Tutorial: VAE as an inference paradigm for neuroimaging

TL;DR

This tutorial introduces Variational Autoencoders as a powerful inference framework for neuroimaging data, covering theory, practical challenges, and applications in uncovering brain patterns.

Contribution

It provides a comprehensive overview of VAE theory, addresses practical issues, and demonstrates their application in neuroimaging analysis.

Findings

VAEs enable interpretable latent representations of neuroimaging data

Strategies like reparameterization improve training stability

Applications reveal meaningful brain patterns related to neurodegeneration

Abstract

In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensional datasets such as neuroimaging. By integrating deep learning with Bayesian inference, VAEs enable the generation of interpretable latent representations. This tutorial outlines the theoretical foundations of VAEs, addresses practical challenges such as convergence issues and over-fitting, and discusses strategies like the reparameterization trick and hyperparameter optimization. We also highlight key applications of VAEs in neuroimaging, demonstrating their potential to uncover meaningful patterns, including those associated with neurodegenerative processes, and their broader implications for analyzing complex brain data.