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A self-adapting approach for the detection of bursts and network bursts in neuronal cultures

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Abstract

Dissociated networks of neurons typically exhibit bursting behavior, whose features are strongly influenced by the age of the culture, by chemical/electrical stimulation or by environmental conditions. To help the experimenter in identifying the changes possibly induced by specific protocols, we developed a self-adapting method for detecting both bursts and network bursts from electrophysiological activity recorded by means of micro-electrode arrays. The algorithm is based on the computation of the logarithmic inter-spike interval histogram and automatically detects the best threshold to distinguish between inter- and intra-burst inter-spike intervals for each recording channel of the array. An analogous procedure is followed for the detection of network bursts, looking for sequences of closely spaced single-channel bursts. We tested our algorithm on recordings of spontaneous as well as chemically stimulated activity, comparing its performance to other methods available in the literature.

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Abbreviations

MEA:

Micro-electrode array

BD:

Burst detection

NBD:

Network burst detection

DIV:

Days in vitro

BIC:

Bicuculline

APV:

D-2-amino-5-phosphonopentanoic acid

ISI:

Inter-spike interval

logISIH:

Logarithmic inter-spike interval histogram

ISIth:

Inter-spike interval threshold

IBeI:

Inter-burst event interval

logIBeIH:

Logarithmic inter-burst event interval histogram

IBeIth:

Inter-burst event interval threshold

newBD:

New burst detection algorithm

SE:

Selinger’s algorithm

CH:

Chiappalone’s algorithm

WA:

Wagenaar’s algorithm

GE:

Gourevitch-Eggermont’s algorithm

SEM:

Standard error of the mean

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Acknowledgments

The authors wish to thank Dr. Mariateresa Tedesco for excellent culture preparation and maintenance and for providing images for Fig. 1(a). The authors are very grateful to Dr Luca Leonardo Bologna, who provided the recordings of spontaneous activity during development.

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Authors and Affiliations

  1. Neuroscience and Brain Technologies Department, Italian Institute of Technology, Via Morego 30, 16163, Genova, Italy

    Valentina Pasquale, Sergio Martinoia & Michela Chiappalone

  2. Neuroengineering and Bio-nanoTechnology Laboratory, Department of Biophysical and Electronic Engineering, University of Genova, Via all’Opera Pia 11A, 16145, Genova, Italy

    Sergio Martinoia

Authors
  1. Valentina Pasquale
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  2. Sergio Martinoia
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  3. Michela Chiappalone
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Correspondence to Valentina Pasquale.

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Action Editor: Rob Kass

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Pasquale, V., Martinoia, S. & Chiappalone, M. A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. J Comput Neurosci 29, 213–229 (2010). https://doi.org/10.1007/s10827-009-0175-1

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  • DOI: https://doi.org/10.1007/s10827-009-0175-1

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