Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In...

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Main Authors:	Łęski, Szymon, Pettersen, Klas H., Tunstall, Beth, Einevoll, Gaute T., Gigg, John, Wójcik, Daniel K.
Format:	Online
Language:	English
Published:	Springer-Verlag 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3214268/

id	pubmed-3214268
recordtype	oai_dc
spelling	pubmed-32142682011-12-09 Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings Łęski, Szymon Pettersen, Klas H. Tunstall, Beth Einevoll, Gaute T. Gigg, John Wójcik, Daniel K. Original Article The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets. Springer-Verlag 2011-03-16 2011-12 /pmc/articles/PMC3214268/ /pubmed/21409556 http://dx.doi.org/10.1007/s12021-011-9111-4 Text en © The Author(s) 2011
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Łęski, Szymon Pettersen, Klas H. Tunstall, Beth Einevoll, Gaute T. Gigg, John Wójcik, Daniel K.
spellingShingle	Łęski, Szymon Pettersen, Klas H. Tunstall, Beth Einevoll, Gaute T. Gigg, John Wójcik, Daniel K. Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
author_facet	Łęski, Szymon Pettersen, Klas H. Tunstall, Beth Einevoll, Gaute T. Gigg, John Wójcik, Daniel K.
author_sort	Łęski, Szymon
title	Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
title_short	Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
title_full	Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
title_fullStr	Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
title_full_unstemmed	Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings
title_sort	inverse current source density method in two dimensions: inferring neural activation from multielectrode recordings
description	The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets.
publisher	Springer-Verlag
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3214268/
_version_	1611487144622161920

Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

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