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Normative modeling is an emerging and promising approach to effectively studydisorder heterogeneity in individual participants. In this study, we propose anovel normative modeling method by combining conditional variationalautoencoder with adversarial learning (ACVAE) to identify brain dysfunction inAlzheimer's Disease (AD). Specifically, we first train a conditional VAE on thehealthy control (HC) group to create a normative model conditioned oncovariates like age, gender and intracranial volume. Then we incorporate anadversarial training process to construct a discriminative feature space thatcan better generalize to unseen data. Finally, we compute deviations from thenormal criterion at the patient level to determine which brain regions wereassociated with AD. Our experiments on OASIS-3 database show that the deviationmaps generated by our model exhibit higher sensitivity to AD compared to otherdeep normative models, and are able to better identify differences between theAD and HC groups.

Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease