Christopher Fields

Neuroimaging studies rarely test whether the variance structure is equivalent across population subgroups. Here, in 4,736 participants from the Adolescent Brain Cognitive Development (ABCD) cohort, we examine racialized heteroscedasticity (i.e., differences in variance across racialized groups) in neuroimaging and behavioral data and test how these differences in variance propagate into predictive modeling. Across neuroimaging modalities, behaviors, and predictive frameworks, variance differences exhibited consistent patterns, indicating that variance structure is a stable property across domains within the dataset. Simulation analyses demonstrated that such differences directly induce subgroup disparities in prediction error and reliability, even in the absence of mean differences. Across neuroimaging modalities, multiple measures demonstrated greater variance in Black participants, particularly in functional imaging modalities. Similar variance patterns were observed in behavioral measures, and predictive models exhibited greater residual dispersion and prediction variance in Black participants even when overall performance metrics were comparable. These findings position variance structure, rather than central tendency, as a critical determinant of model performance, generalizability, and reliability across diverse populations.

This chapter explores the integration of sociological concepts and frameworks into animal models of addictive behaviors, aiming to enhance cross-species validity and deepen understanding of addiction’s multifaceted biopsychosocial roots. Through an interdisciplinary approach merging sociology and behavioral neuroscience, the investigation examines how experiences of discrimination and constrained agency may contribute to addiction vulnerability. Specifically, chronic social defeat stress’s impact on drug-seeking behaviors is explored, drawing parallels between rodent models and human experiences of racial battle fatigue resulting from daily racial microaggressions. The chapter also delves into how lack of autonomy, social isolation, and perceived loss of control due to discrimination may precipitate substance use as a coping mechanism. Additionally, the influence of social hierarchies, bonding patterns, and intergenerational epigenetic effects on resilience versus susceptibility to addiction is considered. Current methodologies are critically evaluated, with a proposal to enhance ecological validity in animal experiments to better simulate nuanced sociocultural stressors associated with addiction risk. Emphasizing the urgent need for cross-disciplinary collaborations between rodent researchers and social scientists, the chapter advocates for the incorporation of relevant sociological concepts into behavioral assays. Ultimately, the integration of diverse perspectives is crucial for comprehensively addressing the psychosocial and neurobiological dynamics underlying addiction across mammalian species and for developing more effective and equitable prevention and treatment approaches.

While the world is aware of America's history of enslavement, the ongoing impact of anti-Black racism in the United States remains underemphasized in health intervention modeling. This Perspective argues that algorithmic bias—manifested in the worsened performance of clinical algorithms for Black vs. white patients—is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity. Racial heteroscedasticity refers to the unequal variance in health outcomes and algorithmic predictions across racial groups, driven by differential exposure to racism-related stress. This may be particularly salient for Black Americans, where anti-Black bias has wide-ranging impacts that interact with differing backgrounds of generational trauma, socioeconomic status, and other social factors, promoting unaccounted for sources of variance that are not easily captured with a blanket “race” factor. Not accounting for these factors deteriorates performance for these clinical algorithms for all Black patients. We outline key principles for anti-racist AI governance in healthcare, including: (1) mandating the inclusion of Black researchers and community members in AI development; (2) implementing rigorous audits to assess anti-Black bias; (3) requiring transparency in how algorithms process race-related data; and (4) establishing accountability measures that prioritize equitable outcomes for Black patients. By integrating these principles, AI can be developed to produce more equitable and culturally responsive healthcare interventions. This anti-racist approach challenges policymakers, researchers, clinicians, and AI developers to fundamentally rethink how AI is created, used, and regulated in healthcare, with profound implications for health policy, clinical practice, and patient outcomes across all medical domains.

Objective: To investigate whether perceived racial and ethnic discrimination mediates the relationship between race and impulsivity, as measured by delay discounting, among adolescents aged 10–11 years.Methods: Utilizing data from the Adolescent Brain Cognitive Development (ABCD) study release 5.1, we analyzed a sample of 3,857 participants who completed the delay discounting task and perceived discrimination measures at the year-one follow-up. Delay discounting was assessed through a hypothetical monetary choice task, with the area under the curve (AUC) serving as the primary measure of impulsivity—lower AUC scores indicate higher impulsivity. Perceived discrimination was measured using an adapted Perceived Discrimination Scale. Linear mixed-effects regression models examined the direct effects of race, socioeconomic factors, age, and gender on impulsivity, as well as the mediating effect of perceived discrimination.Results: Black adolescents exhibited significantly lower AUC scores compared to White adolescents (p < 0.05), indicating higher impulsivity. When perceived discrimination was included in the model, the effect of being Black on impulsivity was no longer significant, suggesting mediation. Perceived discrimination was associated with lower AUC scores (p < 0.05), reflecting greater impulsivity. Males demonstrated higher impulsivity than females (p < 0.001), consistent across models.Conclusion: Perceived racial and ethnic discrimination significantly mediates the relationship between being Black and higher impulsivity in early adolescence. These findings underscore the importance of addressing discrimination to reduce racial disparities in impulsivity and associated risk behaviors. Interventions focusing on both systemic factors contributing to discrimination and enhancing individual coping strategies are crucial for promoting equitable developmental outcomes among minority adolescents.

Perceived discrimination is a pervasive social stressor with significant implications for adolescent mental health and neurodevelopment. Using resting-state functional magnetic resonance imaging (rs-fMRI) data from the Adolescent Brain Cognitive Development (ABCD) Study, we investigated the relationship between perceived discrimination and functional connectivity patterns in the adolescent brain. We applied a Network-Based Statistic (NBS) analysis to 268x268 connectome data from 5,084 participants who provided data on perceived discrimination. Perceived discrimination was coded as a binary variable (Yes/No) based on participants' responses to a validated discrimination scale. Our analysis identified two primary subnetworks associated with perceived discrimination: a positive subnetwork, characterized by increased connectivity between the right prefrontal cortex, left insula, and subcortical regions, and a negative subnetwork, marked by disrupted connectivity between the right prefrontal cortex and left temporal regions. The positive subnetwork suggests enhanced cross-hemispheric communication in adolescents reporting discrimination, while the negative subnetwork indicates reduced cross-hemispheric integration in those reporting no discrimination. Additionally, significant alterations in connectivity across large-scale brain networks were observed. Specifically, adolescents reporting discrimination exhibited increased connectivity between visual processing networks and regions involved in attentional control, while showing decreased connectivity in other network interactions. These findings offer novel insights into the neural mechanisms underlying the effects of perceived discrimination on adolescent brain function, particularly in regions related to emotion regulation, social cognition, and stress responses. Our results underscore the importance of considering discriminatory experiences in the context of adolescent neurodevelopment and mental health.

Background: Racialized heteroscedasticity, characterized by unequal variance in neuroimaging data across racial groups, may challenge the assumptions underlying brain-behavior predictive models. These models often assume homoscedasticity, a fundamental assumption in linear clinical models like Connectome-based predictive models (CPM), particularly when applied across diverse populations. This study investigates the presence of heteroscedasticity across racial groups in functional connectivity data from the Adolescent Brain Cognitive Development (ABCD) study and examines its implications for predictive modeling using resting-state fMRI (rs-fMRI) data.Methods: This study analyzed neuroimaging data across six modalities—Diffusion Tensor Imaging (DTI), T1-weighted structural MRI (SMR), resting-state fMRI (rs-fMRI), and task-based fMRI (Monetary Incentive Delay, Stop-Signal Task, and nBack tasks)—from 5,084 participants in the Adolescent Brain Cognitive Development (ABCD) study. Variance differences between Black and White participants were assessed using Levene's test, adjusting for demographic and socioeconomic covariates with False Discovery Rate (FDR) correction. Behavioral variance across 36 cognitive and psychological measures was also evaluated. We predicted CPM employed to predict performance on behavioral tasks, with bootstrapping used to estimate prediction intervals and assess variance in model accuracy across groups.Results: Racial differences in variance were observed across neuroimaging modalities, behavioral measures, and CPM variance interval analyses. After covariate adjustment, 68.9% of DTI features and 6.6% of rs-fMRI connectivity edges showed greater variance in Black participants, while no significant differences remained for SMR. Task-based fMRI and behavioral variance analyses revealed consistently greater variability in Black participants, with 87.8% of behavioral measures showing significant differences. Increased prediction uncertainty was observed for CPM models with Black relative to White participants across 8 out of the 15 measures tested.Conclusion: Our findings underscore the importance of accounting for racialized heteroscedasticity in neuroimaging-based predictive models. Failing to account for differences in variance may exacerbate health disparities and lead to less reliable predictions for Black participants. These findings emphasize the need for diverse sampling and adaptive modeling approaches to ensure equitable predictive performance across populations. Addressing these disparities is essential for the development of more equitable predictive models in neuroscience and psychiatry.

This study examines longitudinal and geographic trends in perceived racial discrimination among U.S. adolescents using data from the Adolescent Brain Cognitive Development (ABCD) Study. A diverse sample of 11,868 children aged 9-10 at baseline from 22 sites across the U.S. was analyzed, assessing perceived discrimination at ages 10-11, 11-12, and 13-14 using items adapted from the Perceived Discrimination Scale. Binomial logistic regression models were used to evaluate longitudinal trends and geographic variation, adjusting for demographic factors such as race/ethnicity, parental education, and income. Results show that perceived racial discrimination increased significantly from ages 10-11 to 13-14, particularly among Black and Asian adolescents. By age 13-14, nearly half of Black adolescents and over a quarter of Asian adolescents reported discrimination. Geographic analysis revealed that Black adolescents in the Western U.S. and predominantly White affluent neighborhoods had the highest odds of perceived discrimination. Higher state-level anti-Black bias was associated with lower discrimination rates among Black adolescents but higher rates for Asian adolescents. These findings highlight the evolving nature of racial discrimination during adolescence and underscore the need for targeted interventions that address racism’s mental health impacts on adolescents, particularly in high-risk geographic and socio-economic contexts.

While the world is aware of America’s history of enslavement, the ongoing impact of anti-Black racism in the United States remains underemphasized in health intervention modeling. In this Perspective, we argue that algorithmic bias—manifested in the worsened performance of clinical algorithms for Black versus white patients—is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity. We assert that current approaches to developing, implementing, and regulating AI in healthcare are insufficient to address the complex ways in which racism influences health outcomes. Accurately capturing these dynamics, and specifically accounting for the pervasive effects of racism on health, is essential for improving racial parity in clinical algorithm performance. Using psychiatric models as a conceptual framework, we outline key principles for anti-racist AI governance in healthcare, including: 1) mandating the inclusion of Black researchers and community members in AI development; 2) implementing rigorous audits to assess anti-Black bias; 3) requiring transparency in how algorithms process race-related data; and 4) establishing accountability measures that prioritize equitable outcomes for Black patients. By integrating these principles into healthcare AI governance, we can develop more equitable and culturally responsive interventions across medical specialties. This anti-racist approach challenges policymakers, researchers, clinicians, and AI developers to fundamentally rethink how AI is created, used, and regulated in healthcare, with profound implications for health policy, clinical practice, and patient outcomes across all medical domains.