Virtual Histology for Assessing MS Pathologies

项目总结 多发性硬化症(MS)是一种炎性脱髓鞘疾病，最终导致不可逆转的轴突损伤 导致永久性的神经功能障碍。防止疾病进展或治疗进行性多发性硬化症 仍然是一个尚未得到满足的主要临床需求。我们以前开发了一种新的数据驱动模型--选择 扩散基础光谱成像(DBSI)可准确成像炎症、脱髓鞘和轴突损伤， 以及在实验性自身免疫中存在血管源性水肿的情况下量化轴突丢失 脑脊髓炎(EAE)和脊髓损伤小鼠，以及MS的脑WM病理。 MRI不能区分轴突间和轴突内的水信号，反映信号的加权平均 在两个车厢之间。然而，我们最近观察到，dBSI导出了轴向扩散系数(dBSI-λǁ) 在多发性硬化(PWMS)患者的正常外观白质(NAWM)中略有升高。这是升空的 DBSI-λǁ在评估轴突损伤是否增加了不确定性(与↓DBSI-λǁ≈轴突损伤的概念相反) 存在于这些PWMS的NAWM中。在这项拟议的研究中，我们将改进DBSI以进一步提高其敏感度 以及对轴突损伤/丢失、脱髓鞘和炎症的特异性，以准确评估疾病 PWMS的进展和治疗效果。 由于MRI不能区分轴突间和轴突内的水信号，它反映了 轴突间和轴突内信号。在炎症相关的水肿或轻微的轴突丢失的情况下 PWMS，人体扫描仪的扩散时间越长，加上轴突间间隙的增加，就会导致 增加DBSI-λǁ可掩盖轴突损伤的可察觉。因此，通过分离轴突间和轴突内 水室信号、DBSI来源的轴突内λ对轴突损伤的敏感性和特异性 (DBSI-IA-λ||)可能会改进。这一新模型仍将保持各向同性扩散的专一性 炎症和组织丢失。 我们提出了三个具体目标来证明或反驳这一假设：目标1.执行DBSI和DBSI-IA PWMS尸检标本常规组织学和免疫组织化学分析 染色。目的2.应用DBSI和DBSI-IA技术建立蛙坐骨神经灌流模型 造影剂用于分离轴突间/轴突内的空间水信号。目标3a。发展扩散组织学 结合DBSI/DBSI-IA指标和机器/深度学习算法的成像(DHI)方法 总结多发性硬化症病理的组织学特异性。目标3b。转换DBSI-IA模型以分析现有DWI的步骤 来自先前在过期计划项目中成像的PWM队列的数据。

PROJECT SUMMARY Multiple sclerosis (MS) is an inflammatory demyelinating disease with, ultimately, irreversible axonal injury leading to permanent neurological disabilities. Preventing disease progression or treating progressive MS remains a major unmet clinical need. We have previously developed a novel data-driven model-selection diffusion basis spectrum imaging (DBSI) to accurately image inflammation, demyelination, and axonal injury, as well as quantifying axonal loss in the presence of vasogenic edema in experimental autoimmune encephalomyelitis (EAE) and spinal cord injury mice, and brain WM pathologies in MS. MRI does not distinguish inter- from intra-axonal water signals, reflecting a weighted-average of signals between the two compartments. However, our recent observation that DBSI derived axial diffusivity (DBSI-λǁ) was slightly elevated in normal appearing white matter (NAWM) in people with MS (pwMS). This elevated DBSI-λǁ added uncertainty in assessing whether axonal injury (against the notion that ↓DBSI-λǁ ≈ axonal injury) is present in NAWM of these pwMS. In this proposed study, we will refine DBSI to further improve its sensitivity and specificity to axonal injury/loss, demyelination, and inflammation for accurately assessing disease progression and therapeutic efficacy in pwMS. Since MRI does not distinguish inter- from intra-axonal water signals, it reflects a weighted-average between inter- and intra-axonal signals. In the presence of inflammation-associated edema or minor axonal loss in pwMS, the longer diffusion time for human scanners coupled with the increased inter-axonal space will lead to increased DBSI-λǁ masking the detectability of axonal injury. Thus, through separating inter- and intra-axonal water compartment signals, the sensitivity and specificity to axonal injury of DBSI-derived intra-axonal λ|| (DBSI-IA-λ||) may be improved. This new model will still preserve the isotropic diffusion specificity to inflammation and tissue loss. We propose three specific aims to prove or disprove this hypothesis: Aim 1. To perform DBSI and DBSI-IA analyses on autopsy specimens from pwMS followed by conventional histology and immunohistochemical staining. Aim 2. To perform DBSI and DBSI-IA modeling on perfused frog sciatic nerve with and without contrast agent to separate inter-/intra-axonal space water signal. Aim 3a. To develop a Diffusion Histology Imaging (DHI) approach combining DBSI/DBSI-IA metrics and machine/deep learning algorithms to recapitulate histology specificity to MS pathology. Aim 3b. To translate DBSI-IA model to analyze existing DWI data from the cohort of pwMS previously imaged in an expired program project.