Functional Brain Networks: A Novel Approach to Address Clinical Challenges in PD

描述（由申请人提供）：帕金森病（PD）以运动和非运动症状（认知障碍、情感障碍和其他临床特征）为特征。实验动物模型和PD患者的数据表明，这种疾病的表现不能归因于孤立的基底神经节功能障碍。相反，黑质多巴胺细胞的高度局部缺失与广泛的、空间分布的皮质-纹状体-苍白球-丘脑皮质（CSPTC）回路和相关通路的功能异常有关。通过量化空间分布（大规模）功能性脑网络的活动，包括多个相互连接的脑区域，基于图像的现代分析技术可以提供有关广泛的电路异常的有价值的信息，这些异常是神经退行性疾病（如PD）的基础。由Eidelberg博士领导的Feinstein研究所神经科学中心的研究人员率先使用功能性脑成像和网络分析来研究PD和相关的神经退行性疾病。由于这种“小信号”分析中固有的噪声，我们强调从统计和经验的角度严格验证与疾病相关的功能模式。事实上，在定量网络测量被认为是疾病过程及其对治疗反应的潜在生物标志物之前，需要高水平的测量精度。

DESCRIPTION (provided by applicant): Parkinson's disease (PD) is characterized by both motor and non-motor symptoms (cognitive impairment, affective disorder, and other clinical features). Data from experimental animal models and patients with PD indicate that the manifestations of this disease cannot be attributed to isolated dysfunction of the basal ganglia. Rather, the highly localized loss of nigral dopamine cells is associated with a broad, spatially distributed set of functional abnormalities involving cortico-striato-pallido-thalamocortical (CSPTC) loops and related pathways. By quantifying the activity of spatially distributed (large-scale) functional brain networks, comprising multiple interconnected brain regions, modern techniques of image-based analysis can provide valuable information concerning the widespread circuit abnormalities that underlie neurodegenerative disorders such as PD. The investigators at the Center for Neurosciences at The Feinstein Institute, led by Dr. Eidelberg, have pioneered the use of functional brain imaging and network analysis for the study of PD and related neurodegenerative diseases. Because of the noise inherent in "small signals" analyses of this sort, we have emphasized rigorous validation of the disease-related functional patterns from both statistical and empiric standpoints. Indeed, high levels of measurement precision are needed before quantitative network measures can be considered as potential biomarkers of the disease process and its response to treatment. In this proposal, we seek to take this approach to a new level by employing rigorously validated PD-related networks to address a number of vital issues that impact heavily on the care of today's PD patients. Project 1 addresses the serious clinical problem of levodopa-induced dyskinesias, which ultimately affect nearly all PD patients. Project 2 examines the network basis for individual differences in the cognitive response to dopaminergic treatment with a view to predicting which patients will develop untoward cognitive side effects under different treatment conditions. Project 3 aims to establish the feasibility of a new network-based algorithm for providing earlier and more accurate differential diagnosis than is currently possible.