Analytic Methods for Determining Multimodal Biomarkers for Parkinson's Disease

描述(由申请人提供)：在理解帕金森氏病(PD)的生物学方面已经有了相当大的进步。然而，可靠的生物标志物仍然缺乏，用于帕金森病的早期检测和疾病进展的特征。生物技术的进步导致了心理健康研究的出现，这些研究收集了大规模的多维数据集，包括脑成像数据、基因组数据以及生物和临床测量。这样的研究为交叉调查提供了前所未有的机会，有助于加深对帕金森病的了解。然而，多维生物标志物发展的一个主要限制因素是缺乏可用的统计工具来适应不同的、大规模的数据。利用来自蓝斑和黑质的神经黑素磁共振成像(NM-MRI)、化学位移成像(CSI)、扩散张量成像(DTI)、静息状态功能MRI、脑脊液(CSF)分析、基因信息和众多临床变量的数据，我们计划开发新的统计技术来识别多模式PD生物标记物。我们的数据为多模式钯生物标记物发现的交叉方法学进步提供了前所未有的机会。另外，我们将考虑一个在佐治亚州拥有近25万订户的庞大患者数据库。基于我们在开发大规模成像数据的统计和机器学习方法以及帕金森病病理生理学方面的集体专业知识，我们计划通过以下具体目标推进帕金森病生物标志物分析和发现的方法。首先，我们计划开发新的统计技术来揭示PD的多模式生物标记物，包括成像、临床和生物变量。其次，我们计划利用海量的临床数据库来识别早期帕金森病的临床危险因素。第三，我们将开发配备友好图形用户界面(GUI)的软件来实现多模式生物标志物检测方法。

DESCRIPTION (provided by applicant): There has been considerable progress in understanding the biology of Parkinson's disease (PD). Reliable biomarkers are still lacking, however, for early stage detection of PD and for characterizing disease progression. Advances in biotechnology have led to the advent of mental health studies that collect large-scale, multi-dimensional data sets, including brain imaging data, genomic data, and biologic and clinical measures. Such studies provide an unprecedented opportunity for cross-cutting investigations that stand to gain a deeper understanding of PD. A major limiting factor to multidimensional biomarker development, however, is the lack of statistical tools available to accommodate diverse, large-scale data. Leveraging data from neuromelanin magnetic resonance imaging (NM-MRI) of the locus coeruleus and the substantia nigra, chemical shift imaging (CSI), diffusion tensor imaging (DTI), resting-state functional MRI, cerebrospinal fluid (CSF) analytes, genotype information, and numerous clinical variables, we plan to develop novel statistical techniques to identify multimodal PD biomarkers. Our data provide an unprecedented opportunity for cross-cutting methodological advances in multimodal PD biomarker discovery. Separately, we will consider a massive patient database with nearly 250,000 subscribers in Georgia. Building on our collective expertise in developing statistical and machine-learning methods for large-scale imaging data and in the pathophysiology of PD, we plan to advance methods for PD biomarker analyses and discovery through the following specific aims. First, we plan to develop new statistical techniques to reveal multimodal biomarkers for PD including imaging, clinical, and biologic variables. Secondly, we plan to utilize the massive clinical database to identify clinical risk factors for early stage PD. Thirdly, we will develop software equipped with a friendly graphical user interface (GUI) to implement the multimodal biomarker detection methods.