title: Nonlinear local electrovascular coupling. II: From data to neuronal masses.
abstract: In the companion article a local electrovascular coupling (LEVC) model was proposed to explain the continuous dynamics of electrical and vascular states within a cortical unit. These states produce certain mesoscopic reflections whose discrete time series can be reconstructed from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In this article we develop a recursive optimization algorithm based on the local linearization (LL) filter and an innovation method to make statistical inferences about the LEVC model from both EEG and fMRI data, i.e., to estimate the unobserved states and the unknown parameters of the model. For a better understanding, the LL filter is described from a Bayesian point of view, providing the particulars for the case of hybrid data (e.g., EEG and fMRI), which could be sampled at different rates. The dynamics of the exogenous synaptic inputs going into the cortical unit are also estimated by introducing a set of Gaussian radial basis functions. In order to study the dynamics of the electrical and vascular states in the striate cortex of humans as well as their local interrelationships, we applied this algorithm to EEG and fMRI recordings obtained concurrently from two subjects while passively observing a radial checkerboard with a white/black pattern reversal. The EEG and fMRI data from the first subject was used to estimate the electrical/vascular states and parameters of the LEVC model in V1 for a 4.0 Hz reversion frequency. We used the EEG data from the second subject to investigate the changes in the dynamics of the electrical states when the frequency of reversion is varied from 0.5-4.0 Hz. Then we made use of the estimated electrical states to predict the effects on the vasculature that such variations produce.
authors.affiliation: NICHe, Tohoku University, Sendai, Japan. riera@idac.tohoku.ac.jp
authors.forename: J J
authors.initials: JJ
authors.lastname: Riera
authors.forename: J C
authors.initials: JC
authors.lastname: Jimenez
authors.forename: X
authors.initials: X
authors.lastname: Wan
authors.forename: R
authors.initials: R
authors.lastname: Kawashima
authors.forename: T
authors.initials: T
authors.lastname: Ozaki
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D014796:Visual Perception / Q000502:physiology*