A Model-Based Approach to Understanding Memory Impairments in Normal Aging

基于模型的方法来了解正常衰老过程中的记忆损伤

• 批准号: 9127066
• 负责人: Michael Jacob Kahana
• 金额: $ 20万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9127066
• 关键词: Accounting; Adopted; Affect; Age; Aging; Aging-Related Process; Attention; Behavior; Cognition; Cognitive; Cognitive Science; Cognitive aging; Computer Simulation; Cues; Data; Development; Elderly; Ensure; Episodic memory; Event; Exhibits; Foundations; Health; High Performance Computing; Human; Impairment; Individual; Intervention; Laboratories; Laboratory Study; Lead; Learning; Left; Life; Literature; Longevity; Measures; Memory; Memory Loss; Memory impairment; Modeling; Neurocognitive; Pattern; Performance; Play; Process; Research; Resolution; Retrieval; Role; Scheme; Science; Subgroup; System; Testing; Work; abstracting; age difference; age related; base; cognitive process; experience; innovation; memory encoding; memory process; normal aging; novel strategies; operation; prevent; remediation; research study; success; theories; young adult

DESCRIPTION (provided by applicant): One of the most damaging effects of normal aging is an increasing difficulty in recalling recent events especially when those events are subject to interference from related memories learned in different contexts. Although this difficulty in forming and recalling episodic memories is a hallmark of the aging process, the mechanisms that underlie it remain largely unknown. We adopt a computational modeling approach to help elucidate these mechanisms in three major laboratory memory tasks and in more naturalistic "real world" tasks. The key innovation of this application is in its proposal to fit high-resolutio individual subject data obtained in multisession experiments and to examine age-differences in terms of the distributions of best fitting parameters, which will allow us to detect subgroups of older adults that show different profiles of impairment. Given data of sufficiently high resolution this novel approach to aging research has become feasible thanks to advances in high-performance computing. Computational modeling of age-affected memory systems could lead not only to a better understanding of aging memory, but also provide a theoretical basis on which to build potential schemes for memory remediation among older adults suffering age-related memory impairment.

描述(申请人提供)：正常衰老最具破坏性的影响之一是回忆最近发生的事件越来越困难，特别是当这些事件受到在不同背景下学习的相关记忆的干扰时。尽管这种形成和回忆情节记忆的困难是衰老过程的一个标志，但其背后的机制在很大程度上仍然不清楚。我们采用计算建模的方法来帮助解释三个主要的实验室记忆任务和更自然主义的“真实世界”任务中的这些机制。这一应用程序的关键创新之处在于，它建议对多时段实验中获得的高分辨率个体受试者数据进行拟合，并根据最佳拟合参数的分布来检查年龄差异，这将使我们能够检测出表现不同损害特征的老年人亚组。鉴于足够高分辨率的数据，由于高性能计算的进步，这种新的老化研究方法已经变得可行。对受年龄影响的记忆系统进行计算建模不仅可以更好地理解衰老记忆，而且还可以为在遭受年龄相关性记忆损害的老年人中建立潜在的记忆修复方案提供理论基础。