A maximum-entropy method for MEG source imaging

Magnetoencephalographic imaging is the estimation of three-dimensional neuronal current sources on the cortical surface from the measured magnetoencephalogram (MEG). It is a highly under-determined inverse problem as there are many "feasible" images which are consistent with the MEG. Previous approaches to this problem have concentrated on the use of weighted minimum-norm inverse methods. These methods often produce overly smoothed images and exhibit bias towards surface sources. In this work we explore the maximum-entropy approach to obtain better solutions to the problem. This estimation technique selects, from the possible set of feasible images, the image with the highest entropy permitted by the available information. In order to account for the presence of noise in the data, we have also incorporated a noise rejection term into the maximum-entropy method (MEM). This makes our approach mirror a Bayesian maximum a posteriori probability (MAP) formulation. Additional information from other modalities like functional magnetic resonance imaging (fMRI) can also be incorporated into the proposed method in the form of a prior bias function to improve solutions. We demonstrate the method with experimental phantom data from a clinical 122-channel MEG system.