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A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothness 1 1 2 Jessica R. Gilbert *, Laura R. Shapiro *, Gareth R. Barnes 1 School of Life and Health Sciences, Aston University, Birmingham, United Kingdom, 2 The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom 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. Citation: Gilbert JR, Shapiro LR, Barnes GR (2012) A Peak-Clustering Method for MEG Group Analysis to Minimise Artefacts Due to Smoothne