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The novel coronavirus (COVID-19) is a highly contagious disease responsible for millions of deaths worldwide. Effective vaccines against COVID-19 are now available, however, an extreme form of vaccine hesitancy known as anti-vax attitudes challenge vaccine acceptance and distribution efforts. To understand these anti-vax attitudes and their associated psychological characteristics, we examined several predictors of vaccine hesitancy for COVID-19 and anti-vax attitudes generally. We surveyed 1004 adults (M = 47.0 years, SD = 17.1 years, range 18–98 years) in September-October 2020 across the United States (51% female, 49% male; 76.5% White, 23.5% non-White), prior to widespread availability of the COVID-19 vaccines. Attitudes toward vaccinations were influenced by a variety of factors, especially political attitudes. We should therefore anticipate and attempt to mitigate these challenges to achieving widespread vaccination to reduce the spread of COVID-19 and other communicable diseases.

The murder of George Floyd and subsequent mass protest movement in the summer of 2020 brought policing, race, and police brutality to the forefront of American political discourse. We examined mean-levels of attitudes about police and race using online surveys administered at five time points from June 2020 to October 2021 ( n ~ 1000 at each wave) to adults living in the United States. There was a small increase in pro-police attitudes over this time ( d = .24), and some evidence that mean-levels of pro-police attitudes increased more for Black participants ( d = .51) than White participants ( d = .20), and more for Democrats ( d = .40) than Republicans ( d = .15). Pro-police attitudes were much lower among Black participants than White participants (mean d = -1.04), and–relative to political independents–lower among Democrats (mean d = -.66) and higher among Republicans (mean d = .72). Pro-police attitudes had large associations with a variety of conservative or right-wing political attitudes (e.g., approval of Donald Trump) and COVID-19 variables (e.g., disapproval of government mandates and restrictions), but were unrelated to psychiatric problems and substance use. These results validate a new measure of police attitudes, provide information on trends in police attitudes over the 15 months following the largest mass protests against police brutality in American history, and begin to establish the nomological network of police attitudes, finding that pro-police attitudes are firmly within the right-wing coalition of American politics.

There was a large spike in gun purchases and gun violence during the first year of the COVID-19 pandemic in the United States. We used an online U.S. national survey ( N = 1036) to examine the characteristics of people who purchased a gun between March 2020 and October 2021 ( n = 103) and compared them to non-gun owners ( n = 763) and people who own a gun but did not purchase a gun during the COVID-19 pandemic ( n = 170). Compared to non-gun owners, pandemic gun buyers were younger and more likely to be male, White race, and to affiliate with the Republican party. Compared to non-gun owners and pre-pandemic gun owners, pandemic gun buyers exhibited extreme elevations on a constellation of political (QAnon beliefs, pro-gun attitudes, Christian Nationalism, approval of former President Donald Trump, anti-vax beliefs, COVID-19 skepticism; mean Cohen’s d = 1.15), behavioral (intimate partner violence, antisocial behavior; mean d = 1.38), mental health (suicidality, depression, anxiety, substance use; mean d = 1.21), and personality (desire for power, belief in a dangerous world, low agreeableness, low conscientiousness; mean d = 0.95) characteristics. In contrast, pre-pandemic gun owners only endorsed more pro-gun attitudes ( d = 0.67), lower approval of President Joe Biden ( d = -0.41) and were more likely to be male and affiliate with the Republican party relative to non-gun owners. Pandemic gun buyers represent an extreme group in terms of political and psychological characteristics including several risk-factors for violence and self-harm.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.

It remains unclear how the seemingly ubiquitous use of the internet impacts user’s offline personal relationships, particularly those that are romantic or sexual. Therefore, we conducted a national online survey to better understand the associations among internet use, sexual behavior, and adjustment called the Sexual Behaviors, Internet Use, and Psychological Adjustment Survey (SIPS). Here, we report patterns of sexual behavior in a sample of adults ( N = 1987; ages 18–70) in the United States to establish its representativeness and consistency with similar recent surveys. We found age- and sex-related trends in oral, vaginal, and anal sex in terms of prevalence, frequency, number of partners, and age of initiation consistent with prior studies. We also detected differences in sexual behaviors based on relationship status and sexual orientation, but small and relatively few significant differences across racial and ethnic groups. The results confirm and expand upon trends identified in prior national surveys of sexual behavior, establishing the representativeness of the SIPS sample for use in future research examining the links among sexual behaviors and romantic relationships, internet use, and adjustment.

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain–behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective—population neuroscience—that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals’ ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9–10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths’ ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15–20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12–13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths’ symptoms in new data across contexts.

Efficiency of evidence accumulation (EEA), an individual’s ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the “task-positive” frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks’ flexible adaptation to cognitive demands is a key neural underpinning of EEA. Analyses of fMRI data across two large samples show that people’s cognitive efficiency, as measured by a computational model, is strongly related to the degree to which brain networks involved in cognitive control adapt to task difficulty.

Previous studies have shown that children of alcohol use disorder (AUD) parents are more likely to develop alcohol problems as well as antisocial and other behavior problems. The purpose of this study was to examine gender discordance in the effect of early maternal and paternal influences on antisocial behaviors of boys and girls, as well as the environmental factors that moderate the parental effects. Specifically, we examined the effects of childhood and adulthood antisocial behavior of the parents on offspring antisocial behavior as young adults. We also examined whether mothers' and fathers' drinking problems when offspring were young children (6-8 years) affected offspring antisocial behavior as young adults (18-21 years). We evaluated 655 children from 339 families in the Michigan Longitudinal Study (MLS), a prospective study of AUD and non-AUD families. Path models were constructed in order to test for the parental contributions to offspring outcomes. We found that both mothers' and fathers' antisocial behavior contributed to the children's young adult antisocial behavior. Only mothers' drinking problems while their children were little had a significant effect on their sons' later drinking, but not on their daughters'. These different parental effects suggest that maternal and paternal influences may be mediated by different mechanisms.

Having a family history of alcohol use problems (FH+) conveys risk for alcohol use in offspring. Reward-related brain functioning may play a role in this vulnerability. The present study investigated brain function in the nucleus accumbens (NAcc) associated with the anticipation of reward in youth with two biological parents with alcohol use problems (FH+2), one biological parent with alcohol use problems (FH+1), and no biological parents with alcohol use problems (FH-). Participants were from the large, national Adolescent Brain Cognitive Development (ABCD) Study (mean age: 9.93; 48% female; FH+2 n = 223, FH+1 n = 1447, FH- n = 9690) and the Michigan Longitudinal Study (MLS), consisting of community-recruited families with high rates of alcohol use disorder (mean age: 10.54; 39.3% female; FH+2 n = 40, FH+1 n = 51, FH- n = 40). Reward anticipation was measured by the monetary incentive delay task. Regression models were used to assess associations between FH status and the anticipation of large rewards in right and left NAcc regions of interest. In both studies, FH+2 youth showed blunted anticipatory reward responding in the right NAcc compared to FH+1 youth. In the MLS, FH+2 youth also had blunted anticipatory reward responding in the right NAcc compared to the FH- group. Convergent results across two separate samples provide insights into a unique vulnerability of FH+2 youth and suggest that binary FH+ versus FH- categorizations may obscure important differences within FH+ youth.