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一种用于对填充示踪剂或经高尔基浸染的神经元(包括棘突和膨体)进行编码、图形表示和度量分析的“扩展棒状”模型的新进展。

New developments in an "expanded stick" model for coding, graphic representation and metric analysis of tracer-filled or Golgi-impregnated neurons, including spines and varicosities.

作者信息

Freire M

机构信息

Instituto Cajal, Madrid, Spain.

出版信息

J Neurosci Methods. 1991 Mar;37(1):71-9. doi: 10.1016/0165-0270(91)90022-r.

Abstract

A new data model allowing the coding, graphic representation and metric analysis of dendritic and axonal processes including spines and varicosities, is here described. The model is implemented in an interactive light microscope-computer system and stores the three-dimensional coordinates of the selected neuronal points, their topological identifiers, and the width of the processes. In addition codes for "nature", and "shape" are stored in the data array. The "nature" code identifies structures such as perikaryon, axon, apical dendrite, basal dendrite, etc. The "shape" code defines varicosities and spines and allows their graphic representation. At present, the coding for metric analysis is made at a final magnification of x1875, with a resolution of 0.11 microns in the objective plane. The graphic representation of spines and varicosities is an ellipse, whose major axis is the length of spines and varicosities and the minor axis the width of these structures. From this "expanded stick" model a computer program calculates the length, area and form factor of the perikaryon; the mean length, width and area of each neuronal branch; the distribution of varicosity and spine number and their size (length and width) per length interval; the total number of processes, varicosities and spines; and the total length and area of the processes.

摘要

本文描述了一种新的数据模型,该模型可对包括棘突和膨体在内的树突和轴突过程进行编码、图形表示和度量分析。该模型在交互式光学显微镜-计算机系统中实现,存储所选神经元点的三维坐标、它们的拓扑标识符以及过程的宽度。此外,“性质”和“形状”代码存储在数据数组中。“性质”代码识别诸如胞体、轴突、顶端树突、基底树突等结构。“形状”代码定义膨体和棘突,并允许对它们进行图形表示。目前,度量分析的编码是在1875倍的最终放大倍数下进行的,在物镜平面上的分辨率为0.11微米。棘突和膨体的图形表示是一个椭圆,其长轴是棘突和膨体的长度,短轴是这些结构的宽度。根据这个“扩展杆”模型,一个计算机程序可以计算胞体的长度、面积和形状因子;每个神经元分支的平均长度、宽度和面积;膨体和棘突数量的分布以及它们在每个长度间隔内的大小(长度和宽度);过程、膨体和棘突的总数;以及过程的总长度和面积。

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