title: Comparison between realistic and spherical approaches in EEG forward modelling.
abstract: In electroencephalography (EEG) a valid conductor model of the head (forward model) is necessary for predicting measurable scalp voltages from intra-cranial current distributions. All inverse models, capable of inferring the spatial distribution of the neural sources generating measurable electrical and magnetic signals outside the brain are normally formulated in terms of a pre-estimated forward model, which implies considering one (or more) current dipole(s) inside the head and computing the electrical potentials generated at the electrode sites on the scalp surface. Therefore, the accuracy of the forward model strongly affects the reliability of the source reconstruction process independently of the specific inverse model. So far, it is as yet unclear which brain regions are more sensitive to the choice of different model geometry, from both quantitative and qualitative points of view. In this paper, we compare the finite difference method-based realistic model with the four-layers sensor-fitted spherical model using simulated cortical sources in the MNI152 standard space. We focused on the investigation of the spatial variation of the lead fields produced by simulated cortical sources which were placed on the reconstructed mesh of the neocortex along the surface electrodes of a 62-channel configuration. This comparison is carried out by evaluating a point spread function all over the brain cortex, with the aim of finding the lead fields mismatch between realistic and spherical geometry. Realistic geometry turns out to be a relevant factor of improvement which is particularly important when considering sources placed in the temporal or in the occipital cortex. In these situations, using a realistic head model will allow a better spatial discrimination of neural sources when compared to the spherical model.
authors.affiliation: DEEI, University of Trieste, Via A. Valerio 10,Trieste, Italy. meneghini@gnbts.units.it
authors.forename: Fabio
authors.initials: F
authors.lastname: Meneghini
authors.forename: Federica
authors.initials: F
authors.lastname: Vatta
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Esposito
authors.forename: Stefano
authors.initials: S
authors.lastname: Mininel
authors.forename: Francesco
authors.initials: F
authors.lastname: Di Salle
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*