Analytic Methods for Determining Multimodal Biomarkers for Parkinson's Disease

描述（由申请人提供）：在了解帕金森病（PD）的生物学方面已经取得了相当大的进展。然而，对于帕金森病的早期检测和表征疾病进展，仍然缺乏可靠的生物标志物。生物技术的进步导致了心理健康研究的出现，这些研究收集大规模、多维的数据集，包括脑成像数据、基因组数据以及生物和临床测量。此类研究为跨领域研究提供了前所未有的机会，有助于更深入地了解 PD。然而，多维生物标志物开发的一个主要限制因素是缺乏可用于容纳多样化、大规模数据的统计工具。利用蓝斑和黑质的神经黑色素磁共振成像 (NM-MRI)、化学位移成像 (CSI)、扩散张量成像 (DTI)、静息态功能 MRI、脑脊液 (CSF) 分析物、基因型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 生物标志物。我们的数据为多模式 PD 生物标志物发现的跨领域方法学进步提供了前所未有的机会。另外，我们将考虑在佐治亚州建立一个拥有近 250,000 名订阅者的庞大患者数据库。基于我们在开发大规模成像数据统计和机器学习方法以及 PD 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 PD 生物标志物分析和发现方法。首先，我们计划开发新的统计技术来揭示 PD 的多模式生物标志物，包括影像、临床和生物变量。其次，我们计划利用海量临床数据库来识别早期PD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）的软件来实施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.