Multimodal Imaging Biomarkers of Parkinson’s Disease

帕金森病的多模态成像生物标志物

• 批准号: 9552310
• 负责人: F. DuBois Bowman
• 金额: $ 40.01万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9552310
• 关键词: Amygdaloid structure; Anatomy; Atlases; Basal Ganglia; Bayesian Modeling; Biological Markers; Brain; Characteristics; Clinical; Clinical Management; Conduct Clinical Trials; Corpus striatum structure; Data; Data Set; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease Marker; Disease Progression; Dopamine; Dorsal; Early Diagnosis; Evaluation; Forms Controls; Functional Magnetic Resonance Imaging; Functional disorder; Future; Globus Pallidus; Hippocampus (Brain); Image; Investigation; Label; Lead; Limbic System; Link; Magnetic Resonance Imaging; Measures; Mediating; Mediation; Mediator of activation protein; Methods; Modeling; Motor; Multimodal Imaging; National Institute of Neurological Disorders and Stroke; Neurons; Parkinson Disease; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Process; Property; Red nucleus structure; Reproducibility; Research; Research Design; Rest; Role; Scanning; Secondary Parkinson Disease; Structure; Subgroup; Substantia nigra structure; Symptoms; Techniques; Thalamic structure; Validation; Work; analytical tool; base; biomarker discovery; biomarker panel; candidate marker; cingulate gyrus; disorder control; dopaminergic neuron; high dimensionality; imaging biomarker; improved; in vivo; multimodality; neuroimaging; neuroimaging marker; non-motor symptom; novel; pars compacta; programs; progression marker; relating to nervous system; validation studies

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.

项目摘要/摘要：帕金森病 (PD) 显然需要经过充分验证的生物标志物 帮助早期发现、更精确的诊断和临床管理。许多候选标记是 例如，在帕金森病生物标志物计划 (PDBP) 等举措的推动下出现 由国家神经疾病和中风研究所发起。生物标志物的一条有前途的途径 发现涉及使用多模式神经影像来揭示帕金森病的神经病理生理学特征。 PD 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 大脑功能和结构的变化，其中一些通过体内神经影像表现出来（Politis，2014）。识别 有症状的帕金森病患者的神经影像学改变为评估此类作用提供了机会 跟踪疾病进展并最终调查期间是否出现类似的变化 前驱期。从大量多模态神经影像特征中发现生物标志物取决于 批判性地关注先进分析技术的开发和应用。 在之前的研究（U18 NS082143）中，我们开发了一套横截面分析工具 多模态神经影像数据可准确区分轻至中度 PD 患者与健康对照 科目。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）， 静息态功能 MRI (rs-fMRI) 和扩散张量成像 (DTI)，我们确定了三个简约的 强预测性多模态成像标记物组。第一个面板由 24 个功能和 结构标记（MRI、DTI 和 rs-fMRI），共同反映丘脑和边缘系统的变化 （例如海马体、杏仁核、眶额皮质和扣带回）。第二个识别出 23 个标记， 分析结果包括更详细的基底神经节覆盖范围。最后，我们确定了 15- 具有结构面板（MRI 和 DTI），我们预计其不易受到 PD 药物的影响。 从长远来看，每个面板都可能在实践中提供优势。我们在选择过程中嵌入了方法 提高可重复性和模型简约性，同时瞄准高精度。 在这个新项目中，我们将进一步评估我们之前研究中发现的标记物 验证和可能的改进。我们还试图了解这些标记在不同组中的变化 正在服用和停止服用常用 PD 药物的患者，以调查这些横断面和 新的纵向标记来预测进展并确定临床症状之间的关联 和新兴的成像标记。这个项目的一个主要优点是我们拥有三个独立的数据 组，其中两个具有纵向扫描，可以进一步发现和验证。数据来自 帕金森病进展标志物倡议 (PPMI) 和根据 PDBP 进行的两项研究。