Intrinsic Cortical Networks and Cognitive Dysfunction in Parkinson???s Disease

帕金森病的内在皮质网络和认知功能障碍

• 批准号: 8635587
• 负责人: BENZI M KLUGER
• 金额: $ 18.64万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-23 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8635587
• 关键词: Adult; Affect; Alzheimer' s Disease; Area; Biological Markers; Brain; Caregivers; Cephalic; Clinical Data; Clinical Trials; Cognition; Cognitive; Data; Dementia; Development; Disease; Double-Blind Method; Elderly; Foundations; Frequencies; Functional disorder; Funding; Future; Goals; Graph; Impaired cognition; Intervention; K-Series Research Career Programs; Lead; Link; Machine Learning; Magnetism; Magnetoencephalography; Manuscripts; Measures; Medical; Memory; Mentors; Modeling; Motor; Motor Cortex; Neurodegenerative Disorders; Nursing Homes; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathology; Patients; Pattern; Physiological; Physiology; Population; Prevention; Publications; Publishing; Randomized Controlled Trials; Research; Research Activity; Rest; Symptoms; Test Result; Testing; Therapeutic; Therapeutic Intervention; Thinking; Training; Transcranial magnetic stimulation; Work; base; career; career development; clinically relevant; cognitive change; cognitive function; effective therapy; encephalography; high risk; improved; innovation; mild cognitive impairment; neurobiological mechanism; neurophysiology; new therapeutic target; novel; physiologic model; programs; public health relevance; screening; skills; theories; therapeutic target

7. Project Summary/Abstract: Parkinson's disease (PD) affects 1% of adults over age 65. While traditionally defined by motor symptoms, up to 75% of PD patients will eventually develop dementia making it the leading cause of nursing home placement in this population. Although there is currently no cure for PD, our ability to treat motor symptoms has advanced tremendously since the 1960's based on advances in our understanding of motor symptom neurophysiology. I propose that the treatment and prevention of dementia in PD may also prove possible through advances in our understanding of the neurophysiology of cognitive dysfunction. I will use modern network theory as a theoretical and mathematical framework for this endeavor. My long-term goal is to advance our fundamental understanding of the neurophysiology of cognitive dysfunction in PD to provide empirically testable models, clinically relevant biomarkers, and novel therapeutic targets. The central hypothesis of this proposal is that patterns of cortical functional connectivity critical to normal cognitive function are disrupted by subcortical pathology in PD and that interventions which normalize these patterns will improve cognition. This hypothesis has been formulated on the basis of preliminary data presented in this proposal and other previously published work. The research objectives of this proposal are to further our understanding of how cortical connectivity relates to cognitive dysfunction in PD, develop a novel biomarker for cognitive dysfunction in PD based on cortical physiology and to determine whether modulation of cortical connectivity may result in cognitive improvements in PD. We will accomplish the objectives of this proposal through three Specific Aims: 1) Determine whether graph theory measures of functional cortical activity measured with magnetoencephalography (MEG) are associated with cognitive dysfunction in PD subjects with and without mild cognitive impairment (MCI); 2) Develop a novel state-defining biomarker for cognitive dysfunction in PD based on MEG features through a machine learning approach; and 3) Determine the effects of repetitive transcranial magnetic stimulation (rTMS) on MEG measures of cortical connectivity and cognitive outcomes in PD-MCI patients. The approach is innovative because it represents the first study to apply graph theory measures to understanding the relationship of cortical physiology and cognitive dysfunction in PD; the first study to apply machine learning approaches to cognitive PD biomarker development; and the first clinical trial or mechanistic study of rTMS in PD-MCI. The proposed research is significant because it is expected to advance our understanding of the pathophysiology of cognitive dysfunction in PD and will provide biomarkers and pilot data essential to planning future therapeutic interventions. The training objectives and related research activities of this proposal will provide new skills, manuscripts and pilot data related to advanced MEG analysis, graph theory, biomarker development and rTMS trials necessary to establish my independence in these areas and obtain R01 funding to advance this unique research program.

7。项目摘要/摘要：帕金森氏病（PD）影响65岁以上的成年人的1％。而传统上 由运动症状定义，多达75％的PD患者最终会发展为领先的痴呆症 该人群中疗养院安置的原因。尽管目前无法治愈PD，但我们的能力 自1960年代以来，根据我们的理解进展，治疗运动症状已大大提高 运动症状神经生理学。我建议在PD中治疗和预防痴呆症 通过我们对认知功能障碍神经生理学的理解的进步证明。我会 将现代网络理论用作这项工作的理论和数学框架。我的长期目标 是为了促进我们对PD认知功能障碍神经生理学的基本理解，以提供 经验测试的模型，临床相关的生物标志物和新型治疗靶标。中央 该提议的假设是皮质功能连接的模式至关重要的正常认知功能 在PD中受到皮层病理的破坏，使这些模式正常化的干预措施将改善 认识。该假设是根据本提案中提出的初步数据提出的 其他先前发表的作品。该提案的研究目标是进一步了解我们对 皮质连通性与PD中认知功能障碍的关系如何，开发一种新型的认知生物标志物 基于皮质生理学的PD功能障碍，并确定皮质连通性的调节是否调节 可能会导致PD的认知改善。我们将通过三个 具体目的：1）确定图形理论是否用 磁脑摄影（MEG）与有和没有的PD受试者的认知功能障碍有关 轻度认知障碍（MCI）； 2）开发一种新的状态定义生物标志物，用于PD的认知功能障碍 通过机器学习方法基于MEG功能； 3）确定重复的影响 在MEG的皮质连通性和认知结果的MEG测量中，经颅磁刺激（RTMS） PD-MCI患者。该方法具有创新性，因为它代表了第一个应用图理论的研究 了解PD中皮质生理学与认知功能障碍的关系的措施；第一个 将机器学习方法应用于认知PD生物标志物开发的研究；和第一次临床试验 或PD-MCI中RTM的机械研究。拟议的研究很重要，因为它有望 促进我们对PD认知功能障碍的病理生理学的理解，并将提供生物标志物 以及计划未来治疗干预措施必不可少的试验数据。培训目标和相关目标 该建议的研究活动将提供与高级MEG有关的新技能，手稿和试点数据 分析，图理论，生物标志物发展和RTMS试验，以建立我的独立性 这些领域并获得R01资金来推进这项独特的研究计划。