  Normative models in neuroimaging learn the brain patterns of healthy
population distribution and estimate how disease subjects like Alzheimer's
Disease (AD) deviate from the norm. Existing variational autoencoder
(VAE)-based normative models using multimodal neuroimaging data aggregate
information from multiple modalities by estimating product or averaging of
unimodal latent posteriors. This can often lead to uninformative joint latent
distributions which affects the estimation of subject-level deviations. In this
work, we addressed the prior limitations by adopting the
Mixture-of-Product-of-Experts (MoPoE) technique which allows better modelling
of the joint latent posterior. Our model labelled subjects as outliers by
calculating deviations from the multimodal latent space. Further, we identified
which latent dimensions and brain regions were associated with abnormal
deviations due to AD pathology.
