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：显然需要经过充分验证的帕金森病（PD）生物标志物 以帮助早期发现、更精确的诊断和临床管理。许多候选标记物是 例如，在帕金森病生物标志物项目（PDBP）等倡议的推动下， 由国家神经疾病和中风研究所发起。生物标志物的一个有前途的途径 这一发现涉及使用多模态神经成像来揭示PD的神经病理生理学特征。 PD涉及多巴胺产生神经元的严重损失，这预计将导致下游变化 在大脑功能和结构中，其中一些通过体内神经成像表现出来（Politis，2014）。识别 有症状的PD患者的神经影像学改变创造了一个机会，以评估这种变化的作用。 跟踪疾病进展的变化，并最终调查在治疗过程中是否出现类似的变化。 前驱期从大量多模态神经成像特征中发现生物标志物取决于 对先进分析技术的发展和应用持批判态度。 在以前的研究（U18 NS 082143）中，我们开发了一套分析工具，用于横截面 多模式神经影像学数据，以准确区分轻度至中度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）， 静息态功能磁共振成像（rs-fMRI）和扩散张量成像（DTI），我们确定了三个吝啬 强预测性多模态成像标记物组。第一个小组由24个功能和 结构标记物（MRI、DTI和rs-fMRI），共同反映丘脑和边缘系统的改变 (e.g.海马、杏仁核、眶额皮质和扣带回）。第二个识别23个标记， 这是由包括基底神经节的更详细覆盖的分析产生的。最后，我们发现了一个15- 功能结构面板（MRI和DTI），我们预计不太容易受到PD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 以提高可重复性和模型简约性，同时实现高精度。 在这个新项目中，我们将进一步评估我们以前研究中发现的标记， 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， 正在接受和停止常规PD药物治疗的患者，以调查这些横断面和 新的纵向标志物，以预测进展，并确定临床症状之间的关联 和紧急成像标记。这个项目的一个主要优势是我们有三个独立的数据 其中两个具有纵向扫描，能够进一步发现和验证。这些数据来自 帕金森病进展标志物启动（PPMI）和PDBP下进行的两项研究。