Generative Latent Representations of 3D Brain MRI for Multi-Task Downstream Analysis in Down Syndrome

2026-02-14

AI Summary

This study investigates the structure and information content of latent representations in 3D brain MRI generative models, with a focus on their efficacy for clinical discrimination of Down syndrome. Employing various variational autoencoder (VAE) architectures, we compress 3D brain MRIs into compact latent codes that enable high-fidelity reconstruction while supporting downstream multitask analysis. Through principal component analysis visualization and systematic evaluation, we demonstrate that the learned latent space clearly clusters individuals with Down syndrome and neurotypical controls, exhibiting strong discriminative power and interpretability. These findings validate the potential of such latent representations for clinical neuroimaging applications and offer a novel approach to disease representation learning based on generative models.

Technology Category

Machine Learning: Deep Generative Models & AutoencodersComputer Vision: Representation Learning for VisionKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

Search and Retrieval-Augmented AI: Web query analysis, representation and understandingEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Generative models have emerged as powerful tools in medical imaging, enabling tasks such as segmentation, anomaly detection, and high-quality synthetic data generation. These models typically rely on learning meaningful latent representations, which are particularly valuable given the high-dimensional nature of 3D medical images like brain magnetic resonance imaging (MRI) scans. Despite their potential, latent representations remain underexplored in terms of their structure, information content, and applicability to downstream clinical tasks. Investigating these representations is crucial for advancing the use of generative models in neuroimaging research and clinical decision-making. In this work, we develop multiple variational autoencoders (VAEs) to encode 3D brain MRI scans into compact latent space representations for generative and predictive applications. We systematically evaluate the effectiveness of the learned representations through three key analyses: (i) a quantitative and qualitative assessment of MRI reconstruction quality, (ii) a visualisation of the latent space structure using Principal Component Analysis, and (iii) downstream classification tasks on a proprietary dataset of euploid and Down syndrome individuals brain MRI scans. Our results demonstrate that the VAE successfully captures essential brain features while maintaining high reconstruction fidelity. The latent space exhibits clear clustering patterns, particularly in distinguishing individuals with Down syndrome from euploid controls.

Problem

• latent representations
• 3D brain MRI
• Down syndrome
• generative models
• downstream analysis

Innovation

• Generative Latent Representations
• Variational Autoencoders
• 3D Brain MRI
• Down Syndrome Classification
• Latent Space Analysis