Virtual Histology for Assessing MS Pathologies

用于评估多发性硬化症病理学的虚拟组织学

• 批准号: 10308715
• 负责人: SHENG-KWEI SONG
• 金额: $ 45.84万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10308715
• 关键词: Affect; Area; Autopsy; Axon; Biological Markers; Brain; Cellularity; Clinical; Contrast Media; Coupled; Data; Demyelinating Diseases; Demyelinations; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Disease Progression; Edema; Electrophysiology (science); Experimental Autoimmune Encephalomyelitis; Experimental Designs; Fast Blue; Fiber; Gadolinium DTPA; Gadopentetate Dimeglumine; Germany; Glucose; Histologic; Histology; Human; Image; Immunohistochemistry; Inflammation; Inflammatory; Injury; Magnetic Resonance Imaging; Masks; Measurement; Minor; Modeling; Morphology; Multiple Sclerosis; Mus; Myelin; Nerve; Neurologic; Optic Nerve; Pathology; Perfusion; Periodic acid Schiff stain method; Persons; Rana; Reporting; Ringer' s solution; Sampling; Sensitivity and Specificity; Signal Transduction; Silver; Specificity; Specimen; Spinal cord injury; Stains; Structure; Temperature; Therapeutic Intervention; Thinness; Time; Tissues; Tolonium chloride; Translating; Treatment Efficacy; Uncertainty; Validation; Water; Work; axon injury; base; central nervous system demyelinating disorder; central nervous system injury; code development; cohort; data-driven model; deep learning algorithm; deep neural network; density; disability; imaging approach; improved; in vivo; manganese chloride; neurotransmission; novel; preservation; prevent; programs; remyelination; sciatic nerve; severe injury; spectrograph; support vector machine; vasogenic edema; virtual; white matter

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.

项目概要 多发性硬化症 (MS) 是一种炎症性脱髓鞘疾病，最终会导致不可逆的轴突损伤 导致永久性神经功能障碍或治疗进行性多发性硬化症。 仍然是一个未满足的主要临床需求。我们之前开发了一种新颖的数据驱动模型选择。 扩散基础能谱成像 (DBSI) 可准确成像炎症、脱髓鞘和轴突损伤， 以及量化实验性自身免疫中血管源性水肿存在下的轴突损失 脑脊髓炎 (EAE) 和脊髓损伤小鼠，以及 MS 中的脑部 WM 病理。 MRI 不区分轴突间和轴突内水信号，反映的是信号的加权平均值 然而，我们最近观察到 DBSI 得出的轴向扩散率 (DBSI-λǁ) MS (pwMS) 患者的正常白质 (NAWM) 略有升高。 DBSI-λǁ 增加了评估轴突损伤是否存在的不确定性（反对 ↓DBSI-λǁ ≈ 轴突损伤的观点） 存在于这些 pwMS 的 NAWM 中。在这项拟议的研究中，我们将改进 DBSI 以进一步提高其灵敏度。 以及对轴突损伤/损失、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水信号，因此它反映了轴突间和轴突内水信号的加权平均值。 存在炎症相关水肿或轻微轴突丢失的情况下。 pwMS，人体扫描仪的较长扩散时间加上轴突间空间的增加将导致 增加的 DBSI-λǁ 掩盖了轴突损伤的可检测性，因此，通过分离轴突间和轴突内。 水室信号，DBSI 衍生的轴突内 λ|| 对轴突损伤的敏感性和特异性 (DBSI-IA-λ||) 可能会得到改进，这个新模型仍将保留各向同性扩散特异性。 炎症和组织损失。 我们提出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 对 pwMS 尸检标本进行分析，然后进行常规组织学和免疫组织化学分析 目的 2. 对有或没有灌注的青蛙坐骨神经进行 DBSI 和 DBSI-IA 建模。 分离轴突间/轴内空间水信号的造影剂 目标 3a。 成像 (DHI) 方法结合 DBSI/DBSI-IA 指标和机器/深度学习算法 概括 MS 病理学的组织学特异性 目标 3b。 数据来自先前在过期计划项目中成像的 pwMS 队列。