Normative modeling using multimodal variational autoencoders to identify abnormal brain volume deviations in Alzheimer's disease

Sayantan Kumar; Philip R. O. Payne; Aristeidis Sotiras

doi:10.1117/12.2654369

7 April 2023 Normative modeling using multimodal variational autoencoders to identify abnormal brain volume deviations in Alzheimer's disease

Sayantan Kumar, Philip R. O. Payne, Aristeidis Sotiras

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246503 (2023) https://doi.org/10.1117/12.2654369
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Sayantan Kumar, Philip R. O. Payne, and Aristeidis Sotiras "Normative modeling using multimodal variational autoencoders to identify abnormal brain volume deviations in Alzheimer's disease", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246503 (7 April 2023); https://doi.org/10.1117/12.2654369

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