Search

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

IMPORTANCE: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. OBJECTIVE: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. EVIDENCE REVIEW: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. FINDINGS: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. CONCLUSIONS AND RELEVANCE: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

BackgroundPrevious investigations that have examined associations between family history (FH) of alcohol/substance use and adolescent brain development have been primarily cross-sectional. Here, leveraging a large population-based sample of youths, we characterized frontal cortical trajectories among 9- to 13-year-olds with (FH+) versus without (FH-) an FH and examined sex as a potential moderator.MethodsWe used data from 9710 participants in the Adolescent Brain Cognitive Development (ABCD) Study (release 4.0). FH+ was defined as having ≥1 biological parents and/or ≥2 biological grandparents with a history of alcohol/substance use problems (n = 2433). Our primary outcome was frontal cortical structural measures obtained at baseline (ages 9-11) and year 2 follow-up (ages 11-13). We used linear mixed-effects models to examine the extent to which FH status qualified frontal cortical development over the age span studied. Finally, we ran additional interactions with sex to test whether observed associations between FH and cortical development differed significantly between sexes.ResultsFor FH+ (vs. FH-) youths, we observed increased cortical thinning from 9 to 13 years across the frontal cortex as a whole. When we probed for sex differences, we observed significant declines in frontal cortical thickness among boys but not girls from ages 9 to 13 years. No associations were observed between FH and frontal cortical surface area or volume.ConclusionsHaving a FH+ is associated with more rapid thinning of the frontal cortex across ages 9 to 13, with this effect driven primarily by male participants. Future studies will need to test whether the observed pattern of accelerated thinning predicts future substance use outcomes.

INTRODUCTION: Alcohol expectancies predict subsequent alcohol use and related problems among adolescents, although predictors of alcohol expectancies remain unclear. This study examined the longitudinal association between family conflict, a sociocultural factor strongly implicated in adolescent alcohol use, and positive and negative alcohol expectancies of adolescents of diverse racial/ethnic backgrounds. METHODS: Data were from the Adolescent Brain Cognitive Development Study 4.0 release, a multisite longitudinal study (N = 6,231, baseline age 9-10). Linear mixed-effects regression, with interactions between race/ethnicity and family conflict, tested the association between family conflict and alcohol expectancies, for each racial/ethnicity (e.g., Black vs. non-Black; White vs. non-White). RESULTS: Interactions of family conflict with race/ethnicity in predicting negative and positive alcohol expectancies were statistically significant for models testing Black and White adolescents, but not for Asian, Hispanic, and Other. Family conflict at baseline predicted lower negative alcohol expectancy for Black adolescents (B = -.166, p = 0.033) and positive alcohol expectancy for White adolescents (B = 0.71, p = 0.023) at the year 3 follow-up. All models controlled for sex, age, family socioeconomic status, alcohol expectancies at year 1, and family conflict at year 3. CONCLUSION: The results indicate that family conflict is a potential risk factor for problematic alcohol expectancies for Black and White adolescents. Although we did not directly compare Black and White adolescents, our findings indicate that family conflict may operate differently for Black and White adolescents. Prevention and intervention efforts targeting family conflict may be relevant for different aspects of alcohol expectancies in Black and White families.

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.

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.

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohens d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

INTRODUCTION: Impulsivity is a known risk factor for the development of substance use disorders and other psychiatric conditions that is influenced by both genetics and environment. Although research has linked parental mental health to childrens impulsivity, potential mediators of this relationship remain understudied. The current investigation leverages the large national Adolescent Brain Cognitive Development (ABCD) Study to assess the mediating role of family conflict - an important social context for youth development - in the relationship between parental mental health and youth impulsivity. METHODS: Data were from the first three annual waves of the ABCD study (Baseline N = 11,876 children, Mage = 9.9 years; 48% female; 52% White). Parental mental health conditions were self-reported internalizing, externalizing, and total problems. Youth completed the family conflict scale, and Urgency, Planning (lack of), Perseverance (lack of), Sensation Seeking, and Positive Urgency (UPPS-P) scale to measure impulsivity. To determine if within-family change in conflict from baseline to year 1 explained changes in the strength of relations between baseline parental mental health and year 2 youth impulsivity, longitudinal causal mediation analyses were conducted, controlling for demographic factors (i.e., age, sex, race, household income, parental education, marital status), as well as baseline levels of family conflict and outcomes. Separate mediation models were run for each mental health condition and each UPPS-P subscale. RESULTS: Above and beyond bivariate relations, longitudinal mediation models, which included covariates, showed family conflict significantly (ps

This study explored the generality versus specificity of two trait-liability factors for externalizing problems-disinhibition and callousness-in the concurrent and prospective prediction of symptoms of conduct disorder, attention-deficit/hyperactivity disorder (ADHD), and substance use (i.e., alcohol use disorder and history of illicit substance use). Disinhibition involves an impulsive, unrestrained cognitive-behavioral style; callousness entails a dispositional lack of social-emotional sensitivity. Participants were European adolescents from the multisite IMAGEN project who completed questionnaires and clinical interviews at ages 14 (N = 1,504, Mage = 14.41, 51.13% female) and 16 (N = 1,407, Mage = 16.46, 51.88% female). Disinhibition was related concurrently and prospectively to greater symptoms of conduct disorder, ADHD, and alcohol use disorder; higher scores on a general externalizing factor; and greater likelihood of having tried an illicit substance. Callousness was selectively related to greater conduct disorder symptoms. These findings indicate disinhibition confers broad liability for externalizing spectrum disorders, perhaps due to its affiliated deficits in executive function. In contrast, callousness appears to represent more specific liability for antagonistic (aggressive/exploitative) forms of externalizing, as exemplified by antisocial behavior. Results support the utility of developmental-ontogenetic and hierarchical-dimensional models of psychopathology and have important implications for early assessment of risk for externalizing problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Adolescent Brain Cognitive Development (ABCD)SM study aims to retain a demographically diverse sample of youth and one parent across 21 sites throughout its 10-year protocol while minimizing selective (systematic) attrition. To evaluate the effectiveness of these efforts, the ABCD Retention Workgroup (RW) has employed a data-driven approach to examine, track, and intervene via three key metrics: (1) which youth completed visits late; (2) which youth missed visits; and (3) which youth withdrew from the study. The RW actively examines demographic (race, education level, family income) and site factors (visit satisfaction, distance from site, and enrollment in ancillary studies) to strategize efforts that will minimize disengagement and loss of participating youth and parents. Data showed that the most robust primary correlates of late visits were distance from study site, race, and parental education level. Race, lower parental education level, parental employment status, and lower family income were associated with higher odds of missed visits, while being enrolled in one of the ancillary studies was associated with lower odds of missed visits. Additionally, parents who were primary Spanish speakers withdrew at slightly higher rates. These findings provide insight into future targets for proactive retention efforts by the ABCD RW.

BackgroundNicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.MethodsEleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.ResultsThe cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Brain asymmetry reflects left-right hemispheric differentiation, which is a quantitative brain phenotype that develops with age and can vary with psychiatric diagnoses. Previous studies have shown that substance dependence is associated with altered brain structure and function. However, it is unknown whether structural brain asymmetries are different in individuals with substance dependence compared with nondependent participants. Here, a mega-analysis was performed using a collection of 22 structural brain MRI datasets from the ENIGMA Addiction Working Group. Structural asymmetries of cortical and subcortical regions were compared between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis (n = 1,796) and nondependent participants (n = 996). Substance-general and substance-specific effects on structural asymmetry were examined using separate models. We found that substance dependence was significantly associated with differences in volume asymmetry of the nucleus accumbens (NAcc; less rightward; Cohen's d = 0.15). This effect was driven by differences from controls in individuals with alcohol dependence (less rightward; Cohen's d = 0.10) and nicotine dependence (less rightward; Cohen's d = 0.11). These findings suggest that disrupted structural asymmetry in the NAcc may be a characteristic of substance dependence.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

SummaryBrain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (e.g. brain derived, psychiatric, behavioral and physiological variables) and neuroimaging specific data (e.g. brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging-based ML questions, in particular, those queried from large human datasets.Availability and implementationBPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app.

Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.

BackgroundThere are known associations between mental health symptoms and transgender identity among adults. Whether this relationship extends to early adolescents and to gender domains other than identity is unclear. This study measured dimensions of gender in a large, diverse, sample of youth, and examined associations between diverse gender experiences and mental health.MethodsThe ABCD study is an ongoing, longitudinal, US cohort study. Baseline data (release 2.0) include 11,873 youth age 9/10 (48% female); and the 4,951 1-year follow-up visits (age 10/11; 48% female) completed prior to data release. A novel gender survey at the 1-year visit assessed felt-gender, gender noncontentedness, and gender nonconformity using a 5-point scale. Mental health measures included youth- and parent-reports.ResultsRoughly half a percent of 9/10-year-olds (n = 58) responded 'yes' or 'maybe' when asked, 'Are you transgender' at baseline. Recurrent thoughts of death were more prevalent among these youth compared to the rest of the cohort (19.6% vs. 6.4%, χ2  = 16.0, p

Effect sizes are commonly interpreted using heuristics established by Cohen (e.g., small: r = .1, medium r = .3, large r = .5), despite mounting evidence that these guidelines are mis-calibrated to the effects typically found in psychological research. This study's aims were to 1) describe the distribution of effect sizes across multiple instruments, 2) consider factors qualifying the effect size distribution, and 3) identify examples as benchmarks for various effect sizes. For aim one, effect size distributions were illustrated from a large, diverse sample of 9/10-year-old children. This was done by conducting Pearson's correlations among 161 variables representing constructs from all questionnaires and tasks from the Adolescent Brain and Cognitive Development Study® baseline data. To achieve aim two, factors qualifying this distribution were tested by comparing the distributions of effect size among various modifications of the aim one analyses. These modified analytic strategies included comparisons of effect size distributions for different types of variables, for analyses using statistical thresholds, and for analyses using several covariate strategies. In aim one analyses, the median in-sample effect size was .03, and values at the first and third quartiles were .01 and .07. In aim two analyses, effects were smaller for associations across instruments, content domains, and reporters, as well as when covarying for sociodemographic factors. Effect sizes were larger when thresholding for statistical significance. In analyses intended to mimic conditions used in "real-world" analysis of ABCD data, the median in-sample effect size was .05, and values at the first and third quartiles were .03 and .09. To achieve aim three, examples for varying effect sizes are reported from the ABCD dataset as benchmarks for future work in the dataset. In summary, this report finds that empirically determined effect sizes from a notably large dataset are smaller than would be expected based on existing heuristics.

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

The 21-site Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to characterize functional brain development via resting-state functional connectivity (RSFC) and to quantify relationships between RSFC and behavior. This multi-site data set includes potentially confounding sources of variance, such as differences between data collection sites and/or scanner manufacturers, in addition to those inherent to RSFC (e.g., head motion). The ABCD project provides a framework for characterizing and reproducing RSFC and RSFC-behavior associations, while quantifying the extent to which sources of variability bias RSFC estimates. We quantified RSFC and functional network architecture in 2,188 9-10-year old children from the ABCD study, segregated into demographically-matched discovery (N = 1,166) and replication datasets (N = 1,022). We found RSFC and network architecture to be highly reproducible across children. We did not observe strong effects of site; however, scanner manufacturer effects were large, reproducible, and followed a "short-to-long" association with distance between regions. Accounting for potential confounding variables, we replicated that RSFC between several higher-order networks was related to general cognition. In sum, we provide a framework for how to characterize RSFC-behavior relationships in a rigorous and reproducible manner using the ABCD dataset and other large multi-site projects.

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.