title: Incorporating FMRI functional networks in EEG source imaging: a Bayesian model comparison approach.
abstract: Brain functional networks extracted from fMRI can improve the accuracy of EEG source localization. However, the coupling between EEG and fMRI remains poorly understood, i.e., whether fMRI networks provide information about the magnitude of neural activity, and whether neural sources demonstrate temporal correlations within each network. In this paper, we present an improved version of the NEtwork-based SOurce Imaging method (iNESOI) through Bayesian model comparison. Different models correspond to various matching between EEG and fMRI, and the appropriate one is selected by data with the model evidence. Synthetic and real data tests show that iNESOI has potential to select the appropriate fMRI priors to reach a better source reconstruction than some other typical approaches.
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Xu
authors.initials: X
authors.lastname: Lei
authors.forename: Jiehui
authors.initials: J
authors.lastname: Hu
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D010100:Oxygen / Q000097:blood
chemical_list: D010100:Oxygen