Functional anatomy of stereoscopic visual process assessed using functional magnetic resonance imaging and structural equation modelling.

摘要

The purpose of this thesis is to study the functional anatomy of stereoscopicudvision. Although many studies have investigated the physiological mechanisms byudwhich the brain transforms the retinal disparities into three-dimensionaludrepresentations, the invasive nature of the techniques available have restrictedudthem to studies in non-human primates, whilst the research on humans has beenudlimited to psychophysical studies.udModem non-invasive neuroimaging techniques now allow the investigation of theudfunctional organisation of the human brain. Although PET and fMRI studies haveudbeen widely used, few researchers have explored the functional anatomy ofudstereoscopic vision. Most of these studies appear to be pilot work, showingudinconsistency, not only in the areas sensitive to stereo disparities, but also in theudspecific role that each of these possesses in the perception of depth.udIn order to investigate the cortical regions involved in stereoscopic vision, fourudfMRI studies were performed using anaglyph random dot stereo grams. Our resultsudsuggest that the stereo disparity processing is widespread over a network ofudcortical regions which include VI, V3A, V3B and B7. Functionally, the V3Audregion seems to be the main processing centre of pure stereo disparities and theudV3B region to be engaged in motion defined purely by spatio-temporal changes ofudlocal horizontal disparities (stereoscopic -cyclopean- motion).udInterregional connectivity was investigated with two approaches. StructuraludEquation Modelling (SEM), as the classical technique for the analysis of effectiveudconnectivity, was used to assess one connectivity model proposed to· explain theudcortical interaction observed in the first experiment. The implementation andudapplication of this technique permitted us to identify some of its weaknesses inudrepresenting fMRI data. An extension of the SEM technique was introduced as audNon-linear Auto-Regressive Moving Average with eXogenous variablesud(NARMAX) approach. This can be thought of as an attempt to bring SEMudtowards a non-linear dynamic system modelling technique which permits a moreudappropriate representation of effective connectivity models using fMRI timeudseries.