具有物理信息的神经Koopman机用于可解释的纵向个性化阿尔茨海默病预测
Physics-Informed Neural Koopman Machine for Interpretable Longitudinal Personalized Alzheimer's Disease Forecasting
AI总结：
本文提出神经Koopman机，通过整合多模态数据和生物学知识，实现阿尔茨海默病认知下降的可解释性预测。
摘要：
早期预测个体在阿尔茨海默病中的认知下降对于疾病评估和管理至关重要。尽管有进展，但现有方法框架在整合多模态数据进行纵向个性化预测的同时保持可解释性仍具有挑战性。为解决这一差距，我们提出了神经Koopman机（NKM），这是一种受动态系统和注意力机制启发的新机器学习架构，旨在使用多模态遗传、神经影像、蛋白质组和人口统计数据同时预测多个认知评分。NKM整合分析（α）和生物学（β）知识，以指导特征分组并控制分层注意力机制以提取相关模式。通过在Koopman操作符框架内实现融合组感知分层注意力，NKM将复杂的非线性轨迹转化为可解释的线性表示。为了展示NKM的有效性，我们将其应用于阿尔茨海默病神经影像倡议（ADNI）数据集。我们的结果表明，NKM在预测认知下降轨迹方面始终优于传统机器学习方法和深度学习模型。具体而言，NKM（1）同时预测多个认知评分的变化，（2）量化不同生物标志物对预测不同认知评分的贡献，（3）识别最能预测认知退化的脑区。共同而言，NKM通过一个可解释、明确的系统，利用过去多模态数据，推进了阿尔茨海默病未来认知下降的个性化、可解释预测，并揭示了阿尔茨海默病进展的潜在多模态生物学基础。
英文摘要：
Early forecasting of individual cognitive decline in Alzheimer's disease (AD) is central to disease evaluation and management. Despite advances, it is as of yet challenging for existing methodological frameworks to integrate multimodal data for longitudinal personalized forecasting while maintaining interpretability. To address this gap, we present the Neural Koopman Machine (NKM), a new machine learning architecture inspired by dynamical systems and attention mechanisms, designed to forecast multiple cognitive scores simultaneously using multimodal genetic, neuroimaging, proteomic, and demographic data. NKM integrates analytical ($α$) and biological ($β$) knowledge to guide feature grouping and control the hierarchical attention mechanisms to extract relevant patterns. By implementing Fusion Group-Aware Hierarchical Attention within the Koopman operator framework, NKM transforms complex nonlinear trajectories into interpretable linear representations. To demonstrate NKM's efficacy, we applied it to study the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our results suggest that NKM consistently outperforms both traditional machine learning methods and deep learning models in forecasting trajectories of cognitive decline. Specifically, NKM (1) forecasts changes of multiple cognitive scores simultaneously, (2) quantifies differential biomarker contributions to predicting distinctive cognitive scores, and (3) identifies brain regions most predictive of cognitive deterioration. Together, NKM advances personalized, interpretable forecasting of future cognitive decline in AD using past multimodal data through an explainable, explicit system and reveals potential multimodal biological underpinnings of AD progression.
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