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（由适用提供）：帕金森氏病（PD）影响65岁以上的成年人中的1％。尽管传统上由运动症状定义，但多达75％的PD患者最终将发展与PD相关的痴呆症（PDD），这是PD中护士住所的主要原因。尽管目前尚无PD治疗，但自1960年代以来，我们的治疗运动症状的能力已取决于我们对运动症状神经生理学的理解的进步。我们建议通过促进我们对神经生理学潜在认知功能障碍的神经生理学的理解，可以证明治疗和预防PDD。我们将使用现代网络理论作为这项工作的理论和数学框架。长期目标是提高我们对PD中认知功能障碍的神经生理学的理解，以提供可实现的模型，临床相关的生物标志物和新颖的治疗靶标。该提议的中心假设是，在PDD中，皮层病理学的功能连通性与正常认知至关重要的模式受到了破坏。该假设是根据我们自己的初步数据和其他最新研究提出的。我们将通过三个特定目的来实现该提案的目标：1）确定基于a）认知功能障碍的严重程度；或b）受影响的特定认知领域； 2）基于使用机器学习方法对内在网络功能活动的测量，为PDD开发了一种新颖的状态定义的生物标志物； 3）探讨使用动态因果建模（DCM）的皮层源对皮质网络活性和认知的潜在作用。这些目标是可以在两年的时间内实现的，因为它们将涉及对25名控制受试者，25名PDD受试者，25名具有正常认知的PD受试者和25名PD受试者的单一数据集进行分析。该提案的可行性可以通过利用当前 资助Ninds K02独立科学家奖（1 K02 NS080885-01A1）。该R21探索性/发展研究赠款生成的数据和生物标志物将为未来的研究提供基础，以验证国家定义和预测性生物标志物；乙酰胆碱和多巴胺在认知中的作用的机理研究；以及针对生理和区域异常的生理或药物干预措施的治疗研究。该方法是创新的，是第一个采用图理论的研究，以了解PD中认知领域和严重程度之间的皮质生理和认知功能障碍的关系；第一个将机器学习方法应用于PDD生物标志物开发的研究；以及DCM首次使用对PDD的皮质和皮质下贡献进行建模。拟议的研究之所以重要，是因为它将提高我们对与PD相关认知功能障碍的神经生理学的理解，并将为开发更有效的干预措施提供生物标志物，经验模型和热目标。