A total of 25.7 million children in the United States are classified as overweight or obese. Obesity is associated with deficits in executive function, which may contribute to poor dietary ...decision-making. Less is known about the associations between being overweight or obese and brain development.
To examine whether body mass index (BMI) is associated with thickness of the cerebral cortex and whether cortical thickness mediates the association between BMI and executive function in children.
In this cross-sectional study, cortical thickness maps were derived from T1-weighted structural magnetic resonance images of a large, diverse sample of 9 and 10-year-old children from 21 US sites. List sorting, flanker, matrix reasoning, and Wisconsin card sorting tasks were used to assess executive function.
A 10-fold nested cross-validation general linear model was used to assess mean cortical thickness from BMI across cortical brain regions. Associations between BMI and executive function were explored with Pearson partial correlations. Mediation analysis examined whether mean prefrontal cortex thickness mediated the association between BMI and executive function.
Among 3190 individuals (mean SD age, 10.0 0.61 years; 1627 51.0% male), those with higher BMI exhibited lower cortical thickness. Eighteen cortical regions were significantly inversely associated with BMI. The greatest correlations were observed in the prefrontal cortex. The BMI was inversely correlated with dimensional card sorting (r = -0.088, P < .001), list sorting (r = -0.061, P < .003), and matrix reasoning (r = -0.095, P < .001) but not the flanker task. Mean prefrontal cortex thickness mediated the association between BMI and list sorting (mean SE indirect effect, 0.014 0.008; 95% CI, 0.001-0.031) but not the matrix reasoning or card sorting task.
These results suggest that BMI is associated with prefrontal cortex development and diminished executive functions, such as working memory.
•Functional connectivity (FC) patterns derived from fMRI tasks outperform resting-state FC at predicting individual differences in a measure of cognitive task performance and a task-derived ...behavioral inhibition measure.•The improvement in behavioral prediction afforded by fMRI tasks over resting-state is largely associated with the FC of the task model fit.•FC of the task model fit and task design model parameters contain shared and unique behavioral prediction power.
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.
Despite an increased understanding of the pharmacology and long-term cognitive effects of cannabis in humans, there has been no research to date examining its chronic effects upon reward processing ...in the brain. Motivational theories regarding long-term drug use posit contrasting predictions with respect to how drug users are likely to process non-drug incentives. The reward deficiency syndrome (RDS) of addiction posits that there are deficits in dopamine (DA) motivational circuitry for non-drug rewards, such that only drugs of abuse are capable of normalizing DA in the ventral striatum (VS). Alternatively, the opponent process theory (OPT) holds that in individuals prone to drug use, there exists some form of mesolimbic hyperactivity, in which there is a bias towards reward-centred behaviour concomitant with impulsivity. The current study examined BOLD responses during reward and loss anticipation and their outcome deliveries in 14 chronic cannabis users and 14 drug-naive controls during a monetary incentive delay (MID) task. Despite no significant behavioural differences between the two groups, cannabis users had significantly more right VS BOLD activity during reward anticipation. Correlation analyses demonstrated that this right VS BOLD response was significantly correlated with life-time use and reported life-time cannabis joints consumed. No correlations between cannabis abstinence and BOLD responses were observed. We also observed a number of group differences following outcome deliveries, most notably hypoactivity in the left insula cortex in response to loss and loss avoidance outcome notifications in the cannabis group. These results may suggest hypersensitivity during instrumental response anticipation for non-drug rewards and a hyposensitivity to loss outcomes in chronic cannabis users; the implications of which are discussed with respect to the potentially sensitizing effects of cannabis for other rewards.
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.
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of ...behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
The ABCD study is recruiting and following the brain development and health of over 10,000 9–10 year olds through adolescence. The imaging component of the study was developed by the ABCD Data ...Analysis and Informatics Center (DAIC) and the ABCD Imaging Acquisition Workgroup. Imaging methods and assessments were selected, optimized and harmonized across all 21 sites to measure brain structure and function relevant to adolescent development and addiction. This article provides an overview of the imaging procedures of the ABCD study, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature.
Abstract Evidence from a number of substance abuse populations suggests that substance abuse is associated with a cluster of differences in cognitive processes. However, investigations of this kind ...in non-clinical samples are relatively few. The present study examined the ability of alcohol-attentional bias (an alcohol Stroop task), impulsive decision-making (a delay discounting task), and impaired inhibitory control (a GO–NOGO task) to: (a) discriminate problem from non-problem drinkers among a sample of college students; (b) predict scores on the Alcohol Use Disorders Identification Test (AUDIT; a measure of alcohol consumption, drinking behaviour, and alcohol-related problems) across all of the student drinkers; (c) predict AUDIT scores within the subgroups of problem and non-problem student drinkers. In logistic regression controlling for gender and age, student drinkers with elevated alcohol-attentional bias and impulsive decision-making were over twice as likely to be a problem than a non-problem drinker. Multiple regression analysis of the entire sample revealed that all three cognitive measures were significant predictors of AUDIT scores after gender and age had been controlled; the cognitive variables together accounted for 48% of the variance. Moreover, subsequent multiple regressions revealed that impaired inhibitory control was the only significant predictor of AUDIT scores for the group of non-problem drinkers, and alcohol-attentional bias and impulsive decision-making were the only significant predictors of AUDIT scores for the group of problem drinkers. Finally, both impulsive decision-making and impaired inhibitory control were significantly correlated with alcohol-attentional bias across the whole sample. Implications are discussed relating to the development of problematic drinking.
•Describes the ABCD study aims and design.•Covers issues surrounding estimation of meaningful associations, including population inferences, effect sizes, and control of covariates.•Outlines best ...practices for reproducible research and reporting of results.•Provides worked examples that illustrate the main points of the paper.
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.
More than 80% of addicted individuals fail to seek treatment, which might reflect impairments in recognition of severity of disorder. Considered by some as intentional deception, such ‘denial’ might ...instead reflect dysfunction of brain networks subserving insight and self-awareness. Here we review the scant literature on insight in addiction and integrate this perspective with the role of: (i) the insula in interoception, self-awareness and drug craving; (ii) the anterior cingulate in behavioral monitoring and response selection (relevant to disadvantageous choices in addiction); (iii) the dorsal striatum in automatic habit formation; and (iv) drug-related stimuli that predict emotional behavior in addicted individuals, even without conscious awareness. We discuss implications for clinical treatment including the design of interventions to improve insight into illness severity in addiction.
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