Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions.

ABSTRACT: Normative models are increasingly used to characterize individual-level brain deviations in neuroimaging studies, but their performance depends heavily on the reference sample used for training or adaptation. In this study, we systematically investigated how sample size and covariate composition of the reference cohort influence model fit, deviation estimates, and clinical readouts in Alzheimer's disease (AD). Using a discovery dataset (OASIS-3, n = 1032), we trained models on healthy control (HC), subsamples ranging from 5 to 600 individuals, while varying age and sex distributions to simulate biases in reference populations. We further assessed the use of adaptive transfer learning by pre-training models on the UK Biobank (n = 42,747) and adapting them to the clinical dataset applying th

SUBMITTER: Elleaume C 

PROVIDER: S-EPMC12407829 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature