Including the slice geometry in Current Source Density analysis
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1
Nencki Institute of Experimental Biology, Poland
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2
Norwegian University of Life Sciences, Norway
Multielectrode recordings of local field potentials (LFP) from brain slices have become a standard research technique. Because of the long range of the electric fields it is often advantageous to reconstruct the neuronal transmembrane currents from the measured potentials, a procedure known as Current Source Density (CSD) analysis. CSD analysis methods utilize the relation between the transmembrane currents and the potentials (Poisson equation) and have been used succesfully in many contexts. However, CSD methods typically assume that the tissue is isotropic and homogeneous, which is clearly not true in slice recordings.
We have developed a variant of the kernel CSD method which takes into account the finite thickness of the slice and different conductivities of the tissue and the fluid covering the slice. To achieve that, we have employed the method of images and replaced the standard "1/r" solution to the Poisson equation with a series. We have tested the new kCSD variant on model data, in which the spread of electric field was thoroughly modelled using the Finite Element Method. We have found that 1) the reconstructed error is smaller when the correct slice thickness and the inhomogeneity in conductivities are taken into account, but the improvement is relatively minor, 2) it is enough to include just the first two additional terms resulting from the method of images.
Acknowledgements
Supported by the Polish Ministry of Science and Higher Education grant IP2011 030971.
Keywords:
local field potential (LFP),
current source density analysis,
multi-electrode-array,
inhomogeneous neural media,
brain slice
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Poster
Topic:
Electrophysiology
Citation:
Potworowski
J,
Ness
TB,
Łęski
S,
Einevoll
GT and
Wójcik
DK
(2013). Including the slice geometry in Current Source Density analysis.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00019
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Received:
08 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Dr. Szymon Łęski, Nencki Institute of Experimental Biology, Warszawa, Poland, s.leski@nencki.gov.pl