Normative Modeling Via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunctio...

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Bibliographic Details
Published in:Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 4
Main Authors: Wang, Xuetong, Zhou, Rong, Zhao, Kanhao, Leow, Alex, Zhang, Yu, He, Lifang
Format: Conference Proceeding
Language:English
Published: IEEE 18.04.2023
Subjects:
adversarial learning
Alzheimer's disease
Biological system modeling
Brain modeling
Computational modeling
conditional variational autoencoder
Normative modeling
Sensitivity
Solid modeling
Training
ISSN:1945-8452
Online Access:Get full text
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Summary:Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230377