Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

2026-04-24

AI Summary

This work addresses the challenge of modeling sparse longitudinal neuroimaging data in neurodegenerative diseases, where capturing continuous individual anatomical changes remains difficult. The authors propose a novel 4D (3D × time) diffusion generative framework that explicitly learns topology-preserving spatiotemporal deformation distributions. By integrating clinical variables such as health status, age, and sex, the model generates anatomically consistent and temporally coherent trajectories of brain structural evolution. Experiments on two large-scale longitudinal datasets demonstrate that the generated trajectories outperform existing methods in anatomical accuracy, temporal consistency, and clinical relevance, while significantly improving downstream performance in disease classification and brain segmentation tasks.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject. This scarcity of temporal data limits our ability to model and accurately capture the continuous anatomical changes related to disease progression in individual subjects. To address this problem, we propose a novel 4D (3DxT) diffusion-based generative framework that effectively models and synthesizes longitudinal brain anatomy over time, conditioned on available clinical variables such as health status, age, sex, and other relevant factors. Moreover, while most current approaches focus on manipulating image intensity or texture, our method explicitly learns the data distribution of topology-preserving spatiotemporal deformations to effectively capture the geometric changes of brain structures over time. This design enables the realistic generation of future anatomical states and the reconstruction of anatomically consistent disease trajectories, providing a more faithful representation of longitudinal brain changes. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github.

Problem

• neurodegenerative disease
• longitudinal neuroimaging
• anatomical change
• temporal sparsity
• brain morphology

Innovation

• 4D diffusion model
• longitudinal neuroimaging
• topology-preserving deformation
• generative modeling
• neurodegenerative disease progression