Normative modelling is a popular method for studying brain disorders like Alzheimer's Disease (AD) where the normal brain patterns of cognitively normal subjects are modelled and can be used at subject-level to detect deviations relating to disease pathology. So far, deep learning-based normative frameworks have largely been applied on a single imaging modality. We aim to design a multi-modal normative modelling framework based on multimodal variational autoencoders (mmNormVAE) where disease abnormality is aggregated across multiple neuroimaging modalities (T1-weighted and T2-weighted MRI) and subsequently used to estimate subject-level neuroanatomical deviations due to AD.