No. 964

Title:

Nonlinear Local Electro-Vascular Coupling. Part II: From Data to Neuronal Masses

Author(s):

Riera J.J(NICHe, Tohoku Univ.);
Jimenez J.C. (Institute of Cybernetics, Mathematics and Physics);
Wan X. (NICHe, Tohoku Univ.);
Kawashima R. (NICHe, Tohoku Univ.);
Ozaki T. (the Institute of Statistical Mathematics)

Key words:

fusing EEG and fMRI data; state space models; statistical inference.

Abstract:

    In Riera et al. (2005a), a Local Electro-Vascular Coupling (LEVC) model was proposed to explain the continuous dynamics of electric and vascular states within a cortical unit. These states produce certain mesoscopic reflections that can be discretely observed from the ElectronEncephaloGram (EEG) and the functional Magnetic Resonance Imaging (fMRI). In this paper, we develop a local-recursive optimization algorithm based on the Local Linearization (LL) filter and the 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, which could have been sampled at different rates (e.g. EEG and fMRI). The dynamics of the exogenous synaptic inputs going into the cortical unit are also estimated by introducing a set of Gaussia n radial basis functions. In order to study the dynamics of the electrical and vascular states, as well as their local interrelationships, in the striate cortex of humans, we applied this algorithm to EEG and fMRI recordings obtained concurrently from two subjects while observing passively a radial checkerboard with white/black pattern reversal. The EEG and fMRI data from one subject was used to estimate simultaneously the electrical/vascular states and parameters of the LEVC model in V1 for a 4 Hz reversion frequency. We used the EEG data from a second subject to investigate how the dynamics of the electrical states change when the frequency of reversion is varied from 0.5 to 0.4 Hz. Then, we made use of the estimated electrical states to predict the effects on the vasculature that such variations produce.