title: A peak-clustering method for MEG group analysis to minimise artefacts due to smoothness.
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.
authors.affiliation: School of Life and Health Sciences, Aston University, Birmingham, United Kingdom. gilberjr@aston.ac.uk
authors.forename: Jessica R
authors.initials: JR
authors.lastname: Gilbert
authors.forename: Laura R
authors.initials: LR
authors.lastname: Shapiro
authors.forename: Gareth R
authors.initials: GR
authors.lastname: Barnes
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D015225:Magnetoencephalography / Q000379:methods*; D015203:Reproducibility of Results