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出生后人类新皮质发育过程中相对神经元群体的同步变化。

Synchronized changes to relative neuron populations in postnatal human neocortical development.

出版信息

Cogn Neurodyn. 2010 Jun;4(2):151-63. doi: 10.1007/s11571-010-9103-3. Epub 2010 Feb 3.

Abstract

UNLABELLED

Mammalian prenatal neocortical development is dominated by the synchronized formation of the laminae and migration of neurons. Postnatal development likewise contains "sensitive periods" during which functions such as ocular dominance emerge. Here we introduce a novel neuroinformatics approach to identify and study these periods of active development. Although many aspects of the approach can be used in other studies, some specific techniques were chosen because of a legacy dataset of human histological data (Conel in The postnatal development of the human cerebral cortex, vol 1-8. Harvard University Press, Cambridge, 1939-1967). Our method calculates normalized change vectors from the raw histological data, and then employs k-means cluster analysis of the change vectors to explore the population dynamics of neurons from 37 neocortical areas across eight postnatal developmental stages from birth to 72 months in 54 subjects. We show that the cortical "address" (Brodmann area/sub-area and layer) provides the necessary resolution to segregate neuron population changes into seven correlated "k-clusters" in k-means cluster analysis. The members in each k-cluster share a single change interval where the relative share of the cortex by the members undergoes its maximum change. The maximum change occurs in a different change interval for each k-cluster. Each k-cluster has at least one totally connected maximal "clique" which appears to correspond to cortical function.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11571-010-9103-3) contains supplementary material, which is available to authorized users.

摘要

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哺乳动物产前新皮质发育以层和神经元迁移的同步形成为主导。出生后的发育同样包含“敏感时期”,在此期间,如眼优势等功能会出现。在这里,我们引入一种新的神经信息学方法来识别和研究这些活跃发育的时期。尽管该方法的许多方面都可以用于其他研究,但由于人类组织学数据的遗留数据集(Conel 在人类大脑皮质的产后发育,第 1-8 卷。哈佛大学出版社,剑桥,1939-1967),我们选择了一些特定的技术。我们的方法从原始组织学数据中计算标准化变化向量,然后使用变化向量的 k-均值聚类分析来探索来自 37 个新皮质区域的神经元的群体动态,跨越 54 个个体从出生到 72 个月的八个产后发育阶段。我们表明,皮质“地址”(布罗德曼区/亚区和层)提供了将神经元群体变化分离到 k-均值聚类分析的七个相关“k-聚类”的必要分辨率。每个 k-聚类中的成员共享一个单一的变化间隔,成员所占据的皮质相对份额在该间隔内发生最大变化。最大变化发生在每个 k-聚类的不同变化间隔内。每个 k-聚类至少有一个完全连接的最大“团”,这似乎对应于皮质功能。

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本文引用的文献

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