Optimized Arterial Spin Labeling MRI in Mild Cognitive Impairment

描述（由申请人提供）：阿尔茨海默病（AD）研究的一个主要目标是开发对早期疾病敏感的生物标志物，预测轻度症状（例如轻度认知障碍或MCI）的下降，并反映疾病进展。在过去的二十年里，一些候选的神经影像学，分子和心理测量的措施已经证明了可变的成功，在实现这些目标。虽然对特定病理学敏感的分子标志物（例如CSF A 1-42）已经取得了重大进展，但这些技术似乎对临床状态或疾病进展相对不敏感。在经验和理论的基础上，反映突触功能的大脑措施被认为是最敏感的早期AD病理学的后果和预测未来的下降。氟代脱氧葡萄糖（FDG）PET，葡萄糖代谢（CMRGlu）的措施，已证明在这方面相当有前途。动脉自旋标记（ASL）MRI对反映代谢活动的脑血流量（CBF）敏感，可提供 与FDG-PET重叠的信息，但有几个潜在的优势：1）ASL可以在常规MR成像期间在几分钟内获得，大多数患者将获得常规MR成像作为其临床评估的一部分，因此，更便宜且负担更轻~ 2）ASL不需要IV造影剂或辐射暴露~ 3）ASL可能比PET更容易获得~ 4）短激活或任务-相关序列可以更容易地实施，具有对早期功能变化增加敏感性的潜力。此外，由于ASL是与其他MRI序列一起沿着采集的，因此可以潜在地利用脑结构和功能的正交测量，其组合可以提供疾病状态的最充分表征。该提案的中心目标是证明“最先进的”ASL-MRI在遗忘型MCI患者队列中产生与FDG-PET基本等同的信息。特别是，我们将确定这些模式的相对能力，以确定临床状态[MCI与健康对照（HC）]，疾病状态（存在/不存在AD CSF特征），并预测未来的进展。利用数据采集和分析方面的众多进步，“优化”ASL序列也将与阿尔茨海默病神经成像倡议更新（ADNI 2）中实施的市售ASL措施进行比较，以确定这些ASL变体的相对价值。此外，将探索任务相关ASL进一步增强单独静息ASL预测价值的潜力。为了实现这些目标，MCI患者和HC将接受基线ASL-MRI和FDG-PET扫描，我们还将获得CSF分子标志物（tau/A？）。 纵向临床随访和1年重复MRI将允许评估疾病进展并确定这些成像生物标志物的相对预测价值。最后，我们将利用本项目中开发的分析管道来分析来自ADNI 2的ASL数据，这将潜在地增强解决上述一些问题的能力，并作为复制数据集。

DESCRIPTION (provided by applicant): A major goal in Alzheimer's disease (AD) research is to develop biomarkers that are sensitive to early disease, predict decline in those with mild symptoms (e.g. Mild Cognitive Impairment, or MCI), and reflect disease progression. Over the last two decades, a number of candidate neuroimaging, molecular,and psychometric measures have demonstrated variable success in accomplishing these goals. While significant advances have been made with molecular markers (e.g. CSF A¿1-42) that are sensitive to specific pathology, these techniques appear relatively insensitive to clinical status or disease progression. On both empirical and theoretical grounds, brain measures that reflect synaptic function are thought to be the most sensitive to the consequences of early AD pathology and predictive of future decline. Fluorodeoyglucose (FDG) PET, a measure of glucose metabolism (CMRGlu), has demonstrated considerable promise in this regard. Arterial spin labeling (ASL) MRI, which is sensitive to cerebral blood flow (CBF) reflective of metabolic activity, may provide overlapping information with FDG-PET, but has several potential advantages: 1) ASL can be acquired in several minutes during routine MR imaging that most patients will obtain as part of their clinical evaluation, and, thus, is less expensive and burdensome~ 2) ASL does not require IV contrast or radiation exposure~ 3) ASL is potentially more accessible than PET~ 4) Short activation or task-related sequences can more easily be implemented with potential for increased sensitivity to early functional change. Further, since ASL is acquired along with other MRI sequences, one can potentially take advantage of orthogonal measures of brain structure and function, the combination of which may offer the fullest characterization of disease state. The central goal of this proposal is to demonstrate that 'state-of-the art' ASL-MRI produces largely equivalent information to FDG-PET in a cohort of amnestic MCI patients. In particular, we will determine the relative capacity of these modalities to determine clinical status [MCI vs healthy control (HC)], disease state (presence/absence of AD CSF profile), and predict future progression. 'Optimized' ASL sequences, leveraging numerous advancements in data acquisition and analysis, will also be compared to a commercially available ASL measure being implemented in the Alzheimer's disease Neuroimaging Initiative renewal (ADNI 2), to determine the relative value of these ASL variants. Additionally, task-related ASL will be explored for its potential to further enhance the predictive value of rest ASL alone. To achieve these aims, MCI patients and HC will undergo a baseline ASL-MRI and FDG-PET scan~ we will also obtain CSF molecular markers (tau/A¿). Longitudinal clinical follow-up and a 1-year repeat MRI will allow for assessment of disease progression and determination of the relative predictive value of these imaging biomarkers. Finally, we will utilize the analytic pipeline developed in this project o analyze ASL data from ADNI 2, which will potentially enhance power to address some of the above questions and serve as a replication dataset.