Characterizing neuroimaging 'brain-behavior' model performance bias in rural populations

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Principal Investigator: Brendan  Adkinson
Organization: YALE UNIVERSITY
Fiscal Year: 2024
Award: $33,892
Funding agency: National Institute on Minority Health and Health Disparities

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.

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