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.

项目摘要 多发性硬化症（MS）是一种炎症性脱髓鞘疾病，最终是不可逆的轴突损伤 导致永久性神经系统疾病。防止疾病进展或治疗进行性MS 仍然是主要的未满足临床需求。我们以前已经开发了一种新型的数据驱动模型选择 扩散基频谱成像（DBSI）以准确的图像注射，脱髓鞘和轴突损伤， 以及在实验自身免疫性中具有加血管水肿的情况下量化轴突损失 MS中的脑脊髓炎（EAE）和脊髓损伤小鼠以及脑WM病理。 MRI不会区分轴内水信号，反映了信号的加权平均 在两个隔间之间。但是，我们最近的观察结果是DBSI衍生的轴向扩散率（DBSI-λǁ） 在MS（PWMS）的患者中，正常出现的白质（NAWM）在正常出现的白质（NAWM）中略有升高。 DBSI-λǁ在评估轴突损伤时增加了不确定性（针对↓DBSI-λǁ≈轴突损伤的观点） 存在于这些PWM的NAWM中。在这项拟议的研究中，我们将完善DBSI以进一步提高其敏感性 以及轴突损伤/丧失，脱髓鞘和感染的特异性，以精确评估疾病 PWMS的进展和治疗效率。 由于MRI没有区分轴内水信号，因此它反映了加权平均水平 间和轴内信号。在存在与炎症相关的水肿或未成年轴突丧失的情况下 PWMS，人类扫描仪的扩散时间较长，并增加轴间空间的扩散时间将导致 DBSI-λǁ掩盖了轴突损伤的可检测性。因此，通过分离轴内和轴内 水室信号，对DBSI衍生的轴内λ|轴突损伤的敏感性和特异性|| （DBSI-II-λ||）可以改善。该新模型仍然将各向同性扩散特异性保留到 炎症和组织损失。 我们提出了三个特定的目的，以证明或反驳这一假设：目的1。执行DBSI和DBSI-II 对PWMS的尸检标本的分析，然后进行常规组织学和免疫组织化学 染色。 AIM 2。在有或没有 对比剂，分离间/轴内空间水信号。目标3a。开发扩散组织学 成像（DHI）方法将DBSI/DBSI-II指标与机器/深度学习算法结合在一起 概括了对MS病理学的组织学特异性。目标3B。翻译DBSI-II模型以分析现有DWI 先前在过期的程序项目中成像的PWMS的数据。