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.