A Potts Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data

• Activity Number: 144 - Uncover the Essential Truth by Integrating Big and Complex Imaging Data with New Statistical Tools
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: SSC
• Author(s): Yin Song and Farouk Nathoo*
• Companies: University of Victoria and University of Victoria
• Keywords: MEG ; EEG ; Inverse Problem ; Potts Model ; Mixture Model ; Spatiotemporal Model

Abstract

We consider the ill-posed inverse problem arising when magnetoencephalography (MEG) and/or electroencephalography (EEG) are used to measure electromagnetic brain activity over an array of sensors at the scalp and it is of interest to map these data back to the sources of neural activity within the brain. We review some of the existing approaches to solving this inverse problem and discuss the mesostate-space model (MSM) proposed by Daunizeau and Friston (Neuroimage, 2007). We then propose a new model that builds on the MSM and incorporates three major extensions: (i) We combine EEG and MEG data together and formulate a joint model for source reconstruction; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent mesostates; (iii) we formulate the mesostate dynamics in a more flexible manner so that the model can characterize the functional connectivity between mesosources. We formulate the model, discuss computational implementation, and make comparisons to existing methods.