Using quantitative gradient echo MRI to distinguish MOG antibody disorder from multiple sclerosis

项目总结/摘要 髓鞘少突胶质细胞糖蛋白（MOG）是中枢神经系统（CNS）特有的蛋白质 在少突胶质细胞和CNS髓鞘的外表面上发现。MOG自身抗体的存在 在CNS脱髓鞘综合征患者中的天然构象定义了MOG抗体紊乱 （MOGAD）。这种最近表征的病症在临床上与多发性硬化症（MS）重叠， 典型的CNS脱髓鞘疾病，并且将MOGAD患者与MS区分开来可能具有挑战性 仅使用临床特征和常规MRI的患者。 定量梯度回波（qGRE）是华盛顿开发的一种新型成像技术 大学qGRE技术可以检测CNS白色物质（WM）、灰质（GM） 以及MS和其他神经系统疾病中正常出现的WM（NAWM）和GM（NAGM），包括 老年痴呆症由于qGRE从不同的图像生成不同对比度的自然配准图像， 单次扫描，它也非常适合检测中央静脉征（CVS），这是一种先进的成像特征， 以区分MS病变与其他神经炎性疾病。值得注意的是，高分辨率 （1 mm 3体素）qGRE采集在任何标准3 T MRI扫描仪上都需要不到10分钟， 能量，是可再现的，并且不需要造影剂施用。 在此，我们假设病变、NAWM和NAGM内的定量组织损伤以及 病变内CVS的患病率将共同区分MOGAD和MS。为了评估我们的假设，我们 将qGRE方法应用于MOGAD CNS成像，以提供局部脑的定量信息 组织完整性和CVS患病率。 在目标1中，我们将获得20例MOGAD患者的qGRE成像数据，以表征和量化这些 先进的成像功能。MOGAD患者成像数据将与20个已采集的数据进行比较。 年龄和性别匹配的MS患者。 在目标2中，我们将MOGAD患者的qGRE数据与临床测试结果相关联，并比较 这些结果与MS中的结果进行了比较。 对qGRE结果的认知测试可以让我们了解 MOGAD病理学，尚未得到很好的定义。这将与我们拥有的数据进行比较 已经将这些相同的测试结果和MS患者的qGRE数据联系起来。 在成功完成我们的目标后，我们希望qGRE可以在临床上用于简化和 提高MOGAD和MS的诊断准确性，减少误诊。我们还希望qGRE 将有助于更好地了解MOGAD的疾病过程。

Project Summary/Abstract Myelin oligodendrocyte glycoprotein (MOG) is a protein exclusive to the central nervous system (CNS) found on the external surface of oligodendrocytes and CNS myelin. The presence of an autoantibody to MOG in its native conformation in patients with a CNS demyelinating syndrome defines MOG antibody disorder (MOGAD). This recently-characterized condition overlaps clinically with multiple sclerosis (MS), the prototypical CNS demyelinating condition, and it can be challenging to differentiate MOGAD patients from MS patients using only clinical features and conventional MRI. Quantitative gradient recalled echo (qGRE) is a novel imaging technique developed at Washington University. qGRE technique can detect microscopic damage in CNS white matter (WM), gray matter (GM), and normal appearing WM (NAWM) and GM (NAGM) in MS and other neurologic diseases including Alzheimer’s disease. Because qGRE generates naturally co-registered images of different contrasts from a single scan, it is also ideally suited to detect the central vein sign (CVS), an advanced imaging feature thought to differentiate MS lesions from those of other neuroinflammatory conditions. It is notable that high-resolution (1 mm3 voxel) qGRE acquisition takes less than 10 minutes on any standard 3T MRI scanner, deposits little energy, is reproducible, and does not require contrast agent administration. Here, we hypothesize that quantitative tissue damage within lesions, NAWM, and NAGM, as well as the prevalence of CVS within lesions, will together differentiate MOGAD from MS. To assess our hypothesis, we will apply the qGRE approach to MOGAD CNS imaging to provide quantitative information on regional brain tissue integrity and CVS prevalence. In Aim 1, we will obtain qGRE imaging data in 20 MOGAD patients to characterize and quantify these advanced imaging features. MOGAD patient imaging data will be compared to already acquired data from 20 age- and sex-matched MS patients. In Aim 2, we will correlate this qGRE data in MOGAD patients with clinical test results and compare these results to those in MS. A comparison of clinical test scores measuring physical dysfunction in legs and arms and tests of cognition to the qGRE findings may allow us to understand the distribution and severity of MOGAD pathology, which has not yet been well-defined. This will be done in comparison to data we have already acquired linking these same test results and qGRE data in MS patients. Upon successful completion of our aims, we expect that qGRE could be used clinically to simplify and improve the diagnostic accuracy for both MOGAD and MS, limiting misdiagnoses. We also expect that qGRE will help to better understand the disease processes underlying MOGAD.