Non-linear Parameter Estimates from Non-stationary MEG Data

We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do th...

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Main Authors:	Martínez-Vargas, Juan D., López, Jose D., Baker, Adam, Castellanos-Dominguez, German, Woolrich, Mark W., Barnes, Gareth
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993126/

id	pubmed-4993126
recordtype	oai_dc
spelling	pubmed-49931262016-09-05 Non-linear Parameter Estimates from Non-stationary MEG Data Martínez-Vargas, Juan D. López, Jose D. Baker, Adam Castellanos-Dominguez, German Woolrich, Mark W. Barnes, Gareth Neuroscience We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast. Frontiers Media S.A. 2016-08-22 /pmc/articles/PMC4993126/ /pubmed/27597815 http://dx.doi.org/10.3389/fnins.2016.00366 Text en Copyright © 2016 Martínez-Vargas, López, Baker, Castellanos-Dominguez, Woolrich and Barnes. 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	Martínez-Vargas, Juan D. López, Jose D. Baker, Adam Castellanos-Dominguez, German Woolrich, Mark W. Barnes, Gareth
spellingShingle	Martínez-Vargas, Juan D. López, Jose D. Baker, Adam Castellanos-Dominguez, German Woolrich, Mark W. Barnes, Gareth Non-linear Parameter Estimates from Non-stationary MEG Data
author_facet	Martínez-Vargas, Juan D. López, Jose D. Baker, Adam Castellanos-Dominguez, German Woolrich, Mark W. Barnes, Gareth
author_sort	Martínez-Vargas, Juan D.
title	Non-linear Parameter Estimates from Non-stationary MEG Data
title_short	Non-linear Parameter Estimates from Non-stationary MEG Data
title_full	Non-linear Parameter Estimates from Non-stationary MEG Data
title_fullStr	Non-linear Parameter Estimates from Non-stationary MEG Data
title_full_unstemmed	Non-linear Parameter Estimates from Non-stationary MEG Data
title_sort	non-linear parameter estimates from non-stationary meg data
description	We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993126/
_version_	1613631842723823616

Non-linear Parameter Estimates from Non-stationary MEG Data

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