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Author Ekapucu, Fikret Emre ♦ Enarkilahti, Susanna ♦ Mikkonen, Jarno E. ♦ Tanskanen, Jarno M. A. ♦ Eyläoutinen, Laura ♦ Hyttinen, Jari A. K.
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2014-05-22
Copyright Year ©2012
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Neuropsychiatry ♦ Biological psychiatry ♦ Microelectrode array ♦ Developing neuronal networks ♦ Neurosciences ♦ Action potential bursts ♦ Internal medicine ♦ MEA ♦ Medicine ♦ Burst analysis ♦ HESCs
Abstract In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI) histograms. Moreover, the algorithm calculates interspike interval thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA) data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays.
ISSN 16625188
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2012-06-01
e-ISSN 16625188
Journal Frontiers in Computational Neuroscience
Volume Number 6


Source: Directory of Open Access Journals (DOAJ)