Characterizing Intrinsic Functional Cortical Networks in Parkinson Disease Dementia

 描述(申请人提供)：帕金森氏病(PD)影响1%的65岁以上的成年人。虽然传统上以运动症状来定义，但高达75%的PD患者最终将发展为PD相关痴呆(PDD)，使其成为PD患者安置疗养院的主要原因。虽然目前还没有治愈帕金森病的方法，但自20世纪60年代S以来，基于我们对运动症状神经生理学的理解，我们治疗运动症状的能力已经有了很大的进步。我们认为，通过促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。长期目标是促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，以提供经验可检验的模型、临床相关的生物标志物和新的治疗靶点。这一建议的中心假设是，对于正常认知至关重要的功能连接模式在PDD患者的皮质下病理中被破坏。这一假设是基于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)基于a)认知功能障碍的严重程度；或b)受影响的特定认知域，确定网络功能连通性的图论测量是否与PD的认知表型相关；2)使用机器学习方法，基于内在网络功能活动的测量，开发用于PDD的新的状态定义生物标记物；以及3)使用动态因果建模(DCM)来探索皮质下来源对大脑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，因为它们将涉及分析来自25名对照受试者、25名PDD受试者、25名认知正常的帕金森病受试者和25名轻度认知障碍的帕金森病受试者的单一静息脑磁图数据集。这项建议的可行性是通过利用目前的 NINDS K02独立科学家奖(1 K02 NS080885-01A1)。这项R21探索/发展研究基金产生的数据和生物标记物将为未来的研究提供基础，以验证状态定义和预测性生物标记物；乙酰胆碱和多巴胺在认知中的作用的机制研究；以及针对生理和区域异常的生理学或药物干预的治疗性研究。该方法是创新的，因为它是第一个应用图论方法来了解认知领域和严重程度之间的皮质生理学和认知功能障碍的关系的研究；第一个将机器学习方法应用于PDD生物标志物开发的研究；以及第一个使用DCM来模拟皮质和皮质下对PDD的贡献的研究。这项拟议的研究具有重要意义，因为它将促进我们对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必要的生物标志物、经验模型和治疗靶点。

 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.