Novel metrics of brain dynamics in Alzheimer’s disease

项目摘要/摘要 阿尔茨海默病(AD)是一种进行性神经系统疾病，对认知和日常生活产生不利影响 在老年人中发挥作用。功能磁共振成像是一种非侵入性的广泛使用的神经成像工具，可以 用于识别AD的早期表现，然后可以作为补充生物标记物来丰富 对那些最有可能下降的患者进行的临床试验。最近的研究表明，核心大脑 阿尔茨海默病的异常可能不是由结构或功能连接引起的，而是大脑的时间安排 活动。大脑计时异常是认知灵活性下降的一种直观机制，可能是 轻度认知障碍(MCI)和AD的生物标志物。这项研究的目的是调查如何改变 在时程上，功能连接性与MCI和AD相关。具体目标是：1) 描述认知完整个体和有认知障碍的个体的功能连通性的时间特征 认知障碍(MCI和AD)；2)检查持续的和基于滞后的连接在 有认知障碍的个体，并确定哪些生物和认知因素可以预测变化；以及3) 评估持续连接和基于滞后的连接作为认知生物标志物的预测性能 减损。这项建议旨在促进杰斯·金博士在收购 必要的培训、实践经验和知识，才能成为脑相关领域的首席研究人员 与MCI和AD相关的功能障碍。作为一名独立研究人员，金博士将获得研究成果 以下方面的技能：1)阿尔茨海默病的神经心理、神经生物学和临床表现；2) 纵向研究设计和分析；以及3)涉及机器学习的高级统计方法 强调如何使用这些工具进行行为表型分析。这项研究将进行 在犹他大学，并得到具有协作经验的多学科指导团队的支持，但 独特的专业知识。达夫博士(首席导师)是MCI和AD神经心理学评估方面的专家。 福斯特博士(共同导师)是诊断和治疗阿尔茨海默病的专家。美岛博士(共同导师)是 他因发现阿尔茨海默病早期征兆和传播诊断统计图谱而闻名国际 技术菲利普斯博士(共同导师)在机器学习和高级统计学方面拥有专业知识。齐林斯基医生的 (共同导师)具有多模式磁共振分析和纵向研究设计方面的专业知识。称赞这支球队是 一支专注于AD诊断和治疗、高级统计建模、 和先进的功能磁共振数据分析。成功完成此奖项将为申请者提供 进行R01应用研究纵向脑功能所需的背景和培训 人口老龄化。拟议的研究对该领域做出了重大贡献，因为它有可能 对AD背后的认知神经机制提供新的见解，这将有助于实施 有针对性的干预和预防努力。

PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) is a progressive neurological disorder that adversely affects cognition and daily functioning in older adults. Functional MRI is a non-invasive and widely available neuroimaging tool that can be used to identify early manifestations of AD, which can then be used as a complimentary biomarker to enrich clinical trials with those patients most likely to decline. Recent investigations have suggested that the core brain abnormalities in AD may not arise from structural or even functional connectivity, but rather the timing of brain activity. Abnormal brain timing is an intuitive mechanism for decreased cognitive flexibility, and may serve as a biomarker for mild cognitive impairment (MCI) and AD. The purpose of this study is to investigate how changes in the temporal duration of functional connectivity are related to MCI and AD. The specific aims are: 1) Characterize the temporal aspects of functional connectivity in cognitively intact individuals and those with cognitive impairment (MCI and AD); 2) Examine the evolution of sustained and lag-based connectivity in individuals with cognitive impairment and establish which biological and cognitive factors predict change; and 3) Evaluate the predictive performance of sustained and lag-based connectivity as a biomarker for cognitive impairment. This proposal is designed to facilitate the career development of Dr. Jace King in acquiring the necessary training, practical experience, and knowledge to become a lead investigator in brain related dysfunction associated with MCI and AD. To excel as an independent researcher, Dr. King will obtain research skills in the following areas: 1) neuropsychological, neurobiological and clinical manifestations of AD; 2) longitudinal study design and analysis; and 3) advanced statistical methods involved in machine learning with an emphasis on how these tools can be used to conduct behavioral phenotyping. This research will be conducted at the University of Utah and supported by a multidisciplinary mentorship team with collaborative experience but distinct expertise. Dr. Duff (primary mentor) is an expert in the neuropsychological assessment of MCI and AD. Dr. Foster (co-mentor) is an expert in the diagnosis and treatment of AD. Dr. Minoshima (co-mentor) is internationally known for his discovery of early signs of AD and dissemination of diagnostic statistical mapping technology. Dr. Phillips (co-mentor) has expertise in machine learning and advanced statistics. Dr. Zielinski’s (co-mentor) expertise are in multimodal MRI analysis and longitudinal study design. Complimenting this team is a dedicated team of consultants with expertise in AD diagnosis and treatment, advanced statistical modeling, and advanced fMRI data analysis. Successful completion of this award will provide the applicant with the background and training necessary to pursue an R01 application to investigate longitudinal brain function in aging populations. The proposed research makes significant contributions to the field as it has the potential to provide novel insights in the cognitive neural mechanisms underlying AD, which will aid in the implementation of targeted intervention and prevention efforts.