Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04789 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917067198824448 |
|---|---|
| author | Wang, Xiaoda Zhao, Yuji Han, Kaiqiao Luo, Xiao van Rooij, Sanne Stevens, Jennifer He, Lifang Zhan, Liang Sun, Yizhou Wang, Wei Yang, Carl |
| author_facet | Wang, Xiaoda Zhao, Yuji Han, Kaiqiao Luo, Xiao van Rooij, Sanne Stevens, Jennifer He, Lifang Zhan, Liang Sun, Yizhou Wang, Wei Yang, Carl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting Wang, Xiaoda Zhao, Yuji Han, Kaiqiao Luo, Xiao van Rooij, Sanne Stevens, Jennifer He, Lifang Zhan, Liang Sun, Yizhou Wang, Wei Yang, Carl Machine Learning 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. |
| title | Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.04789 |