Optimized Arterial Spin Labeling MRI in Mild Cognitive Impairment

描述(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记物，预测那些有轻微症状(如轻度认知障碍，MCI)的人的衰退，并反映疾病进展。在过去的二十年里，一些候选的神经成像、分子和心理测量方法已经证明在实现这些目标方面取得了不同程度的成功。虽然对特定病理敏感的分子标记(如脑脊液A�1-42)已取得重大进展，但这些技术似乎对临床状态或疾病进展相对不敏感。在经验和理论上，反映突触功能的大脑指标被认为对AD早期病理的后果最敏感，并预测未来的衰退。氟脱氧葡萄糖(FDG)正电子发射计算机断层扫描(FDG)是一种测量葡萄糖代谢(CMRGlu)的方法，在这方面显示出相当大的前景。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 ASL与FDG-PET有重叠的信息，但有几个潜在的优点：1)在常规MR成像中，大多数患者可以在几分钟内获得ASL，作为临床评估的一部分，因此，更便宜和负担更低；2)ASL不需要静脉造影剂或放射曝光；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现，有可能增加对早期功能变化的敏感性。此外，由于ASL是与其他MRI序列一起获得的，因此可以潜在地利用大脑结构和功能的正交测量，它们的组合可能提供疾病状态的最全面表征。这项建议的中心目标是证明在一组遗忘型MCI患者中，最先进的ASL-MRI在很大程度上与FDG-PET产生相同的信息。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(AD-CSF图谱的存在/缺失)以及预测未来的进展。“优化的”ASL序列利用了数据采集和分析方面的众多进步，还将与在阿尔茨海默病神经成像计划更新(ADNI 2)中实施的商业ASL测量进行比较，以确定这些ASL变体的相对价值。此外，还将探索与任务相关的ASL的潜力，以进一步提高REST ASL单独的预测价值。为了达到这些目的，MCI患者和HC患者将接受基线ASL-MRI和FDG-PET扫描，我们还将获得脑脊液分子标志物(tau/A�)。纵向临床随访和一年的重复核磁共振成像将允许评估疾病进展和确定这些成像生物标记物的相对预测价值。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，并用作复制数据集。

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.