Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex

Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from se...

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Main Authors:	Głąbska, Helena T., Norheim, Eivind, Devor, Anna, Dale, Anders M., Einevoll, Gaute T., Wójcik, Daniel K.
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4724720/

id	pubmed-4724720
recordtype	oai_dc
spelling	pubmed-47247202016-01-31 Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex Głąbska, Helena T. Norheim, Eivind Devor, Anna Dale, Anders M. Einevoll, Gaute T. Wójcik, Daniel K. Neuroscience Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population. Frontiers Media S.A. 2016-01-25 /pmc/articles/PMC4724720/ /pubmed/26834620 http://dx.doi.org/10.3389/fninf.2016.00001 Text en Copyright © 2016 Głąbska, Norheim, Devor, Dale, Einevoll and Wójcik. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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	Głąbska, Helena T. Norheim, Eivind Devor, Anna Dale, Anders M. Einevoll, Gaute T. Wójcik, Daniel K.
spellingShingle	Głąbska, Helena T. Norheim, Eivind Devor, Anna Dale, Anders M. Einevoll, Gaute T. Wójcik, Daniel K. Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
author_facet	Głąbska, Helena T. Norheim, Eivind Devor, Anna Dale, Anders M. Einevoll, Gaute T. Wójcik, Daniel K.
author_sort	Głąbska, Helena T.
title	Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
title_short	Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
title_full	Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
title_fullStr	Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
title_full_unstemmed	Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
title_sort	generalized laminar population analysis (glpa) for interpretation of multielectrode data from cortex
description	Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4724720/
_version_	1613527645209755648

Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex

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