Empirical validation of race-neutral normative brain morphometry models across ethnoracially diverse populations

AI Summary

Pretrained CentileBrain race-neutral, sex-specific models generalise across diverse ethnoracial samples with high accuracy: MAE <10% for volumes and areas, <5% for thickness.
Model performance was highly concordant across self-identified and genetically defined ethnoracial groups, demonstrating classification-independent accuracy.
CentileBrain models perform comparably to a population-specific Chinese model in ancestry-matched samples, supporting broad utility for individualised neuroimaging assessment.

Proc Natl Acad Sci U S A. 2026 May 19;123(20):e2521055123. doi: 10.1073/pnas.2521055123. Epub 2026 May 12.

ABSTRACT

Normative models of brain morphometry quantify individual deviations from typical anatomical patterns and hold promise for enhancing clinical decision-making. However, their clinical utility depends critically on demonstrating generalizability across diverse ethnoracial populations. We previously developed sex-specific, race-neutral normative models for cortical thickness, surface area, and subcortical volumes using brain scans from a large international sample of healthy individuals, as part of the CentileBrain Project, a global initiative to provide open-access, neuroimaging reference models. The primary aim of the present study was to empirically evaluate the generalizability and accuracy of these pretrained models across multiple ethnoracial groups. To this end, we tested model performance in independent samples of healthy individuals from Africa, Asia, Europe, and the Americas, with ethnoracial classification defined either by self-identification or genetic ancestry (N = 4,862). We further compared performance against normative models developed exclusively from a single-population Chinese cohort. Across all groups, as well as in the pooled sample, the pretrained CentileBrain models demonstrated consistently high accuracy, with relative mean absolute error values below 10% for subcortical volume and surface area and below 5% for cortical thickness. Model performance was highly concordant across self-identified and ancestry-defined groups. In a separate analysis, the CentileBrain models performed comparably to a population-specific model when applied to an independent ancestry-matched sample. These findings provide empirical support for the generalizability of race-neutral normative models developed on large and diverse samples and underscore their potential utility for individualized neuroimaging assessment across ethnoracially diverse populations.

PMID:42118844 | DOI:10.1073/pnas.2521055123

Document this CPD