Within-person dynamics of cognition and personality in healthy aging and Alzheimer's disease.

健康老龄化和阿尔茨海默氏病中认知和人格的内部动态。

• 批准号: 10565861
• 负责人: Andrew ASCHENBRENNER
• 金额: $ 10.7万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10565861
• 关键词: Achievement; Adult; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid; Attention; Bayesian Modeling; Biological; Clinical; Clinical Trials; Clinical assessments; Cognition; Cognition Disorders; Cognitive; Data Set; Dementia; Disease; Disease Progression; Elderly; Encapsulated; Equation; Evaluation; Goals; Impaired cognition; Impairment; Individual; International; Intervention; Intervention Studies; Measurement; Measures; Mentors; Minor; Modeling; Moods; Motivation; Nerve Degeneration; Noise; Outcome; Performance; Personality; Personality Assessment; Persons; Phenotype; Play; Positron-Emission Tomography; Prevention trial; Process; Research; Research Design; Research Technics; Secondary Prevention; Severities; Statistical Models; Stimulus; Techniques; Testing; Time; Training; Validation; Variant; advanced analytics; attentional control; behavior prediction; career development; cognitive change; cognitive function; cognitive performance; cognitive process; cognitive testing; cohort; design; disease phenotype; drug efficacy; experience; healthy aging; improved; in vivo; innovation; insight; intervention effect; longitudinal analysis; longitudinal design; magnetic resonance imaging biomarker; novel; pre-clinical; predictive modeling; psychologic; research clinical testing; risk prediction; tau Proteins; young adult

Project Abstract / Summary Intervention studies of Alzheimer disease (AD) require accurate measurement of cognitive function across many years. Adequate description of function is hampered by the fact that cognition can significantly fluctuate from moment-to-moment and from day-to-day. This additional variance adds measurement noise which impairs sensitivity to detect intervention effects. However, rather than ascribing cognitive variability to measurement error, there is accumulating evidence to suggest that fluctuations in performance are reflective of meaningful biological and psychological processes, including variations in daily mood, motivation and attention. Many of these psychological mechanisms can be captured via standard assessments of personality, which have been shown to be important behavioral predictors of AD risk. The overall goal of this project is to apply intensive longitudinal research techniques to the analysis of cognitive function in order to describe and explain performance variability in healthy aging and in individuals with mild or questionable cognitive impairment. If daily variability in cognition is predictive of later cognitive decline or other clinically meaningful outcomes, it may be useful as an additional or alternative cognitive endpoint in clinical trials. This proposal aims to apply dynamic structural equation models (DSEM) to a three-week intensive longitudinal research design. DSEM models allow for direct and robust statistical evaluation of variability as a sensitive marker of critical late life outcomes including cognitive decline and progression to AD. I will collect daily measures of cognitive and psychological (e.g., personality) function for a three-week period on healthy older adults and those with questionable impairment. DSEM will test the hypothesis that within-person variability in cognition is related to clinical status. Reanalysis of an existing dataset will provide further validation of this approach by relating cognitive variability to disease progression and in vivo AD biomarkers. I will supplement my extensive experience measuring cognition in healthy aging and early stage AD by gaining additional, didactic training in advanced analytical techniques including Bayesian modeling and dynamic structural equation modeling with emphasis on intensive longitudinal research designs. In addition, I will gain experience providing assessments of clinical function and judging the presence / severity of dementia. The mentors selected for this application, Drs. Joshua Jackson and John Morris are internationally recognized experts in the fields of personality assessment and longitudinal modeling in healthy aging, and clinical assessment of AD respectively and are well suited to serve as mentors on this project. Through the training and research plan described in this application, I will produce new cognitive endpoints for AD research. Techniques developed in this proposal can be readily extended to other neurodegenerative or clinical disorders where cognition plays a key role.

项目摘要/摘要 阿尔茨海默病（AD）的干预研究需要准确测量认知功能， 多年对功能的充分描述受到认知可以显著波动这一事实的阻碍 从每时每刻，从每一天。这种额外的方差增加了测量噪声， 损害检测干预效果的灵敏度。然而，与其将认知变异归因于 尽管存在测量误差，但越来越多的证据表明，业绩波动反映了 有意义的生物和心理过程，包括日常情绪，动机和注意力的变化。 这些心理机制中的许多都可以通过标准的人格评估来捕捉， 已被证明是AD风险的重要行为预测因子。该项目的总体目标是应用 深入的纵向研究技术来分析认知功能，以描述和解释 在健康老龄化和轻度或可疑认知障碍的个体中的性能变异性。如果 认知的每日变化可预测后期认知下降或其他有临床意义的结果， 可能在临床试验中用作额外或替代的认知终点。该提案旨在适用于 动态结构方程模型（DSEM），为期三周的密集纵向研究设计。DSEM 模型允许对变异性进行直接和可靠的统计评估，作为关键晚期生命的敏感标志 结果包括认知下降和进展为AD。我会收集每天的认知和 心理的（例如，人格）功能，为期三周的健康老年人和那些 可疑的损害。DSEM将检验以下假设：认知中的人内变异性与以下因素有关： 临床状态对现有数据集的重新分析将通过以下方式进一步验证该方法： 认知变异性对疾病进展和体内AD生物标志物的影响。 我将补充我在健康老龄化和早期AD认知测量方面的丰富经验， 在高级分析技术方面的额外教学培训，包括贝叶斯建模和动态 结构方程模型，重点是密集的纵向研究设计。此外，我将获得 提供临床功能评估和判断痴呆症的存在/严重程度的经验。的 为该应用程序选择的导师，约书亚杰克逊和约翰莫里斯博士是国际公认的 健康老龄化人格评估和纵向建模领域的专家，以及临床 他们分别评估AD，非常适合担任这个项目的导师。 通过本申请中描述的培训和研究计划，我将为 AD研究。在这项建议中开发的技术可以很容易地扩展到其他神经退行性疾病或 认知起关键作用的临床疾病。