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ImportanceMost research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment.ObjectiveTo identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC.Design, setting, and participantsMulticenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history.ExposureSARS-CoV-2 infection.Main outcomes and measuresPASC and 89 prolonged symptoms across 9 symptom domains.ResultsA total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise-related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents.Conclusions and relevanceThis study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

IMPORTANCE: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or Long COVID) in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. OBSERVATIONS: We describe the protocol for the Pediatric Observational Cohort Study of the NIHs REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. CONCLUSIONS AND RELEVANCE: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. CLINICAL TRIALS.GOV IDENTIFIER: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011.

Background and aimsRecently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.Design and settingCross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).CasesCases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.MeasurementsGraph theory metrics of segregation and integration were used to summarize SCN.FindingsMirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.ConclusionStructural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Findings in adults have shown that crystallized measures of intelligence, which are more culturally sensitive than fluid intelligence measures, have greater heritability; however, these results have not been found in children. The present study used data from 8,518 participants between 9 and 11 years old from the Adolescent Brain Cognitive Development (ABCD) Study. We found that polygenic predictors of intelligence test performance (based on genome-wide association meta-analyses of data from 269,867 individuals) and of educational attainment (based on data from 1.1 million individuals) predicted neurocognitive performance. We found that crystallized measures were more strongly associated with both polygenic predictors than were fluid measures. This mirrored heritability differences reported previously in adults and suggests similar associations in children. This may be consistent with a prominent role of gene-environment correlation in cognitive development measured by crystallized intelligence tests. Environmental and experiential mediators may represent malleable targets for improving cognitive outcomes.

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

BackgroundEarly detection is critical for easing the rising burden of psychiatric disorders. However, the specificity of psychopathological measurements and genetic predictors is unclear among youth.MethodsWe measured associations between genetic risk for psychopathology (polygenic risk scores (PRS) and family history (FH) measures) and a wide range of behavioral measures in a large sample (n = 5,204) of early adolescent participants (9-11 years) from the Adolescent Brain and Cognitive Development StudySM . Associations were measured both with and without accounting for shared variance across measures of genetic risk.ResultsWhen controlling for genetic risk for other psychiatric disorders, polygenic risk for problematic opioid use (POU) is uniquely associated with lower behavioral inhibition. Attention deficit hyperactivity disorder (ADHD), depression (DEP), and attempted suicide (SUIC) PRS shared many significant associations with externalizing, internalizing, and psychosis-related behaviors. However, when accounting for all measures of genetic and familial risk, these PRS also showed clear, unique patterns of association. Polygenic risk for ASD, BIP, and SCZ, and attempted suicide uniquely predicted variability in cognitive performance. FH accounted for unique variability in behavior above and beyond PRS and vice versa, with FH measures explaining a greater proportion of unique variability compared to the PRS.ConclusionOur results indicate that, among youth, many behaviors show shared genetic influences; however, there is also specificity in the profile of emerging psychopathologies for individuals with high genetic risk for particular disorders. This may be useful for quantifying early, differential risk for psychopathology in development.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

ImportanceHereditary hemochromatosis (HH) is an autosomal recessive genetic disorder that leads to iron overload. Conflicting results from previous research has led some to believe the brain is spared the toxic effects of iron in HH.ObjectiveTo test the association of the strongest genetic risk variant for HH on brainwide measures sensitive to iron deposition and the rates of movement disorders in a substantially larger sample than previous studies of its kind.Design, setting, and participantsThis cross-sectional retrospective study included participants from the UK Biobank, a population-based sample. Genotype, health record, and neuroimaging data were collected from January 2006 to May 2021. Data analysis was conducted from January 2021 to April 2022. Disorders tested included movement disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], codes G20-G26), abnormalities of gait and mobility (ICD-10 codes R26), and other disorders of the nervous system (ICD-10 codes G90-G99).ExposuresHomozygosity for p.C282Y, the largest known genetic risk factor for HH.Main outcomes and measuresT2-weighted and T2* signal intensity from brain magnetic resonance imaging scans, measures sensitive to iron deposition, and clinical diagnosis of neurological disorders.ResultsThe total cohort consisted of 488 288 individuals (264 719 female; ages 49-87 years, largely northern European ancestry), 2889 of whom were p.C282Y homozygotes. The neuroimaging analysis consisted of 836 individuals: 165 p.C282Y homozygotes (99 female) and 671 matched controls (399 female). A total of 206 individuals were excluded from analysis due to withdrawal of consent. Neuroimaging analysis showed that p.C282Y homozygosity was associated with decreased T2-weighted and T2* signal intensity in subcortical motor structures (basal ganglia, thalamus, red nucleus, and cerebellum; Cohen d >1) consistent with substantial iron deposition. Across the whole UK Biobank (2889 p.C282Y homozygotes, 485 399 controls), we found a significantly increased prevalence for movement disorders in male homozygotes (OR, 1.80; 95% CI, 1.28-2.55; P = .001) but not female individuals (OR, 1.09; 95% CI, 0.70-1.73; P = .69). Among the 31 p.C282Y male homozygotes with a movement disorder, only 10 had a concurrent HH diagnosis.Conclusions and relevanceThese findings indicate increased iron deposition in subcortical motor circuits in p.C282Y homozygotes and confirm an increased association with movement disorders in male homozygotes. Early treatment in HH effectively prevents the negative consequences of iron overload in the liver and heart. Our work suggests that screening for p.C282Y homozygosity in high-risk individuals also has the potential to reduce brain iron accumulation and to reduce the risk of movement disorders among male individuals who are homozygous for this mutation.

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca's area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Key Points: Question: What prolonged symptoms experienced by youth are most associated with SARS-CoV-2 infection? Findings: Among 5367 participants in the RECOVER-Pediatrics cohort study, 14 symptoms in both school-age children (6-11 years) and adolescents (12-17 years) were more common in those with vs without SARS-CoV-2 infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. Empirically derived indices for PASC research and associated clustering patterns were developed. Meaning: This study developed research indices for characterizing pediatric PASC. Symptom patterns were similar but distinguishable between school-age children and adolescents, highlighting the importance of characterizing PASC separately in different age groups. Importance: Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment. Objective: To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC. Design, Setting, and Participants: Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: PASC and 89 prolonged symptoms across 9 symptom domains. Results: A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise–related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents. Conclusions and Relevance: This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges. This observational cohort study examines the symptoms experienced by children after SARS-CoV-2 infection and how these symptoms differ by age (6-11 years vs 12-17 years). [ABSTRACT FROM AUTHOR]

The data release of Adolescent Brain Cognitive Development® (ABCD) Study represents an extensive resource for investigating factors relating to child development and mental wellbeing. The genotype data of ABCD has been used extensively in the context of genetic analysis, including genome-wide association studies and polygenic score predictions. However, there are unique opportunities provided by ABCD genetic data that have not yet been fully tapped. The diverse genomic variability, the enriched relatedness among ABCD subsets, and the longitudinal design of the ABCD challenge researchers to perform novel analyses to gain deeper insight into human brain development. Genetic instruments derived from the ABCD genetic data, such as genetic principal components, can help to better control confounds beyond the context of genetic analyses. To facilitate the use genomic information in the ABCD for inference, we here detail the processing procedures, quality controls, general characteristics, and the corresponding resources in the ABCD genotype data of release 4.0. [ABSTRACT FROM AUTHOR]