BIOMARKERS AND PATHOGENESIS OF MS: FROM MOUSE TO HUMAN

在最初的资助期间，我们团队开发了一种新的扩散基光谱成像(DBSI) 一种同时检测体内炎症、脱髓鞘和轴突损伤的方法 扩散成像(Wang等人2011年)。DBSI大大提高了我们之前的准确性和特异性 弥散张量成像(DTI)方法，克服了弥散张量成像的主要不足。我们假设 DBSI可以量化MS患者中枢神经系统轴突损伤、脱髓鞘和炎症的比例。 我们之前使用小鼠模型显示，DBSI检测和量化轴突和髓鞘损伤， 通过标准成像和DTI逃脱了检测。我们的初步数据现在包括验证 使用尸检和活检的人类样本进行DBSI，与人类有良好的相关性 组织学。我们现在还拥有跨越1.5年的纵向数据，并将DBSI与 磁化传递成像(MTI)。在项目3中，我们将DBSI应用于患有多发性硬化症的人类，并将其与 标准MRI、DTI和MTI。项目3将根据轴突损伤的指标对多发性硬化症病变亚型进行分类， 脱髓鞘和炎症(细胞增多和因浮肿或组织丢失而增加的自由水)，然后 对4年以上的患者确定临床恶化的预测因素和相关因素。我们期望实现这一目标 应用DBSI区分显著轴突损伤与轴突保留、脱髓鞘与髓鞘 保存/再髓鞘形成。我们将研究已建立的持久黑洞(PBH)(新子目标)， 并对Gd+MS病变进行纵向评估，以确定DBSI是否会 预测PBH的形成，代表着严重的轴突丢失。它具有描绘病变和正常外观的能力 CNS、DBSI可帮助无创性地阐明MS病变形成的底物并检测炎症反应 在完整的血-中枢神经系统屏障后面(Gd+检测不到)。DBSI具有帮助开发和测试的潜力 对于进展性多发性硬化症的新疗法，轴突和组织完整性的丧失被认为起着重要作用。

During the initial funding period, our group developed a novel Diffusion Basis Spectrum Imaging (DBSI) method to simultaneously detect and quantitate inflammation, demyelination and axon injury in vivo using diffusion imaging (Wang et al. 2011). DBSI has substantially improved the accuracy and specificity of our prior diffusion tensor imaging (DTI) approach, by overcoming the main inadequacies of DTI. We hypothesize that DBSI can quantitate the proportions of axon injury, demyelination, and inflammation in CNS of MS patients. We previously showed using a mouse model that DBSI detects and quantitates axonal and myelin injuries that had escaped detection by standard imaging, and by DTI. Our preliminary data now include validation of DBSI using autopsied and biopsied human specimens, with favorable correlations with human histology. We also now have longitudinal data spanning 1.5 yrs, and comparisons of DBSI with magnetization transfer imaging (MTI). In Project 3, we will apply DBSI to humans with MS, comparing it to standard MRI, DTI and MTI. Project 3 will classify MS lesion subtypes by measures of axon injury, demyelination, and inflammation (cellularity and increased free water due to edema or tissue loss), and follow the patients over 4 years to identify predictors and correlates of clinical deterioration. We expect to achieve this using DBSI by differentiating prominent axonal injury vs. axon preservation, and demyelination vs. myelin preservation/ remyelination. We will examine established persistent black holes (PBHs) (new sub-aim), and perform longitudinal assessments of gadolinium-enhancing (Gd+) MS lesions to determine if DBSI will predict PBH formation, representing severe axon loss. With its ability to profile lesions and normal-appearing CNS, DBSI could help non-invasively elucidate the substrate of MS lesion formation and detect inflammation behind an intact blood-CNS-barrier (not detected by Gd+). DBSI has potential to aid development and testing of new therapies for progressive MS where loss of axons and tissue integrity are believed to play a large role.