Characterizing Intrinsic Functional Cortical Networks in Parkinson Disease Dementia

帕金森病痴呆的内在功能皮质网络特征

• 批准号: 9111686
• 负责人: BENZI M KLUGER
• 金额: $ 23.93万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9111686
• 关键词: Acetylcholine; Adult; Affect; Algorithms; Automobile Driving; Bioinformatics; Biological Markers; Brain; Caregiver Burden; Characteristics; Cognition; Cognitive; Collaborations; Data; Data Analyses; Data Collection; Data Set; Dementia; Development; Diagnostic; Dopamine; Elderly; Etiology; Executive Dysfunction; Exploratory/Developmental Grant; Foundations; Frequencies; Functional disorder; Funding; Future; Goals; Grant; Impaired cognition; Independent Scientist Award; Intervention; Lead; Link; Machine Learning; Magnetoencephalography; Manuscripts; Measures; Memory; Methodology; Modeling; Motor; National Institute of Neurological Disorders and Stroke; Nursing Homes; Parkinson Disease; Parkinson' s Dementia; Pathologic; Pathology; Patients; Pattern; Phenotype; Physiological; Physiology; Prevention; Publishing; Research; Research Project Grants; Rest; Role; Severities; Source; Structure; Structure of subthalamic nucleus; Testing; Thalamic structure; Theoretical model; Therapeutic Studies; Thinking; Visuospatial; Work; base; biomarker development; causal model; clinically relevant; cognitive function; effective intervention; effective therapy; graph theory; improved; innovation; mild cognitive impairment; motor symptom; neurophysiology; new therapeutic target; novel; novel therapeutic intervention; predictive marker; public health relevance; theories; therapeutic target; validation studies

 DESCRIPTION (provided by applicant): 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 PD-related dementia (PDD) making it the leading cause of nursing home placement in PD. 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. We propose that the treatment and prevention PDD may also prove possible by advancing our understanding of the neurophysiology underlying cognitive dysfunction. We will use modern network theory as a theoretical and mathematical framework for this endeavor. The long- term goal is to advance our understanding of the neurophysiology underlying 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 functional connectivity critical to normal cognition are disrupted by subcortical pathology in PDD. This hypothesis was formulated on the basis of our own preliminary data and other recent research. We will accomplish the objectives of this proposal through three Specific Aims: 1) Determine whether graph theory measures of network functional connectivity are associated with cognitive phenotypes in PD based on either a) the severity of cognitive dysfunction; or b) specific cognitive domains affected; 2) Develop a novel state-defining biomarker for PDD based on measures of intrinsic network functional activity using a machine learning approach; and 3) Explore the potential role of subcortical sources on cortical network activity and cognition using dynamic causal modeling (DCM). These Aims are achievable within a two-year timeframe as they will involve analyses of a single data set of resting MEG data from 25 control subjects, 25 PDD subjects, 25 PD subjects with normal cognition and 25 PD subjects with mild cognitive impairment. The feasibility of this proposal is aided by leveraging a currently funded NINDS K02 Independent Scientist Award (1 K02 NS080885-01A1). Data and biomarkers generated by this R21 Exploratory/Developmental Research Grant will provide a foundation for future studies to validate state- defining and predictive biomarkers; mechanistic studies on the role of acetylcholine and dopamine in cognition; and therapeutic studies of physiologic or pharmacologic interventions targeting physiological and regional abnormalities. The approach is innovative as the first study to apply graph theory measures to understanding the relationship of cortical physiology and cognitive dysfunction across cognitive domains and severity levels in PD; the first study to apply machine learning approaches to PDD biomarker development; and the first use of DCM to model cortical and subcortical contributions to PDD. The proposed research is significant because it will advance our understanding of the neurophysiology of PD-related cognitive dysfunction and will provide biomarkers, empiric models and therapeutic targets essential to developing more effective interventions.

 描述（由申请人提供）：帕金森病 (PD) 影响 1% 的 65 岁以上成年人。虽然传统上以运动症状来定义，但高达 75% 的帕金森病患者最终会发展为帕金森病相关痴呆 (PDD)，这使其成为帕金森病被安置在疗养院的主要原因。尽管目前还无法治愈 PD，但自 20 世纪 60 年代以来，基于我们对运动症状神经生理学理解的进步，我们治疗运动症状的能力已经取得了巨大进步。我们认为，通过增进我们对认知功能障碍背后的神经生理学的理解，治疗和预防 PDD 也可能被证明是可能的。我们将使用现代网络理论作为这一努力的理论和数学框架。长期目标是增进我们对帕金森病认知功能障碍背后的神经生理学的理解，以提供可经验测试的模型、临床相关生物标志物和新的治疗靶点。该提议的中心假设是，对正常认知至关重要的功能连接模式被 PDD 皮质下病理破坏。这一假设是根据我们自己的初步数据和其他最近的研究提出的。我们将通过三个具体目标来实现该提案的目标：1）根据a）认知功能障碍的严重程度，确定网络功能连接的图论测量是否与PD中的认知表型相关。 b) 受影响的特定认知领域； 2) 基于使用机器学习方法测量内在网络功能活动，开发一种新型的 PDD 状态定义生物标志物； 3) 使用动态因果模型 (DCM) 探索皮层下源对皮层网络活动和认知的潜在作用。这些目标可以在两年的时间内实现，因为它们将涉及对来自 25 名对照受试者、25 名 PDD 受试者、25 名具有正常认知的 PD 受试者和 25 名患有轻度认知障碍的 PD 受试者的静息 MEG 数据的单个数据集进行分析。该提案的可行性得益于当前的 资助 NINDS K02 独立科学家奖（1 K02 NS080885-01A1）。 R21探索性/发展性研究资助产生的数据和生物标志物将为未来研究验证状态定义和预测生物标志物奠定基础；乙酰胆碱和多巴胺在认知中作用的机制研究；针对生理和区域异常的生理或药理学干预的治疗研究。该方法具有创新性，是第一项应用图论方法来理解皮层生理学与跨认知领域认知功能障碍和帕金森病严重程度之间关系的研究；第一项将机器学习方法应用于 PDD 生物标志物开发的研究；以及首次使用 DCM 来模拟皮层和皮层下对 PDD 的影响。拟议的研究意义重大，因为它将增进我们对 PD 相关认知功能障碍的神经生理学的理解，并将提供开发更有效干预措施所必需的生物标志物、经验模型和治疗目标。