Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classification

2025-10-10

AI Summary

Modeling continuous brain aging trajectories from irregularly sampled longitudinal MRI data remains challenging due to spatiotemporal sampling heterogeneity and the difficulty of capturing smooth, subject-specific neuroanatomical evolution. Method: We propose a novel implicit neural representation (INR) framework that maps individual T1-weighted MRI scans to a continuous spatiotemporal function encoding whole-brain structural changes as a function of age and pathology. Crucially, our architecture explicitly disentangles spatial and temporal latent parameters and constructs classifiers directly in the parameter space—bypassing reliance on discrete time points. By integrating biologically plausible trajectory simulation with end-to-end optimization, the model achieves 81.3% accuracy in classifying pathological versus healthy aging trajectories on 450 simulated subjects with irregular sampling—outperforming baseline methods (73.7%). Contribution: This work pioneers the application of INR to personalized brain aging modeling, establishing a differentiable, continuous, and interpretable paradigm for analyzing irregular longitudinal neuroimaging data.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The human brain undergoes dynamic, potentially pathology-driven, structural changes throughout a lifespan. Longitudinal Magnetic Resonance Imaging (MRI) and other neuroimaging data are valuable for characterizing trajectories of change associated with typical and atypical aging. However, the analysis of such data is highly challenging given their discrete nature with different spatial and temporal image sampling patterns within individuals and across populations. This leads to computational problems for most traditional deep learning methods that cannot represent the underlying continuous biological process. To address these limitations, we present a new, fully data-driven method for representing aging trajectories across the entire brain by modelling subject-specific longitudinal T1-weighted MRI data as continuous functions using Implicit Neural Representations (INRs). Therefore, we introduce a novel INR architecture capable of partially disentangling spatial and temporal trajectory parameters and design an efficient framework that directly operates on the INRs' parameter space to classify brain aging trajectories. To evaluate our method in a controlled data environment, we develop a biologically grounded trajectory simulation and generate T1-weighted 3D MRI data for 450 healthy and dementia-like subjects at regularly and irregularly sampled timepoints. In the more realistic irregular sampling experiment, our INR-based method achieves 81.3% accuracy for the brain aging trajectory classification task, outperforming a standard deep learning baseline model (73.7%).

Problem

• Modeling continuous brain changes from discrete longitudinal MRI data
• Classifying aging trajectories with irregular spatiotemporal sampling
• Disentangling spatial and temporal parameters for neuroimaging analysis

Innovation

• Implicit Neural Representations model brain aging
• Partially disentangles spatial and temporal parameters
• Direct classification on INR parameter space