Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC

Abstract

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayesian inference employing Markov Chain Monte Carlo (MCMC) techniques provides a straightforward way to combine disparate forms of information while dealing with the uncertainty in each. In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. The usefulness and feasibility of the method are verified through testing with both simulated and empirical data.

Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents

“…First, a classification can be made in focal current modeling, spatial scanning/beamforming and distributed current modeling. Focal current modeling attempts to reconstruct the real current using a small number of equivalent current dipoles with arbitrary locations and orientations (Jun et al, 2008;Mosher et al, 1992;Scherg and Cramon, 1985). When the number of sources is unknown or the current distribution might have a larger spatial extent, focal current models are not suitable.…”

Computing Sources of Epileptic Discharges Using the Novel BMA Approach

“…Others have defended this combination with BMA, since fMRI provides additional information for EEG source localization rather than acts as a constraint. 13,15,16,29,44,46 …”

“…Additionally, this method has only been validated based on simulated EEG/MEG data or by using EEG recorded during well-studied evoked steady-state responses. 3,[13][14][15][16][17][18] In this sense, the present work is also novel in that it uses patients with a rigorous clinical diagnosis as a gold standard.…”

Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors

“…In terms of ECD methods, it is possible to constrain the location of the dipoles to be within fMRI active areas (George et al, 1995) or to use them as starting points for the optimization algorithm (dipole seeding) (Hillyard et al, 1997). More recently, an ECD method using a Bayesian formulation with an fMRI location prior and Markov Chain Monte Carlo sampling has been proposed (Jun et al, 2008). In the distributed formulation, fMRI active areas can be assigned different weights when using a weighted minimum norm method (Liu et al, 1998), or principal component analysis (PCA) and independent component analysis (ICA) can be used to obtain basis signals which can explain both the EEG and fMRI observations (Brookings et al, 2009).…”