Computational modeling of semantic decline in Alzheimer's disease

阿尔茨海默病语义衰退的计算模型

• 批准号: 9164777
• 负责人: Joseph Larry Austerweil
• 金额: $ 20.66万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9164777
• 关键词: Accounting; Affect; Alzheimer' s Disease; American; Area; Behavior; Behavior Therapy; Behavioral; Categories; Clinical; Cognitive; Computer Simulation; Data; Data Set; Dementia; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Early Diagnosis; Early Intervention; Emotional; Family; Free Association; Huntington Disease; Impairment; Individual; Intervention; Knowledge; Lead; Life; Machine Learning; Maps; Measures; Memory; Methods; Modeling; Neurodegenerative Disorders; Neurologic; Patients; Pattern; Performance; Phenotype; Population; Process; Quality of life; Research; Retrieval; Semantic memory; Semantics; Staging; Structure; Techniques; Testing; Time; Walking; Work; behavioral impairment; computerized tools; contagion; disorder risk; improved; learning strategy; memory retrieval; neurocognitive test; novel; outcome forecast; pre-clinical; research study; theories; tool; trait; transmission process

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.

项目摘要 为了成功地互动、沟通和导航世界，人们必须从 语义记忆（对事实和一般知识的记忆）。患有阿尔茨海默病的人 从疾病的早期就很难获得这些知识，并且随着疾病的发展， 疾病传播，这一过程被称为语义衰退。本项目研究了 通过开发和应用新的计算工具，研究阿尔茨海默病患者的语义衰退。 阿尔茨海默病患者语义记忆受损的程度可以通过使用 行为实验阿尔茨海默氏症患者以及那些有患病风险的人显示出一种模式， 在这些任务上的行为与健康个体不同。尽管经过几十年的研究， 行为障碍几乎完全集中在认知机制，可以解释病人的电流 在给定时间点的行为，但没有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无法解释语义记忆和 由于阿尔茨海默氏症，记忆提取过程会随着时间的推移而退化，限制了我们对阿尔茨海默氏症的理解。 疾病的发展，以及阻碍我们的预后能力，早期检测措施， 可能的干预。该项目将测试疾病如何传播的计算模型， 对语义记忆衰退的定量预测。此外，我们将开发一种新的机器， 学习方法，可用于映射个人的语义记忆的结构，创建 个性化行为干预的机会，以改善语义记忆和提高质量 老年痴呆症患者的生命周期。