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

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dc.contributor.author Kapucu, Fikret E.
dc.contributor.author Tanskanen, Jarno M. A.
dc.contributor.author Mikkonen, Jarno
dc.contributor.author Ylä-Outinen, Laura
dc.contributor.author Narkilahti, Susanna
dc.contributor.author Hyttinen, Jari A. K.
dc.date.accessioned 2012-12-07T12:19:46Z
dc.date.available 2012-12-07T12:19:46Z
dc.date.issued 2012 fi
dc.identifier.citation Kapucu, F., Tanskanen, J., Mikkonen, J., Ylä-Outinen, L., Narkilahti, S., & Hyttinen, J. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Frontiers in Computational Neuroscience, 6 (38). doi:10.3389/fncom.2012.00038
dc.identifier.issn 1662-5188
dc.identifier.uri http://hdl.handle.net/123456789/40550
dc.description.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 (hESCs), 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 ISI thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average (CMA) 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. fi
dc.language.iso eng
dc.publisher Frontiers Research Foundation
dc.relation.ispartofseries Frontiers in Computational Neuroscience
dc.relation.uri http://www.frontiersin.org/Computational_Neuroscience
dc.rights © 2012 Kapucu, Tanskanen, Mikkonen, Ylä-Outinen, Narkilahti and Hyttinen. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
dc.rights.uri http://www.creativecommons.org/licenses/by-nc/3.0/
dc.subject.other solut
dc.subject.other toimintapotentiaaliryhmät
dc.subject.other toimintapotentiaalipurskeet
dc.subject.other purskeanalyysi
dc.subject.other hESCs
dc.subject.other ihmisalkion kantasolu
dc.subject.other kehittyvät hermoverkot
dc.subject.other MEA
dc.subject.other mikroelektordihila
dc.subject.other spike trains
dc.subject.other action potential bursts
dc.subject.other burst analysis
dc.subject.other hESCs
dc.subject.other human embryonic stem cells
dc.subject.other developing neuronal networks
dc.subject.other MEA
dc.subject.other microelectrode array
dc.title Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics fi
dc.type Article en
dc.identifier.urn URN:NBN:fi:jyu-201212073318
dc.subject.kota 312
dc.contributor.laitos Psykologian laitos fi
dc.contributor.laitos Department of Psychology en
dc.contributor.oppiaine psykologia
dc.type.uri http://purl.org/eprint/type/JournalArticle
dc.identifier.doi 10.3389/fncom.2012.00038
dc.description.version Published version
eprint.status http://purl.org/eprint/type/status/PeerReviewed

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