用连续模型预测帕金森病进展，更准且能个性化建模。
Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting
- 用神经微分方程建模脑结构变化的连续过程
- 联合学习个体起始时间和进展速度，对齐轨迹
- 在PPMI数据集上优于现有方法，适合临床预测
帕金森病（PD）表现出异质性、动态演变的脑形态学特征。建模这些纵向轨迹有助于揭示疾病机制、推动治疗研发，并实现个体化‘数字孪生’预测。然而，现有方法多采用循环神经网络和Transformer架构，依赖离散、规则采样的数据，在处理帕金森队列中不规则、稀疏的磁共振成像（MRI）时表现不佳，且难以捕捉疾病起始时间、进展速率和症状严重程度等个体差异，这正是PD的核心特征。为此，我们提出CNODE（Conditional Neural ODE），一种用于连续、个体化帕金森病进展预测的新框架。其核心是使用神经微分方程（Neural ODE）将脑形态变化建模为连续时间过程，并联合学习患者特异性的初始时间与进展速度，以对齐个体轨迹至共享的进展轨迹。我们在帕金森病进展标志物倡议（Parkinson's Progression Markers Initiative, PPMI）数据集上验证了CNODE。实验结果表明，该方法在预测纵向帕金森病进展方面优于当前最先进基线。
原文摘要 · Abstract (English)
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.
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