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Information extraction from biphasic concentration-response curves for data obtained from neuronal activity of networks cultivated on multielectrode-array-neurochip

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Kerstin Lenk
Kerstin Lenk
Kerstin Lenk
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Matthias Reuter at Clausthal University of Technology
Matthias Reuter
Olaf HU Schroeder
Olaf HU Schroeder
Olaf HU Schroeder
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Alexandra Gramowski at University of Rostock
Alexandra Gramowski
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Information extraction from biphasic concentration-response curves for data obtained from neuronal activity of networks cultivated on multielectrode-array-neuroch
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POSTER PRESENTATION Open Access
Information extraction from biphasic concentration-
response curves for data obtained from neuronal
activity of networks cultivated on multielectrode-
array-neurochips
Kerstin Lenk
1*
, Matthias Reuter
2
, Olaf HU Schroeder
3
, Alexandra Gramowski
3
, Konstantin Jügelt
3
, Barbara Priwitzer
1
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
San Antonio, TX, USA. 24-30 July 2010
We aim to fit biphasic concentration-response curves to
extract information about the effect of given biochem-
ical substances to in-vitro neurons.
Neurons extracted from embryonic mice are cultivated
on multielectrode-array-neurochips (MEA-neurochip) [1].
The activity of single neurons in such networks is
recorded especially the change of network activity caused
by long-term application of neuroactive substances. This
results in quasi-stable patterns of neuronal activity. Based
on the data, different features [2] are calculated adapted
from spikes and bursts and separately displayed in concen-
tration-response curves [3]. These concentration-response
curves can exhibit non sigmoid shape, then indicating that
different mechanisms affect the neuronal activity. Hence,
the concentration-response curves presumably include
currently hidden and unused information.
Methods
The concentration-response curve under consideration
is given as mean spike rate depending on the logarithm
of concentration. We present two methods to calculate
biphasic concentration-response curves.
Firstly, a fitting algorithm, extending the method
described in [3] is developed, leading not only to mono-
phasic but also biphasic concentration-response curves.
The fitting parameters gained with this method exhibit
new features describing the effect of neuroactive sub-
stances in a new way.
Secondly, a smoothing spline [4] is applied to the data.
Thereby efforts are being made to keep close at the data
as well as to achieve a smooth curve. Computational
Geometry is used to calculate the minimal and maximal
curvature,theareaunderthecurveaswellasthelocal
extrema of the fitted curve. These values quantify con-
centration dependent effects of the used substances.
We applied both approaches to datasets which are
derived by adding agmatine or bicuculline, respectively,
to the neuronal network (data by courtesy from Neuro-
proof GmbH). As these substances have biphasic or
monophasic concentration-response curves, we were
able to compare the values of the new features for these
different effects.
Conclusion
The methods described above lead to new features
describing the effect of increasing concentration on the
mean spike rate of in-vitro neuronal networks. We aim
to use these features for classification with machine
learning algorithms like neuronal networks or support
vector machines to identify unknown substances.
Author details
1
Department of Information Technology/Electronics/Mechanical Engineering,
Lausitz University of Applied Sciences, Senftenberg, Brandenburg, Germany.
2
Department of Informatics, Clausthal University of Technology, Clausthal-
Zellerfeld, Lower Saxony, Germany.
3
NeuroProof GmbH, Rostock,
Mecklenburg-Vorpommern, Germany.
Published: 20 July 2010
References
1. Gross GW, Rhoades BK, Azzazy HME, Wu MC: The use of neuronal
networks on multielectrode arrays as biosensors. Biosensors &
Bioelectronics 1995, 10:553-567.
2. Schroeder OHU, Gramowski A, Jügelt K, Teichmann C, Weiss DG: Spike
train data analysis of substance-specific network activity: Application to
* Correspondence: kerstin.lenk@hs-lausitz.de
1
Department of Information Technology/Electronics/Mechanical Engineering,
Lausitz University of Applied Sciences, Senftenberg, Brandenburg, Germany
Lenk et al.BMC Neuroscience 2010, 11(Suppl 1):P168
http://www.biomedcentral.com/1471-2202/11/S1/P168
© 2010 Lenk et al; licensee BioMed Centra l Ltd.
functional screening in preclinical drug development. 6th Int. Meeting on
Substrate-Integrated Microelectrodes 2008.
3. Motulsky H, Christopoulos A: Fitting Models to Biological Data Using
Linear and Nonlinear Regression: A Practical Guide to Curve Fitting.
San Diego CA: GraphPad Software Inc. 2003 [http://www.graphpad.com].
4. deBoor C: A practical guide to splines, Revised Edition. New York:
Springer-Verlag 2001.
doi:10.1186/1471-2202-11-S1-P168
Cite this article as: Lenk et al.: Information extraction from biphasic
concentration-response curves for data obtained from neuronal activity of
networks cultivated on multielectrode-array-neurochips. BMC Neuroscience
2010 11(Suppl 1):P168.
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Lenk et al.BMC Neuroscience 2010, 11(Suppl 1):P168
http://www.biomedcentral.com/1471-2202/11/S1/P168
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