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.

项目摘要/摘要 帕金森氏病（PD）是美国成长最快的神经退行性疾病之一 认知能力下降是其最令人衰弱的非运动症状之一。随着疾病的发展，大多数 个人有时会发展痴呆症。但是，认知下降的轨迹在 个人 - 寻求即将下降的危险因素。 2019年，瑞安（Ryan）及其同事提出了 精确老化模型，并建议典型的与年龄相关的认知下降受到三个宽阔的影响 “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）， 和脑血管功能障碍（例如，白质超强度）。过去的研究一直衡量 这些大脑驱动因素孤立，dospite这些因素均属于互连的神经生物学 系统。这是拟议的研究的目的是确定PD认知差异是否得到更好的解释 通过这些神经生物学风险因素的组合，相对于孤立的因素。中心假设是 每种类别的大脑驱动因素（即神经病理学，神经炎症，脑血管功能障碍）将 与认知表现（特别是执行功能和内存）独特地相关，以便在每个添加 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 现有的，充分表征的患有特发性PD的人没有痴呆症（n = 112）来确定 大脑驱动因素与认知性能横截面和纵向之间的关联（在2-处 一年的随访）。为此，将孤立评估与认知的大脑驱动因素关系（使用相关性） 并组合（使用分层线性回归，增加每个大脑驱动程序类别的因素 依次）。总体而言，此方法将重点转向了精确的医学方法，以检查 多个大脑驱动因素可以使个人认知能力下降的个性化风险有更多的了解 与PD。改善对认知风险的评估可以为PD患者和 允许更有针对性的参与者参加实验试验，以减缓即将来临的认知 衰退。拟议的培训计划将为申请人提供以外的其他培训经验 她的博士学位程序。具体的培训目标包括（1）在方法测量中获得专业知识 神经炎症和神经病理学生物标志物及其解释，（2）熟练熟练 磁共振成像（获取，处理和解释）测量白质 超强度（脑血管功能障碍的度量），（3）提高统计能力和 实验性严谨，（4）专业和职业技能发展。拟议的项目和培训目标 将通过强大的研究环境的资源和支持完成，包括产品 指导团队在拟议的研究领域具有特定的专业知识。拟议的研究在一起 当申请人转变为独立调查员的职业时，其他活动将有助于为申请人做好准备。