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

Gilbert, JR; Shapiro, LR; Barnes, GR; (2012) Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness. PLOS ONE , 7 (9) , Article e45084. 10.1371/journal.pone.0045084. Green open access

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Abstract

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.

Type: Article
Title: Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0045084
Publisher version: http://dx.doi.org/10.1371/journal.pone.0045084
Language: English
Additional information: © Gilbert et al. 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 credited. The authors have no support or funding to report.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1363163
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