported Short Moods and Feelings Questionnaires on ten occasions between 10-25 years, were used. Trajectories were summarised using functional principal component analysis (FPCA) and P-splines linear mixed-effect (PLME) models. Estimated derivatives were used to obtain magnitude and age of peak symptoms and peak symptoms velocity. Both methods performed comparably, but PLME models tended to over-smooth trajectories. Peak symptoms and peak velocity were higher and occurred >1 year earlier in females than males. All early-life factors were associated with higher peak symptoms, and most associated with higher and earlier peak velocity. Abuse and bullying additionally associated with earlier age of peak symptoms. FPCA is a useful alternative for characterising depressive symptoms trajectories and informing time-sensitive preventative measures to reduce impact of depression before symptoms reach their peak. Early-life stressors may accelerate timeline and intensity of symptoms escalation during adolescence. Lay summaryUnderstanding development of depressive symptoms and factors shaping them is crucial for designing effective mental health support initiatives. This study used data from over 8,000 young people regularly followed up from before birth to compare two cutting-edge methods for describing depressive symptoms trajectories and examined how known risk factors for adulthood depression relate to the severity and rate of change of depressive symptoms in adolescence. We found that both methods performed well and that the peaks in depressive symptoms and their rate of change were, on average, higher and occurred over a year earlier in females than males. Our findings additionally suggest that early-life stressors (e.g., abuse, bullying) may accelerate the development of depression, highlighting the importance of early prevention.

Objective: While ADHD symptoms often decline from childhood into adulthood, the underlying neurobiological mechanisms, such as altered brain maturation or neural reorganization, remain incompletely understood. This study investigated how grey matter development relates to ADHD symptom trajectories into adulthood. Method: We analyzed data of individuals with ADHD and controls from the longitudinal Dutch NeuroIMAGE cohort, utilizing dimensional ADHD symptom scores (Conners Parent Rating Scale) from three waves and T1-weighted structural MRI scans from the final two waves. Using General Linear Models with permutation-based inference, we examined: 1) cross-sectional associations between ADHD symptoms and vertex-wise cortical thickness and surface area, and subcortical volumes at Wave 1 (n = 765, mean age = 16.95 years); and 2) longitudinal associations between symptom progression and brain morphometric changes (Wave 0 to 1: n = 644, mean age = 11.55-17.24 years; Wave 1 to 2: n = 149, mean age = 16.45-20.11 years). Results: Cross-sectionally, at Wave 1, more ADHD symptoms were related to widespread reductions in surface area, most prominently in the frontal cortex, and smaller volumes of the cerebellum, amygdala, and hippocampus. Longitudinally, symptom improvement from Wave 1 to Wave 2 was associated with stronger reductions in surface area, particularly in prefrontal and occipital regions, and with more pronounced cortical thinning across multiple brain regions. Conclusion: These findings suggest an association between symptom trajectories and structural brain changes, indicating that clinical improvement in ADHD behaviors might coincide with ongoing neural refinement during the transition to adulthood.

Background. Brain development is altered in neonates with congenital heart disease (CHD). However, development in the perioperative period remains incompletely understood. Purpose. This study used Structural Covariance Component (SCC) analysis to identify brain regions showing spatial patterns of coordinated expansion and contraction that differ between neonates with CHD after cardiac intervention and healthy controls, as well as pre-to postoperative changes and effects of perioperative risk factors. Study type. Prospective. Population. The cohort included 41 neonates with CHD who underwent cardiac surgery or catheterization and 359 healthy neonates. Field strength and sequence. 3 Tesla T2-weighted turbo-spin-echo sequence. Assessment: Brain MRI were motion-corrected and reconstructed using an established neonatal algorithm. Jacobian determinants calculated from non-linear registration of MRI to a neonatal template were input into an Independent Component Analysis to identify SCCs (N=40). SCC weightings were extracted, reflecting the degree to which the pattern of covariance is expressed in each neonate. Statistical tests. Postoperative SCC weightings were compared to healthy neonates using a general linear model or robust regression. Pre- and postoperative SCC weightings were compared using a linear mixed effect model. Pre- to postoperative differences were calculated and associations with age at surgery, cardiopulmonary bypass duration, and postoperative paediatric intensive care unit stay were assessed using partial spearman's rank correlation. Analyses were adjusted for covariates and corrected for multiple comparisons using False Discovery Rate. Results. 16/40 SCCs showed significant differences between neonates with CHD after surgery and controls, including white matter, cortical- and deep grey matter, brainstem, and CSF regions, with seven also showing significant perioperative change. A further nine SCCs only showed significant perioperative change. Perioperative risk factors were not associated with perioperative change. Data conclusion. This data-driven approach highlights region-specific postoperative alterations and perioperative changes in brain morphology of neonates with CHD. Evidence level. 1. Technical Efficacy. Stage 3.

Adult ADHD is increasingly recognized across the lifespan, yet the psychometric equivalence of the Adult ADHD Self-Report Scale (ASRS) remains unverified for older populations. This study examined age-related Differential Item Functioning (DIF) in 600 adults (n = 100 per decade, ages 20-80) who completed the 18-item ASRS. Using a bi-factor Graded Response Model, we extracted latent ADHD trait scores ({omega}H = .895) and assessed DIF via ordinal logistic regression with adaptive age modeling. Five of 18 items exhibited significant uniform DIF. At equivalent latent severity, older adults were less likely to endorse hyperactivity symptoms in Part A (fidgeting, feeling "driven by a motor") but more likely to endorse specific symptoms in Part B (careless mistakes, misplacing items, interrupting). From ages 20 to 80, expected Part A scores decreased by 1.36 points (~0.27 per decade), while Part B scores increased by 1.15 points (~0.23 per decade). These findings indicate a phenotypic redistribution of ADHD symptoms as individuals age. Because the 6-item Part A screener serves as the primary clinical gatekeeper, its concentration of negative DIF suggests standard screening practice may systematically underestimate ADHD severity in older adults. We recommend using the full 18-item ASRS when screening older populations and suggest that developing age-adjusted norms would improve diagnostic accuracy.

Brain development follows a precisely regulated biological timetable, with defined periods of vulnerability increasingly recognized in congenital disorders affecting early brain development. This biological timing can be captured by the emerging concept of brain age, a measure of brain maturation, enabling the detection of deviation from normative developmental trajectories. Clinical conditions affect the degree of brain development during this critical period, including preterm birth and congenital heart disease (CHD). We developed a deep learning-based brain age estimation framework across the fetal-neonatal period (21-44 gestational weeks) to quantify neurodevelopment from structural MRI. Using 1056 scans from six datasets acquired at three centers, Zurich, Shanghai, and the Developing Human Connectome Project, we trained models on normative fetal and neonatal MRI data. Both structural MRI-based and segmentation-derived cortical morphology-based models were implemented to assess representation effects and cross-center generalisability. The framework was applied to two clinically relevant conditions, preterm birth and CHD, to estimate the brain age gap (BAG), defined as the difference between predicted brain age and chronological age. In preterm neonates scanned at term-equivalent age (n=90, 37-44 weeks), BAG was progressively more negative with lower gestational age at birth. Neonates born before 28 weeks showed delays of -0.7 to -0.8 weeks relative to term-born controls. In CHD (n=50, 22-34 weeks), fetal brain age did not differ from center-matched controls and no association with cardiac defect severity was observed. After birth, neonates with CHD (n=110, 37-44 weeks) showed significant (p<0.05) negative BAGs before surgery (-1.3 to -1.8 weeks) and BAGs increased significantly (p<0.05) after surgery (up to -3 weeks in center-specific analyses), indicating a delay in brain maturation from postnatal stage, but not in prenatal stage in CHD patients. These patterns were found across both structural MRI-based models and cortical morphology-based models, despite the need for cross-center calibration to minimize systematic bias. Voxel-based morphometry showed that a larger BAG was associated with regional contraction in deep frontal and peri-Rolandic white matter in preterm neonates, and perioperative spatial shifts in neonates with CHD. Saliency maps converged on deep white matter and periventricular regions, highlighting a potential link between BAG and delayed maturation of rapidly developing projection pathways. These findings may indicate neurodevelopmental delays in preterm birth and a postnatally emerging maturational gap in CHD that increases following cardiac intervention. Despite limited generalisability of our methods, these results support a continuous fetal-neonatal brain age metric as a sensitive marker of global neurological maturational timing.

BackgroundChildren who are HIV-exposed but uninfected (CHEU) face elevated risks of hearing loss and language deficits compared to HIV-unexposed peers. The central auditory system (CAS) undergoes substantial maturational changes during adolescence, yet no neuroimaging study has examined its structural or functional integrity in CHEU. Prior work in this cohort identified white matter (WM) alterations in regions adjacent to the CAS at age 7, and reduced auditory working memory in CHEU relative to unexposed children (CHUU). AimTo characterise WM integrity and functional connectivity (FC) of the CAS and related regions in CHEU at age 11, to investigate structural and functional network topology, and to examine associations between imaging outcomes and neurocognitive function. MethodsForty-eight children aged 11-12 (20 CHEU, 28 CHUU) from an ongoing longitudinal neurodevelopmental cohort underwent 3T MRI including diffusion tensor imaging (DTI) and resting-state fMRI (RS-fMRI). CAS regions (cochlear nucleus/superior olivary complex, inferior colliculus [IC], medial geniculate nucleus [MGN], and primary auditory cortex [PAC]) were manually segmented and combined with an automated atlas. DTI probabilistic tractography was performed, extracting FA, MD, AD, RD, fractional number of tracts, and tract volume. FC was computed using Pearson correlations between regional time series. Graph theory measures (degree, strength, transitivity, nodal and local efficiency) were derived for structural and functional networks. RS-fMRI group comparisons used Bayesian multilevel modelling (matrix-based and region-based analyses), while DTI comparisons used linear models with FDR correction. Neurocognitive testing employed the KABC-II. ResultsNo significant group differences in DTI WM metrics (FA, MD, AD, RD) were observed after FDR correction. CHEU demonstrated higher structural nodal strength in the left IC (FDR-significant) and in the bilateral rostral middle frontal cortex (rMFC) and right cuneus. RS-fMRI revealed lower FC between the bilateral IC in CHEU, alongside reduced FC in the left caudate, left hippocampus CA3, left pericalcarine, and left lingual gyrus. CHEU showed higher FC between the left MGN and right precentral, left postcentral, and right rMFC; the right PAC also showed higher FC to the right rMFC and left postcentral gyrus. No significant group differences were observed in functional nodal measures. No significant associations were found between structural or functional imaging outcomes and neurocognitive scores after multiple comparison correction. DiscussionStructural and functional alterations within the CAS were most prominent in the IC, with increased nodal strength in CHEU potentially reflecting compensatory structural connectivity, and reduced interhemispheric FC between the bilateral IC suggesting disrupted auditory integration. Altered FC between the MGN/PAC and cortical regions, including the rMFC and sensorimotor cortices, may reflect differences in top-down auditory processing. The absence of imaging-cognition associations at age 11 suggests that these connectivity differences do not, at this stage, translate into measurable deficits in auditory or language-related neurocognitive performance. ConclusionThis is the first study to examine functional and structural connectivity of the CAS in CHEU children. HIV exposure is associated with subtle but discernible alterations in IC connectivity and in CAS links to cortical regions at age 11, without detectable neurocognitive correlates. Longitudinal follow-up and inclusion of audiological and ART exposure data are needed to clarify the developmental and functional consequences of these findings.

BackgroundMindfulness-based interventions (MBIs) have been increasingly adopted in educational settings to support cognitive development in youth. Executive function (EF)--encompassing inhibitory control, working memory, and cognitive flexibility--is a plausible target of MBI given its reliance on attention regulation. However, prior reviews have yielded mixed conclusions, partly due to inconsistent construct definitions and the pooling of heterogeneous outcome measures. ObjectivesTo (1) estimate the pooled effect of MBI on EF in youth aged 3-18 years using only construct-validated, direct EF measures, (2) examine potential moderators including age group, EF domain, and risk of bias, and (3) test dose-response relationships via meta-regression on intervention duration. MethodsWe searched PubMed, PsycINFO, CINAHL, Scopus, and Web of Science from inception to March 2026, supplemented by reference-list searches from two existing systematic reviews and a scoping review. Only English-language publications were eligible. Eligible studies were randomised controlled trials (RCTs) or quasi-RCTs of MBI (excluding yoga-only interventions) in typically developing youth, with at least one direct behavioural or computerised EF outcome. Risk of bias was assessed using Cochrane RoB 2. Hedges g was computed for each study, and pooled using a DerSimonian-Laird random-effects model. Subgroup analyses by age group, EF domain, and risk of bias were conducted, alongside leave-one-out sensitivity analyses, Eggers regression test, trim-and-fill, and Knapp-Hartung-adjusted meta-regression on intervention duration. Evidence certainty was rated using GRADE. ResultsThirteen RCTs (nine school-age, four preschool; total N = 1,560) met inclusion criteria. The pooled effect was g = 0.365 (95% CI 0.264 to 0.465; p < .00001), with negligible heterogeneity (I2 = 0.0%; Q = 6.76, p = .87). Effects were consistent across age groups (school-age g = 0.389; preschool g = 0.318) and EF domains (inhibitory control, working memory, cognitive flexibility; pbetween = .60). Meta-regression on intervention duration (4-20 weeks) was non-significant (p = .79). The effect was robust in leave-one-out analyses, in the low risk-of-bias subgroup (g = 0.361; k = 8), and after trim-and-fill adjustment (g = 0.354). The 95% prediction interval (0.252 to 0.477) was entirely positive. GRADE certainty was rated MODERATE, downgraded once for risk of bias. ConclusionsMBIs appear to produce a small, statistically significant improvement in EF in youth aged 3-18 years, with moderate certainty of evidence per the GRADE framework. The effect is consistent across preschool and school-age samples and across EF domains, with no significant dose-response relationship within the 4-20 week range studied. Emerging mediation evidence suggests that EF improvement may serve as an important pathway through which MBI supports emotion regulation, though this requires replication. Further large-scale, pre-registered RCTs with active control conditions and longitudinal follow-up are warranted.