Linear time invariant models provide the bridge between inferential models employed by statistical mapping and conventional signal processing approaches.

This can be exploited to ensure that both the design and analysis are as efficient as possible. The general ideas behind statistical parametric mapping are then described and illustrated with attention to the different sorts of inferences that can be made with different experimental designs.Д®EG, MEG and fMRI data lend themselves to a signal processing perspective. Before considering statistical modelling, we deal briefly with how images are realigned and normalized into some standard anatomical space. In order to assign an observed response to a particular brain structure, or cortical area, the data are usually mapped into an anatomical space. Statistical parametric mapping is a voxel-based approach, employing topological inference, to make some comment about regionally specific responses to experimental factors. The complementary perspective, namely functional integration, requires a different set of approaches that examine the relationship among changes in one brain region relative to changes in others. Statistical parametric mapping is used to identify regionally specific effects in neuroimaging data and is a prevalent approach to characterizing functional anatomy, specialization and disease-related changes. Friston, in Statistical Parametric Mapping, 2007 Background

Disadvantages of using imagej software