An Interpretable Representation Learning Approach for Diffusion Tensor Imaging

TL;DR

This paper introduces a novel 2D DTI tractography representation processed by a specialized variational autoencoder to produce interpretable embeddings, improving downstream classification and disentanglement over existing methods.

Contribution

It proposes a new 2D representation of DTI data and a Beta-Total Correlation Variational Autoencoder for more interpretable and effective brain connectivity analysis.

Findings

15.74% improvement in sex classification F1 score

Better disentanglement than 3D representations

Enhanced interpretability of DTI embeddings

Abstract

Diffusion Tensor Imaging (DTI) tractography offers detailed insights into the structural connectivity of the brain, but presents challenges in effective representation and interpretation in deep learning models. In this work, we propose a novel 2D representation of DTI tractography that encodes tract-level fractional anisotropy (FA) values into a 9x9 grayscale image. This representation is processed through a Beta-Total Correlation Variational Autoencoder with a Spatial Broadcast Decoder to learn a disentangled and interpretable latent embedding. We evaluate the quality of this embedding using supervised and unsupervised representation learning strategies, including auxiliary classification, triplet loss, and SimCLR-based contrastive learning. Compared to the 1D Group deep neural network (DNN) baselines, our approach improves the F1 score in a downstream sex classification task by 15.74% and shows a better disentanglement than the 3D representation.