Abstract

In the last talk, I want to describe how hierarchical Bayesian modeling (HBM) is used to address various challenges arising from the inverse problem of Electroencephalography (EEG) / Magnetoencephalography (MEG) based source reconstruction: Measuring the induced electromagnetic fields at the head surface to estimate the underlying, activity-related ion currents in the brain is a challenging, severely ill-posed inverse problem. Due to the under-determinedness and the spatial characteristics of the direct problem, especially the recovery of brain networks involving deep-lying sources is still a challenging task for any inverse method. Apart from these theoretical problems, many practical challenges and uncertainties arise in the analysis of EEG /MEG data. However, the high temporal resolution of EEG /MEG recordings and their direct correspondence to the neuronal activity makes them indispensable tools for neuroimaging. I want to sketch how hierarchical Bayesian modeling (HBM) can be used as a convenient framework to address some of the above aspects and show own results on the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high resolution Finite Element (FE) head models.

Slides of this talk can be downloaded at http://wwwmath.uni-muenster.de/num/burger/organization/lucka/talks/Cambridge3_15_11_2012.pdf