Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting

TL;DR

This paper introduces CNODE, a novel continuous neural ODE framework that models individual Parkinson's disease progression trajectories from irregular MRI data, outperforming existing methods in forecasting disease evolution.

Contribution

The paper presents a new neural ODE-based approach for personalized PD progression modeling that handles irregular data and captures individual heterogeneity.

Findings

CNODE outperforms state-of-the-art baselines in forecasting PD progression.

The method effectively models irregular and sparse MRI data.

It captures individual differences in disease onset and progression rate.

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.