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分享神经元形态的数字重建成果与回报。

Successes and rewards in sharing digital reconstructions of neuronal morphology.

作者信息

Ascoli Giorgio A

机构信息

Krasnow Inst. for Advanced Study and Neuroscience Program, George Mason University, Fairfax, VA, USA.

出版信息

Neuroinformatics. 2007 Fall;5(3):154-60. doi: 10.1007/s12021-007-0010-7.

Abstract

The computer-assisted three-dimensional reconstruction of neuronal morphology is becoming an increasingly popular technique to quantify the arborization patterns of dendrites and axons. The resulting digital files are suitable for comprehensive morphometric analyses as well as for building anatomically realistic compartmental models of membrane biophysics and neuronal electrophysiology. The digital tracings acquired in a lab for a specific purpose can be often re-used by a different research group to address a completely unrelated scientific question, if the original investigators are willing to share the data. Since reconstructing neuronal morphology is a labor-intensive process, data sharing and re-analysis is particularly advantageous for the neuroscience and biomedical communities. Here we present numerous cases of "success stories" in which digital reconstructions of neuronal morphology were shared and re-used, leading to additional, independent discoveries and publications, and thus amplifying the impact of the "source" study for which the data set was first collected. In particular, we overview four main applications of this kind of data: comparative morphometric analyses, statistical estimation of potential synaptic connectivity, morphologically accurate electrophysiological simulations, and computational models of neuronal shape and development.

摘要

计算机辅助的神经元形态三维重建正日益成为一种流行的技术,用于量化树突和轴突的分支模式。生成的数字文件适用于全面的形态计量分析,也适用于构建膜生物物理学和神经元电生理学的解剖学逼真的房室模型。如果原始研究者愿意分享数据,那么在实验室中为特定目的获取的数字追踪结果通常可以被不同的研究小组重新使用,以解决一个完全不相关的科学问题。由于重建神经元形态是一个劳动密集型过程,数据共享和重新分析对神经科学和生物医学领域尤其有利。在这里,我们展示了许多“成功案例”,其中神经元形态的数字重建被共享和重新使用,从而带来了额外的、独立的发现和出版物,进而扩大了首次收集数据集的“源”研究的影响力。特别是,我们概述了这类数据的四个主要应用:比较形态计量分析、潜在突触连接性的统计估计、形态学精确的电生理模拟以及神经元形状和发育的计算模型。

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