  One of the challenges of studying common neurological disorders is disease
heterogeneity including differences in causes, neuroimaging characteristics,
comorbidities, or genetic variation. Normative modelling has become a popular
method for studying such cohorts where the 'normal' behaviour of a
physiological system is modelled and can be used at subject level to detect
deviations relating to disease pathology. For many heterogeneous diseases, we
expect to observe abnormalities across a range of neuroimaging and biological
variables. However, thus far, normative models have largely been developed for
studying a single imaging modality. We aim to develop a multi-modal normative
modelling framework where abnormality is aggregated across variables of
multiple modalities and is better able to detect deviations than uni-modal
baselines. We propose two multi-modal VAE normative models to detect subject
level deviations across T1 and DTI data. Our proposed models were better able
to detect diseased individuals, capture disease severity, and correlate with
patient cognition than baseline approaches. We also propose a multivariate
latent deviation metric, measuring deviations from the joint latent space,
which outperformed feature-based metrics.
