Realistic and Spherical Head Modeling for EEG Forward Problem Solution: A Comparative Cortex-Based Analysis

The accuracy of forward models for electroencephalography (EEG) partly depends on head tissues geometry and strongly affects the reliability of the source reconstruction process, but it is not yet clear which brain regions are more sensitive to the choice of different model geometry. In this paper we compare different spherical and realistic head modeling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data. Computer simulations are presented for three different four-shell head models, two with realistic geometry, either surface-based (BEM) or volume-based (FDM), and the corresponding sensor-fitted spherical-shaped model. Point Spread Function (PSF) and Lead Field (LF) cross-correlation analyses were performed for 26 symmetric dipole sources to quantitatively assess models' accuracy in EEG source reconstruction. Realistic geometry turns out to be a relevant factor of improvement, particularly important when considering sources placed in the temporal or in the occipital cortex

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

Pregroup Analysis of Persian Sentences

In muscle tissue the protein caveolin-3 forms caveolae – flask-shaped invaginations localized on the cytoplasmic surface of the sarcolemmal membrane. Caveolae have a key role in the maintenance of plasma membrane integrity and in the processes of vesicular trafficking and signal transduction. Mutations in the caveolin-3 gene lead to skeletal muscle pathology through multiple pathogenetic mechanisms. Indeed, caveolin-3 deficiency is associated to sarcolemmal membrane alterations, disorganization of skeletal muscle T-tubule network and disruption of distinct cell-signaling pathways. To date, there have been 30 caveolin-3 mutations identified in the human population. Caveolin-3 defects lead to four distinct skeletal muscle disease phenotypes: limb girdle muscular dystrophy, rippling muscle disease, distal myopathy, and hyperCKemia. In addition, one caveolin-3 mutant has been described in a case of hypertrophic cardiomyopathy. Many patients show an overlap of these symptoms and the same mutation can be linked to different clinical phenotypes. This variability can be related to additional genetic or environmental factors. This review will address caveolin-3 biological functions in muscle cells and will describe the muscle and heart disease phenotypes associated with caveolin-3 mutations