SPM version 8b statistical parametric mapping, SPM approach

http://www.fil.ion.ucl.ac.uk/spm/software/spm8b/#Introduction

http://www.fil.ion.ucl.ac.uk/spm/doc/intro/

http://www.fil.ion.ucl.ac.uk/spm/ext/

The SPM approach in brief

The Statistical Parametric Mapping approach is voxel based:

• Images are realigned, spatially normalised into a standard space, and smoothed.
• Parametric statistical models are assumed at each voxel, using the General Linear Model GLM to describe the data in terms of experimental and confounding effects, and residual variability.
• For fMRI the GLM is used in combination with a temporal convolution model.
• Classical statistical inference is used to test hypotheses that are expressed in terms of GLM parameters. This uses an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{Z}, SPM{F})
• For such classical inferences, the multiple comparisons problem is addressed using continuous random field theory RFT, assuming the statistic image to be a good lattice representation of an underlying continuous stationary random field. This results in inference based on corrected p-values.
• Bayesian inference can be used in place of classical inference resulting in Posterior Probability Maps PPMs .
• For fMRI, analyses of effective connectivity can be implemented using Dynamic Causal Modelling DCM.