Brain Drivers, Cognition, and Parkinson's Disease: A Psychometric Approach

大脑驱动因素、认知和帕金森病：心理测量方法

• 批准号: 10604827
• 负责人: Lauren Kenney
• 金额: $ 4.19万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-16 至 2025-05-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604827
• 关键词: Accounting; Address; Affect; Age; Age-associated memory impairment; Aging; Amyloid beta-Protein; Area; Astrocytes; Biological Markers; Brain; C-reactive protein; Categories; Cerebrovascular Disorders; Cluster Analysis; Cognition; Cognitive; Competence; Data; Dementia; Disease Progression; Doctor of Philosophy; Education; Environment; Equation; Evaluation; Executive Dysfunction; Functional disorder; Future; Gender; Goals; Grant; Homocysteine; Idiopathic Parkinson Disease; Impaired cognition; Individual; Interleukin-6; Linear Regressions; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Memory; Memory Loss; Mentors; Methodology; Methods; Modeling; Motor; Movement Disorders; Neurobiology; Neurodegenerative Disorders; Parkinson Disease; Participant; Performance; Persons; Phenotype; Plasma; Predictive Factor; Predictive Value; Price; Productivity; Professional Competence; Psychometrics; Research; Research Personnel; Resources; Risk; Risk Factors; Sampling; Subgroup; System; TNF gene; Training; United States; United States National Institutes of Health; Variant; Visit; White Matter Hyperintensity; alpha synuclein; career; cerebrovascular; clinical prognosis; cognitive change; cognitive performance; cohort; cytokine; executive function; experience; falls; follow-up; hypoperfusion; improved; motor symptom; neuroinflammation; neuropathology; non-motor symptom; patient prognosis; precision medicine; programs; risk prediction; sex; skill acquisition; stroke risk; tau Proteins

PROJECT SUMMARY/ABSTRACT Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders in the United States, with cognitive decline being among its most debilitating non-motor symptoms. With disease progression, most individuals eventually develop dementia. However, the trajectory of cognitive decline varies between individuals—leading to a search for risk factors of impending decline. In 2019, Ryan and colleagues proposed a precision aging model and suggested that typical age-related cognitive decline was influenced by three broad categories of “brain drivers”: neuropathology (e.g., alpha-synuclein, tau), neuroinflammation (e.g., cytokines), and cerebrovascular dysfunction (e.g., white matter hyperintensities). Past research has consistently measured these brain driver factors in isolation, despite these factors all belonging to an interconnected, neurobiological system. Thus, the goal of the proposed study is to determine whether cognitive variation in PD is better explained by a combination of these neurobiological risk factors, relative to isolated factors. The central hypothesis is that each category of brain drivers (i.e., neuropathology, neuroinflammation, cerebrovascular dysfunction) will uniquely relate to cognitive performance (specifically executive function and memory), such that adding in each category will better explain changes in each cognitive domain. The proposed study will examine data from an existing, well-characterized cohort of individuals with idiopathic PD without dementia (N=112) to determine the association between brain driver factors and cognitive performance cross-sectionally and longitudinally (at a 2- year follow-up). To do so, brain driver relationships with cognition will be assessed in isolation (using correlations) and in combination (using hierarchical linear regressions, adding in factors from each brain driver category sequentially). Overall, this method shifts the focus towards a precision medicine approach—whereby examining multiple brain drivers may allow for greater understanding of individualized risk of cognitive decline in individuals with PD. Improving the assessment of cognitive risk could inform both clinical prognosis for patients with PD and allow for a more targeted selection of participants into experimental trials aiming to slow impending cognitive decline. The proposed training plan will provide the applicant with additional training experiences beyond that of her Ph.D. program. Specific training goals include (1) gaining expertise in the methodologies measuring neuroinflammatory and neuropathology biomarkers and their interpretation, (2) gaining proficiency with structural magnetic resonance imaging (acquisition, processing, and interpretation) to measure white matter hyperintensities (a metric of cerebrovascular dysfunction), (3) advancing statistical competencies and experimental rigor, and (4) professional and career skills development. The proposed project and training goals will be completed with the resources and support of a strong research environment, including a productive mentoring team with specific expertise in the proposed area of study. Taken together, the proposed research and other activities will help prepare the applicant as she transitions into a career as an independent investigator.

项目概要/摘要 帕金森病 (PD) 是美国增长最快的神经退行性疾病之一， 随着疾病的进展，认知能力下降是最令人衰弱的非运动症状之一。 每个人最终都会患上痴呆症，但是认知能力下降的轨迹因人而异。 2019 年，Ryan 同事提出了一项研究，旨在寻找即将到来的衰退的风险因素。 精确衰老模型表明，典型的与年龄相关的认知能力下降受到三大因素的影响 “大脑驱动因素”的类别：神经病理学（例如，α-突触核蛋白、tau）、神经炎症（例如，细胞因子）、 过去的研究一直在测量 尽管这些因素都属于相互关联的神经生物学因素，但这些大脑驱动因素是孤立的 因此，本研究的目的是确定帕金森病的认知变异是否可以得到更好的解释。 相对于孤立的因素，这些神经生物学危险因素的组合。 每类大脑驱动因素（即神经病理学、神经炎症、脑血管功能障碍）都会 与认知表现（特别是执行功能和记忆）独特相关，因此将每个 类别将更好地解释每个认知领域的变化。拟议的研究将检查来自某个认知领域的数据。 现有的、特征明确的无痴呆特发性帕金森病患者队列 (N=112) 以确定 大脑驱动因素与认知表现之间的横向和纵向关联（2- 为此，将单独评估大脑驱动因素与认知的关系（使用相关性）。 并结合起来（使用分层线性回归，添加每个大脑驱动类别的因素 总体而言，该方法将重点转向精准医学方法，从而进行检查。 多个大脑驱动因素可能有助于更好地了解个体认知能力下降的个体化风险 改善对帕金森病认知风险的评估可以为帕金森病患者的临床预后提供信息。 允许更有针对性地选择参与者进行实验试验，旨在减缓即将发生的认知 拟议的培训计划将为申请人提供超出现有培训经验的额外培训经验。 她的博士课程的具体培训目标包括（1）获得测量方法方面的专业知识。 神经炎症和神经病理学生物标志物及其解释，(2) 熟练掌握结构 磁共振成像（采集、处理和解释）测量白质 高信号（脑血管功能障碍的衡量标准），（3）提高统计能力和 实验培训；(4) 专业和职业技能发展。 将在强大的研究环境的资源和支持下完成，包括富有成效的研究环境 在拟议研究领域具有特定专业知识的指导团队综合起来，提出了拟议的研究。 以及其他活动将帮助申请人为过渡到独立调查员的职业生涯做好准备。