论文标题
建模阿尔茨海默氏病和后皮质萎缩的神经解剖学进展
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy
论文作者
论文摘要
为了找到有关阿尔茨海默氏病（AD）的有效治疗方法，我们需要尽早确定有AD风险的受试者。为此，最近开发的疾病进展模型可用于进行早期诊断，并预测受试者的疾病阶段和未来的进化。但是，这些模型尚未应用于罕见的神经退行性疾病，不适合了解生物标志物的复杂动力学，仅在大型多模式数据集上工作，并且其预测性能尚未得到客观验证。在这项工作中，我开发了疾病进展的新型模型，并将其应用于估计阿尔茨海默氏病和后皮质萎缩的进展，这是一种罕见的神经退行性综合征，导致视觉缺陷。我的第一个贡献是使用已经开发的模型：基于事件的模型（EBM）和微分方程模型（DEM）的模型，研究后皮质萎缩的进展。我的第二个贡献是Dive的发展，潜水是一种新型的疾病进展时空模型，该模型估计了病理学的细粒空间模式，可能使我们能够理解与沿着脑网络沿着脑网络的病理传播有关的复杂疾病机制。我的第三个贡献是疾病知识转移（DKT）的发展，这是一个新型的疾病进展模型，该模型通过从典型的典型神经退行性疾病的较大的多模式数据集中传输信息，从而估算了从有限的，单峰数据集中从有限的，单峰数据集中从稀有神经退行性疾病进行的多模式发展。我的第四个贡献是开发EBM和DEM的新型扩展，以及对此类模型绩效评估的新型措施的发展。我最后的贡献是the the挑战的组织，该竞赛旨在确定最能预测AD演变的算法和功能。
In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict the subjects' disease stages and future evolution. However, these models have not yet been applied to rare neurodegenerative diseases, are not suitable to understand the complex dynamics of biomarkers, work only on large multimodal datasets, and their predictive performance has not been objectively validated. In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer's disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing visual deficits. My first contribution is a study on the progression of Posterior Cortical Atrophy, using models already developed: the Event-based Model (EBM) and the Differential Equation Model (DEM). My second contribution is the development of DIVE, a novel spatio-temporal model of disease progression that estimates fine-grained spatial patterns of pathology, potentially enabling us to understand complex disease mechanisms relating to pathology propagation along brain networks. My third contribution is the development of Disease Knowledge Transfer (DKT), a novel disease progression model that estimates the multimodal progression of rare neurodegenerative diseases from limited, unimodal datasets, by transferring information from larger, multimodal datasets of typical neurodegenerative diseases. My fourth contribution is the development of novel extensions for the EBM and the DEM, and the development of novel measures for performance evaluation of such models. My last contribution is the organization of the TADPOLE challenge, a competition which aims to identify algorithms and features that best predict the evolution of AD.
