ABSTRACT
This chapter reviews the statistical tools available for the analysis of distributed activation maps defined either on the 2D cortical surface or throughout the 3D brain volume. It describes the general linear modeling (GLM) approach, where the Magnetoencephalography (MEG) data are first mapped into brain space, and then fit to a univariate or multivariate model at each surface or volume element. The chapter also describes GLM formulations for the analysis of induced and evoked response in MEG. It analyzes MEG data using either mass-univariate or multivariate approaches. The chapter discusses by showing that the GLM framework is parsimonious in MEG analysis. It explains Time–frequency representations of brain activity are computed from MEG. After selection of a GLM approach, the MEG observations are fitted to the models and a contrast (or linear combination) of the parameters is computed. On account of their flexibility, permutation tests are more commonly used in MEG than the parametric random field theory.
