Spatial spreading of representational geometry through source estimation of magnetoencephalography signals

To clarify where and when information is represented in the human brain, close investigation of brain activity at high spatiotemporal resolution is important. However, no current neuroimaging method is able to achieve such high spatiotemporal resolution. One attempt to extract necessary information from measured data under the limitation is combination of magnetoencephalography (MEG) source estimation and multivariate pattern analysis (MVPA). This combination may allow accurate localization of informative brain areas in fine time steps. However, because MEG source estimation is underdetermined, the source cortical current from a particular brain area can spread to other brain areas. In addition, information represented by the source cortical current may spread, too. Therefore, we should evaluate the accuracy of the localization of informative brain areas when combining MEG source estimation and MVPA. In this study, we used representational similarity analysis (RSA) as one of major methods of MVPA to investigate whether its result was influenced by the spreading of the cortical current through MEG source estimation. We found that relationship of the distance between brain activity patterns for multiple experimental conditions, or representational geometry, spread to brain areas where information about the experimental conditions was not represented as difference in brain activity patterns. These results suggest that we should be aware of the spreading of representational geometry through MEG source estimation, which may yield false positive interpretation about the localization of informative brain areas. Finally, we demonstrated that the possibility of mislocalization of informative brain areas can be reduced by weighting results of RSA with the reliability of the representational dissimilarity matrices.