Assessing Spatial Patterns of Epidermal Nerve Fibers

评估表皮神经纤维的空间模式

• 批准号: 6561707
• 负责人: Lance A Waller
• 金额: $ 17.65万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6561707
• 关键词: biomarker; blister; clinical research; diabetic neuropathy; form /pattern perception; human data; measurement; morphology; morphometry; nerve; skin; statistics /biometry

DESCRIPTION (provided by applicant): Skin blister biopsies allows a "bird's eye" view of the spatial pattern of epidermal nerve fibers (ENFs) across the epidermis. Visual comparison of blister images from diabetic and nondiabetic subjects shows the spatial pattern of ENFs becomes more "clustered" as neuropathy advances. Accurate quantification of these observations via spatial statistics would provide both a minimally invasive screening technique, and a tool to assess other proposed biomarkers of early neuropathy. We propose a focused two year research program (R21) to assess the accuracy and reliability of the use of spatial statistics to quantify observed spatial patterns of ENFs in blister images. We will: 1. Calculate intra- and interindividual variation for measures of spatial clustering. We will use spatial point process methods to define the amount of clustering observed within an image and assess the associated between/within individual variation using a recent extensive collection of blister images. 2. Establish normative ranges for measures of spatial clustering. As a by-product of the first specific aim, we will establish normative ranges for the degree of spatial clustering by body site and diabetes status, stratified by age and sex. 3. Incorporate morphologic measurements. ENF images include information regarding the morphologic structure of the nerve fibers themselves including length, direction, numbers of branches, etc. We propose investigation of statistical models addressing such measures to isolate structural changes in ENFs due to diabetes status. Preliminary results for each aim are promising but require replication on a large number of images to determine their overall utility for screening or as a standard of comparison for assessing performance of other biomarkers. The R21 program structure combined with a recently available collection of blister images provide an ideal mechanism for careful development and critical assessment of spatial analyses of ENF blister images for identifying and verifying progression of neuropathy.

描述（由申请人提供）： 皮肤水泡活检可以“鸟瞰”整个表皮的表皮神经纤维 (ENF) 的空间模式。糖尿病和非糖尿病受试者的水泡图像的视觉比较显示，随着神经病变的进展，ENF 的空间模式变得更加“聚集”。通过空间统计对这些观察结果进行准确量化将提供一种微创筛查技术，以及评估其他提出的早期神经病变生物标志物的工具。我们提出了一项为期两年的重点研究计划 (R21)，以评估使用空间统计来量化泡罩图像中观察到的 ENF 空间模式的准确性和可靠性。我们将： 1. 计算空间聚类度量的个体内和个体间变异。我们将使用空间点处理方法来定义图像内观察到的聚类量，并使用最近广泛收集的泡罩图像来评估个体变异之间/内部的相关性。 2. 建立空间聚集度量的规范范围。作为第一个具体目标的副产品，我们将为身体部位和糖尿病状况的空间聚集程度建立规范范围，并按年龄和性别分层。 3. 结合形态学测量。 ENF 图像包含有关神经纤维本身形态结构的信息，包括长度、方向、分支数量等。我们建议研究统计模型，以解决此类措施，以隔离由于糖尿病状态而导致的 ENF 结构变化。每个目标的初步结果都充满希望，但需要在大量图像上进行复制，以确定其筛选的总体效用或作为评估其他生物标志物性能的比较标准。 R21 程序结构与最近可用的水泡图像集合相结合，为仔细开发和严格评估 ENF 水泡图像的空间分析提供了理想的机制，以识别和验证神经病变的进展。