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：帕金森病 (PD) 影响 1% 的 65 岁以上成年人。 根据运动症状定义，高达 75% 的 PD 患者最终会发展为痴呆症，这使其成为最主要的疾病 在此人群中安置疗养院的原因。尽管目前帕金森病尚无治愈方法，但我们有能力 自 20 世纪 60 年代以来，基于我们理解的进步，治疗运动症状取得了巨大进步 运动症状神经生理学。我建议帕金森病痴呆的治疗和预防也可能 通过我们对认知功能障碍的神经生理学理解的进步，证明了这一点是可能的。我会 使用现代网络理论作为这一努力的理论和数学框架。我的长期目标 是为了增进我们对帕金森病认知功能障碍的神经生理学的基本理解，以提供 可经验测试的模型、临床相关的生物标志物和新的治疗靶点。中央 该提案的假设是皮质功能连接模式对正常认知功能至关重要 帕金森病的皮层下病理学会扰乱这些模式，使这些模式正常化的干预措施将会改善 认识。该假设是根据本提案中提供的初步数据制定的，并且 其他先前发表的作品。本提案的研究目标是加深我们对 皮质连接如何与 PD 认知功能障碍相关，开发一种新型认知生物标志物 基于皮质生理学的 PD 功能障碍，并确定是否调节皮质连接 可能会导致帕金森病的认知改善。我们将通过三个方面来实现本提案的目标 具体目标： 1) 确定图论是否可以测量功能性皮层活动 脑磁图 (MEG) 与患有或不患有帕金森病的受试者的认知功能障碍相关 轻度认知障碍（MCI）； 2) 开发一种用于 PD 认知功能障碍的新型状态定义生物标志物 通过机器学习方法基于 MEG 特征； 3）确定重复的效果 经颅磁刺激 (rTMS) 对 MEG 测量皮质连接和认知结果的影响 PD-MCI 患者。该方法具有创新性，因为它代表了第一个应用图论的研究 了解帕金森病皮质生理学与认知功能障碍之间关系的措施；第一个 研究将机器学习方法应用于认知 PD 生物标志物开发；以及第一个临床试验 或 rTMS 在 PD-MCI 中的机制研究。拟议的研究意义重大，因为预计 增进我们对帕金森病认知功能障碍病理生理学的理解，并将提供生物标志物 以及对规划未来治疗干预措施至关重要的试点数据。培训目标及相关内容 该提案的研究活动将提供与先进 MEG 相关的新技能、手稿和试点数据 分析、图论、生物标志物开发和 rTMS 试验是建立我的独立性所必需的 这些领域并获得 R01 资金来推进这一独特的研究计划。