Developmental Cognitive Neuroscience

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.

Regional volumetric assessment of perinatal brain development is currently limited by the lack of consistent high quality multi-regional segmentation methods applicable to both fetal and neonatal MRI. We present Multi-BOUNTI, a deep learning pipeline for automated multi-lobe segmentation of fetal and neonatal T2w brain MRI. The method is based on a dedicated 43-label parcellation protocol and a 3D Attention U-Net trained on brain MRI datasets of subjects spanning 21-44 weeks gestational/postmenstrual age. The pipeline integrates preprocessing, segmentation and volumetric analysis, and was evaluated on independent datasets, demonstrating fast (< 10 min/case) and accurate performance with high agreement to manually refined labels. We demonstrate the application of the framework with 267 fetal and 593 neonatal MRI datasets from the developing Human Connectome Project without reported clinically significant brain anomalies to derive normative volumetric growth models across 21-44 weeks GA/PMA. These models were used to characterise developmental trajectories, assess differences between fetal and preterm neonatal cohorts, and analyse longitudinal changes. The resulting normative models were integrated into an automated reporting framework enabling subject-specific volumetric assessment via centiles and z-scores. Multi-BOUNTI provides a unified and scalable approach for perinatal brain segmentation and volumetry, supporting large-scale studies and facilitating future clinical translation. The full pipeline is publicly available at https://github.com/SVRTK/perinatal-brain-mri-analysis.

Early behavioral and temperamental differences are important indicators of later socioemotional development and psychopathology risk, yet their neural bases near birth remain incompletely understood. Using resting-state fMRI data from the Developing Human Connectome Project, we examined whether neonatal functional connectivity predicts 18-month behavioral and temperament outcomes in 397 infants (277 term-born, 120 preterm-born). Outcomes were assessed using the Child Behavior Checklist (CBCL) and the Early Childhood Behavior Questionnaire (ECBQ). We applied a stability-driven, ROI-constrained connectome-based predictive modeling framework to identify robust whole-brain connectivity features associated with later externalizing, internalizing, surgency, negative affect, and effortful control. Significant predictive models were observed for multiple outcomes across the whole cohort as well as within term-born and preterm-born groups, with clear differences in predictive architecture between cohorts. Across analyses, prefrontal and temporoparietal systems were repeatedly implicated, alongside medial temporal, fusiform, parahippocampal, and orbitofrontal-related regions. These findings indicate that large-scale neonatal functional organization is meaningfully related to later socioemotional and behavioral variation, and that preterm birth is associated with partly distinct predictive connectivity patterns.

BackgroundPersistent automatic approach tendencies toward alcohol cues that resist goal-directed control are a key feature of harmful alcohol use, yet the causal neural mechanisms underlying this imbalance remain poorly understood. Converging evidence implicates the frontoparietal network (FPN) in actively regulating alcohol approach-avoidance behavior, but whether its constituent nodes make dissociable causal contributions has not been established. MethodsIn a within-subject, active-sham counterbalanced design, inhibitory continuous theta burst stimulation (cTBS) was applied to right dorsolateral prefrontal cortex (rDLPFC) and right posterior parietal cortex (rPPC) in separate groups of non-clinical alcohol users (rDLPFC: n = 29; rPPC: n = 28), followed by an Alcohol Approach-Avoidance Task. ResultsActive rDLPFC cTBS selectively slowed down alcohol push responses, whereas rPPC suppression produced a bidirectional action-specific shift in response to alcohol cues, where pull responses accelerated, and push slowed simultaneously. Suppression of either node shifted automatic tendencies toward greater alcohol approach through mechanistically distinct routes. ConclusionThese dissociable profiles indicate that rDLPFC is causally necessary for effortful top-down avoidance control, while rPPC supports the priority-based selection of alcohol cue-driven actions. These findings provide the first node-specific causal evidence for functional specialization within the FPN in the context of automatic tendencies towards alcohol. Alcohol avoidance emerges as an active, prefrontal-dependent process, whereas priority-based regulation emerges as a parietal-dependent process, together indicating rDLPFC and rPPC as mechanistically independent targets for intervention in maladaptive alcohol approach behavior.

Emotions often occur within social interactions where affective cues are accessible or inferable by others. This raises questions regarding how and to which degree social context modulates subjective, physiological and behavioural affective responses, as well as their coherence, questions which remain points of tension in emotion research. To investigate this, we measured subjective affective ratings, autonomic sympathetic and parasympathetic activity, and facial behaviour while participants completed an emotion-induction task. In the social-context condition (but not control), participants believed that their video feed was accessible to a potential future interaction partner. Results show that even such "minimal social context" selectively and differentially modulated affective response modalities, characterised by both intensification of autonomic responses and dampening of overt facial and subjective affect. Multivariate dimensionality analysis further identified a cross-modal affective dimension Interestingly, social context reduced participants coupling with this shared affective response structure, indicating weaker cross-modal coherence. These findings suggest that emotional responding relies on a flexible, rather than rigid, configuration of affective features, likely recruited to meet the socioemotional demands of a given context. This has important implications for understanding the structure and function of emotion, as well as typical and atypical socioemotional responding.

Background: Early adolescence is a common period of onset for depressive symptoms. In part, this may reflect a developmental manifestation of individual's genetic propensities as they undergo physiological and hormonal changes and interact with new environments. Many commonly proposed mechanisms assume direct effects of an individual's own genes on emerging variation in their depressive symptomatology. However, estimates of genetic influence based on analyses in unrelated individuals capture not only direct genetic effects but also genetic effects from parents and other biologically related family members. Aim: In data from the Norwegian Mother, Father and Child Cohort (MoBa), we used linear mixed models to distinguish developmentally-stable and adolescence-specific direct and parental indirect genetic effects. We examined effects of polygenic scores for major depressive disorder (MDD), ADHD, anxiety disorders, and educational attainment (EA) on depressive symptoms, which were assessed by maternal reports at ages 8 and 14. Results: Children's own MDD polygenic scores showed adolescence-specific effects on depressive symptoms ( b_PGS*wave=0.041, [95% CI: 0.017, 0.065]). Developmentally-stable direct effects from children's polygenic scores for MDD (b=0.016, [0.006, 0.039]), ADHD (b=0.024, [0.008, 0.041]) and EA (b=-0.02, [ -0.038, -0.002]) were also evident. The only evidence of indirect genetic effects was a stable effect of maternal EA polygenic scores (b=0.04, [0.024, 0.054]). Conclusion: Direct genetic effects linked to genetic liability to MDD accounted for emerging variation in depressive symptoms in adolescence. These results imply that specific etiological mechanisms related to MDD may become particularly relevant for depressive symptoms during early adolescence compared to at earlier ages.

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency {rho} [&ge;]0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, {rho}=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, {rho} =-0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies.

Emotional face processing is a critical component in the development of social cognition through childhood. The neural mechanisms supporting this development can be understood by tracking age-related changes in brain activity. Prior work in this area with MEG relies on static, bandlimited, or region-specific measures, which do not capture the dynamic, distributed nature of brain activity. Here we used Dynamic Network Modes (DyNeMo) to analyze MEG data from a large cohort of typically developing individuals (N=224, ages 5-40) during an emotional faces vigilance task. DyNeMo is a data-driven, generative modelling approach which captures functional networks as a set of whole-brain spatiospectral "modes" whose relative mixture (i.e., activation levels) can change dynamically in response to stimuli. We inferred six modes from the MEG data and characterized developmental trajectories in task-related activation and mode connectivity. Across modes we observed distinct developmental trajectories in both measures. With respect to mode activation, a visual mode and a frontotemporal mode (whose labels reflect their respective spatial profiles) increased nonlinearly with age; meanwhile, activation in temporal and sensorimotor modes decreased linearly with age. Meanwhile, connectivity broadly increased with age in all modes, but with different degrees of nonlinearity. These results suggest developmental dissociations between different modes (e.g., visual versus sensorimotor), as well as within individual modes (task-related mode activation versus connectivity). These results provide a comprehensive and complex picture of functional network development underlying emotional face processing. Significance StatementBrain networks supporting social cognition undergo profound changes from childhood through adolescence to adulthood. However, current understanding of how these networks develop has been limited by conventional analyses of brain imaging data, which typically provide a static picture of brain activity. Here we leveraged a cutting-edge, data-driven modeling approach (DyNeMo) to characterize age-related changes in distributed and dynamic networks supporting emotional face processing, in a large cohort of children and young adults (N=224). We inferred six functional networks whose activation levels changed rapidly in response to emotional faces. Network activation and connectivity exhibited profound and distinct non-linear changes with age, indicating that emotional face processing is supported by complex interactions among multiple dynamic networks, each with different maturational trajectories.

There is a dearth of information on how different cocktails sweetened with different sugars impact brain activity. Glucose enters the brain faster and in greater concentration than fructose and directly affects neuronal activity of melanin-concentrating hormone (MCH) neurons. MCH signaling promotes both glucose drinking and alcohol intake by integrating central and sensory inputs, but it is currently unknown how MCH neuronal activity relates to sweetened cocktail drinking. This study sought to investigate the relationship between MCH activity and sugar-sweetened alcoholic cocktail drinking. We also sought to compare MCH neuronal responses to the sugar solutions without alcohol as well as their response to sensory stimuli. In female and male rats, we used fiber photometry to monitor MCH neurons in response to sensory stimuli and during drinking of 10% glucose, 10% fructose, and glucose or fructose cocktails with 1.25% or 10% alcohol. We found that MCH activity rises in response to a variety of sensory stimuli and peaks before the start of drinking for all cocktails, before returning to baseline near the start of drinking. The cocktail type impacted the dynamics of MCH activity, where increased alcohol concentration resulted in earlier MCH activity for fructose but not glucose cocktails. Finally, we found that peak MCH activity during drinking is correlated with approach behavior for all sugar and cocktail types. These findings suggest that glucose and alcohol may interact to directly influence MCH activity. Further, MCH neurons may regulate cocktail drinking in response to sugar type and alcohol concentration. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/719280v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@b992c3org.highwire.dtl.DTLVardef@1526895org.highwire.dtl.DTLVardef@1504c6dorg.highwire.dtl.DTLVardef@c990fc_HPS_FORMAT_FIGEXP M_FIG C_FIG New and noteworthyFiber photometry was used to monitor lateral hypothalamic melanin-concentrating hormone (MCH) neurons in male and female rats during sensory stimuli and drinking of glucose, fructose, or glucose- or fructose-sweetened alcoholic cocktails. Subsecond-scale changes in MCH activity occurred after stimuli. Peak MCH activity during drinking was correlated with approach behavior. Alcohol concentration only impacted MCH activity with fructose cocktails. We discuss the implications of MCH dynamics towards brain function, associative learning, and alcohol use disorder.

Background: The timing of energy intake could be important in the development of obesity. However, most observational evidence stems from adults, anthropometric defined obesity outcomes, single meal timing phenotyping, and traditional regression modeling. Objective: We aimed to describe meal timing patterns in adolescents and determine if they associated with fat mass by modeling the median and all other percentiles of the frequency distribution. Methods: We analyzed data from the Sleep and Growth Study 2 (S-Grow2, N=286, 12-13y). Participants completed 3-day 24-hour dietary recalls and time stamped eating occasions were used to define 8 meal timing traits, with aide from self-reported wake and bed timing. Principal component analysis (PCA) identified multi-dimensional meal timing patterns. Fat mass index (FMI) was estimated using dual energy X-ray absorptiometry. Quantile regression assessed if there were associations between meal timing traits and FMI across the entire FMI frequency distribution. Results: The typical first and last eating occasions were 8:00am (40 minutes after waking) and 8:00pm (2.7 hours before sleep), respectively, thus the eating period typically lasted 11.5 hours per day. The typical eating period midpoint was 2:15pm, and the timing when 50% of energy intake was consumed typically occurred at 3:15pm. PCA revealed three meal timing patterns: 1) Delayed Start, Condensed Eating Period (43% of variance; shorter eating period and delayed timing of first eating); 2) Late, Sleep Proximal Eating (30% of variance; later timing of last eating and extended eating period), and 3) Later Energy Intake (10% of variance; delayed energy intake midpoint). Higher scores for the Delayed Start, Condensed Eating Period pattern associated with higher body mass index and FMI at the upper tails of their distributions. Conclusions: Distinct multidimensional meal timing patterns emerged in early adolescence, with the delayed start, condensed eating period pattern potentially associated with higher adiposity.

Food preference influences behavior toward food-related stimuli, yet the large-scale neural mechanisms underlying this process remain unclear. This study investigated whether preferred and nonpreferred food cues are associated with distinct patterns of cortical functional connectivity during the observation of food images. Data from 25 of the 40 recruited healthy adults were included in the final analysis after excluding individuals with highly unbalanced response tendencies. Participants viewed 150 food images and rated each image on a four-point preference scale. Trials were classified as favorite food (FF) or disliked food (DF). High-density electroencephalography (EEG) was recorded during the task, and source-level ROI-to-ROI functional connectivity was analyzed using amplitude envelope correlation in the alpha (8-13 Hz) and beta (13-25 Hz) frequency bands over the 1000-ms period after food-picture onset. Response time did not differ significantly between FF and DF trials. However, distinct functional connectivity patterns were observed between conditions in both frequency bands. In the alpha band, FF trials involved a network including the cuneus, parietal regions, cingulate regions, and lateral occipital cortex, whereas DF trials involved the isthmus cingulate, caudal middle frontal gyrus, inferior temporal cortex, superior parietal lobule, and lateral occipital cortex. In the beta band, FF trials involved the isthmus cingulate, precuneus, parietal regions, and pericalcarine cortex, whereas DF trials additionally involved frontal regions, including the superior frontal gyrus and pars triangularis. These findings indicate that food preference is associated with distinct large-scale cortical functional connectivity patterns during food image observation, suggesting differential neural processing of preferred and nonpreferred food cues.

ObjectiveThere is little longitudinal research investigating links between violence exposure and mental disorders among children in low- and middle-income countries (LMICs), despite high rates of violence. We examined cross-sectional and longitudinal violence-mental health associations among children in a large South African birth cohort, the Drakenstein Child Health Study, including direct clinical interviews capturing childrens mental disorders. MethodIn this birth cohort (N=974), we assessed lifetime violence exposure and four subtypes (witnessed community, community victimization, witnessed domestic, domestic victimization) at ages 4.5 and 8-years via caregiver reports. At 8-years, caregivers completed the Child Behaviour Checklist; and psychiatric disorders were assessed using the Mini-International Neuropsychiatric Interview for Children and Adolescents, a self-report measure. We tested for associations using linear/logistic regressions, adjusted for confounders. ResultsMost children (91%) had experienced violence by 8-years. Cross-sectionally, total violence exposure was associated with total (B =0.49 [95% CI 0.32, 0.66]), internalizing (0.32 [0.17, 0.47]), and externalizing problems (0.46 [0.31, 0.61]), and with increased odds of disorder at 8 years (aOR=1.09 [1.05, 1.13]). Longitudinally, total violence exposure up to 4.5-years was associated with total (B=0.27 [0.03, 0.52]), internalizing (0.24 [0.04. 0.44]), and externalizing scores (0.23 [0.008, 0.45]) at 8-years, but not with increased risk of psychiatric disorders. The strongest and most consistent associations were observed for domestic versus community violence subtypes. ConclusionOur strong cross-sectional but weaker longitudinal findings suggest that recent violence exposures may be more critical than early exposures for childrens mental health. Longitudinal exploration of other violence-affected LMIC populations is urgently needed.

Cognitive reappraisal, the deliberate reinterpretation of emotional events, is widely considered an effective emotion regulation strategy, and modulation of the late positive potential (LPP) during negative affect reduction has become the primary electrophysiological evidence for volitional emotional control. Experimental instructions, however, impose dual-task demands that free viewing does not, confounding reappraisal with cognitive load. By including instructions to increase emotional responses to pictures ("enhance") as well as instructions to decrease ("suppress"), different predictions are generated. If the LPP reflects regulation, then, compared to free viewing, suppress instructions should decrease LPP amplitude, and enhance instructions should increase LPP amplitude. If modulation instead reflects cognitive load, both instructions should reduce the LPP, as both impose an additional cognitive task. In a sample of 107 participants, evaluative ratings confirmed that regulation instructions modulated reported emotional intensity in the expected directions (Enhance > View > Suppress), but that both enhance and suppress instructions reduced LPP amplitude compared to free viewing, with Bayesian model comparisons providing strong evidence against direction-specific regulation and in favor of cognitive load. Whole-scalp multivariate pattern analysis confirmed that no instruction-related neural signal exists at any scalp location or latency within the first second after stimulus onset. These data indicate that LPP modulation following both instruction types reflects dual-task cognitive load rather than volitional emotional control. Significance StatementCognitive reappraisal is considered the gold standard of emotion regulation, and reduced late positive potential (LPP) amplitude during negative emotion suppression is the primary neural evidence that humans can voluntarily control emotional responses. The current data are inconsistent with this regulatory account and instead support a cognitive load interpretation. Whether instructed to enhance or suppress emotional responses, LPP amplitude was reduced in both conditions relative to free viewing, consistent with attentional resource competition rather than directional regulatory control. The same participants reported successfully regulating emotional experience in opposite directions, producing a clear dissociation between neural and behavioral measures. These findings challenge a basic tenet of emotional regulation and raise questions concerning LPP modulation as a biomarker of regulatory capacity.

Real-time functional magnetic resonance imaging (fMRI) offers a powerful means of studying brain function adaptively, enabling experimental parameters to be updated dynamically in response to ongoing neural activity. However, current approaches remain limited by complex infrastructure requirements, bespoke implementations, and a lack of flexible frameworks for closed-loop neuroimaging, with many primarily focusing on neurofeedback experimental designs. Here we present AutoNeuro, an open-source framework for real-time fMRI acquisition, preprocessing, feature extraction, and adaptive experimental control. AutoNeuro connects directly to the MRI scanner, receiving reconstructed slices as soon as they become available, and streams them into a modular analysis pipeline designed for low-latency processing. Neural features are estimated at the temporal resolution of acquisition and are passed to a Bayesian optimisation agent that selects task conditions to maximise a user-defined objective function. Experimental conditions are represented within a bounded "experiment space", allowing heterogeneous conditions to be explored within a common coordinate system. We demonstrate AutoNeuro in a real-time fMRI experiment in which the system adaptively sampled task conditions to obtain a continuous map of brain response to the range of conditions contained within the experimental space. The system operated within the temporal constraints of real-time preprocessing and analysis, maintaining stable model estimates across iterations, converging on experimental conditions most relevant to the measured brain metric. These results establish AutoNeuro as a flexible platform for closed-loop neuroimaging, supporting hypothesis-driven optimisation as well as exploratory mapping of brain metrics across large experimental spaces.

Objective: To investigate in the National School Feeding Program (PNAE) the local level drivers and barriers to the implementation of four guidelines: the banning of sugary drinks; restrictions on the procurement of processed and ultra-processed foods; the mandatory increase in weekly servings of fruits and vegetables offered to students; and mandatory direct procurement from family farmers. Design: Qualitative study that used semi-structured interviews. Street level bureaucracy theory informed the theoretical framework and thematic analysis. Setting: Brazilian municipalities, across the country five geographic regions (North, Northeast, Southeast, South, and Midwest). Participants: Stakeholders (e.g. nutritionists, school cooks, and food procurement managers) involved in the local implementation of the PNAE program across the country. Results: Ninety stakeholders were interviewed. Stakeholders reported having autonomy to perform their activities, collaboration and support from other members within the local government and food providers, adequate infrastructure such as a well-equipped kitchens, the availability of trained personnel, and political commitment as drivers for optimum program implementation. Reported barriers included lack of support and resistance to change among cooks, teachers and parents; insufficient physical and human resources; and limited political commitment. When barriers outweighed drivers, interviewees reported adapting their practices, often in restrictive ways that could compromise the implementation of the program. Conclusions: Drivers and barriers to local PNAE implementation were generally similar across studied municipalities, although their magnitude varied. In contexts of greater economic vulnerability and fiscal constraint, additional support and targeted actions from the federal government may be required to strengthen local implementation

Importance: Adverse neighborhood conditions can lead to poorer health outcomes, potentially through accelerated biological aging. However, whether these relationships are explained by individual- or neighborhood-level factors remains unclear. Objective: To examine the association between neighborhood deprivation, measured by the Area Deprivation Index (ADI), and epigenetic age acceleration and assess whether individual- and neighborhood-level characteristics mediate or modify these associations. Design: Cross-sectional study using data from a Yale Stress Center study between 2008 and 2012. Data analysis was conducted from July 2025 to January 2026. Setting: Community-based sample from the greater New Haven, CT area. Participants: A total of 370 healthy adults aged 18 to 50 years without major psychiatric, medical, or cognitive disorders who provided blood samples for DNA methylation analysis. Main Outcomes and Measures: Epigenetic age acceleration measured from DNA methylation using four second-generation epigenetic clocks, with associations assessed among aging, neighborhood deprivation, and individual- and neighborhood-level factors. Results: Data were analyzed from 370 participants (212 women [57.3%], 158 men [42.7%]; mean [SEM] age, 29.3 [0.46] years). Greater neighborhood deprivation was associated with greater lifetime adversity ({beta}=0.112, p<.001) and lower educational attainment ({beta}=-0.019, p=.012), and accelerated epigenetic aging as measured by GrimAge ({beta}=0.037, p<.001), PCGrimAge ({beta}=0.019, p<.001), and PCPhenoAge ({beta}=0.041, p<.001), but not PhenoAge (p=.23). In multivariable models accounting for individual factors, neighborhood deprivation remained associated with these three clocks. Lifetime adversity partially mediated the association between ADI and accelerated GrimAge (20.3% of total effect) and PCGrimAge (23.3%). Race moderated the direct association between ADI and epigenetic aging, with stronger associations between neighborhood deprivation and accelerated GrimAge ({beta}=0.061, p=.004) and PCPhenoAge ({beta}=0.057, p=.02) observed among Black participants compared to White. Conclusions: Greater neighborhood deprivation was associated with accelerated epigenetic aging across multiple second-generation clocks, with lifetime adversity partially mediating these associations. Stronger effects were observed among Black participants. These findings suggest that neighborhood environments and cumulative stress may contribute to biological aging and racial disparities in aging trajectories.

Beta activity (13-30 Hz) is a prominent feature of motor control, widely distributed across the cerebral cortex. However, how beta bursts (transient high-amplitude events) propagate along cortical organizational axes, such as the posterior-anterior gradient that coordinates cortical structure and function from sensory to association regions, remains poorly understood. Using magnetoencephalography (MEG) data from the Cambridge Centre for Ageing and Neuroscience (CamCAN) dataset, beta burst propagation was characterized using burst onset timing and optical flow analysis during motor tasks and rest in 573 participants (ages 18-88). Beta bursts propagated systematically along the posterior-anterior cortical axis during motor tasks, with propagation direction reversing between movement phases and exhibiting hemispheric asymmetry. In contrast, the resting state exhibited no consistent spatial organization of beta burst propagation. Propagation patterns in motor tasks significantly correlated with cortical distributions of GABAA, cholinergic, and mu-opioid receptors in a hemisphere-specific and phase-dependent manner. Propagation energy was highest in sensorimotor regions and decreased towards more peripheral areas of the cortex. Older adults exhibited significant temporal expansion of beta activity (earlier pre-movement, later post-movement), suggesting a mediation effect of age on reaction time. These results suggest that the propagation of beta bursts is influenced by cortical architecture and may provide a mechanistic explanation for the age-related slowing of motor function.

Progress in pharmacological treatment development for neurodevelopmental disorders is hindered by a misalignment between targeted mechanisms, outcome measures, and trial designs. This study was initiated as a post-trial access pathway for bumetanide and later expanded with treatment-naive participants. Within this framework, we implemented a parent-cocreated sensory outcome measure set (PROMset) in an unmasked, multiple-baseline single-case experimental design with randomized baseline periods of 2-12 weeks, followed by 6 months of bumetanide treatment (up to 1.5 mg twice daily). Participants (7-19 years) had atypical sensory reactivity and a diagnosis of ASD, ADHD, epilepsy, or TSC. The primary outcome was a PROMset comprising seven PROMIS item banks assessing anxiety, depressive symptoms, sleep disturbance, fatigue, sleep-related impairment, cognitive function, and peer relationships. Secondary outcomes included SSP, SRS-2, RBS-R, and ABC. Of 113 enrolled participants (mean age 13.2 [SD 2.7], 64% male), 102 completed the trial and 95 had analyzable PROMsets. At baseline, PROMset scores showed substantial impairment across domains (mean deviation =9.0 T-score points, p<.001) and correlated with sensory reactivity (SSP; r=-0.40, p<.001). Individual-level analyses showed improvement in 24-41% of participants per PROM domain, most frequently in anxiety and depressive symptoms (41% and 38%; mean across-case Cohen's d=-1). Overall, 83% improved on at least one domain. Group-level analyses showed improvement across all secondary outcomes (p<.001), with superiority over historic placebo for RBS-R and SSP. Integrating PROMsets with individualized trial designs can reveal clinically meaningful changes, supporting a more sensitive and patient-centered framework for treatment evaluation in heterogeneous populations.

AimAutistic children have a high but varied prevalence of internalizing and externalizing problems. This study aimed to identify the subtypes of internalizing and externalizing problems among autistic preschool children in Japan, examine their temporal stability, and investigate differences in participation in daily life and family outcomes across these subtypes. MethodsA prospective cohort study was conducted with 275 caregivers of autistic children aged 51-75 months. Internalizing and externalizing problems were assessed using the Strengths and Difficulties Questionnaire. ResultsLatent transition analysis identified five subtypes: Low-symptom, High-emotional, Externalizing, Comorbid, and Peer-difficulty groups. Membership in the High-emotional and Externalizing groups was relatively stable over time, whereas the Peer-difficulty group showed frequent transitions to subtypes with higher levels of internalizing or externalizing problems. Significant differences in participation in daily life and family outcomes were observed across subtypes, but these patterns were inconsistent with a simple gradient of symptom levels. ConclusionsThe novel findings that the temporal stability of subtype membership varied and that differences in participation in daily life and family outcomes were observed across the subtypes suggest that the heterogeneity of internalizing and externalizing problems may be associated with variations in childrens participation in daily life and family outcomes over time. Plain Language SummaryAutistic preschool children often experience emotional and behavioral difficulties, but the way these difficulties manifest varies widely across individuals. This study aimed to identify the patterns of these difficulties, examine how they change over time, and investigate how participation in daily life and family outcomes differ across autistic preschool children. We conducted a study with 275 caregivers of autistic children aged 4-6 years in Japan. From caregiver reports of childrens emotional and behavioral difficulties, five distinct patterns were identified: a group with mainly emotional difficulties, a group with mainly behavioral difficulties, a group with both types of difficulties, a group with relatively low levels of difficulties, and a group characterized primarily by peer-related difficulties. Our findings suggest that different patterns of emotional and behavioral difficulties are associated with differences in childrens participation in daily life and family outcomes. These differences could not be explained simply by the overall severity of difficulties but rather reflect distinct patterns based on the type of difficulty. The results indicate that autistic children face diverse difficulties that change over time.