  Normative modeling is an emerging and promising approach to effectively study
disorder heterogeneity in individual participants. In this study, we propose a
novel normative modeling method by combining conditional variational
autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in
Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the
healthy control (HC) group to create a normative model conditioned on
covariates like age, gender and intracranial volume. Then we incorporate an
adversarial training process to construct a discriminative feature space that
can better generalize to unseen data. Finally, we compute deviations from the
normal criterion at the patient level to determine which brain regions were
associated with AD. Our experiments on OASIS-3 database show that the deviation
maps generated by our model exhibit higher sensitivity to AD compared to other
deep normative models, and are able to better identify differences between the
AD and HC groups.

Machine Learning;Artificial Intelligence;Quantitative Methods