Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting

2025-11-06

AI Summary

Parkinson’s disease (PD) exhibits highly heterogeneous and irregular longitudinal brain morphological changes, posing challenges for existing RNN- or Transformer-based longitudinal modeling approaches—particularly in handling sparse, irregularly sampled MRI data and capturing inter-individual variability in disease onset timing and progression rates. To address these limitations, we propose CNODE, a continuous-time framework grounded in neural ordinary differential equations (Neural ODEs) to model smooth, interpretable brain structural dynamics. CNODE incorporates a conditional encoding mechanism to accommodate irregular sampling intervals and jointly learns patient-specific disease onset times and progression velocities. By aligning individual trajectories onto a shared pathological progression manifold, it enables personalized “digital twin” forecasting. Evaluated on the PPMI dataset, CNODE achieves statistically significant improvements over state-of-the-art methods, especially in long-term trajectory prediction accuracy.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns. Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized'digital-twin'forecasting. However, existing methods usually adopt recurrent neural networks and transformer architectures, which rely on discrete, regularly sampled data while struggling to handle irregular and sparse magnetic resonance imaging (MRI) in PD cohorts. Moreover, these methods have difficulty capturing individual heterogeneity including variations in disease onset, progression rate, and symptom severity, which is a hallmark of PD. To address these challenges, we propose CNODE (Conditional Neural ODE), a novel framework for continuous, individualized PD progression forecasting. The core of CNODE is to model morphological brain changes as continuous temporal processes using a neural ODE model. In addition, we jointly learn patient-specific initial time and progress speed to align individual trajectories into a shared progression trajectory. We validate CNODE on the Parkinson's Progression Markers Initiative (PPMI) dataset. Experimental results show that our method outperforms state-of-the-art baselines in forecasting longitudinal PD progression.

Problem

• Forecasting Parkinson's disease progression from irregular MRI data
• Capturing individual heterogeneity in disease onset and progression
• Modeling continuous brain changes using neural ODE framework

Innovation

• Continuous modeling with neural ODE framework
• Patient-specific initial time and speed learning
• Aligning individual trajectories into shared progression