Investigating spatial specificity and data averaging in MEG.

This study shows that the spatial specificity of MEG beamformer estimates of electrical activity can be affected significantly by the way in which covariance estimates are calculated. We define spatial specificity as the ability to extract independent timecourse estimates of electrical brain activity from two separate brain locations in close proximity. Previous analytical and simulated results have shown that beamformer estimates are affected by narrowing the time frequency window in which covariance estimates are made. Here we build on this by both experimental validation of previous results, and investigating the effect of data averaging prior to covariance estimation. In appropriate circumstances, we show that averaging has a marked effect on spatial specificity. However the averaging process results in ill-conditioned covariance matrices, thus necessitating a suitable matrix regularisation strategy, an example of which is described. We apply our findings to an MEG retinotopic mapping paradigm. A moving visual stimulus is used to elicit brain activation at different retinotopic locations in the visual cortex. This gives the impression of a moving electrical dipolar source in the brain. We show that if appropriate beamformer optimisation is applied, the moving source can be tracked in the cortex. In addition to spatial reconstruction of the moving source, we show that timecourse estimates can be extracted from neighbouring locations of interest in the visual cortex. If appropriate methodology is employed, the sequential activation of separate retinotopic locations can be observed. The retinotopic paradigm represents an ideal platform to test the spatial specificity of source localisation strategies. We suggest that future comparisons of MEG source localisation techniques (e.g. beamformer, minimum norm, Bayesian) could be made using this retinotopic mapping paradigm.