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Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

Frontiers in Computational Neuroscience. 2012;6 DOI 10.3389/fncom.2012.00038

 

Journal Homepage

Journal Title: Frontiers in Computational Neuroscience

ISSN: 1662-5188 (Online)

Publisher: Frontiers Media S.A.

LCC Subject Category: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS


Fikret Emre eKapucu (Tampere University of Technology)

Fikret Emre eKapucu (Institute of Biosciences and Medical Technology)

Jarno M. A. Tanskanen (Tampere University of Technology)

Jarno M. A. Tanskanen (Institute of Biosciences and Medical Technology)

Jarno E Mikkonen (University of Jyväskylä)

Laura eYlä-Outinen (University of Tampere and Tampere University Hospital)

Laura eYlä-Outinen (Institute of Biosciences and Medical Technology)

Susanna eNarkilahti (University of Tampere and Tampere University Hospital)

Susanna eNarkilahti (Institute of Biosciences and Medical Technology)

Jari A. K. Hyttinen (Tampere University of Technology)

Jari A. K. Hyttinen (Institute of Biosciences and Medical Technology)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 14 weeks

 

Abstract | Full Text

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.