Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.
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.
Jun
Sung C
SC
Los Alamos National Laboratory, Los Alamos, NM 87545, USA. scjun@gist.ac.kr
George
John S
JS
Kim
Woohan
W
Paré-Blagoev
Juliana
J
Plis
Sergey
S
Ranken
Doug M
DM
Schmidt
David M
DM
D000465:Algorithms; D001499:Bayes Theorem; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D008390:Markov Chains; D008953:Models, Anatomic; D015233:Models, Statistical; D009010:Monte Carlo Method