  Normative modelling is an emerging method for understanding the underlying
heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying
how each patient deviates from the expected normative pattern that has been
learned from a healthy control distribution. Since AD is a multifactorial
disease with more than one biological pathways, multimodal magnetic resonance
imaging (MRI) neuroimaging data can provide complementary information about the
disease heterogeneity. However, existing deep learning based normative models
on multimodal MRI data use unimodal autoencoders with a single encoder and
decoder that may fail to capture the relationship between brain measurements
extracted from different MRI modalities. In this work, we propose multi-modal
variational autoencoder (mmVAE) based normative modelling framework that can
capture the joint distribution between different modalities to identify
abnormal brain structural patterns in AD. Our multi-modal framework takes as
input Freesurfer processed brain region volumes from T1-weighted (cortical and
subcortical) and T2-weighed (hippocampal) scans of cognitively normal
participants to learn the morphological characteristics of the healthy brain.
The estimated normative model is then applied on Alzheimer Disease (AD)
patients to quantify the deviation in brain volumes and identify the abnormal
brain structural patterns due to the effect of the different AD stages. Our
experimental results show that modeling joint distribution between the multiple
MRI modalities generates deviation maps that are more sensitive to disease
staging within AD, have a better correlation with patient cognition and result
in higher number of brain regions with statistically significant deviations
compared to a unimodal baseline model with all modalities concatenated as a
single input.
