title: Model driven EEG/fMRI fusion of brain oscillations.
abstract: This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of forward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian computations the practical estimation of very large scale EEG/fMRI models shall soon be possible.
authors.affiliation: Cuban Neuroscience Center, Havana, Cuba. peter@cneuro.edu.cu
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Valdes-Sosa
authors.forename: Jose Miguel
authors.initials: JM
authors.lastname: Sanchez-Bornot
authors.forename: Roberto Carlos
authors.initials: RC
authors.lastname: Sotero
authors.forename: Yasser
authors.initials: Y
authors.lastname: Iturria-Medina
authors.forename: Yasser
authors.initials: Y
authors.lastname: Aleman-Gomez
authors.forename: Jorge
authors.initials: J
authors.lastname: Bosch-Bayard
authors.forename: Felix
authors.initials: F
authors.lastname: Carbonell
authors.forename: Tohru
authors.initials: T
authors.lastname: Ozaki
mesh_terms: D001499:Bayes Theorem; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology