Learning Streamline Embeddings with Variational Autoencoder for Intersubject Bundle Comparison in Alzheimer’s Disease

Tractograms generated from diffusion MRI (dMRI) can be used to evaluate white matter abnormalities in Alzheimer's Disease patients (AD). However, the large quantity of streamlines causes problems for downstream analysis. Here we use a variational autoencoder (VAE) with a 1D convolutional layer (ConvVAE) to embed streamlines in a 2D latent space. The generative nature of ConvVAE allows us to apply Euclidean distances directly to embeddings and perform intersubject fiber bundle comparisons.