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

@article{citeulike:2675612, 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.}, author = {Jun, Sung C. and George, John S. and Kim, Woohan and Par\'{e}-Blagoev, Juliana and Plis, Sergey and Ranken, Doug M. and Schmidt, David M. }, citeulike-article-id = {2675612}, doi = {10.1016/j.neuroimage.2007.12.029}, journal = {NeuroImage}, keywords = {bayesian, fmri}, month = {May}, number = {4}, pages = {1581--1594}, posted-at = {2008-04-16 00:06:31}, priority = {3}, title = {Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC}, url = {http://dx.doi.org/10.1016/j.neuroimage.2007.12.029}, volume = {40}, year = {2008} }

TY - JOUR ID - citeulike:2675612 L3 - citeulike-article-id:2675612 TI - Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC JF - NeuroImage VL - 40 IS - 4 SP - 1581 EP - 1594 N2 - 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. KW - bayesian KW - fmri AU - Jun, Sung AU - George, John AU - Kim, Woohan AU - Paré-Blagoev, Juliana AU - Plis, Sergey AU - Ranken, Doug AU - Schmidt, David PY - 2008/05/01/ UR - http://dx.doi.org/10.1016/j.neuroimage.2007.12.029 ER -