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：显然需要对帕金森氏病（PD）进行精心验证的生物标志物 帮助早期检测，更精确的诊断和临床管理。许多候选标记是 例如，诸如帕金森氏病生物标志物计划（PDBP）之类的计划刺激了新兴 由美国国家神经系统疾病和中风研究所发起。生物标志物的一条有希望的途径 发现涉及使用多模式神经影像来揭示PD的神经病理生理特征。 PD涉及多巴胺产生神经元的严重丧失，预计会导致下游变化 在大脑功能和结构中，其中一些通过体内神经影像体现出来（Politis，2014年）。识别 有症状的PD患者的强大神经成像改变创造了评估这种作用的机会 追踪疾病进展的变化，并最终研究是否在 前驱时期。从一组大量多模式神经影像特征中发现生物标志物取决于 对高级分析技术的开发和应用至关重要。 在先前的研究（U18 NS082143）中，我们开发了一套用于横截面的分析工具 多模式神经影像学数据，以准确地将轻度至中度PD患者与健康对照分解 主题。在这个非常成功的发现阶段，我们使用了大规模的磁共振成像（MRI）， 静止状态功能性MRI（RS-FMRI）和扩散张量成像（DTI），我们鉴定 强烈预测性多模式成像标记的面板。第一个面板由24个功能和 结构标记（MRI，DTI和RS-FMRI），共同反映了丘脑和边缘系统的改变 （例如海马，杏仁核，眶额皮质和扣带回）。第二个标识23个标记， 由于分析包括对基底神经节的更详细的覆盖范围。最后，我们确定了15- 具有结构面板（MRI和DTI），我们希望它不太容易受到PD药物的影响。 长期，每个小组在实践中可能会带来优势。我们嵌入了选择过程方法 促进可重复性和模型模型，同时瞄准高精度。 在这个新项目中，我们将进一步评估我们先前研究中发现的标记 验证和可能的改进。我们还试图以不同的集合了解这些标记的变化 开出通常使用PD药物的患者，以调查这些横截面和 新的纵向标记以预测进展，并确定临床症状之间的关联 以及新兴的成像标记。该项目的主要优点是我们有三个独立数据 套件，其中两个具有纵向扫描，从而实现了进一步的发现和验证。数据来自 帕金森的进展标记式（PPMI）以及在PDBP下进行的两项研究。