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小鼠桶状皮层VI层神经元的形态异质性。

Morphological heterogeneity of layer VI neurons in mouse barrel cortex.

作者信息

Chen Chia-Chien, Abrams Svetlana, Pinhas Alex, Brumberg Joshua C

机构信息

Neuropsychology Doctoral Subprogram, The Graduate Center, City University of New York, New York, New York 10016, USA.

出版信息

J Comp Neurol. 2009 Feb 20;512(6):726-46. doi: 10.1002/cne.21926.

Abstract

Understanding the basic neuronal building blocks of the neocortex is a necessary first step toward comprehending the composition of cortical circuits. Neocortical layer VI is the most morphologically diverse layer and plays a pivotal role in gating information to the cortex via its feedback connection to the thalamus and other ipsilateral and callosal corticocortical connections. The heterogeneity of function within this layer is presumably linked to its varied morphological composition. However, so far, very few studies have attempted to define cell classes in this layer using unbiased quantitative methodologies. Utilizing the Golgi staining technique along with the Neurolucida software, we recontructed 222 cortical neurons from layer VI of mouse barrel cortex. Morphological analyses were performed by quantifying somatic and dendritic parameters, and, by using principal component and cluster analyses, we quantitatively categorized neurons into six distinct morphological groups. Additional systematic replication on a separate population of neurons yielded similar results, demonstrating the consistency and reliability of our categorization methodology. Subsequent post hoc analyses of dendritic parameters supported our neuronal classification scheme. Characterizing neuronal elements with unbiased quantitative techniques provides a framework for better understanding structure-function relationships within neocortical circuits in general.

摘要

了解新皮层的基本神经元组成部分是理解皮层回路组成的必要第一步。新皮层第VI层是形态最多样化的一层,通过其与丘脑的反馈连接以及其他同侧和胼胝体皮质-皮质连接,在向皮层传递信息方面发挥着关键作用。该层内功能的异质性大概与其多样的形态组成有关。然而,到目前为止,很少有研究尝试使用无偏定量方法来定义该层中的细胞类别。利用高尔基染色技术和Neurolucida软件,我们从小鼠桶状皮层第VI层重建了222个皮层神经元。通过量化体细胞和树突参数进行形态学分析,并使用主成分分析和聚类分析,我们将神经元定量分为六个不同的形态组。在另一群神经元上进行的额外系统重复实验也得到了类似结果,证明了我们分类方法的一致性和可靠性。随后对树突参数的事后分析支持了我们的神经元分类方案。用无偏定量技术表征神经元成分,总体上为更好地理解新皮层回路中的结构-功能关系提供了一个框架。

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