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: 9113654
• 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/9113654
• 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; Health; 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; 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; accurate diagnosis; 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; personalized diagnostics; progression marker; 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)的诊断对患者来说是一件意义深远的改变一生的事件。然而，由于各种原因，帕金森病的诊断很困难，尤其是在病程早期。例如，帕金森病患者通常会预先设定完整临床综合征的片段。此外，一些预后非常不同的疾病的症状与帕金森病的症状重叠。即使在诊断确定的情况下，临床上也观察到了广泛的疾病特定分类中的亚群，例如震颤显性疾病(TD-PD)与那些以姿势不稳定和步态障碍为主的人(PIGD-PD)。这些亚群在症状和进展速度上有明显的差异。因此，被诊断为帕金森病的人的预后因人而异。本项目评估弥散张量成像(DTI)作为一种提高帕金森病诊断水平的方法的实用性。我们将DTI与Ioflupane I123 SPECT(DATcan)进行比较。DaTcan是一种核医学模式，已被批准用于帮助诊断帕金森氏症。这项测试可以确定大脑多巴胺系统是否存在缺陷，但不能区分帕金森病和其他帕金森氏症的原因，也不能识别帕金森病患者的亚群。此外，DaTcan价格昂贵，在可获得性方面存在一些限制，而且涉及到放射性碘的暴露，这已成为一个令人担忧的问题。信息密集的磁共振图像有足够的嵌入信息来生成疾病特异性诊断地图，这一直是研究人员的普遍命题。我们的实验室使用高性能计算来补偿脑扫描中个体受试者的变异性，并提取诊断信号。公社已经出版了 数据显示，静息功能磁共振成像可以区分帕金森病患者和健康对照组，敏感性为92%，特异性为87%。统计技术的进一步发展，与UAB统计部门的同事合作，导致了一种方法的开发，该方法能够使用扩散张量成像(DTI)生成地图，该方法可以预测我们分析中遗漏的受试者的群体成员(PD或控制)，具有高度的敏感性和特异性。我们小组正在采用他的诊断方法，这些方法提供了可靠的、特定于对象的 分类，作为科学发现帕金森病早期可靠受影响区域的有力工具。 本项目将评估弥散张量成像作为一种辅助方法在改善早期诊断中的作用。 警察。我们认为，DTI将为早期诊断帕金森病(而不是帕金森病)提供更高的敏感性和特异性。我们提出，与那些姿势不稳和步态障碍(PIGD-PD)的患者相比，震颤主导性疾病(TD-PD)患者的DTI检查结果会有所不同。在这项研究中，我们将评估两个群体：1)来自临床DATcan的不确定PD诊断的个体的本地群体，以及2)基于已建立的共识标准的PD特征良好的个体，来自帕金森进展标记物倡议(PPMI)群体。在这项研究中，我们对每个人群都有不同的假设。对于第一组，我们将比较临床DAT扫描和基线MRI在识别多巴胺缺乏状态和预测36个月后最终诊断方面的敏感性和特异性。PPMI数据集中的第二组包括对照人群和早期特征良好的帕金森病患者(所有受试者的临床特征，包括对照，包括临床DATcan)。DTI这种情况发生在多个地点，使用定义的协议。我们评估了第二组中的一些DTI测量方法，包括基于张量的形态测量(TBM)作为一种提高诊断准确性的方法，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测量的关系，以及DTI预测疾病表型的能力。