Developmental Cognitive Neuroscience

Background: Infants with critical congenital heart disease (CHD) are at high risk for abnormal brain development and later neurodevelopmental impairment. We hypothesized that the trajectory of perioperative whole-brain network development would predict neurodevelopmental outcomes in early childhood. Methods: This prospective longitudinal cohort of neonates with critical CHD (n = 97) underwent preoperative and/or postoperative brain MRI with diffusion imaging. Whole-brain network measures were derived from structural connectomes. Neurodevelopment was assessed between 1 and 4 years using the Bayley Scales of Infant and Toddler Development. Results: White matter injury was associated with slower perioperative growth in global efficiency (p = 0.013), a measure of network integration, whereas cardiac physiology was not associated with network development. Infants with greater perioperative increases in global efficiency had higher cognitive (p = 0.001), language (p < 0.001), and motor (p = 0.008) scores. For each 1-standard deviation increase in the trajectory of global efficiency, cognitive scores increased by 8.2 points (95% CI, 3.64-12.78), independent of brain injury and socioeconomic factors. Conclusion: In infants with critical CHD, longitudinal whole-brain network development was associated with neurodevelopment across multiple domains. Early network development may represent a candidate biomarker of neurodevelopmental risk and resilience in this population.

Although language acquisition delays are frequently observed in children with autism spectrum disorder (autism), our current understanding of the neurobiological mechanisms underlying language development in autism is sparse. Previous studies have found resting-state electroencephalography (EEG) power to be associated with language abilities in autistic children. However, longitudinal studies examining resting-state EEG phase coherence in relation to language development in preschool-aged children with autism are limited. This study aimed to characterize age- and group-related changes in whole-brain coherence in neurotypical children and in autistic children with and without language delay. Resting-state EEG and language data were collected at 2, 3, and 4 years of age. Peak phase coherence within the alpha band (6-11 Hz) was calculated at each timepoint and differences in the developmental trajectory of peak alpha coherence (PAC) were analyzed. In neurotypical children, PAC increased between 2 and 4 years of age. In contrast, PAC did not significantly change with age in children with autism. However, when examining autistic children based on language delay status, PAC increased with age in autistic children without language delay, but not in children with language delay. Exploratory analysis revealed evidence for an interaction between PAC and age, suggesting that the direction of the association between PAC and VDQ varied across age. Overall, these results support previous findings of altered oscillatory connectivity in autism and suggest that differences become apparent early in development. Importantly, phase coherence may not only differentiate diagnostic groups but also capture meaningful variability within the autism group. Future research should further investigate the use of EEG coherence as a biomarker of language development in autism.

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

Background: In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations. Methods: We present a modified version of the SLCMA - called direct and mediated effects (DME)-SLCMA - which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n=336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms. Results: We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example. Conclusion: DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Objectives To explore caregiver and clinicians perspectives on implementing mental health conversations and supports for caregivers of children with chronic conditions in paediatric outpatient clinics. Specifically, views were sought on (a) screening approaches and measures (phase 1) and (b) how feedback and support could be provided to caregivers experiencing mental health difficulties (phase 2). Methods Caregivers and clinicians from two outpatient clinics (neuromuscular and diabetes) at a tertiary paediatric hospital in Melbourne, Australia participated in online focus groups in July and August 2024. Caregivers were recruited from outpatient clinics and clinicians were recruited via email. Both groups were combined for phase 1 before separating into breakout rooms for phase 2. Two authors conducted reflexive thematic analysis of transcripts using NVivo. Results Sixteen participants (caregivers n = 8; and clinicians n = 8) took part in in two semi-structured focus groups. Analysis generated two overarching domains, each comprising multiple themes. Domain 1, Addressing caregiver mental health, captured themes of overwhelm and invisibility, diverse caregiving roles, and the need for time and resources to support wellbeing conversations. Domain 2, Housing the mental health conversation, encompassed themes of screening preferences, caregiver agency in confidentiality, delivery of feedback, and access to tailored supports. Conclusions Caregivers and clinicians support routine caregiver mental health discussions in paediatric outpatient settings. Caregivers favour screening at diagnosis and key transitions, with clear, and actionable feedback delivered away from the child. Questions about record-keeping warrant further exploration, as do the perspectives of fathers.

Background: Pregnancy loss has important implications for womens health. Although maternal age is a well-established risk factor, the contribution of routinely measured cardiometabolic and behavioral markers at population-scale remains incompletely characterized. Objective: To examine associations between cardiometabolic, nutritional, and behavioral risk markers and pregnancy loss among U.S. women of reproductive age. Methods: We conducted a cross-sectional analysis of 4,842 U.S. women aged 20-44 years with [&ge;]1 pregnancy using the National Health and Nutrition Examination Survey data (2013-2023). Pregnancy loss was defined as [&ge;]1 prior miscarriages. Exposures included body mass index, smoking exposure (cotinine), lipid biomarkers, vitamin D and folate, and a composite cardiometabolic-nutritional risk score. Survey-weighted logistic regression estimated adjusted odds ratios (aORs) and 95% confidence intervals, with bootstrap resampling for predictor robustness. Results: The weighted prevalence of pregnancy loss was 23%. Higher odds of pregnancy loss were associated with increasing age (aOR per year=1.02; 95% CI: 1.00-1.04), Non-Hispanic Black race (aOR=1.32; 95% CI: 1.00-1.74), overweight (aOR=1.56; 95% CI: 1.16-2.11), obesity (aOR=2.06; 95% CI: 1.39-3.05), and smoking (aOR=1.58; 95% CI: 1.19-2.10). Adverse lipid profiles, particularly elevated triglycerides (aOR=1.83; 95% CI: 1.16-2.90) and high low-density lipoprotein (aOR=2.97; 95% CI: 1.45-6.61), were independently associated with pregnancy loss. Vitamin D/folate were not stable predictors. Higher composite cardiometabolic-nutritional risk scores were observed among women with pregnancy loss (P=0.026). Conclusion: Pregnancy loss clustered with adverse cardiometabolic and behavioral risk markers in a nationally representative population. These findings highlight pregnancy loss as a marker of broader metabolic vulnerability supporting the need for longitudinal studies and cardiometabolic profiling to inform preconception care and risk stratification.

Background: The traditional wealth index, based on principal component analysis (PCA), used in the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), suffers from urban bias, distorting estimates of health inequality. We compared the traditional index (PEAR1) with an alternative two-component polychoric PCA index (POLY2) using annual expenditure from 12 LSMS surveys as the gold standard to determine which provides more accurate SEP measures for equitable policy targeting. Methods: We compared the traditional wealth index (PEAR1) with a two-component polychoric PCA approach (POLY2) using 12 LSMS (Living Standards Measurement Study) surveys (2015-2022) from 12 African countries. Annual household consumption expenditure was the gold standard. We assessed agreement using weighted Cohen's kappa and validated against education (proportion of households with secondary or higher education) using the concentration index (CIX) and slope index of inequality (SII). Results: The POLY2 index showed higher agreement with expenditure quintiles (average national weighted kappa = 43.3%) than the PEAR1 index (35.1%), with notable improvements in urban (43.5% vs. 27.5%) and rural (35.3% vs. 22.4%) areas. POLY2 also attenuated extreme household distributions observed in PEAR1. Education validation showed that POLY2 produced intermediate inequality gradients between the flatter expenditure-based gradient and the steeper PEAR1-based gradient. Conclusion: The POLY2 wealth index is superior to the traditional index, reducing urban-rural bias and providing more accurate socioeconomic classifications. Its adoption in large-scale surveys such as DHS and MICS is recommended to improve equitable monitoring of health inequalities in low- and middle-income countries.

Introduction Gender norms and roles are important determinants of physical and mental health in the key period of adolescence. Yet, the gendered pathways to mental health in adolescents are not fully understood. Using a conceptual framework for global adolescent mental health that we developed based on a Delphi process, we empirically investigated the associations between six gender-related constructs and adolescent mental health. Methods We used cross-sectional Gender and Adolescence: Global Evidence (GAGE) data from Ethiopia (2020) to explore the associations between sex, gender norms, psychological competencies, gender attitudes, gender roles, with the latter two also serving as mediators, and psychological distress (GHQ-12), using Structural Equation Modelling (SEM). Results The SEM model contained measurements from 1,584 adolescents, including 843 girls and 741 boys, with a median age of 13 years. Out of 14 pathways tested, we found statistically significant associations between psychological competencies and psychological distress; sex and gender attitudes; and between gender norms and psychological competencies, gender attitudes, and gender roles. Hence, the gender-related constructs were mostly associated with each other, rather than with psychological distress. Conclusion The gender-related constructs are strongly interrelated, thereby attenuating their individual effects on psychological distress. The interplay of gender-related constructs should be considered when developing interventions to promote mental health in adolescents.

Background: Childhood and adolescent hearing loss affects not only communication and cognitive development but also motor skills and school participation. Consequently, it generates inequalities in learning and educational inclusion. Nevertheless, no systematic review has yet analyzed these differences from an inclusive education perspective. Methods: A systematic review with meta-analysis was conducted following PRISMA guidelines and registered in PROSPERO. Observational studies comparing physical fitness between children and adolescents with hearing loss and their hearing peers were included. Methodological quality was assessed using the Newcastle--Ottawa Scale, and standardized effect sizes were calculated with a random-effects model. Results: Five studies (n=404) were analyzed. Findings revealed significant differences in strength, agility, speed, and balance. Moreover, the meta-analysis showed a large standardized effect favoring hearing children (ES=-2.35; 95% CI: -3.34 to -1.37). Conclusions: Children and adolescents with hearing loss present significantly lower physical fitness, which may affect the planning of physical education activities if their capacities are misinterpreted. Implementing inclusive and adapted strategies within the school curriculum is essential to ensure equal opportunities, improve physical fitness, and promote educational equity.

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) frequently co-occur in Veterans, producing overlapping symptoms and shared autonomic dysregulation. Heart rate variability (HRV) offers a noninvasive measure of autonomic function. Univariate HRV analyses often fail to capture complex, multivariate patterns associated with comorbidity. This study applied machine learning to HRV features extracted from MEG-derived electrocardiogram (M-ECG) signals to differentiate Veterans with TBI alone (TBI-alone; n = 42) from those with comorbid PTSD (TBI+PTSD; n = 40). Time-domain, frequency-domain, geometric, and nonlinear HRV metrics were analyzed using nested cross-validated Random Forest and XGBoost classifiers, with Boruta-based feature selection and SHapley Additive exPlanations for model interpretability. Both classifiers achieved above-chance discrimination (Random Forest AUC = 0.663; XGBoost AUC = 0.635). Multivariate models identified distributed autonomic signatures in TBI+PTSD, including altered sympathovagal balance, increased low-frequency proportion, and greater heart rate complexity. In contrast, univariate HRV differences were subtle and did not survive correction for multiple comparisons. These findings demonstrate how using multivariate machine learning HRV analysis could help with detecting comorbidity-specific autonomic patterns, suggesting that HRV-derived signatures may serve as exploratory biomarkers for risk assessment and targeted interventions in Veterans with TBI and PTSD.

Background. Psychotic-like experiences (PLEs) index early risk for psychotic disorders and are consistently associated with childhood trauma, yet underlying biological mechanisms remain poorly understood. DNA methylation (DNAm) may capture the biological embedding of early adversity, while adolescent exposures such as cannabis use may modify these processes. We examined epigenome-wide associations of childhood trauma and PLEs, tested the moderating role of early cannabis use, and evaluated DNAm as a potential mediator. Methods. We analysed data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK population-based birth cohort. Childhood trauma was assessed prospectively and retrospectively. Epigenome-wide DNAm was measured in peripheral blood at ~17 years using the Illumina 450K array, and PLEs were assessed at 18 using a structured interview. Epigenome-wide association studies were conducted for trauma-DNAm and DNAm-PLEs associations in the final sample (n = 1,457), adjusting for demographic, biological, and technical covariates. Differentially methylated regions (DMRs) were identified using DMRff, followed by functional enrichment analyses. Cannabis use at 15.5 was modelled as a moderator with multiple imputation for missing data. Mediation was tested using the Divide-Aggregate Composite-null Test (DACT). Results. Childhood trauma was associated with widespread DNAm differences, primarily at the regional level, with enrichment in pathways related to cellular stress responses. In contrast, DNAm associated with PLEs was more limited and implicated loci involved in epigenetic regulatory processes. These signatures were largely distinct, and there was no evidence supporting mediation after multiple testing correction. Incorporating cannabis use altered the pattern and extent of DNAm associations, with stronger and more significant signals observed at both CpG and regional levels, although these did not translate into evidence of mediation. Conclusion. Childhood trauma and PLEs show distinct DNAm signatures in adolescence, with trauma-related DNAm reflecting broad stress-related processes and PLE-associated DNAm implicating regulatory mechanisms. We found little evidence that DNAm mediates the trauma-PLE association. Instead, adolescent exposures, particularly cannabis use, may distinctly influence trauma-related epigenetic variation with limited detectable downstream effects on PLEs. These findings support a context-dependent model of epigenetic risk and highlight the need for larger longitudinal studies to clarify causal pathways linking early adversity to psychosis.

During the COVID-19 pandemic, limited testing capacity and reporting delays complicated epidemic surveillance and decision-making in Mexico. We calibrated \textit{covidestim}, a Bayesian nowcasting model, to estimate the total SARS-CoV-2 infections from reported cases and deaths using Mexican surveillance data. Disease-progression distribution priors were calibrated using Mexico City records and validated through comparisons with national seroprevalence surveys, hospitalization data, and annual reported severe-case rates across all states. Using the reconstructed estimates of active infections, we implemented an event-based risk framework that quantifies the probability of encountering at least one infectious individual in gatherings of different sizes. This probability was subsequently translated into a four-level epidemiological traffic-light indicator and computed at both state and municipality levels. The resulting estimates revealed substantial spatial heterogeneity that is obscured by state-level aggregation, particularly in states with marked differences between urban and rural municipalities. To evaluate consistency with public-health indicators, we compared the proposed risk classification with the official Mexican epidemiological traffic-light system, considering interpretable gathering sizes relevant to public-health decision making. Weekly reports derived from this framework were delivered to policymakers in the State of Queretaro in Mexico, as an anticipation tool for school reopening and public-space management. This demonstrates that this Bayesian reconstruction of infections combined with event-based risk metrics can provide an interpretable and generalizable municipality-level complement to routine surveillance systems, particularly in regions with limited testing capacity and heterogeneous local transmission dynamics.

Background: Obesity is becoming a global health crisis, and it leads to various metabolic disorders. Body mass index fails to differentiate fat mass from lean mass and systematically misclassifies adiposity risk - a limitation particularly pronounced in South Asian adults, who exhibit characteristically elevated visceral adiposity and reduced appendicular lean mass at a normal BMI. The 2025 Lancet Commission explicitly recommends direct adiposity measurement beyond BMI for obesity diagnosis. Weight loss interventions - whether dietary, behavioural, or pharmacological - are consistently associated with concurrent reductions in both fat mass and lean mass, making body composition monitoring essential beyond scale weight alone. Although DEXA is globally accepted as a gold standard for body composition analysis, the accessibility of DEXA is limited, particularly in resource-constrained low and middle-income countries such as India. BIA devices are a convenient low-cost option to DEXA and can be used for body composition analysis more frequently than a DEXA scan to provide longitudinal data. The aim of this study is to validate 8 electrode BIA devices as a viable alternative to DEXA scan for the South Asian population. Methods: A prospective cross-sectional validation study was conducted following ethics committee approval, with a priori sample size estimation ( = 0.05, power = 80%). Fifty-eight healthy adults (n=58) underwent three BIA measurements and one DEXA scan each. To ensure statistical independence, the three BIA readings per participant were averaged, yielding 58 final measurements for validation. Body fat percentage, lean mass and fat mass were evaluated using Python with statistical analyses like Bland Altman analysis, Pearson correlation, ICC and regression analysis. Results: In this BIA vs DEXA study, the Pearson correlation was strong across all three outcomes (fat%: r = 0.97; fat mass: r = 0.98; lean mass: r = 0.96), with ICC (2,1) values of 0.94, 0.97, and 0.91 confirming excellent absolute agreement. Mean absolute error was 3.40% for fat percentage, 1.96 kg for fat mass, and 3.37 kg for lean mass. BIA systematically underestimated body fat percentage (bias -1.96%, 95% CI: -2.91% to -1.01%; LoA: -9.04% to +5.12%) and fat mass (bias -0.72 kg, 95% CI: -1.38 to -0.07 kg; LoA: -5.59 to +4.14 kg), while overestimating lean mass by +3.08 kg (95% CI: +2.34 to +3.82 kg; LoA: -2.46 to +8.62 kg). Conclusions: The 8-electrode BIA device shows clinically acceptable agreement with DEXA for body composition assessment in healthy Indian adults. It offers a radiation-free, cost-effective, accessible, and portable alternative to DEXA, making it suitable for longitudinal monitoring and trend detection. The device is particularly valuable for obesity screening and for tracking body composition changes during weight loss interventions at the population level, addressing the critical need for accessible body composition assessment in resource-limited settings.

Background. Tethered cord syndrome (TCS) is classically associated with a low-lying conus medullaris, yet many surgically treated children have a normally positioned conus (occult TCS). Large-scale normative data on conus position in children, and the diagnostic value of quantitative conus assessment, are limited. Purpose. To establish a large-cohort reference distribution for conus medullaris termination level in children, to quantify conus position in children surgically treated for presumed (occult) TCS, and to test whether automated conus segmentation and radiomics can distinguish TCS from normal. Materials and Methods. In this retrospective single-center study, conus termination level was extracted from structured radiology reports of consecutive pediatric lumbosacral MRI examinations and encoded numerically (L1 = 1, L2 = 2, etc.). Children surgically treated for tethered cord were identified by linkage to an operative registry (name and date of birth) and restricted to preoperative examinations. A deep-learning model (nnU-Net) was trained for conus segmentation on axial T2-weighted images. IBSI-compliant radiomic features were extracted; reproducibility was assessed by intra- and inter-observer intraclass correlation (ICC). A case-control radiomics analysis used batch-only ComBat harmonization and cross-validated L1-penalized logistic regression; discrimination was compared with conus level by paired bootstrap. Results. Among 9,808 examinations with a parseable conus level (98.5% of reports; parser validated against dual blinded annotation, 99.4% agreement, weighted kappa 0.946), the conus terminated in the L1 region in 85.7% and the L2 region in 14.3% of the reference cohort (postoperative examinations excluded, n = 9,655); a low-lying conus (>=L3) occurred in only 0.05% (5/9,655), and remained rare (0.14%, 14/9,808) including operated examinations (median L1; mean 1.13 +/- 0.33). A slightly more cephalad position was seen with increasing age (negligible correlation). Among 475 preoperative children surgically treated for tethered cord, 99.6% had a normally positioned conus (<=L2) and only 0.4% were low-lying. Automated conus segmentation achieved a held-out Dice of 0.85. Conus radiomics likewise did not distinguish TCS from controls (equivalence-tested null; full segmentation/radiomics pipeline reported in the companion methodological paper). Conclusion. In children, the conus medullaris terminates at L1-L2 in more than 99% of cases and is normally positioned in virtually all children surgically treated for TCS. Within the conus, neither position nor texture (radiomics) identifies tethered cord; whether the filum terminale carries a diagnostic signal was not tested here.

Accurate assessment of body composition is important to risk stratification and management of metabolic, musculoskeletal, and aging-related diseases, yet reference modalities such as Dual-energy X-ray absorptiometry (DXA) are costly and impractical for frequent monitoring. Commodity 3D body scans offer a low-cost, radiation-free alternative, but extracting meaningful and predictive shape features from scans remains challenging due to nonuniform point density, variable body size and cross-device differences. We introduce BodyMAE, a self-supervised, surface-area aware masked autoencoder for metric-scale 3D body scans. The pipeline integrates area-adjusted sampling, a long-range focused encoder, and a lightweight decoder regularized to promote locally uniform reconstructions. Trained and evaluated on 917 paired 3D body scans paired with clinical DXA reports, BodyMAE achieves strong accuracy on fat percentage (root-mean-square error (RMSE) 3.825 percentage points, R^2 0.908), fat mass (RMSE 3.694 kg, R^2 0.968), and lean mass (RMSE 3.608 kg, R^2 0.901), with competitive performance on bone mineral content (RMSE 0.284 kg, R^2 0.754).We also assess feature stability across pretrained baselines, finding higher retrieval accuracy for our representations (Top-1 90.131%). These results indicate that combining metric-aware sampling, long-range relational encoding, and local geometric regularization enables accurate body composition estimation from 3D body scans, as validated by comparisons to DXA-derived measurements.

Mood and anxiety disorders emerge predominantly in adolescence, yet they are usually identified only once symptoms have consolidated, when intervention can only be reactive. A marker that registers the loss of healthy brain function before symptoms crystallise would allow earlier and more targeted treatment, much as caged canaries once warned miners of danger before it became apparent. Here we report such a marker using a single baseline resting-state functional MRI scan in 150 adolescents in the Human Connectome Project Boston Adolescent Neuroimaging of Depression and Anxiety (HCP BANDA) cohort, allowing us to prospectively predict depression and anxiety symptoms one year later in held-out participants at r = 0.60, substantially above the effect-size ceiling reported for functional connectivity in the same data. The marker is not computed from raw functional connectivity but read out from a whole-brain generative model fitted to each individual's dynamics, which gives access to interference structure that covariance-based features cannot represent. The regions driving the prediction, including precuneus, ventromedial prefrontal and anterior cingulate cortices, are among those previously implicated in internalising disorders, and the same signature tracks cognitive variation in healthy participants and is mechanistically linked to the efficiency of task-related computation. These findings establish a mechanistically interpretable and prospectively predictive marker of adolescent mental health and define a clear path towards external validation and clinical use.

Electronic health record (EHR) prediction models often summarize longitudinal histories as static patient-level features, which may omit potentially informative event ordering. We developed a simplified spike-timing-dependent plasticity (STDP)-inspired framework that represents asynchronous EHR data as sparse, directional transition features. The approach encodes whether one clinical event precedes another within prespecified temporal windows, preserving event identity, directionality, and approximate timing while retaining feature-level interpretability. We evaluated this framework in two retrospective prediction tasks with different temporal scales: incident acute kidney injury (AKI) prediction in 17,351 MIMIC-IV ICU stays and early postoperative recurrence prediction in 713 CUMC patients with pancreatic ductal adenocarcinoma (PDAC). Models were compared with static burden features (demographics, comorbidities, raw lab measurements) and in addition with STDP transitional feature sets using patient-level cross-validation and rolling prediction horizons. In AKI, a calibrated STDP ensemble model showed higher discrimination than static burden alone at the 24-hour decision snapshot for AKI by 72 hours, with AUROC 0.838 versus 0.800, and at 48 hours for near-term AKI prediction, with AUROC 0.868 versus 0.827. In PDAC, STDP transition features modestly improved Day -30 preoperative recurrence prediction, with AUROC 0.611 versus 0.587 and AUPRC 0.323 versus 0.318 for static burden and showed similar performance at Day 0 (7 days before recorded surgery date), with AUROC 0.681 and AUPRC 0.363. Decision-curve and feature analyses suggested that selected temporal transitions were clinically interpretable across renal, inflammatory, hepatobiliary, hematologic, glycemic, and nutritional trajectories. These findings suggest that STDP-inspired transition features may provide a practical, interpretable way to incorporate temporal ordering into EHR-based risk prediction across both acute and longitudinal settings

Background. Nascent findings suggest that people with attention-deficit/hyperactivity disorder (ADHD) experience higher rates of mortality. To date, study samples have been insufficiently well-characterized to examine the mechanisms via which this neurodevelopmental condition elevates mortality risk. Methods. We used data from the 2007 and 2011 waves of the US National Health Interview Survey, a general population-based cohort study comprising 52097 adults (28675 women) aged 18 years or older at baseline. ADHD diagnosis and an array of demographic, socioeconomic, lifestyle, and co-morbidity (somatic and psychiatric) covariates were self-reported. Findings. At baseline, compared with unaffected individuals, participants with ADHD were more likely to be socioeconomically disadvantaged, smoke cigarettes, consume alcohol, and report symptoms of psychological distress. A median 7.75 years of mortality surveillance (range: 7.25-12.25) gave rise to 6597 deaths from all-causes. After adjustment for age, sex, ethnicity, and survey year, ADHD was associated with a markedly elevated risk of death (hazard ratio [95% confidence interval]: 1.58 [1.20-2.09]). Statistical adjustment for socioeconomic circumstances (11% attenuation), physical co-morbidities (15%), and lifestyle factors (17%) had only a modest impact on the ADHD-death gradient, with the greatest explanatory power apparent for symptoms of depression and anxiety (58%). The magnitude of the association of ADHD with mortality was commensurate to that for several well-established risk factors such as poverty (1.66 [1.55-1.78]), hypertension (1.41 [1.32-1.51]), and diabetes (1.71 [1.59-1.85]) but somewhat lower than cigarette smoking (2.51 [2.29-2.76]) after controlling for age, sex, ethnicity, and survey year. Associations between ADHD and cause-specific mortality from cardiovascular disease, cancer, and chronic respiratory disease were inconclusive. Interpretation. In the present study, the influence of ADHD on total mortality appears to be largely embodied via a series of malleable characteristics, particularly mental illness. If confirmed elsewhere, these results raise the possibility that risk factor modification via standard pharmacological and behavioral interventions could help reduce rates of premature mortality in this patient group. Funding. This paper received no direct funding. GDB is supported by the UK Medical Research Council (MR/P023444/1) and the US National Institute on Aging (1R56AG052519-01, 1R01AG052519-01A1).

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

Introduction: Early hospital presentation after stroke onset is necessary for rapid assessment and access to time-dependent acute management. This study examined the correlates of late presentation for stroke care among patients recorded at a tertiary hospital in Ondo State, Nigeria. Methods: A retrospective records review was conducted using secondary data from the Stroke Registry of the University of Medical Sciences Teaching Hospital, radiology department records, referral notes, and ambulance records. Records of stroke cases documented within the preceding 24 months were reviewed. Late presentation was defined as hospital presentation more than four hours after symptom onset. Frequencies, chi-square tests, and modified Poisson regression with robust standard errors were used to estimate adjusted prevalence ratios. Results: The analysis included 371 stroke cases. Of these, 317 (85.4%) presented after four hours, and the median time to presentation was 24 hours (interquartile range: 9-72 hours). Late presentation differed significantly by employment status, first-contact route, and pathway complexity at bivariate analysis. After adjustment, non-hospital first contact remained strongly associated with late presentation: patients whose first documented contact was non-hospital-based had almost 3 times the prevalence of delay compared with those whose first contact was hospital-based (adjusted prevalence ratio = 2.89; 95% confidence interval: 2.15-3.90; p < 0.001). Conclusion: Late presentation was pervasive in this tertiary hospital record cohort and was primarily associated with the initial direction of care-seeking. Stroke response interventions should emphasise immediate hospital presentation and strengthen urgent referral from non-hospital first-contact points.