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Objective: Generalized linear models (GLMs) such as logistic and Poisson regression are among the most common statistical methods for modeling binary and count outcomes. Though single-coefficient tests (odds ratios, incidence rate ratios) are the most common way to test predictor-outcome relations in these models, they provide limited information on the magnitude and nature of relations with outcomes. We assert that this is largely because they do not describe direct relations with quantities of interest (QoIs) such as probabilities and counts. Shifting focus to QoIs makes several nuances of GLMs more apparent.Method: To bolster interpretability of these models, we provide a tutorial on logistic and Poisson regression and suggestions for enhancements to current reporting practices for predictor-outcome relations in GLMs.Results: We first highlight differences in interpretation between traditional linear models and GLMs, and describe common misconceptions of GLMs. In particular, we highlight that link functions A) introduce non-constant relations between predictors and outcomes and B) make predictor-QoI relations dependent on other covariates. Each of these properties causes interpretation of GLM coefficients to diverge from interpretations of linear models. Next, we argue for a more central focus on QoIs (probabilities and counts). Finally, we propose and provide graphics and tables, with sample R code, for enhancing presentation and interpretation of QoIs.Conclusions: By improving present practices in the reporting of predictor-outcomerelations in GLMs, we hope to maximize the amount of actionable information generated by statistical analyses and provide a tool for building a cumulative science of substance use disorders.Public Health Significance: We propose several enhancements to current reporting practices for statistical analyses of binary outcomes (e.g., psychiatric diagnoses) and count outcomes (e.g., number of alcoholic drinks consumed). We encourage researchers to interpret results in terms of quantities of interest (probabilities for binary models, counts for count models) and provide a tutorial and R code for implementing these analyses. Doing so can provide richer information about a statistical analysis, make study results easier for research consumers to understand, and facilitate comparison of results across studies.

BACKGROUND In the US, methamphetamine-involved overdoses tripled from 2015-2020 and continue to rise. However, efficacious treatments like contingency management (CM) are often unavailable within health systems. OBJECTIVE We conducted a single-arm pilot study to evaluate the feasibility, engagement, and usability of a fully remotely-delivered mobile health (mHealth) CM program that was offered to adult outpatients who use methamphetamine and were receiving healthcare within a large university health system. METHODS Participants were referred by primary care or behavioral health clinicians between 09/2021 and 07/2022. Eligibility criteria were screened by telephone and included self-reported methamphetamine use on ≥5 of the past 30 days and a goal of reducing or abstaining from methamphetamine use. Eligible participants who agreed to participate then completed an initial “welcome phase” that included two videoconference calls to register for and learn about the CM program and two “practice” saliva-based substance tests that were prompted by the smartphone app. Participants who completed these welcome phase activities could then receive the remotely-delivered CM intervention for 12 weeks. The intervention included approximately 24 randomly scheduled smartphone alerts requesting a video recording of themselves taking a saliva-based substance test to verify recent methamphetamine abstinence, 12 weekly calls with a CM guide, 35 self-paced cognitive-behavioral therapy (CBT) modules, and multiple surveys, including an intervention usability questionnaire completed at the study midpoint. Financial incentives were disbursed via reloadable debit cards for completing these activities. RESULTS 37 patients completed telephone screens, with 28 meeting eligibility criteria and consenting to participate. Most participants self-reported symptoms consistent with severe methamphetamine use disorder (88%), and most had other co-occurring non-methamphetamine substance use disorders (79%) and co-occurring mental health disorders (86%) according to existing electronic health records. 15 participants (54%) successfully completed the “welcome phase” and were able to receive the CM intervention. Among those participants, engagement with substance testing, calls with CM guides, and CBT modules varied, although rates of verified methamphetamine abstinence in substance testing were generally low. Participants rated the intervention as easy to use and satisfactory, and reported a mixture of positive and neutral opinions regarding the intervention’s clinical usefulness. CONCLUSIONS A fully remote CM program can be feasibly delivered within healthcare settings that lack existing CM programs. While remote delivery may help reduce some barriers to treatment access, a sizeable proportion of patients who use methamphetamine may struggle to engage during initial onboarding stages. High rates of co-occurring psychiatric conditions in the patient population may also contribute to uptake and engagement challenges. Future efforts could leverage greater human-to-human connection, more streamlined onboarding procedures, larger incentives, longer duration, and incentivization of non-abstinence-based recovery goals to increase uptake and engagement with fully-remote mHealth-based CM. CLINICALTRIAL none

Author(s): Stinson, Elizabeth A; Sullivan, Ryan M; Peteet, Bridgette J; Tapert, Susan F; Baker, Fiona C; Breslin, Florence J; Dick, Anthony S; Gonzalez, Marybel Robledo; Guillaume, Mathieu; Marshall, Andrew T; McCabe, Connor J; Pelham, William E; Van Rinsveld, Amandine M; Sheth, Chandni S; Sowell, Elizabeth R; Wade, Natasha E; Wallace, Alexander L; Lisdahl, Krista M | Abstract: BackgroundDuring the COVID-19 pandemic in the United States, mental health among youth has been negatively impacted. Youth with a history of adverse childhood experiences (ACEs), as well as youth from minoritized racial-ethnic backgrounds, may be especially vulnerable to experiencing COVID-19-related distress. The current aims are to examine whether exposure to pre-pandemic ACEs predicts mental health during the COVID-19 pandemic in youth and whether racial-ethnic background moderated these effects.MethodsFrom May to August 2020, 7,983 youth (Mage= 12.5, range= 10.6-14.6 years old) in the Adolescent Brain Cognitive Development StudySM (ABCD Study®) completed at least one of three online surveys measuring the impact of the pandemic on their mental health. Data was evaluated in relation to youth's pre-pandemic mental health and ACEs.ResultsPre-pandemic ACE history significantly predicted poorer mental health (across all outcomes) and greater COVID-19-related stress and impact of fears on well-being. Youth reported improved mental health during the pandemic (from May to August 2020). While reporting similar levels of mental health, youth from minoritized racial-ethnic backgrounds had elevated COVID-19-related worry, stress, and impact on well-being. Race and ethnicity generally did not moderate ACE effects. Older youth, girls, and those with greater pre-pandemic internalizing symptoms also reported greater mental health symptoms.ConclusionsYouth who experienced greater childhood adversity reported greater negative affect and COVID-19-related distress during the pandemic. Although they reported generally better mood, Black, Asian American, and multiracial youth reported greater COVID-19-related worry and experienced COVID-19 related discrimination compared to non-Hispanic White youth, highlighting potential health disparities.

Purpose Evaluate changes in early adolescent substance use during the coronavirus disease 2019 (COVID-19) pandemic using a prospective, longitudinal, nationwide cohort. Methods Participants were enrolled in the Adolescent Brain Cognitive Development Study. A total of 7,842 youth (mean age = 12.4 years, range = 10.5–14.6) at 21 study sites across the U.S. completed a three-wave assessment of substance use between May and August 2020. Youth reported whether they had used alcohol, nicotine, cannabis, or other substances in the past 30 days. Data were linked to prepandemic surveys that the same youth had completed in the years 2018–2020, before the advent of the COVID-19 pandemic. Results Past-30-day substance use remained stable in the 6 months since stay-at-home orders were first issued in U.S. states/counties; was primarily episodic (1–2 days in the past month); and was typically limited to a single substance. Using pretest/posttest and age-period designs, we found that compared to before the pandemic, fewer youth were using alcohol and more youth were using nicotine or misusing prescription drugs. During the pandemic, youth were more likely to use substances when they were more stressed by pandemic-related uncertainty; their family experienced material hardship; their parents used alcohol or drugs; or they experienced greater depression or anxiety. Neither engagement in social distancing nor worry about COVID-19 infection was associated with substance use. Several risk factors were stronger among older (vs. younger) adolescents. Conclusions Among youth in early adolescence, advent of the COVID-19 pandemic was associated with decreased use of alcohol and increased use of nicotine and misuse of prescription drugs.

Objective:Verbal memory deficits are linked to cannabis use. However, self-reported episodic use does not allow for assessment of variance from other factors (e.g., cannabis potency, route of consumption) that are important for assessing brain-behavior relationships. Further, co-occurring nicotine use may moderate the influence of cannabis on cognition. Here we utilized objective urinary measurements to assess the relationship between metabolites of cannabis, 11-nor-9-carboxy-∆9-tetrahydrocannabinol (THCCOOH), and nicotine (cotinine) on verbal memory in young adults.Method:Adolescents and young adults (n = 103) aged 16–22 completed urinary drug testing and verbal memory assessment (RAVLT). Linear regressions examined the influence of THCCOOH and cotinine quantitative concentrations, and their interaction, on RAVLT scores, controlling for demographics and alcohol. Cannabis intake frequency was also investigated. Secondary analyses examined whether past month or recency of use related to performance, while controlling for THCCOOH and cotinine concentrations.Results:THCCOOH concentration related to both poorer total learning and long delay recall. Cotinine concentration related to poorer short delay recall. Higher frequency cannabis use status was associated with poorer initial learning and poorer short delay. When comparing to self-report, THCCOOH and cotinine concentrations were negatively related to learning and memory performance, while self-report was not.Conclusions:Results confirm the negative relationship between verbal memory and cannabis use, extending findings with objective urinary THCCOOH, and cotinine concentration measurements. No moderating relationship with nicotine was found, though cotinine concentration independently associated with negative short delay performance. Findings support the use of both urinary and self-report metrics as complementary methods in substance use research.

BackgroundDual systems theories suggest that greater imbalance between higher reward sensitivity and lower cognitive control across adolescence conveys risk for behaviors such as heavy episodic drinking (HED). Prior research demonstrated that psychological analogues of these systems, sensation seeking and premeditation, change from childhood through emerging adulthood, and each has been independently linked with HED. However, few studies have assessed whether change over time in these developing analogues is prospectively associated with HED. Moreover, we know of no research that has shown whether within-person differences between higher sensation seeking and relatively lower premeditation across the adolescent period predict HED in emerging adulthood.MethodsProspective data from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study (n=715) were used to examine the association of sensation seeking and premeditation with HED among adolescents ages 16 to 20years. We used novel applications of latent difference score modeling and growth curve analysis to test whether increasing sensation seeking, premeditation, and their imbalance over time are associated with HED across the study period, and whether these associations differed by sex.ResultsWhereas premeditation increased linearly from adolescence through emerging adulthood across sexes, males reported growth and females reported decline in sensation seeking. Sensation seeking in adolescence (and not premeditation) was associated with higher levels of HED by emerging adulthood. Importantly, greater imbalance between sensation seeking and premeditation was associated with higher levels of HED by emerging adulthood though we note that variability capturing this imbalance correlated highly (r=0.86) with baseline levels of sensation seeking.ConclusionsDevelopmental imbalance between higher sensation seeking and lower premeditation in late adolescence may be a risk factor for greater HED in emerging adulthood.

Sexual-minority women (SMW) report higher rates of substance use and disorder across the life span and greater levels of minority stress in adolescence and young adulthood. Minority stress mediation models propose that higher levels of social stressors may increase emotion dysregulation, which in turn increases the propensity toward substance misuse. Few studies, however, have prospectively examined the impact of stressors and emotion dysregulation among SMW on early and escalating substance use. In this longitudinal study, we examined whether emotion dysregulation and social stress mediated the association between sexual-minority status and developing substance use (ages 17–22) in a sample of 2,201 heterosexual and 246 SMW participants in the Pittsburgh Girls Study. Results supported serial mediation processes of marijuana-use risk: SMW reported higher levels of social stress in late adolescence, which in turn predicted greater emotion dysregulation that was associated with greater marijuana use by young adulthood.

Between 20 % and 30 % of teens suffer from depression or anxiety before reaching adulthood, and up to half also use or misuse alcohol. Although theories suggest bidirectional links between harmful alcohol use (e.g., binge drinking) and internalizing symptoms (i.e., depression and anxiety), empirical evidence to-date has been mixed. Systematic reviews have attributed mixed findings to limitations in study design, such as the utilization of between-person analyses and the focus on unidirectional effects. The goal of this study was to address these limitations by assessing bidirectional within-person associations between internalizing symptoms and binge drinking over the course of 5 years in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) sample, a large cohort recruited at ages 12-21 and followed annually on substance use and psychiatric functioning.We used latent curve models with structured residuals to examine within-person lagged associations between depression, anxiety, and past month counts of binge drinking using NCANDA data (N = 831). Analyses were supplemented with post-hoc power simulations.We found marginal evidence linking binge drinking with subsequent depression symptoms one year later among females. We found no evidence that depression or anxiety predicted subsequent binge drinking despite sufficient power.Social and cognitive consequences of binge drinking may predict later depression symptoms in adolescence and young adulthood for young women, though there was little evidence favoring self-medication models for binge drinking. We note several moderating variables and common factor mechanisms that may better explain this link.

Adolescents exposed to violence are at elevated risk of developing most forms of psychopathology, including depression, anxiety, and alcohol abuse. Prior research has identified emotional reactivity and difficulties with emotion regulation as core mechanisms linking violence exposure with psychopathology. Scant research has examined behavioral responses to distress as a mechanism in this association. This study examined the association of violence exposure with distress tolerance—the ability to persist in the face of distress—and whether lower distress tolerance linked violence exposure with subsequent increases in depression, anxiety, and alcohol abuse problems during adolescence. Data were collected prospectively in a sample of 287 adolescents aged 16–17 (44.3% male; 40.8% White). At Time 1, participants provided self-report of demographics, violence exposure, and psychopathology, and completed a behavioral measure of distress tolerance, the Paced Auditory Serial Addition Task. Four months later, participants (n = 237) repeated the psychopathology assessments. Violence exposure was associated with lower distress tolerance (β = −.21 p = .009), and elevated concurrent psychopathology (β = .16–.45, p = .001–.004). Low distress tolerance was prospectively associated with greater likelihood of abusing alcohol over time (OR = .63, p = .021), and mediated the association between violence exposure and greater levels (β = .02, 95% CI [.001, .063]) and likelihood (OR = .03, 95% CI [.006, .065]) of alcohol use over time. In contrast, low distress tolerance was not associated concurrently or prospectively with internalizing symptoms. Results persisted after controlling for socio-economic status. Findings suggest that distress tolerance is shaped by early experiences of threat and plays a role in the association between violence exposure and development of problematic alcohol use in adolescence.

Purpose Adolescence is characterized by dramatic physical, social, and emotional changes, making teens particularly vulnerable to the mental health effects of the COVID-19 pandemic. This longitudinal study identifies young adolescents who are most vulnerable to the psychological toll of the pandemic and provides insights to inform strategies to help adolescents cope better in times of crisis. Methods A data-driven approach was applied to a longitudinal, demographically diverse cohort of more than 3,000 young adolescents (10–14 years) participating in the ongoing Adolescent Brain Cognitive Development Study in the United States, including multiple prepandemic visits and three assessments during the COVID-19 pandemic (May–August 2020). We fitted machine learning models and provided a comprehensive list of predictors of psychological distress in individuals. Results Positive affect, stress, anxiety, and depressive symptoms were accurately detected with our classifiers. Female sex and prepandemic internalizing symptoms and sleep problems were strong predictors of psychological distress. Parent- and youth-reported pandemic-related psychosocial factors, including poorer quality and functioning of family relationships, more screen time, and witnessing discrimination in relation to the pandemic further predicted youth distress. However, better social support, regular physical activities, coping strategies, and healthy behaviors predicted better emotional well-being. Conclusions Findings highlight the importance of social connectedness and healthy behaviors, such as sleep and physical activity, as buffering factors against the deleterious effects of the pandemic on adolescents' mental health. They also point to the need for greater attention toward coping strategies that help the most vulnerable adolescents, particularly girls and those with prepandemic psychological problems.

Our study characterized associations between three indicators of COVID-19's community-level impact in 20 geographically diverse metropolitan regions and how worried youth and their caregivers in the Adolescent Brain Cognitive Development℠ Study have been about COVID-19. County-level COVID-19 case/death rates and monthly unemployment rates were geocoded to participants' addresses. Caregivers' (vs. youths') COVID-19-related worry was more strongly associated with COVID-19's community impact, independent of sociodemographics and pre-pandemic anxiety levels, with these associations varying by location. Public-health agencies and healthcare providers should avoid adopting uniform "one-size-fits-all" approaches to addressing COVID-19-related emotional distress and must consider specific communities' needs, challenges, and strengths.

Socioeconomic disadvantage is associated with larger COVID-19 disease burdens and pandemic-related economic impacts. We utilized the longitudinal Adolescent Brain Cognitive Development Study to understand how family- and neighborhood-level socioeconomic disadvantage relate to disease burden, family communication, and preventative responses to the pandemic in over 6,000 youth-parent/caregiver dyads. Data were collected at three timepoints (May to August 2020). Here, we show that both family- and neighborhood-level disadvantage were associated with parents’ reports of greater family COVID-19 exposure risk and diagnoses, less perceived exposure risk, more frequent parent-youth conversations about COVID-19 risk/prevention and reassurance, and greater youth preventative behaviors. More disadvantaged families may be adaptively incorporating more protective strategies to reduce emotional distress and likelihood of COVID-19 infection. The results highlight the importance of parent-youth communication and disease-preventative practices for buffering the economic and disease burdens of COVID-19, along with policies and programs that reduce these burdens for families with socioeconomic disadvantage.

Many researchers hope to examine interaction effects using generalized linear models (GLMs) to predict outcomes on nonlinear scales. For instance, logistic and Poisson GLMs are used to estimate associations between predictors and outcomes in nonlinear probability and count scales, respectively. However, we (McCabe et al., 2021; Halvorson et al., in press) and others (Ai & Norton, 2003; Mize, 2019; Norton, Wang, & Ai, 2004) have shown that testing and interpreting interaction effects on these scales is not straightforward. GLMs require the application of partial derivatives and/or discrete differences to compute and probe interaction effects appropriately when models are interpreted on their nonlinear scale. Currently available open-source software does not provide methods of computing these interaction effects on probability and count scales, reflecting a central limitation in applying these methods in research practice. Here, we introduce `modglm`, an R-based software package that accompanies our manuscript providing recommendations for computing interaction effects in nonlinear probability and counts (McCabe et al., 2021). This software produces the interaction effect between two variables in generalized linear models of probabilities and counts and provides additional statistics and plotting utilities for evaluating and describing this effect.

Psychology research frequently involves the study of probabilities and counts. These are typically analyzed using generalized linear models (GLMs), which can produce these quantities via nonlinear transformation of model parameters. Interactions are central within many research applications of these models. To date, typical practice in evaluating interactions for probabilities or counts extends directly from linear approaches, in which evidence of an interaction effect is supported by using the product term coefficient between variables of interest. However, unlike linear models, interaction effects in GLMs describing probabilities and counts are not equal to product terms between predictor variables. Instead, interactions may be functions of the predictors of a model, requiring non-traditional approaches for interpreting these effects accurately. Here, we define interactions as change in a marginal effect of one variable as a function of change in another variable, and describe the use of partial derivatives and discrete differences for quantifying these effects. Using guidelines and simulated examples, we then use these approaches to describe how interaction effects should be estimated and interpreted for GLMs on probability and count scales. We conclude with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model.

Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Keywords: Longitudinal methods, Growth curve models, Change over time

Interaction plots are used frequently in psychology research to make inferences about moderation hypotheses. A common method of analyzing and displaying interactions is to create simple-slopes or marginal-effects plots using standard software programs. However, these plots omit features that are essential to both graphic integrity and statistical inference. For example, they often do not display all quantities of interest, omit information about uncertainty, or do not show the observed data underlying an interaction, and failure to include these features undermines the strength of the inferences that may be drawn from such displays. Here, we review the strengths and limitations of present practices in analyzing and visualizing interaction effects in psychology. We provide simulated examples of the conditions under which visual displays may lead to inappropriate inferences and introduce open-source software that provides optimized utilities for analyzing and visualizing interactions.

Infrequency scales are becoming a popular mode of data screening, due to their availability and ease of implementation. Recent research has indicated that the interpretation and functioning of infrequency items may not be as straightforward as had previously been thought (Curran & Hauser, 2015), yet there are no empirically based guidelines for implementing cutoffs using these items. In the present study, we compared two methods of detecting random responding with infrequency items: a zero-tolerance threshold versus a threshold that balances classification error rates. The results showed that a traditional zero-tolerance approach, on average, screens data that are less indicative of careless responding than those screened by the error-balancing approach. Thus, the de facto standard of applying a “zero-tolerance” approach when screening participants with infrequency scales may be too stringent, so that meaningful responses may also be removed from analyses. Recommendations and future directions are discussed.

Objective: Adolescents with ADHD are at risk of functional problems that may be mitigated by consistent ADHD treatment. This study pilots a brief intervention for adolescents with ADHD and their parents to increase treatment knowledge and family motivation to seek treatment. Method: The 3-hr curriculum was developed by a multidisciplinary team and included psychoeducation, goal setting, and motivational interviewing. Fifteen adolescents and 20 caregivers participated in the workshop, completed pre- and post-test assessments, and reported on acceptability. Results: Acceptability and satisfaction with the intervention were high. Perceived knowledge of ADHD increased post intervention; stigma was unchanged. Parents reported more acceptability of stimulant medications and less willingness to use special diets or cognitive games. Family feedback informed modifications to the curriculum. Conclusion: The Teen ADHD Workshop is a feasible and acceptable intervention to increase knowledge of ADHD and evidence-based treatments. Further research will evaluate effects on treatment participation.

Introduction Structural equation modeling (SEM) is a multivariate data analytic technique used in many domains of addictive behaviors research. SEM results are usually summarized and communicated through statistical tables and path diagrams, which emphasize path coefficients and global fit without showing specific quantitative values of data points that underlie the model results. Data visualization methods are often absent in SEM research, which may limit the quality and impact of SEM research by reducing data transparency, obscuring unexpected data anomalies and unmodeled heterogeneity, and inhibiting the communication of SEM research findings to research stakeholders who do not have advanced statistical training in SEM. Methods and results In this report, we show how data visualization methods can address these limitations and improve the quality of SEM-based addictive behaviors research. We first introduce SEM and data visualization methodologies and differentiate data visualizations from model visualizations that are commonly used in SEM, such as path diagrams. We then discuss ways researchers may utilize data visualization in SEM research, including by obtaining estimates of latent variables and by visualizing multivariate relations in two-dimensional figures. R syntax is provided to help others generate data visualizations for several types of effects commonly modeled in SEM, including correlation, regression, moderation, and simple mediation. Discussion The techniques outlined here may help spur the use of data visualization in SEM-based addictive behaviors research. Using data visualization in SEM may enhance methodological transparency and improve communication of research findings.

Infrequency scales are becoming a popular mode of data screening, due to their availability and ease of implementation. Recent research has indicated that the interpretation and functioning of infrequency items may not be as straightforward as had previously been thought (CurranHauser, 2015), yet there are no empirically based guidelines for implementing cutoffs using these items. In the present study, we compared two methods of detecting random responding with infrequency items: a zero-tolerance threshold versus a threshold that balances classification error rates. The results showed that a traditional zero-tolerance approach, on average, screens data that are less indicative of careless responding than those screened by the error-balancing approach. Thus, the de facto standard of applying a "zero-tolerance" approach when screening participants with infrequency scales may be too stringent, so that meaningful responses may also be removed from analyses. Recommendations and future directions are discussed.

Background Some respondents may respond at random to self-report surveys, rather than responding conscientiously (Meade and Craig, 2012), and this has only recently come to the attention of researchers in the addictions field (Godinho et al., 2016). Almost no research in the published addictions literature has reported screening for random responses. We illustrate how random responses can bias statistical estimates using simulated and real data, and how this is especially problematic in skewed data, as is common with substance use outcomes. Method We first tested the effects of varying amounts and types of random responses on covariance-based statistical estimates in distributions with varying amounts of skew. We replicated these findings in correlations from a real dataset (Add Health) by replacing varying amounts of real data with simulated random responses. Results Skew and the proportion of random responses influenced the amount and direction of bias. When the data were not skewed, uniformly random responses deflated estimates, while long-string random responses inflated estimates. As the distributions became more skewed, all types of random responses began to inflate estimates, even at very small proportions. We observed similar effects in the Add Health data. Conclusions Failing to screen for random responses in survey data produces biased statistical estimates, and data with only 2.5% random responses can inflate covariance-based estimates (i.e., correlations, Cronbach’s alpha, regression coefficients, factor loadings, etc.) when data are heavily skewed. Screening for random responses can substantially improve data quality, reliability and validity.

The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data. Keywords: Longitudinal modeling, Functional magnetic resonance imaging (fMRI), General Linear Model, Structural Equation Modeling, Developmental change

© 2015 Elsevier Inc. The main objective of the multi-site Pediatric Imaging, Neurocognition, and Genetics (PING) study was to create a large repository of standardized measurements of behavioral and imaging phenotypes accompanied by whole genome genotyping acquired from typically-developing children varying widely in age (3 to 20 years). This cross-sectional study produced sharable data from 1493 children, and these data have been described in several publications focusing on brain and cognitive development. Researchers may gain access to these data by applying for an account on the PING portal and filing a data use agreement. Here we describe the recruiting and screening of the children and give a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available. We also cite sources of more detailed information about the methods and data. Finally we describe the procedures for accessing the data and for using the PING data exploration portal.

Young adulthood is a peak period for externalizing behaviors such as substance abuse and antisocial conduct. Evidence from developmental neuroscience suggests that externalizing conduct within this time period may be associated with a “developmental asymmetry” characterized by an early peak in sensation seeking combined with a relatively immature impulse control system. Trait measures of impulsivity— sensation seeking and premeditation—are psychological manifestations of these respective systems, and multiple prior studies suggest that high sensation seeking and low premeditation independently confer risk for distinct forms of externalizing behaviors. The goal of the present study was to test this developmental asymmetry hypothesis, examining whether trait premeditation moderates the effect of sensation seeking on substance use and problems, aggression, and rule-breaking behavior. Using a cross-sectional sample of college-enrolled adults (n = 491), we applied zero-inflated modeling strategies to examine the likelihood and level of risky externalizing behaviors. Results indicated that lower premeditation enhanced the effect of higher sensation seeking on higher levels of positive and negative alcohol consequences, more frequent drug use, and more problematic drug use, but was unrelated to individual differences in antisocial behaviors. Our findings indicate that the developmental asymmetry between sensation seeking and a lack of premeditation is a risk factor for individual differences in problematic substance use among young adults, and may be less applicable for antisocial behaviors among high functioning individuals.

© 2015, Springer-Verlag Berlin Heidelberg. Anxiety is a risk factor for many adverse neuropsychiatric and socioeconomic outcomes, and has been linked to functional and structural changes in the ventromedial prefrontal cortex (VMPFC). However, the nature of these differences, as well as how they develop in children and adolescents, remains poorly understood. More effective interventions to minimize the negative consequences of anxiety require better understanding of its neurobiology in children. Recent research suggests that structural imaging studies may benefit from clearly delineating between cortical surface area and thickness when examining these associations, as these distinct cortical phenotypes are influenced by different cellular mechanisms and genetic factors. The present study examined relationships between cortical surface area and thickness of the VMPFC and a self-report measure of anxiety (SCARED-R) in 287 youths aged 7–20 years from the Pediatric Imaging, Neurocognition, and Genetics (PING) study. Age and gender interactions were examined for significant associations in order to test for developmental differences. Cortical surface area and thickness were also examined simultaneously to determine whether they contribute independently to the prediction of anxiety. Anxiety was negatively associated with relative cortical surface area of the VMPFC as well as with global cortical thickness, but these associations diminished with age. The two cortical phenotypes contributed additively to the prediction of anxiety. These findings suggest that higher anxiety in children may be characterized by both delayed expansion of the VMPFC and an altered trajectory of global cortical thinning. Further longitudinal studies will be needed to confirm these findings.

Objective: The NIH Toolbox Cognition Battery (NTCB) was designed to provide a brief, efficient computerized test of key neuropsychological functions appropriate for use in children as young as 3 years of age. This report describes the performance of a large group of typically developing children and adolescents and examines the impact of age and sociocultural variables on test performance. Method: The NTCB was administered to a sample of 1,020 typically developing males and females ranging in age from 3 to 20 years, diverse in terms of socioeconomic status (SES) and race/ethnicity, as part of the new publicly accessible Pediatric Imaging, Neurocognition, and Genetics (PING) data resource, at 9 sites across the United States. Results: General additive models of nonlinear age-functions were estimated from age-differences in test performance on the 8 NTCB subtests while controlling for family SES and genetic ancestry factors (GAFs). Age accounted for the majority of the variance across all NTCB scores, with additional significant contributions of gender on some measures, and of SES and race/ethnicity (GAFs) on all. After adjusting for age and gender, SES and GAFs explained a substantial proportion of the remaining unexplained variance in Picture Vocabulary scores. Conclusions: The results highlight the sensitivity to developmental effects and efficiency of this new computerized assessment battery for neurodevelopmental research. Limitations are observed in the form of some ceiling effects in older children, some floor effects, particularly on executive function tests in the youngest participants, and evidence for variable measurement sensitivity to cultural/socioeconomic factors. © 2013 American Psychological Association.

Structural MRI allows unparalleled in vivo study of the anatomy of the developing human brain. For more than two decades [1], MRI research has revealed many new aspects of this multifaceted maturation process, significantly augmenting scientific knowledge gathered from postmortem studies. Postnatal brain development is notably protracted and involves considerable changes in cerebral cortical [2-4], subcortical [5], and cerebellar [6, 7] structures, as well as significant architectural changes in white matter fiber tracts [8-11] (see [12]). Although much work has described isolated features of neuroanatomical development, it remains a critical challenge to characterize the multidimensional nature of brain anatomy, capturing different phases of development among individuals. Capitalizing on key advances in multisite, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental brain phase can be assessed with much greater precision than has been possible using other biological measures, accounting for more than 92% of the variance in age. Further, our composite metric of morphology, diffusivity, and signal intensity shows that the average difference in phase among children of the same age is only about 1 year, revealing for the first time a latent phenotype in the human brain for which maturation timing is tightly controlled. Video Abstract: © 2012 Elsevier Ltd. All rights reserved.