PROJECT SUMMARY Nearly one-fifth of the Unites States population resides in a rural region, and approximately one-fifth of those residents suffers from a mental illness. While these rates of mental illness are similar to urban areas, individuals living in rural regions face a disproportionate burden of negative psychiatric outcomes. Modern advances in psychiatric research have focused on using machine learning and human neuroimaging to predict diagnoses and treatment outcomes. However, recent evidence suggests that machine learning models themselves may drive health disparities through performance bias. Specifically, clinical decision-making models created in majority populations may not perform as well in populations that were underrepresented during the creation of the model (e.g., poorer likelihood of choosing the correct treatment if patients are rural). Given that virtually all neuroimaging ‘brain-behavior’ predictive models in psychiatry research are generated from data collected in highly populated metropolitan areas, this study will evaluate ‘brain-behavior’ models for performance bias in rural populations. It will also investigate means of eliminating this bias that creates further health disparities in rural populations. In Aim 1, I will use neuroimaging data from 9,811 individuals in the Adolescent Brain and Cognitive Development Study to create a ‘brain-behavior’ predictive model of cognition. In Aim 2, I will evaluate this model for urban-rural performance bias and pursue strategies to reduce model bias. This study will have important implications for understanding how algorithms in healthcare drive health disparities and how we can reduce these disparities by designing models that perform equitably within underrepresented populations.

PROJECT NARRATIVE Rural individuals face a disproportionate burden of negative mental health outcomes. Emerging technology in psychiatry uses machine learning and human neuroimaging data to inform clinical decisions such as treatment choices. This study investigates how this machine learning technology itself may drive mental health disparities by performing more poorly in rural populations and means of reducing disparities by creating models that perform equitably within underrepresented populations.