Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

翻译：阿尔茨海默病（AD）是一种以β-淀粉样蛋白、病理性tau蛋白及神经退化为特征的异质性、多因素神经退行性疾病。晚期阿尔茨海默病缺乏有效治疗方法，亟需早期干预。然而，现有AD亚型识别的统计推断方法忽略了病理领域知识，可能导致结果违背基本神经学原理的病态问题。通过整合系统生物学建模与机器学习，我们提出一种新型病理导向分层网络（PSSN），该网络通过反应-扩散模型融入已建立的AD病理领域知识，考虑主要生物标志物间的非线性相互作用及其在脑结构网络上的扩散过程。基于纵向多模态神经影像数据训练后，该生物学模型可预测捕捉个体进展模式的长程轨迹，填补稀疏影像数据之间的空白。随后构建深度预测神经网络以挖掘时空动态特征，将神经学检测与临床特征相联系，并在个体层面生成亚型分配概率。通过大规模仿真，我们进一步识别出进化疾病图谱以量化亚型转变概率。本分层方法在多种临床评分的簇间异质性与簇内同质性方面均取得优越性能。将本方法应用于老龄化人群富集样本后，我们识别出涵盖AD谱系的六个亚型，每个亚型均呈现与其临床结局一致的独特生物标志物模式。PSSN为症状前诊断提供洞见，并为临床治疗提供实践指导，该方法可进一步推广至其他神经退行性疾病。