Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer's disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. This provides a combination of mechanisms that best explains the observations for each individual from the ADNI dataset. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms.

翻译：神经退行性病变的计算模型旨在模拟阿尔茨海默病等神经退行性疾病期间脑内病理变化的演化模式。以往研究对病理产生与扩散机制做出了特定选择，或假设所有受试者遵循相同的疾病进展轨迹。然而，神经退行性病理的复杂性与异质性表明，多种机制可能通过复杂交互作用产生协同效应，同时各机制在个体间的贡献程度可能存在差异。为此，我们提出了一种耦合机制建模框架，该框架将网络拓扑引导的病理表现与动态建模系统中的病理扩散过程进行非线性组合。通过个体层面拟合模型，我们允许疾病起始点和进展速率在不同受试者间变化，从而解释疾病异质性。我们构建了贝叶斯模型选择框架来评估特征重要性与参数不确定性，该方法能够从ADNI数据集中为每个个体找出最佳解释观测结果的机制组合。基于各受试者机制重要性的分布特征，我们能够识别出共享相似表观机制组合的患者亚群。