Modelling And Analysis Of Electrical Potentials Recorded In Microelectrode Arrays (MEAs)
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1
Norwegian University of Life Sciences, Department of Mathematical Sciences and Technology, Norway
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2
Nencki Institute of Experimental Biology, Department of Neurophysiology, Poland
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3
Nencki Institute of Experimental Biology, Department of Neurophysiology, Poland
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4
Nencki Institute of Experimental Biology, Department of Neurophysiology, Poland
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5
Nencki Institute of Experimental Biology, Department of Neurophysiology, Poland
Motivation
Microelectrode arrays (MEAs), substrate-integrated planar arrays of up to thousands of closely spaced metal electrode contacts, have long been used to record neuronal activity in in vitro brain slices with high spatial and temporal resolution. However, the analysis of the MEA potentials has generally been mainly qualitative. Here we present a biophysical forward modelling framework for modelling extracellular potentials, and use it to thoroughly investigate the impact on the extracellular potential of effects such as neural tissue inhomogeneity and anisotropy, MEA recording electrodes and the saline cover (Figure 1).
Material and Methods
We use a biophysical forward-modelling formalism based on the finite element method (FEM) to establish quantitatively accurate links between neural activity in the slice and potentials recorded in the MEA set-up (Figure 1). Then we develop a simpler approach based on the method of images (MoI) from electrostatics, which allows for computation of MEA potentials by simple formulas similar to what is used for homogeneous volume conductors. As we find MoI to give accurate results in most situations of practical interest, including anisotropic slices covered with highly conductive saline and MEA-electrode contacts of sizable physical extensions, a Python software package (ViMEAPy) has been developed to facilitate forward-modelling of MEA potentials generated by biophysically detailed multicompartmental neurons (Ness et al., 2015). This software makes modelling of extracellular potentials originating from neural activity easy to accomplish for the broad scientific community. We also use this MoI forward-modelling formalism to estimate the current-source density (CSD) using the kernel Current Source Density method (Potworowski et al., 2012). This inverse method incorporates the changing conductivity between the slice and the saline bath. This implementation is also available as Python scripts (github.com/Neuroinflab/kCSD-python/).
Results
We apply our scheme to investigate the influence of the MEA set-up on single-neuron spikes as well as on potentials generated by a cortical network comprising more than 3000 model neurons (Ness et al., 2015). The generated MEA potentials are substantially affected by both the saline bath covering the brain slice and a (putative) inadvertent saline layer at the interface between the MEA chip and the brain slice. However, even with exaggerated assumed anisotropies and inhomogeneities in the electrical conductivity of neural tissue, compared to what has been measured in cortex (Goto et al. 2010), the effects from anisotropies and inhomogeneities seem to be small for cortical slices. We further explore methods for estimation of CSD from MEA potentials, and find the results to be much less sensitive to the experimental set-up.
Conclusion
We present an open-source Python package, ViMEAPy, for calculation of extracellular potentials originating from neural activity in in vitro MEA slice recordings. We believe can this can be of great use to the MEA community both for analysis, modelling and increased understanding of extracellular potentials. We also provide evidence that tissue anisotropy and inhomogeneity are of minor importance for extracellular potentials and CSD analysis for cortical slices, while the surrounding saline bath on the other hand can have a large effect on the measured potentials, but not on the estimated CSD.
References
Ness, T. V., Chintaluri, C., Potworowski, J., Łęski, S., Głąbska, H., Wójcik, D. K., & Einevoll, G. T. (2015). Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs). Neuroinformatics, 13(4), 403–426.
Potworowski, J., Jakuczun, W., Lȩski, S., & Wójcik, D. K. (2012). Kernel current source density method. Neural Computation, 24(2), 541–75.
Goto, T., Hatanaka, R., Ogawa, T., Sumiyoshi, A., Riera, J., & Kawashima, R. (2010). An evaluation of the conductivity profile in the somatosensory barrel cortex of Wistar rats. Journal of Neurophysiology, 104(6), 3388–3412.
Figure Legend
Schematic illustration of present MEA set-up with an in vitro slice of brain tissue immersed in saline on top of a substrate-integrated microelectrode array (MEA). The metal electrodes at the MEA chip (embedded in glass substrate) measure the potential set up by the transmembrane currents of the neurons in the brain slice. The dot with protruding arrows represents a point current source at position (x’, y’, z’). Short dotted lines on the right denote the depth coordinates corresponding to the bottom (z = 0) and top of the brain slice (z = h), respectively.
Keywords:
modelling,
MEA,
extracellular potentials,
Finite element method,
Current Source Density,
slice,
method of images
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
Ness
TV,
Chintaluri
C,
Potworowski
J,
Łęski
S,
Głąbska
H,
Wójcik
DK and
Einevoll
G
(2016). Modelling And Analysis Of Electrical Potentials Recorded In Microelectrode Arrays (MEAs).
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00011
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Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Torbjørn V Ness, Norwegian University of Life Sciences, Department of Mathematical Sciences and Technology, Ås, Norway, torbjorn.ness@nmbu.no