Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

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/.

Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity.

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.

Persistent and distressing psychotic-like experiences using adolescent brain cognitive development℠study data.

Childhood psychotic-like experiences (PLEs) are associated with a range of impairments; a subset of children experiencing PLEs will develop psychiatric disorders, including psychotic disorders. A potential distinguishing factor between benign PLEs versus PLEs that are clinically relevant is whether PLEs are distressing and/or persistent. The current study used three waves of Adolescent Brain Cognitive Development℠ (ABCD) study PLEs assessments to examine the extent to which persistent and/or distressing PLEs were associated with relevant baseline risk factors (e.g., cognition) and functioning/mental health service utilization domains. Four groups varying in PLE persistence and distress endorsement were created based on all available data in ABCD Release 3.0, with group membership not contingent on complete data: persistent distressing PLEs (n = 272), transient distressing PLEs (n = 298), persistent non-distressing PLEs (n = 221), and transient non-distressing PLEs (n = 536) groups. Using hierarchical linear models, results indicated youth with distressing PLEs, whether transient or persistent, showed delayed developmental milestones (ß = 0.074, 95%CI:0.013,0.134) and altered structural MRI metrics (ß = -0.0525, 95%CI:-0.100,-0.005). Importantly, distress interacted with PLEs persistence for the domains of functioning/mental health service utilization (ß = 0.079, 95%CI:0.016,0.141), other reported psychopathology (ß = 0.101, 95%CI:0.030,0.170), cognition (ß = -0.052, 95%CI:0.-0.099,-0.002), and environmental adversity (ß = 0.045, 95%CI:0.003,0.0.86; although no family history effects), with the interaction characterized by greatest impairment in the persistent distressing PLEs group. These results have implications for disentangling the importance of distress and persistence for PLEs with regards to impairments, including functional, pathophysiological, and environmental outcomes. These novel longitudinal data underscore that it is often only in the context of distress that persistent PLEs were related to impairments.

Effects of Anticholinergic Burden on Verbal Memory Performance in First-Episode Psychosis.

OBJECTIVES: Antipsychotics are widely used to treat first-episode psychosis but may have an anticholinergic burden, that is, a cumulative effect of medications that block the cholinergic system. Studies suggest that a high anticholinergic burden negatively affects memory in psychosis, where cognitive deficits, particularly those in verbal memory, are a core feature of the disease. The present study sought to replicate this in a large cohort of well-characterized first-episode psychosis patients. We expected that patients in the highest anticholinergic burden group would exhibit the poorest verbal memory compared to those with low anticholinergic burden and healthy controls at baseline (3 months following admission). We further hypothesized that over time, at month 12, patients' verbal memory performance would improve but would remain inferior to controls. METHODS: Patients (n = 311; low anticholinergic burden [n = 241] and high anticholinergic burden [n = 70], defined by a Drug Burden Index cut-off of 1) and healthy controls (n = 128) completed a clinical and neurocognitive battery including parts of the Wechsler Memory Scale at months 3 and 12. RESULTS: Cross-sectionally, using an analysis of variance, patients in the highest anticholinergic burden group had the poorest performance in verbal memory when compared to the other groups at month 3, F(2,430) = 52.33, P < 0.001. Longitudinally, using a Generalized Estimating Equation model, the verbal memory performance of all groups improved over time. However, patients' performance overall remained poorer than the controls. CONCLUSION: These findings highlight the importance of considering the anticholinergic burden when prescribing medications in the early stages of the disease.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.

INTRODUCTION: There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). METHODS: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. RESULTS: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. DISCUSSION: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

Generalization of cortical MOSTest genome-wide associations within and across samples.

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.

Unique prediction of developmental psychopathology from genetic and familial risk.

BACKGROUND: Early detection is critical for easing the rising burden of psychiatric disorders. However, the specificity of psychopathological measurements and genetic predictors is unclear among youth. METHODS: We 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. RESULTS: When 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. CONCLUSION: Our 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.

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology.

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.

Altered hippocampal centrality and dynamic anatomical covariance of intracortical microstructure in first episode psychosis.

Hippocampal circuitry has been posited to be fundamental to positive symptoms in psychosis, but its contributions to other factors important for outcome remains unclear. We hypothesized that longitudinal changes in the hippocampal circuit and concomitant changes of intracortical microstructure are altered in first episode psychosis (FEP) patients and that such changes are associated with negative symptoms and verbal memory. Longitudinal brain scans (2-4 visits over 3-15 months) were acquired for 27 FEP and 29 age- and sex-matched healthy controls. Quantitative T1 maps, sensitive to myelin content, were used to sample the microstructure of the hippocampal subfields and output circuitry (fimbria, alveus, fornix, mammillary bodies), and intracortical regions. Dynamic anatomical covariance in pair-wise regional trajectories were assessed for each subject, and graph theory was used to calculate a participation coefficient metric that quantifies the similarity/divergence between hippocampal and intracortical microstructure. The mean participation coefficient of the hippocampus was significantly reduced in FEP patients compared with controls, reflecting differences in output hippocampal regions. Importantly, lower participation coefficient of the hippocampal circuit was associated with worse negative symptoms, a relationship that was mediated by changes in verbal memory. This study provides evidence for reduced hippocampal centrality in FEP and concomitant changes in intracortical anatomy. Myelin-rich output regions of the hippocampus may be an important biological trigger in early psychosis, with cascading effects on broader cortical networks and resultant clinical profiles.

Intersection of verbal memory and expressivity on cortical contrast and thickness in first episode psychosis.

BACKGROUND: Longitudinal studies of first episode of psychosis (FEP) patients are critical to understanding the dynamic clinical factors influencing functional outcomes; negative symptoms and verbal memory (VM) deficits are two such factors that remain a therapeutic challenge. This study uses white-gray matter contrast at the inner edge of the cortex, in addition to cortical thickness, to probe changes in microstructure and their relation with negative symptoms and possible intersections with verbal memory. METHODS: T1-weighted images and clinical data were collected longitudinally for patients (N = 88) over a two-year period. Cognitive data were also collected at baseline. Relationships between baseline VM (immediate/delayed recall) and rate of change in two negative symptom dimensions, amotivation and expressivity, were assessed at the behavioral level, as well as at the level of brain structure. RESULTS: VM, particularly immediate recall, was significantly and positively associated with a steeper rate of expressivity symptom decline (r = 0.32, q = 0.012). Significant interaction effects between baseline delayed recall and change in expressivity were uncovered in somatomotor regions bilaterally for both white-gray matter contrast and cortical thickness. Furthermore, interaction effects between immediate recall and change in expressivity on cortical thickness rates were uncovered across higher-order regions of the language processing network. CONCLUSIONS: This study shows common neural correlates of language-related brain areas underlying expressivity and VM in FEP, suggesting deficits in these domains may be more linked to speech production rather than general cognitive capacity. Together, white-gray matter contrast and cortical thickness may optimally inform clinical investigations aiming to capture peri-cortical microstructural changes.

Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis.

There is growing evidence that psychosis is characterized by brain network abnormalities. Analyzing morphological abnormalities with T1-weighted structural MRI may be limited in discovering the extent of deviations in cortical associations. We assess whether structural associations of either cortical white-gray contrast (WGC) or cortical thickness (CT) allow for a better understanding of brain structural relationships in first episode of psychosis (FEP) patients. Principal component and structural covariance analyses were applied to WGC and CT derived from T1-weighted MRI for 116 patients and 88 controls, to explore sets of brain regions that showed group differences, and associations with symptom severity and cognitive ability in patients. We focused on 2 principal components: one encompassed primary somatomotor regions, which showed trend-like group differences in WGC, and the second included heteromodal cortices. Patients' component scores were related to general psychopathology for WGC, but not CT. Structural covariance analyses with WGC revealed group differences in pairwise correlations across widespread brain regions, mirroring areas derived from PCA. More group differences were uncovered with WGC compared with CT. WGC holds potential as a proxy measure of myelin from commonly acquired T1-weighted MRI and may be sensitive in detecting systems-level aberrations in early psychosis, and relationships with clinical/cognitive profiles.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Head motion: the dirty little secret of neuroimaging in psychiatry

Psychiatry is at a crossroads when choosing final samples for analysis of neuroimaging data. Many patient populations exhibit significantly increased motion in the scanner compared with healthy controls, suggesting that more patients would need to be excluded to obtain a clean sample. However, this need is often overshadowed by the extensive amount of time and effort required to recruit these valuable and uncommon samples. This commentary sheds light on the impact of motion on imaging studies, drawing examples from psychiatric patient samples to better understand how head motion can confound interpretation of clinically oriented questions. We discuss the impact of even subtle motion artifacts on the interpretation of results as well as how different levels of stringency in quality control can affect findings within nearly identical samples. We also summarize recent initiatives toward harmonization of quality-control procedures as well as tools to prospectively and retrospectively correct for motion artifacts.

Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation.

Accurate automated quantification of subcortical structures is a greatly pursued endeavour in neuroimaging. In an effort to establish the validity and reliability of these methods in defining the striatum, globus pallidus, and thalamus, we investigated differences in volumetry between manual delineation and automated segmentations derived by widely used FreeSurfer and FSL packages, and a more recent segmentation method, the MAGeT-Brain algorithm. In a first set of experiments, the basal ganglia and thalamus of thirty subjects (15 first episode psychosis [FEP], 15 controls) were manually defined and compared to the labels generated by the three automated methods. Our results suggest that all methods overestimate volumes compared to the manually derived "gold standard", with the least pronounced differences produced using MAGeT. The least between-method variability was noted for the striatum, whereas marked differences between manual segmentation and MAGeT compared to FreeSurfer and FSL emerged for the globus pallidus and thalamus. Correlations between manual segmentation and automated methods were strongest for MAGeT (range: 0.51 to 0.92; p<0.01, corrected), whereas FreeSurfer and FSL showed moderate to strong Pearson correlations (range 0.44-0.86; p<0.05, corrected), with the exception of FreeSurfer pallidal (r=0.31, p=0.10) and FSL thalamic segmentations (r=0.37, p=0.051). Bland-Altman plots highlighted a tendency for greater volumetric differences between manual labels and automated methods at the lower end of the distribution (i.e. smaller structures), which was most prominent for bilateral thalamus across automated pipelines, and left globus pallidus for FSL. We then went on to examine volume and shape of the basal ganglia structures using automated techniques in 135 FEP patients and 88 controls. The striatum and globus pallidus were significantly larger in FEP patients compared to controls bilaterally, irrespective of the method used. MAGeT-Brain was more sensitive to shape-based group differences, and uncovered widespread surface expansions in the striatum and globus pallidus bilaterally in FEP patients compared to controls, and surface contractions in bilateral thalamus (FDR-corrected). By contrast, after using a recommended cluster-wise thresholding method, FSL only detected differences in the right ventral striatum (FEP>Control) and one cluster of the left thalamus (Control>FEP). These results suggest that different automated pipelines segment subcortical structures with varying degrees of variability compared to manual methods, with particularly pronounced differences found with FreeSurfer and FSL for the globus pallidus and thalamus.

The MNI data-sharing and processing ecosystem.

Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.

Quality over quantity: powering neuroimaging samples in psychiatry.

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Sociodemographic correlates of parent and youth-reported eating disorder symptoms in the Adolescent Brain Cognitive Development Study.

Purpose: Eating Disorders (EDs) often start in adolescence, though ED-related concerns in diverse youth samples remain understudied. We leveraged data from the Adolescent Brain Cognitive Development (ABCD) Study to identify the prevalence of parent- and youth-reported ED symptoms and their sociodemographic characteristics. Methods: Data were drawn from baseline (ages 9-11 years, n=11,868) and 2-year follow-up (ages 11-14 years; n=10,908) from the ABCD Study. A tetrachoric factor analysis summarized clusters of ED symptoms, which were compared between parent and youth reports and across sociodemographic variables. Results: Three factors emerged reflecting "weight distress", "weight control", and "binge eating" (prevalence range: 1.5-7.3%). Symptoms loaded onto similar factors between reporters. Rates of symptom endorsement were similar between sexes, with disproportionately higher endorsement rates for youth who self-identified as sexual minority, Hispanic, Black, or Mixed race participants, and those from a disadvantaged socioeconomic background, compared to the reference ABCD sample. Youth and parent reports at 2-year showed ~12% overlap. Conclusions: ED-related concerns among historically understudied racial and sexual minority groups call for greater attention to the detection and treatment of these symptoms in these groups. Applying a transdiagnostic approach to ED symptoms can inform effective detection and intervention efforts.