Multimodal Imaging Biomarkers of Parkinson’s Disease

项目摘要/摘要：帕金森氏病(PD)生物标志物的有效验证显然是有必要的 以帮助早期发现，更准确的诊断和临床管理。许多候选标记是 例如，在帕金森氏病生物标记物计划(PDBP)等倡议的推动下出现了 由国家神经疾病和中风研究所发起。一条很有希望的生物标记物之路 发现涉及到使用多模式神经成像来揭示帕金森病的神经病理生理学特征。 帕金森病涉及产生多巴胺的神经元的严重丧失，预计这将导致下游变化 在大脑功能和结构方面，其中一些通过活体神经成像表现出来(Poltis，2014)。识别 有症状的帕金森病患者的强健的神经影像改变创造了评估这种作用的机会 用于跟踪疾病进展的变化，并最终调查在 前驱期。从大量多模式神经成像特征中发现生物标记物取决于 对先进分析技术的发展和应用持批评态度。 在之前的研究(U18 NS082143)中，我们开发了一套横截面分析工具 多模式神经影像数据准确区分轻、中度帕金森病患者与健康对照 研究对象。在这个非常成功的发现阶段，我们使用了大规模磁共振成像(MRI)， 静息状态功能磁共振成像(rS-fMRI)和扩散张量成像(DTI)，我们发现了三种节约型 由强预测性多模式成像标记组成的面板。第一个小组由24个职能部门和 结构标志物(MRI、DTI和RS-fMRI)，共同反映丘脑和边缘系统的改变 (例如，海马体、杏仁体、眶前叶皮质和扣带回)。第二个标识23个标记， 这是由一项分析得出的，该分析包括更详细的基底节覆盖。最后，我们确定了一个15- 功能结构面板(MRI和DTI)，我们预计它不太容易受到帕金森病药物的影响。 从长远来看，每个小组都可能在实践中提供优势。我们在选拔过程中嵌入了方法 促进重复性和模型简洁性，同时目标是高精度。 在这个新项目中，我们将进一步评估在我们之前的研究中发现的标记 验证和可能的改进。我们还试图了解这些标志物在不同的组中的变化 断断续续服用帕金森病药物的患者，研究这些横断面和 新的纵向标记物用于预测进展并确定临床症状之间的关联 以及新出现的成像标记。这个项目的一个主要优势是我们有三个独立的数据 Set，其中两个具有纵向扫描，从而能够进一步发现和验证。这些数据来自于 帕金森进展标记物启动(PPMI)和根据PDBP进行的两项研究。

Project Summary/Abstract: There is a clear need for well-validated biomarkers for Parkinson's disease (PD) to aid early detection, more precise diagnosis, and clinical management. Numerous candidate markers are emerging, for example, spurred by initiatives such as the Parkinson's Disease Biomarker Program (PDBP) launched by the National Institute of Neurological Disorders and Stroke. One promising path to biomarker discovery involves the use of multimodal neuroimaging to reveal neuropathophysiologic characteristics of PD. PD involves a severe loss of dopamine producing neurons, which is expected to lead to downstream changes in brain function and structure, some of which manifest through in vivo neuroimaging (Politis, 2014). Identifying robust neuroimaging alterations in symptomatic PD patients creates an opportunity to assess the role of such changes for tracking disease progression and eventually to investigate whether similar changes emerge during the prodromal period. Biomarker discovery from a massive set of multimodal neuroimaging features depends critically on the development and application of advanced analytic techniques. In previous research (U18 NS082143), we developed a suite of analytic tools for cross-sectional multimodal neuroimaging data to accurately dissociate patients with mild to moderate PD from healthy control subjects. In this highly successful discovery phase, we used large-scale magnetic resonance imaging (MRI), resting-state functional MRI (rs-fMRI), and diffusion tensor imaging (DTI), and we identified three parsimonious panels of strongly predictive multimodal imaging markers. The first panel consists of 24 functional and structural markers (MRI, DTI, and rs-fMRI), which collectively reflect thalamic and limbic system alterations (e.g. hippocampus, amygdala, orbitofrontal cortex, and cingulate gyrus). The second identifies 23 markers, resulting from an analysis that includes more detailed coverage of the basal ganglia. Lastly, we identified a 15- feature structural panel (MRI and DTI), which we expect to be less susceptible to effects from PD medications. Long-term, each panel may offer advantages in practice. We embedded in our selection processes methods to promote reproducibility and model parsimony, while targeting high accuracy. In this new project, we will further evaluate the markers discovered in our previous research for validation and possibly refinement. We also seek to understand changes in these markers in distinct sets of patients who are on and off of their usual PD medications, to investigate the ability of these cross-sectional and new longitudinal markers to forecast progression, and to determine associations between clinical symptoms and the emergent imaging markers. A major advantage of this project is that we have three independent data sets, two of which have longitudinal scans, enabling further discovery and validation. The data come from the Parkinson's Progression Markers Intiative (PPMI) and from two studies conducted under the PDBP.