MEG-BASED IMAGING OF FOCAL NEURONAL CURRENT SOURCES

We describe a new approach to imaging neural current sources from meas urements of the magnetoencephalogram (MEG) associated with sensory, mo tor, or cognitive brain activation. Many previous approaches to this p roblem have concentrated on the use of weighted minimum norm (WMN) inv erse methods. While these methods ensure a unique solution, they do no t introduce information specific to the MEG inverse problem, often pro ducing overly smoothed solutions and exhibiting severe sensitivity to noise. We describe a Bayesian formulation of the inverse problem in wh ich a Gibbs prior is constructed to reflect the sparse focal nature of neural current sources associated with evoked response data. We demon strate the method with simulated and experimental phantom data, compar ing its performance with several WMN methods.