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Activity Number: 65
Type: Topic Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: SSC
Abstract - #309787
Title: Variational Bayes Spatial Analysis of Combined MEG, EEG, and fMRI Data
Author(s): Farouk S. Nathoo*+ and Arif Babul and Alexander Moiseev and Naznin Virji-Babul and Faisal Beg
Companies: University of Victoria and Physics and Astronomy, University of Victoria and Down Syndrome Research Foundation and Physical Therapy, British Columbia and Engineering Science, Simon Fraser
Keywords: variational Bayes ; multimodal imaging ; EEG, MEG, fMRI

In this talk we present a new variational Bayes approach for electromagnetic brain mapping using combined MEG, EEG, and fMRI data. We propose a model for solving the neuroelectromagnetic inverse problem, a high-dimensional problem that involves estimating the time-varying neural activity at a large number of locations within the brain, from time series of electric and magnetic fields recorded at a relatively small number of locations on or near the scalp. Framing this problem within the context of variable selection in an underdetermined dynamic linear model, we propose a mixture formulation based on a binary latent process representing the spatial profile of brain activity, and serving as a shared parameter linking the EEG and MEG data through a joint spatiotemporal model. The latent process is governed by an autologistic specification, and this specification accommodates spatial clustering in brain activation, while also allowing for the inclusion of auxiliary information derived from fMRI data. We develop a variational approach for fast Bayesian inference, and we apply our approach for analysis in a multimodal imaging study of face perception.

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