BIOMARKERS AND PATHOGENESIS OF MS: FROM MOUSE TO HUMAN

在最初的资助期间，我们的团队开发了一种新的扩散基光谱成像（DBSI） 一种同时检测和定量体内炎症、脱髓鞘和轴突损伤的方法， 弥散成像（Wang et al. 2011）。DBSI大大提高了我们先前的准确性和特异性， 扩散张量成像（DTI）的方法，通过克服DTI的主要不足之处。我们假设 DBSI可以定量MS患者CNS中轴突损伤、脱髓鞘和炎症的比例。 我们之前使用小鼠模型表明，DBSI检测和定量轴突和髓鞘损伤， 标准成像和弥散张量成像都没有发现。我们的初步数据现在包括验证 DBSI使用尸检和活检的人类标本，与人类 组织学我们现在也有跨越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MS患者，并将其与 标准MRI、DTI和MTI。项目3将通过轴突损伤的测量对MS病变亚型进行分类， 脱髓鞘和炎症（由于水肿或组织损失导致的细胞结构和游离水增加），并随后 4年以上的患者，以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 通过区分突出的轴突损伤与轴突保留，以及脱髓鞘与髓鞘，使用DBSI 保存/髓鞘再生。我们将研究已建立的持久黑洞（PBH）（新的子目标）， 并对钆增强（Gd+）MS病变进行纵向评估，以确定DBSI是否会 预测PBH形成，代表严重的轴突损失。由于它能够描绘病变和正常外观 CNS、DBSI有助于无创性地阐明MS病变形成的底物和检测炎症 在完整的血液-CNS屏障后面（未被Gd+检测到）。DBSI具有帮助开发和测试的潜力 新的治疗进展性MS的方法，其中轴突和组织完整性的损失被认为发挥了很大的作用。

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.