Diagnosis of Parkinson's Disease using Diffusion Tensor Imaging

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，由于各种原因，PD的诊断，特别是在疾病的早期诊断是困难的。例如，PD患者通常会预设完整临床综合征的片段。此外，许多预后非常不同的疾病的症状与PD的症状重叠。即使诊断明确，临床也观察到在“PD”的广泛疾病特定分类中的亚群体，例如震颤显性疾病（TD-PD）与主要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上有明显差异。因此，帕金森氏症患者的预后因人而异。本项目评估扩散张量成像（DTI）作为一种提高PD诊断的方法的效用。我们比较DTI与碘氟烷I123 SPECT （DaTscan）。DaTscan是一种核医学模式，已被批准用于帕金森病的诊断。该测试可以确定脑多巴胺系统是否存在缺陷，但不能区分PD和其他原因的帕金森病，也不能识别PD患者中的亚群。此外，DaTscan很昂贵，在可用性上有一些限制，并且涉及到放射性碘的暴露，这已经引起了人们的关注。研究人员普遍认为，信息密集的磁共振图像具有足够的嵌入信息来生成疾病特异性诊断图。我们的实验室使用高性能计算来补偿个体受试者在脑部扫描中的可变性，并提取诊断信号。PI发表了

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.