Strategies for statistical thresholding of source localization maps in magnetoencephalography and estimating source extent

Background: Magnetoencephalography allows defining non-invasively the spatio-temporal activation of brain networks thanks to source localization algorithms. A major difficulty of MNE and beamforming methods, two classically used techniques, is the definition of proper thresholds that allow deciding the extent of activated cortex. New method: We investigated two strategies for computing a threshold, taking into account the difficult multiple comparison issue. The strategies were based either on parametric statistics (Bonferroni, FDR correction) or on empirical estimates (local FDR and a custom measure based on the survival function). Results: We found thanks to the simulations that parametric methods based on the sole estimation of H-0 (Bonferroni, FDR) performed poorly, in particular in high SNR situations. This is due to the spatial leakage originating from the source localization methods, which give a 'blurred' reconstruction of the patch extension: the higher the SNR, the more this effect is visible. Comparison with existing methods: Adaptive methods such as local FDR or our proposed 'concavity thresh-old' performed better than Bonferroni or classical FDR. We present an application to real data originating from auditory stimulation in MEG. Conclusion: In order to estimate source extent, adaptive strategies should be preferred to parametric statistics when dealing with 'leaking' source reconstruction algorithms. (C) 2017 Elsevier B.V. All rights reserved.