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A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness

Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of fi...

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Detalles Bibliográficos
Autores principales: Gilbert, Jessica R., Shapiro, Laura R., Barnes, Gareth R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443237/
https://www.ncbi.nlm.nih.gov/pubmed/23024795
http://dx.doi.org/10.1371/journal.pone.0045084
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author Gilbert, Jessica R.
Shapiro, Laura R.
Barnes, Gareth R.
author_facet Gilbert, Jessica R.
Shapiro, Laura R.
Barnes, Gareth R.
author_sort Gilbert, Jessica R.
collection PubMed
description Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant’s volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images.
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spelling pubmed-34432372012-09-28 A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness Gilbert, Jessica R. Shapiro, Laura R. Barnes, Gareth R. PLoS One Research Article Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant’s volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images. Public Library of Science 2012-09-14 /pmc/articles/PMC3443237/ /pubmed/23024795 http://dx.doi.org/10.1371/journal.pone.0045084 Text en © 2012 Gilbert et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gilbert, Jessica R.
Shapiro, Laura R.
Barnes, Gareth R.
A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title_full A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title_fullStr A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title_full_unstemmed A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title_short A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
title_sort peak-clustering method for meg group analysis to minimise artefacts due to smoothness
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443237/
https://www.ncbi.nlm.nih.gov/pubmed/23024795
http://dx.doi.org/10.1371/journal.pone.0045084
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