The authors describe inverse methods for using the magnetoencephalogram (MEG) to image neural current sources associated with functional activation in the cerebral cortex. A Bayesian formulation is presented that is based on a Gibbs prior which reflects the sparse, focal nature of neural activation. The model includes a dynamic component so that the authors can utilize the full spatio-temporal data record to reconstruct a sequence of images reflecting changes in the current source amplitudes during activation. The model consists of the product of a binary field, representing the areas of activation in the cerebral cortex, and a time series at each site which represents the dynamic changes in the source amplitudes at the active sites. The authors' estimation methods are based on the optimization of three different functions of the posterior density. Each of these methods requires the estimation of a binary field which the authors compute using a mean field annealing method. They demonstrate and compare their methods in application to computer generated and experimental phantom data.
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