Toward Real-Time Bayesian Inference for Magnetoencephalography

• Activity Number: 512
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Adam Michael Johansen* and Alberto Sorrentino and John Aston and Tom E. Nichols and Wilfrid S. Kendall
• Companies: University of Warwick and and University of Cambridge and University of Warwick and University of Warwick
• Keywords: filtering ; sequential monte carlo ; dipole model ; MEG

Abstract

We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, careful algorithm design is necessary. Previous sequential Monte Carlo algorithms applied in the MEG setting employed a bootstrap filter and an artificial dynamic structure in which dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the approach developed here. The method is applied to real data illustrating the potential of computationally intensive Bayesian methods in this setting.