RETRACTED: No Normal Brain: How Demographic Exclusion Undermines Neuroimaging AI Validity

Neuroimaging AI systems increasingly influence clinical decisions, yet demographic exclusions in training datasets may compromise their scientific validity and clinical safety. This analysis examines how demographic concentration in major neuroimaging repositories undermines the fundamental neurobiological principle that there is no single "normal" brain, potentially creating AI systems that pathologize neurological diversity rather than embrace it as biological reality.

I analyzed five major neuroimaging datasets (HCP, ADNI, UK Biobank, ABCD, OpenNeuro) representing >55,000 participants. For each dataset, I collected participant characteristics across race/ethnicity, sex/gender, age distribution, geographic origin, socioeconomic status, and educational attainment. I analyzed published literature documenting performance disparities in neuroimaging AI systems across demographic groups.

Major neuroimaging datasets demonstrate severe demographic concentration: 52-94% White representation despite 16% global population share, with 85-100% from North America/Europe representing only 15% globally. These exclusions produce predictable AI performance disparities, including 27% lower seizure detection sensitivity in adolescent females and doubled false positive rates in dementia screening for Black participants. Contemporary COVID-19 brain aging research demonstrates bias amplification in demographically restricted training data.

Demographic exclusion in neuroimaging AI represents a fundamental threat to research validity and clinical safety. Diversity, equity, and inclusion initiatives provide essential mechanisms for ensuring scientific rigor by acknowledging neurological diversity as biological reality rather than statistical inconvenience.