Diagnosis of Parkinson's Disease using Diffusion Tensor Imaging

使用扩散张量成像诊断帕金森病

• 批准号: 9310359
• 负责人: Frank M. Skidmore
• 金额: $ 18.59万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9310359
• 关键词: Affect; Anisotropy; Atrophic; Brain; Brain scan; Caliber; Cerebellum; Classification; Clinical; Collaborations; Consensus; Data; Data Set; Defect; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Diffusion; Diffusion Magnetic Resonance Imaging; Discipline of Nuclear Medicine; Disease; Disease Progression; Dopamine; Early Diagnosis; Enrollment; Event; Exposure to; Failure; Fiber; Functional Magnetic Resonance Imaging; Gait abnormality; Globus Pallidus; Gold; High Performance Computing; Hypertrophy; Image; Individual; Intervention; Investigational Therapies; Lateral; Lead; Left; Life Change Events; Lobule; Magnetic Resonance Imaging; Maps; Measures; Medical; Methods; Modality; Modeling; Motor; Operative Surgical Procedures; Outcome; Output; Parkinson Disease; Parkinsonian Disorders; Pathway interactions; Patients; Phenotype; Population; Population Control; Population Group; Protocols documentation; Publishing; Radioactive Iodine; Research Personnel; Rest; Sampling; Scanning; Sensitivity and Specificity; Signal Transduction; Site; Specificity; Structure of subthalamic nucleus; Study Subject; Substantia nigra structure; Symptoms; Syndrome; Techniques; Testing; Thalamic structure; Tremor; Work; accurate diagnosis; base; clinical Diagnosis; clinically relevant; cohort; disease diagnosis; disease phenotype; disorder control; dopamine system; dopamine transporter; emotional distress; experience; fimbria; follow-up; imaging modality; improved; interest; morphometry; outcome forecast; personalized diagnostics; posture instability; progression marker; public health relevance; rate of change; single photon emission computed tomography; statistics; tool; unnecessary treatment; white matter

DESCRIPTION (provided by applicant): A diagnosis of Parkinson disease (PD) is a profound and life-changing event for a patient. However diagnosis of PD, particularly early in the course of illness is difficult for a variety of reasons. For example, individuals with PD will often preset with a fragment of the full clinical syndrome. Further, a number of disorders with very different prognoses have symptoms that overlap with the symptoms of PD. Even when diagnosis is firm, sub- populations within the broad disease specific classification of "PD" have been clinically observed, such as individuals with tremor dominant disease (TD-PD) vs. those with predominant postural instability and gait disorder (PIGD-PD). These sub-populations have distinct differences in symptoms and rate of progression. Therefore, prognosis for individuals with a diagnosis of Parkinson disease varies dramatically from one individual to another. This project evaluates the utility of diffusion tensor imaging (DTI) as a method to improve diagnosis of PD. We compare DTI to Ioflupane I123 SPECT (DaTscan). The DaTscan is a nuclear medicine modality that been approved to aid in diagnosis of Parkinsonism. This test can determine whether there is a defect in brain dopamine systems, but cannot distinguish between PD and other causes of Parkinsonism, or identify subsets within those with PD. Moreover, DaTscan is expensive, has some limitations in availability, and involves exposure to radioactive iodine, which has been raised as a concern. It has been a general thesis of the investigator that information dense MR images have sufficient embedded information to generate disease-specific diagnostic maps. Our lab uses high performance computing to compensate for individual subject variability in brain scans, and extract diagnostic signals. The PI has published data showing that resting fMRI can segregate individuals with PD from healthy controls with 92% sensitivity and 87% specificity. Further development of statistical techniques, in collaboration with colleagues in the UAB department of statistics, has resulted in development of a method that is able to generate a map using Diffusion Tensor Imaging (DTI) that can predict group membership (PD or Control) of subjects left out of our analysis with a high sensitivity and specificity. Our group is adapting his diagnostic methods, which provide reliable, subject-specific classification, as a potent tool for scientific discovery of regions reliably affected early in PD. This project will evaluate the utility of DTI as an adjunctive method to improve early diagnosis of PD. We propose DTI will provide a superior sensitivity and specificity to DaTscan for early diagnosis of PD (as opposed to Parkinsonism). We propose findings on DTI will differ in individuals with tremor predominant disease (TD- PD) compared to those with prominent postural instability and gait disorder (PIGD-PD). We will evaluate two populations in this study: 1) a local group drawn from individuals with uncertain PD diagnosis referred for clinical DaTscan, and 2) individuals with well characterized PD based on established consensus criteria, drawn from the Parkinson's Progression Markers Initiative (PPMI) population. We have distinct hypotheses surrounding each population group within the study. For group 1, we will compare the sensitivity and specificity of a clinical DaTscan with a baseline MRI for identificatio of a dopamine deficient state, and prediction of final diagnosis at 36 months. Group 2 from the PPMI dataset includes a control population, and individuals with early, well characterized PD (clinical characterization of all subjects, including controls, includes a clinical DaTscan). DTI i this case has occurred at multiple sites, using a defined protocol. We evaluate a number of DTI measures in group 2, including tensor-based morphometry (TBM) as a method to improve diagnostic precision, as well as the relationship between atrophy and hypertrophy of particular fiber tracts and disease progression. In addition, we evaluate the relationship of disease phenotype (TD-PD vs. PIGD-PD) to DTI measures, and the capacity of DTI to predict disease phenotype.

描述（由申请人提供）：帕金森病 (PD) 的诊断对于患者来说是一个意义深远且改变生活的事件。然而，由于多种原因，PD 的诊断，特别是病程早期的诊断很困难。例如，患有帕金森病的个体通常会预设完整临床综合征的一部分。此外，许多预后截然不同的疾病的症状与帕金森病的症状重叠。即使诊断明确，“PD”广泛疾病特定分类内的亚群也已在临床上观察到，例如患有震颤显性疾病（TD-PD）的个体与患有主要姿势不稳定和步态障碍的个体（PIGD-PD）。这些亚群在症状和进展速度方面存在明显差异。因此，被诊断患有帕金森病的个体的预后因人而异。该项目评估弥散张量成像 (DTI) 作为改善 PD 诊断方法的实用性。我们将 DTI 与 Ioflupane I123 SPECT (DaTscan) 进行比较。 DaTscan 是一种核医学模式，已被批准用于帮助诊断帕金森病。该测试可以确定大脑多巴胺系统是否存在缺陷，但无法区分帕金森病和帕金森病的其他原因，也无法识别帕金森病患者的子集。此外，DaTscan 价格昂贵，在可用性方面存在一些限制，并且涉及放射性碘暴露，这已成为人们关注的问题。研究人员的一个普遍论点是，信息密集的 MR 图像具有足够的嵌入信息来生成特定疾病的诊断图。我们的实验室使用高性能计算来补偿大脑扫描中的个体差异，并提取诊断信号。 PI 已发布 数据显示，静息功能磁共振成像可以以 92% 的敏感性和 87% 的特异性将帕金森病患者与健康对照者区分开来。与 UAB 统计系的同事合作，进一步开发统计技术，开发出一种方法，能够使用扩散张量成像 (DTI) 生成地图，该地图可以以高灵敏度和特异性预测未分析的受试者的组成员身份（PD 或对照）。我们的小组正在调整他的诊断方法，该方法提供了可靠的、针对特定主题的诊断方法 分类，作为科学发现早期帕金森病受影响区域的有效工具。 该项目将评估 DTI 作为辅助方法的效用，以改善早期诊断 PD。我们建议 DTI 将为 DaTscan 提供卓越的敏感性和特异性，用于 PD 的早期诊断（相对于帕金森病）。我们认为，与具有明显姿势不稳定性和步态障碍的个体（PIGD-PD）相比，震颤为主的疾病（TD-PD）个体的 DTI 结果会有所不同。我们将在本研究中评估两个人群：1）从帕金森病进展标志物倡议（PPMI）人群中抽取的局部群体，该群体选自接受临床 DaTscan 的帕金森病诊断不确定的个体，2）根据既定共识标准具有明确特征的帕金森病个体。我们对研究中的每个人群都有不同的假设。对于第 1 组，我们将比较临床 DaTscan 与基线 MRI 的敏感性和特异性，以识别多巴胺缺乏状态并预测 36 个月时的最终诊断。 PPMI 数据集中的第 2 组包括对照人群和患有早期、特征明确的 PD 的个体（所有受试者（包括对照）的临床特征，包括临床 DaTscan）。 DTI 我这种情况发生在多个站点，使用定义的协议。我们评估了第 2 组中的许多 DTI 测量值，包括基于张量的形态测量 (TBM) 作为提高诊断精度的方法，以及特定纤维束的萎缩和肥大与疾病进展之间的关系。此外，我们评估了疾病表型（TD-PD 与 PIGD-PD）与 DTI 测量值的关系，以及 DTI 预测疾病表型的能力。