Dynamic MEG imaging of focal neuronal sources

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