mmNormVAE: Normative Modeling on Multimodal Neuroimaging Data Using Variational Autoencoders

Abstract

Normative modelling is a popular method for studying brain disorders like Alzheimer's Disease (AD) where the normal brain patterns of cognitively normal subjects are modelled and can be used at subject-level to detect deviations relating to disease pathology. So far, deep learning-based normative frameworks have largely been applied on a single imaging modality. We aim to design a multi-modal normative modelling framework based on multimodal variational autoencoders (mmNormVAE) where disease abnormality is aggregated across multiple neuroimaging modalities (T1-weighted and T2-weighted MRI) and subsequently used to estimate subject-level neuroanatomical deviations due to AD.

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Cite

Text

Kumar et al. "mmNormVAE: Normative Modeling on Multimodal Neuroimaging Data Using Variational Autoencoders." NeurIPS 2023 Workshops: DGM4H, 2023.

Markdown

[Kumar et al. "mmNormVAE: Normative Modeling on Multimodal Neuroimaging Data Using Variational Autoencoders." NeurIPS 2023 Workshops: DGM4H, 2023.](https://mlanthology.org/neuripsw/2023/kumar2023neuripsw-mmnormvae/)

BibTeX

@inproceedings{kumar2023neuripsw-mmnormvae,
  title     = {{mmNormVAE: Normative Modeling on Multimodal Neuroimaging Data Using Variational Autoencoders}},
  author    = {Kumar, Sayantan and Payne, Philip and Sotiras, Aristeidis},
  booktitle = {NeurIPS 2023 Workshops: DGM4H},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/kumar2023neuripsw-mmnormvae/}
}