Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease

Sayantan Kumar, Philip Payne, Aristeidis Sotiras
January 2021
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Abstract

Normative modelling is a 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. Existing deep learning based normative models have been applied on only single modality Magnetic Resonance Imaging (MRI) neuroimaging data. However, these do not take into account the complementary information offered by multimodal MRI, which is essential for understanding a multifactorial disease like AD. To address this limitation, we propose a multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify abnormal brain volume deviations due to 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 AD patients to quantify the deviation in brain volumes and identify abnormal brain pattern deviations due to the progressive stages of AD. We compared our proposed mmVAE with a baseline unimodal VAE having a single encoder and decoder and the two modalities concatenated as unimodal input. Our experimental results show that deviation maps generated by mmVAE 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 the unimodal baseline model.

Type
Conference paper
Publication
Medical Imaging 2023: Computer-Aided Diagnosis.
Aristeidis Sotiras
Aristeidis Sotiras
Principal Investigator at CBICA
Associate Professor
Department of Radiology
Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics

I am an Associate Professor of Radiology in the Division of Informatics, Department of Biostatistics, Epidemiology, and Informatics, at the University of Pennsylvania. I am a principal investigator in the Center for Biomedical Image Computing and Analytics (CBICA), where I lead the Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL).