%0 Journal Article
%T A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness
%A Jessica R. Gilbert
%A Laura R. Shapiro
%A Gareth R. Barnes
%J PLOS ONE
%D 2012
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0045084
%X 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.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0045084