Uncovering and mitigating bias in large, automated MRI analyses of brain development.

Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk . However, MRI studies of youth are especially susceptible to motion and other artifacts . These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses.