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

- Different strategies are compared for defining a threshold on MEG source maps.
- Parametric and adaptive thresholding were compared on simulated data.
- Adaptive thresholds were the best, across a range of signal to noise ratios.
- Extent of active cortex can be retrieved with performance close to optimal.

BackgroundMagnetoencephalography 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 methodWe 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).ResultsWe found thanks to the simulations that parametric methods based on the sole estimation of H0 (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 methodsAdaptive methods such as local FDR or our proposed 'concavity threshold' performed better than Bonferroni or classical FDR. We present an application to real data originating from auditory stimulation in MEG.ConclusionIn order to estimate source extent, adaptive strategies should be preferred to parametric statistics when dealing with 'leaking' source reconstruction algorithms.