Elections present unique opportunities to study how sociopolitical events influence individual processes. The current study examined 286 young adults' mood and diurnal cortisol responses to the 2016 U.S. presidential election in real-time: two days before the election, election night, and two days after the election of Donald Trump, with the goal of understanding whether (and the extent to which) the election influenced young adults' affective and biological states. Utilizing piecewise trajectory analyses, we observed high, and increasing, negative affect leading up to the election across all participants. Young adults who had negative perceptions of Trump's ability to fulfill the role of president and/or were part of a non-dominant social group (i.e., women, ethnic/racial minority young adults) reported increased signs of stress before the election and on election night. After the election, we observed a general "recovery" in self-reported mood; however, diurnal cortisol indicators suggested that there was an increase in biological stress among some groups. Overall, findings underscore the role of macro-level factors in individuals' health and well-being via more proximal attitudes and physiological functioning.

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We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

We leveraged nationally representative data from the Panel study of Income Dynamics-Child Development Supplement (N = 3,562) and the Early Childhood Longitudinal study (N = 18,174), to chart the development of working memory, indexed via verbal forward and backward digit span task performance, from 3 to 19 years of age. Results revealed nonlinear growth patterns for forward and backward digit span tasks, with the most rapid growth occurring during childhood followed by a brief accelerated period of growth during early adolescence. We also found similar developmental trajectories on digit span task performance for males and females across the U.S. population. Together, this study highlights the relative importance of the childhood period for working memory development and provides researchers with a reference against which to compare the developmental changes of working memory in individual studies. From a practical perspective, clinicians and educators can also use this information to understand important periods of working memory growth using national developmental trends.

Executive functioning (EF) is fundamental to positive development. Yet, little is known about how to best characterize constellations of EF skills that may inform disparate associations between EF and behavior during adolescence. In the current study, cross‐validated latent profile analysis (LPA) was used to derive profiles of EF based on measures of inhibitory control, working memory, and cognitive flexibility using data from 11,672 youth (52.2% male, mean age = 9.91 years) in the Adolescent Brain and Cognitive Development study. Four meaningful EF profiles emerged from the data representing Average EF, High EF, Low Inhibitory Control, and Low EF. Boys, youth from low‐income households, and early developing youth were more likely to be in profiles distinguished by lower EF. Profile membership also predicted differences in externalizing, internalizing, and other problem behaviors assessed one year later. Findings indicate that youth may have distinct constellations of EF skills, underscoring the need for person‐centered approaches that focus on patterns of individual characteristics.

As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.