NormVAE: Normative Modeling on Neuroimaging Data using Variational Autoencoders

• URL: http://arxiv.org/abs/2110.04903v1
• Date: Sun, 10 Oct 2021 20:55:34 GMT
• Title: NormVAE: Normative Modeling on Neuroimaging Data using Variational Autoencoders
• Authors: Sayantan Kumar and Aristeidis Sotiras
• Abstract summary: Deep autoencoders have been implemented as normative models, where patient-level deviations are modelled as the squared difference between the actual and reconstructed input without any uncertainty estimates in the deviations. In this study, we assessed a novel normative modeling based variational autoencoder (VAE) which calculates subject-level normative abnormality maps (NAM) for quantifying uncertainty in the deviations.
• Score: 0.776692116806953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Normative modeling is an emerging method for understanding the heterogeneous biology underlying neuropsychiatric and neurodegenerative disorders at the level of the individual participant. Deep autoencoders have been implemented as normative models, where patient-level deviations are modelled as the squared difference between the actual and reconstructed input without any uncertainty estimates in the deviations. In this study, we assessed NormVAE, a novel normative modeling based variational autoencoder (VAE) which calculates subject-level normative abnormality maps (NAM) for quantifying uncertainty in the deviations. Our experiments on brain neuroimaging data of Alzheimer's Disease (AD) patients demonstrated that the NormVAE-generated patient-level abnormality maps exhibit increased sensitivity to disease staging compared to a baseline VAE, which generates deterministic subject-level deviations without any uncertainty estimates.

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