Much methodological research has been devoted to developing sophisticated Bayesian source imaging inversion schemes, while dipoles have received less attention. Dipole models have their advantages; they are often appropriate summaries of evoked responses or helpful first approximations. In SPM8, we have implemented a variational Bayesian algorithm that enables the fast Bayesian inversion of dipole models. The approach allows for specification of priors on all the model parameters. The posterior distributions can be used to form Bayesian confidence intervals for interesting parameters, like dipole locations. Furthermore, competing models (e.g., models with different numbers of dipoles) can be compared using their evidence or marginal likelihood.
 
S.J. Kiebel, J. Daunizeau, C. Phillips, and K.J. Friston. Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG. NeuroImage, 39(2):728-741, 2008.
 
These descriptions of the new features are taken from the SPM8 Release Notes
 
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