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海马体培养物中的功能聚类:关联网络结构和动力学。

Functional clustering in hippocampal cultures: relating network structure and dynamics.

机构信息

Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Phys Biol. 2010 Oct 26;7(4):046004. doi: 10.1088/1478-3975/7/4/046004.

Abstract

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.

摘要

在这项工作中,我们研究了分离大鼠海马培养物中总解剖结构网络性质、神经元动力学和由此产生的功能结构之间的关系。具体来说,我们研究了在两种条件下发育的培养物:第一种支持神经胶质细胞生长(高神经胶质组),第二种抑制神经胶质细胞生长(低神经胶质组)。然后,我们比较了结构网络特性和神经元的时空活动模式。这两组之间的动力学差异可能与胶质网络对神经元网络的影响有关,因为培养物在发育过程中。我们还实现了一种称为功能聚类算法(FCA)的新算法,以获得结果功能网络结构。我们表明,该新算法对于捕获网络随时间演变的功能网络结构变化是有用的。FCA 检测到功能结构的变化与由于胶质网络的影响而导致的预期动力学差异一致。高神经胶质组中的培养物随着培养物的老化而表现出全局同步性的增加,而低神经胶质组中的培养物保持局部同步性。我们还使用 FCA 来量化培养物中存在的同步量,并表明高神经胶质组中的总同步水平强于低神经胶质组。这些结果表明,分离培养物中存在胶质和神经元网络之间的相互依存关系。

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