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: 8822029
• 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/8822029
• 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; Emotional; 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; Operative Surgical Procedures; Outcome; Output; Parkinson Disease; Parkinsonian Disorders; Pathway interactions; Patients; Population; Population Control; Population Group; Protocols documentation; Publishing; Radioactive Iodine; Relative (related person); 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; White Matter Disease; Work; base; clinical Diagnosis; clinically relevant; cohort; disease diagnosis; disease phenotype; disorder control; dopamine system; dopamine transporter; experience; fimbria; follow-up; imaging modality; improved; interest; morphometry; non-motor symptom; outcome forecast; progression marker; public health relevance; single photon emission computed tomography; statistics; tool; 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）的诊断对患者来说是一个深刻的和改变生活的事件。然而，由于多种原因，PD的诊断，特别是在病程早期很困难。例如，患有PD的个体通常会预设完整临床综合征的片段。此外，许多具有非常不同症状的疾病具有与PD症状重叠的症状。即使当诊断是确定的时，也已经在临床上观察到“PD”的广泛疾病特异性分类内的亚群，例如具有震颤显性疾病（TD-PD）的个体与具有显性姿势不稳定和步态障碍（PIGD-PD）的个体。这些亚群在症状和进展速度方面存在明显差异。因此，诊断为帕金森病的个体的预后从一个个体到另一个个体变化很大。本项目评估扩散张量成像（DTI）作为提高PD诊断的方法的实用性。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症。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PD和帕金森症的其他原因，或识别PD患者的子集。此外，DaTscan价格昂贵，在可用性方面有一些限制，并且涉及暴露于放射性碘，这已经引起了人们的关注。信息密集的MR图像具有足够的嵌入信息来生成疾病特异性诊断图，这一直是研究者的一般论点。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发布了 数据显示，静息功能磁共振成像可以将PD患者与健康对照者分开，灵敏度为92%，特异性为87%。与UAB统计部门的同事合作，进一步开发了统计技术，开发了一种能够使用扩散张量成像（DTI）生成地图的方法，该方法可以预测我们分析中遗漏的受试者的组成员（PD或对照），具有高灵敏度和特异性。我们的团队正在调整他的诊断方法，这些方法提供了可靠的，特定于主题的 分类，作为科学发现PD早期可靠受影响区域的有力工具。 本项目将评估DTI作为一种有效的方法，以提高早期诊断， 警局我们建议DTI将提供一个上级敏感性和特异性的DaTscan的早期诊断PD（而不是帕金森综合征）。我们提出，与具有显著姿势不稳定和步态障碍（PIGD-PD）的个体相比，具有震颤主导型疾病（TD-PD）的个体的DTI结果将有所不同。我们将在本研究中评估两个人群：1）从临床DaTscan转诊的PD诊断不确定的个体中抽取的当地组，以及2）根据已建立的共识标准，从帕金森病进展标志物倡议（PPMI）人群中抽取的PD特征良好的个体。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识别多巴胺缺乏状态的灵敏度和特异性，并预测36个月时的最终诊断。来自PPMI数据集的第2组包括对照群体和具有早期、良好表征的PD的个体（包括对照的所有受试者的临床表征包括临床DaTscan）。在这种情况下，DTI在多个部位发生，使用定义的协议。我们评估了第2组中的一些DTI测量，包括基于张量的形态测量（TBM）作为提高诊断精度的方法，以及特定纤维束萎缩和肥大与疾病进展之间的关系。此外，我们还评估了疾病表型（TD-PD与PIGD-PD）与DTI指标的关系，以及DTI预测疾病表型的能力。