Total Number Is Important: Using the Disector Method in Design-Based Stereology to Understand the Structure of the Rodent Brain.
Total Number Is Important: Using the Disector Method in Design-Based Stereology to Understand the Structure of the Rodent Brain.
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DOI：
10.3389/fnana.2018.00016
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发表时间：
2018
影响因子：
2.9
通讯作者：
Napper RMA
中科院分区：
文献类型：
--
作者：
Napper RMA
自从Sterio描述了基于设计的体视学方法以来，在许多出版物中强调了使用基于设计的体视学收集定量数据的优势。这篇综述文章讨论了用偏向器方法得出的总数的重要性，作为一个必须继续用于了解啮齿类动物大脑的关键变量，以及这些数据可以用来建立大脑的定量网络。这篇综述文章将强调总数对我们对啮齿类动物大脑的理解产生的巨大影响，并建议神经科学家需要意识到越来越多的研究使用密度而不是总数作为定量衡量标准。它将强调，密度可能会导致误导性的数据，最常见的是在未知的方向上，我们面临着这种类型的数据被集体神经科学知识数据库接受的风险。它还将表明，使用定向方法的基于设计的体视学可以与电子显微镜的最新发展一起使用，例如连续的块面扫描电子显微镜(SEM)，以在超微结构水平上非常有效地获得总数数据。在整篇文章中，总数量被讨论作为理解啮齿类动物大脑微网络的关键参数，因为它们可以被表示为解剖网络和定量网络。
The advantages of using design-based stereology in the collection of quantitative data, have been highlighted, in numerous publications, since the description of the disector method by Sterio. This review article discusses the importance of total number derived with the disector method, as a key variable that must continue to be used to understand the rodent brain and that such data can be used to develop quantitative networks of the brain. The review article will highlight the huge impact total number has had on our understanding of the rodent brain and it will suggest that neuroscientists need to be aware of the increasing number of studies where density, not total number, is the quantitative measure used. It will emphasize that density can result in data that is misleading, most often in an unknown direction, and that we run the risk of this type of data being accepted into the collective neuroscience knowledge database. It will also suggest that design-based stereology using the disector method, can be used alongside recent developments in electron microscopy, such as serial block-face scanning electron microscopy (SEM), to obtain total number data very efficiently at the ultrastructural level. Throughout the article total number is discussed as a key parameter in understanding the micro-networks of the rodent brain as they can be represented as both anatomical and quantitative networks.
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影响因子：
2.9
作者：
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通讯作者：
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DOI：
10.1046/j.1365-2818.2001.00958.x
发表时间：
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期刊：
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发表时间：
2004-11-01
期刊：
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