Burst on Hurst algorithm for detecting activity patterns in networks of cortical neurons

Burst on Hurst algorithm for detecting activity

patterns in networks of cortical neurons

G. Stillo, L. Bonzano, M. Chiappalone, A. Vato, F. Davide, and S. Martinoia

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Abstract—Electrophysiologicalsignals were recorded from

primary cultures of dissociated rat cortical neurons coupled toMicro-

ElectrodeArrays(MEAs).The neuronal discharge patterns may

change under varying physiologicaland pathological conditions. For

this reason, we developed anewburst detection method able to

identifyburstswithpeculiar features in different experimental

conditions (i.e. spontaneous activity and under theeffectofspecific

drugs).Themainfeatureof our algorithm (i.e. Burst On Hurst),

based on the auto-similarity or fractal property of the recorded signal,

is the independence from the chosen spikedetectionmethodsinceit

works directly on the raw data.

Keywords— Burst detection, cortical neuronal networks, Micro-

Electrode Array (MEA), wavelets.

I.INTRODUCTION

UEto its extremecomplexity, nowadays the neural code

(i.e.,how neurons in the brain represent, store and

process information) is still poorly understood. Awayto

approachthe problem consists in the study of the behavior of

reduced and simplified systems, such asin-vitro networks of

neurons, where the properties of the nervous systemcanbe

qualitatively characterized and modulated [1].

Neuronal networks process information by generating

actionpotentials(i.e. spikes) and propagating them through

spatio-temporal patterns of electricalactivity that change with

the development of the network and are modulated byexternal

chemical and electrical stimulations.

Manuscript received November 12, 2004.Thiswork was supported in part by the NeuroBit project IST-2001-33564,“A bioartificial brain with an artificial body: training a culturedneuraltissueto support the purposive of an artificial body”.L. Bonzano’s Ph.D. fellowship is sponsored by Telecom Italia S.P.A.G.Stillois with Telecom Italia Learning Services (TILS), 00148, Roma,Italy, (e-mail: ue002493@guest.telecomitalia.it).L.Bonzano is with the Neuroengineering and Bio-nanoTechnologies(NBT)group at DIBE, University of Genova, 16154, Genova, (e-mail:laura.bonzano@dibe.unige.it).M.Chiappaloneis with the Neuroengineering and Bio-nanoTechnologies(NBT)group at DIBE, University of Genova, 16154, Genova, (e-mail:michela@dibe.unige.it).A. Vato is with the Neuroengineering and Bio-nanoTechnologies (NBT)groupatDIBE, University of Genova, 16154, Genova, (corresponding authorphone +390103532761; fax +390103532133; e-mail: vato@dibe.unige.it).F. Davide is with Telecom Italia Learning Services (TILS),00148,Roma,Italy, (e-mail: fabrizio.davide@telecomitalia.it).S. Martinoia is with the Neuroengineering and Bio-nanoTechnologies(NBT)group at DIBE, University of Genova, 16154, Genova, (e-mail:martinoia@dibe.unige.it).Electrophysiological activity is characterized bysingle

spikes, bursts and silent periods. Commonlythetermburstis

used to identify a period in whicha spike train is characterized

byamuch higher firing rate than other periods in the same

train [2]. Using this qualitative definition,thedetectionofa

burst is neither easy nor unambiguous.

In this work, we present a new method to identify the

neuronal network bursting activity; we developed an

algorithmbased on the self-similarity property of the recorded

electrophysiological signals and on the observation of the

fractal-likeness increase during the “bursting” active state.

Toconfirm the efficacy of this new algorithm, a

comparison with the results obtainedusing a spike analysis-

based approach is presented.

II.MATERIALS AND METHODS

A.Neuronal preparation and experimental set-up

Neuronalcultures were taken from cerebral cortices of

embryonic rats at gestation day 18 (E18). Thecerebralcortex

was dissociated using Trypsin. Cells were platedon60

channelsMicro-Electrode Arrays (MultichannelSystems,

Reutlingen, Germany), pre-coated withadhesionpromoting

molecules (Poly-D-Lysine and Laminin), at thefinaldensity

of 6-8󰂘104cells/deviceand maintained in Neurobasal medium

(Gibco) supplemented with 2% B-27 and 1%Glutamax-I

(both Gibco). Measurements were carried outinphysiological

medium(NaCl 150mM, CaCl2 1.3mM, MgCl2 0.7mM, KCl

2.8mM, Glucose 10mM, HEPES buffer 10mM).

The electrophysiological signals were recordedusinga

standard commercially available experimentalset-upfor

extracellular measurements (MultichannelSystems).Each

channel was sampled at a frequency of 10 kHz.

B.Data set

Extracellular neuronal recordings were obtainedby

dissociated cultures of cortical neurons coupled to MEAs.

The data set employed to test thepresentedalgorithm

consists of electrophysiological signalsrecordedinthree

different experimental conditions: spontaneous activity

(control), activity under additionof 30µM BIC (bicuculline,

an antagonist of the GABAA receptor), activity under the

addition of 100µM DL-TBOA (d,l-threo-beta-

benzyloxyaspartate, a neuronal and glial inhibitorofglutamate

transport). Recordings lasting 5 minutes of each experimental

phase were considered for this analysis. DPROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY VOLUME 2 JANUARY 2005 ISSN 1307-6884

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