Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis
This work addresses the need for transparent MRI-based biomarkers for cognitive decline in neurological diseases like multiple sclerosis, though it is incremental as it extends an existing method.
The authors tackled the problem of interpretable prediction from 3D brain MRI in multiple sclerosis by developing InfoVAE-Med3D, which outperformed other VAE variants in reconstruction and prediction tasks, supporting accurate brain-age and SDMT regression.
We present InfoVAE-Med3D, a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline. Standard statistical models and shallow machine learning often lack power, while most deep learning methods behave as black boxes. Our method extends InfoVAE to explicitly maximize mutual information between images and latent variables, producing compact, structured embeddings that retain clinically meaningful content. We evaluate on two cohorts: a large healthy-control dataset (n=6527) with chronological age, and a clinical multiple sclerosis dataset from Charles University in Prague (n=904) with age and Symbol Digit Modalities Test (SDMT) scores. The learned latents support accurate brain-age and SDMT regression, preserve key medical attributes, and form intuitive clusters that aid interpretation. Across reconstruction and downstream prediction tasks, InfoVAE-Med3D consistently outperforms other VAE variants, indicating stronger information capture in the embedding space. By uniting predictive performance with interpretability, InfoVAE-Med3D offers a practical path toward MRI-based biomarkers and more transparent analysis of cognitive deterioration in neurological disease.