Analytic Methods for Determining Multimodal Biomarkers for Parkinson's Disease

确定帕金森病多模式生物标志物的分析方法

• 批准号: 8889317
• 负责人: F. DuBois Bowman
• 金额: $ 21.58万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8889317
• 关键词: Analytic; Methods; Determining; Multimodal; Biomarkers

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.

描述（由申请人提供）：了解帕金森氏病（PD）的生物学取得了很大进展。然而，仍然缺乏可靠的生物标志物，用于早期检测PD和表征疾病进展。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，包括大脑成像数据，基因组数据以及生物学和临床指标。这样的研究为跨切割调查提供了前所未有的机会，可以深入了解PD。但是，多维生物标志物开发的一个主要限制因素是缺乏可容纳多种大规模数据的统计工具。从基因座的神经元素磁共振成像（NM-MRI）中利用数据，化学位移成像（CSI），扩散张量成像（DTI），静止状态功能MRI，静止功能MRI，脑脊髓流体（CSF）分析，统计型统计信息，并确定许多统计信息，以识别统计信息，以识别统计信息，以下几乎没有统计信息，以下是统计的变量。 PD生物标志物。我们的数据为多模式PD生物标志物发现中的横切方法学进步提供了前所未有的机会。另外，我们将考虑一个大量的患者数据库，佐治亚州有近25万个订户。在我们开发用于大规模成像数据以及PD病理生理学的统计和机器学习方法方面的集体专业知识的基础上，我们计划通过以下特定目标推进PD生物标记分析和发现的方法。首先，我们计划开发新的统计技术，以揭示PD的多模式生物标志物，包括成像，临床和生物学变量。其次，我们计划利用大量的临床数据库来确定早期PD的临床风险因素。第三，我们将开发配备有友好图形用户界面（GUI）的软件，以实现多模式生物标志物检测方法。