权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10517501
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
项目类别：
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/10517501
关键词：
Affect Area Autopsy Axon Biological Markers Brain Cellularity Classification Clinical Contrast Media Coupled Data Demyelinating Diseases Demyelinations Diffuse Axonal Injury 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 Measurement Minor Modeling Morphology Multiple Sclerosis Mus Myelin Nerve Neurologic Optic Nerve Pathology Perfusion Periodic acid Schiff stain method Persons Protocols documentation Rana Reporting Ringer&apos s solution Sampling Sensitivity and Specificity Signal Transduction Silver Specificity Specimen Spinal cord injury Stains Structure Temperature Therapeutic Intervention Time Tissues Tolonium chloride Translating Treatment Efficacy Uncertainty Validation Water Work axon injury central nervous system demyelinating disorder central nervous system injury code development cohort data-driven model deep learning algorithm deep neural network density detection sensitivity disability imaging approach improved in vivo manganese chloride multiple sclerosis patient neurotransmission novel preservation prevent programs remyelination sciatic nerve severe injury spectrograph support vector machine vasogenic edema virtual white matter

项目摘要

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.

项目总结