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Please use this identifier to cite or link to this item: http://hdl.handle.net/10077/4522

Title: Multimodal functional neuroimaging: new insights from novel head modeling methodologies
Authors: Meneghini, Fabio
Supervisor/Tutor: Vatta, Federica
Co-supervisor: Di Salle, Francesco
Issue Date: 31-Mar-2011
Publisher: Università degli studi di Trieste
Abstract: Neuroimaging plays a critically important role in neuroscience research and management of neurological and mental disorders. Modern neuroimaging techniques rely on various “source” signals that change across different spatial and temporal scales in accompany with neuronal activity. Nowadays, several types of noninvasive neuroimaging modalities are available based on biophysical signals related to either brain electrophysiology or hemodynamics/metabolism. In this dissertation, advanced model-based neuroimaging methods for the estimation of cortical brain activity from combined high-resolution electroencephalography (EEG), multimodal Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) data are presented. The present dissertation begins with a review of the current state-of-the-art in the major neuroimaging techniques. Particular attention has been devoted to EEG modelling since such signals propagate (virtually) instantaneously from the activated neuronal tissues via volume conduction to the recording sites on/above the scalp surface. The instantaneous nature of EEG indicates an intrinsically high temporal resolution and precision, which make it well suited for studying brain functions on the neuronal time scale. The collective nature suggests low spatial resolution and specificity, which impede mapping brain functions in great regional details. However, this is regardless of recent advancements in electromagnetic source imaging, which has led to great strides in improving the EEG/MEG spatial resolution to a centimetre scale or even smaller. These methods entail: 1) modeling the brain electrical activity; 2) modeling the head volume conduction process so as to link the modeled electrical activity to EEG; and 3) reconstructing the brain electrical activity from recorded EEG data. For this aim, a subject's multicompartment head model (scalp, skull, CSF, brain cortex, white matter) is constructed from either individual magnetic resonance images or approximated geometry models. We compared different spherical and realistic head modelling 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. Both statistical and imaging analysis point to the realistic geometry as a relevant factor of improvement, 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. Moreover a brief overview of Diffusion Weighted Imaging and Diffusion Tensor Imaging is also given, as their application in modelling refinement is increasing the accuracy and the complexity of the brain models. Both fMRI and EEG represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales. The purpose of the last chapter is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modelling. We define a common source space for fMRI and EEG signal projection and novel framework for the spatial and temporal comparative analysis. We use simultaneous EEG-fMRI in order to explore the relationship between the envelope of spontaneous neuronal oscillations in the alpha frequency band (8-13 Hz) recorded with EEG during eyes closed rest and spontaneous fluctuations of the fMRI BOLD signal. We showed on a single-subject analysis how the presented approach, when combined to an accurate realistic head modelling, is able to localize the alpha rhythmic modulation in the occipital visual area and the parieto-occipital sulcus. This finding is in line with recent studies, asserting that, within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition. Given the lack in the scientific literature of group-analysis experimental studies performed with realistic modelling approach in this field, this topic will be further investigated in future work.
PhD cycle: XXII Ciclo
PhD programme: INGEGNERIA DELL'INFORMAZIONE
Description: 2009/2010
Keywords: eeg
fmri
multimodal integration
brain source imaging
Main language of document: en
Type: Tesi di dottorato
Doctoral Thesis
Scientific-educational field: ING-INF/06 BIOINGEGNERIA ELETTRONICA E INFORMATICA
NBN: urn:nbn:it:units-9025
Appears in Collections:Ingegneria industriale e dell'informazione

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