arXiv 2511.201542025-11-25cs.CV

基于不规则采样纵向数据流形映射的阿尔茨海默病进展预测

Alzheimers Disease Progression Prediction Based on Manifold Mapping of Irregularly Sampled Longitudinal Data

College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Key Laboratory of Computer Vision and Machine Learning in Fujian Province（福建省计算机视觉与机器学习重点实验室）；College of Information Science and Engineering,Ritsumeikan University（立命馆大学信息科学与工程学院）

Xin Hong, Ying Shi, Yinhao Li, Yen-Wei Chen

备注：: 10 pages, 3 figures

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AI总结：

本文提出基于流形映射和时间感知神经微分方程的框架，用于预测阿尔茨海默病的进展，通过保留数据内在几何结构提升预测准确性。

摘要：

临床检查的不确定性经常导致纵向影像数据出现不规则的观测间隔，这对建模疾病进展提出了挑战。大多数现有的基于影像的疾病预测模型在欧几里得空间中运行，假设数据呈平坦表示，无法充分捕捉不规则采样纵向影像的内在连续性和非线性几何结构。为了解决从不规则采样纵向结构性磁共振成像（sMRI）数据建模阿尔茨海默病（AD）进展的挑战，我们提出了一个黎曼流形映射，一个时间感知的流形神经微分方程，以及一个基于注意力的黎曼门控递归单元（R-TNAG）框架。我们的方法首先将从高维sMRI中提取的特征投影到流形空间，以保留疾病进展的内在几何结构。在此表示上，一个时间感知的神经微分方程（TNODE）建模了观测之间的潜在状态的连续演变，而一个基于注意力的黎曼门控递归单元（ARGRU）则自适应地整合历史和当前信息以处理不规则间隔。这种联合设计提高了时间一致性，并在不规则采样下实现了稳健的AD轨迹预测。实验结果表明，所提出的方法在疾病状态预测和认知分数回归方面均优于最先进的模型。消融研究验证了每个模块的贡献，突显了它们在提高预测准确性中的互补作用。此外，该模型在不同序列长度和缺失数据率下表现出稳定的性能，表明了强大的时间泛化能力。跨数据集验证进一步证实了其鲁棒性和在不同临床环境中的适用性。

英文摘要：

The uncertainty of clinical examinations frequently leads to irregular observation intervals in longitudinal imaging data, posing challenges for modeling disease progression.Most existing imaging-based disease prediction models operate in Euclidean space, which assumes a flat representation of data and fails to fully capture the intrinsic continuity and nonlinear geometric structure of irregularly sampled longitudinal images. To address the challenge of modeling Alzheimers disease (AD) progression from irregularly sampled longitudinal structural Magnetic Resonance Imaging (sMRI) data, we propose a Riemannian manifold mapping, a Time-aware manifold Neural ordinary differential equation, and an Attention-based riemannian Gated recurrent unit (R-TNAG) framework. Our approach first projects features extracted from high-dimensional sMRI into a manifold space to preserve the intrinsic geometry of disease progression. On this representation, a time-aware Neural Ordinary Differential Equation (TNODE) models the continuous evolution of latent states between observations, while an Attention-based Riemannian Gated Recurrent Unit (ARGRU) adaptively integrates historical and current information to handle irregular intervals. This joint design improves temporal consistency and yields robust AD trajectory prediction under irregular sampling.Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art models in both disease status prediction and cognitive score regression. Ablation studies verify the contributions of each module, highlighting their complementary roles in enhancing predictive accuracy. Moreover, the model exhibits stable performance across varying sequence lengths and missing data rates, indicating strong temporal generalizability. Cross-dataset validation further confirms its robustness and applicability in diverse clinical settings.

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