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Small‐world network topology of hippocampal neuronal network is lost, in an in vitro glutamate injury model of epilepsy
Author(s) -
Srinivas Kalyan V.,
Jain Rishabh,
Saurav Subit,
Sikdar Sujit K.
Publication year - 2007
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/j.1460-9568.2007.05559.x
Subject(s) - neuroscience , hippocampal formation , network topology , biological neural network , glutamate receptor , computer science , biology , computer network , receptor , biochemistry
Neuronal network topologies and connectivity patterns were explored in control and glutamate‐injured hippocampal neuronal networks, cultured on planar multielectrode arrays. Spontaneous activity was characterized by brief episodes of synchronous firing at many sites in the array (network bursts). During such assembly activity, maximum numbers of neurons are known to interact in the network. After brief glutamate exposure followed by recovery, neuronal networks became hypersynchronous and fired network bursts at higher frequency. Connectivity maps were constructed to understand how neurons communicate during a network burst. These maps were obtained by analysing the spike trains using cross‐covariance analysis and graph theory methods. Analysis of degree distribution, which is a measure of direct connections between electrodes in a neuronal network, showed exponential and Gaussian distributions in control and glutamate‐injured networks, respectively. Although both the networks showed random features, small‐world properties in these networks were different. These results suggest that functional two‐dimensional neuronal networks in vitro are not scale‐free. After brief exposure to glutamate, normal hippocampal neuronal networks became hyperexcitable and fired a larger number of network bursts with altered network topology. The small‐world network property was lost and this was accompanied by a change from an exponential to a Gaussian network.