Computational modeling of semantic decline in Alzheimer's disease

项目摘要 为了成功地交互、交流和导航世界，人们必须从以下位置检索相关信息 他们的语义记忆(对事实和常识的记忆)。患有阿尔茨海默病的人 从疾病的早期恢复这种知识是困难的，并随着疾病的恶化而逐渐恶化 疾病传播，这一过程被称为语义衰退。这个项目研究了 通过开发和应用新的计算工具，阿尔茨海默病患者的语义衰退。 阿尔茨海默病患者的语义记忆受损程度可以用 行为实验。阿尔茨海默氏症患者以及疾病高危人群表现出一种模式 在这些任务中的行为与健康的人不同。尽管进行了数十年的研究，但对这些问题的解释 行为障碍几乎完全集中在可能解释患者电流的认知机制上 在给定时间点的行为，但不考虑从正常的、症状前行为的转变 完全受损的行为。现有的模型不能解释语义记忆和 由于阿尔茨海默氏症，记忆提取过程随着时间的推移而退化，限制了我们对 疾病的发展，以及阻碍我们的预后能力，早期发现措施，以及 可能的干预措施。这个项目将测试疾病如何传播的计算模型，具体来说 对语义记忆衰退的定量预测。此外，我们还将开发一种新型机器 一种可以用来映射个体语义记忆结构的学习方法，创造 为改善语义记忆和提高质量提供个性化行为干预的机会 阿尔茨海默氏症患者的生活质量。

Project Summary To interact, communicate, and navigate the world successfully, people must retrieve relevant information from their semantic memory (memory for facts and general knowledge). Individuals with Alzheimer's disease have difficulty retrieving such knowledge from early in the course of the disease and progressively gets worse as the disease spreads, a process known as semantic decline. This project examines the mechanisms underlying semantic decline in individuals with Alzheimer's disease by developing and applying novel computational tools. The extent to which semantic memory is impaired in individuals with Alzheimer's disease can be probed using behavioral experiments. Individuals with Alzheimer's as well as those at-risk for the disease display a pattern of behavior on these tasks distinct from healthy individuals. Despite decades of research, explanations of these behavioral impairments focus almost exclusively on cognitive mechanisms that may explain a patient's current behavior at a given time point, but without an account of the transition from normal, pre-symptomatic behavior to fully impaired behavior. Existing models fail to explain the mechanisms by which semantic memory and memory retrieval processes degrade over time due to Alzheimer's, limiting our understanding of the development of the disease, as well as hindering our ability for prognosis, early detection measures, and possible interventions. This project will test computational models of how the disease spreads, making specific quantitative predictions about the decline of semantic memory. Additionally, we will develop a novel machine learning method that can be used to map the structure of an individual's semantic memory, creating opportunities for individualized behavioral interventions to improve semantic memory and improve the quality of life for individuals with Alzheimer's disease.