Investigation of oscillatory mechanisms and thalamo-cortical circuitry of the visual system by simultaneous EEG-fMRI

Abstract

Neural oscillation is an indispensable phenomena in the functioning of the cortical networks. Evoked neural oscillations triggered by external rhythmic stimulation mimic spontaneous ongoing oscillations, thus could shed light on the intrinsic specialization and tuning of the cortical networks. In this thesis, ickering light stimulation is used to constitute steady state for a wide range of temporal frequencies (6-46 Hz) during simultaneous electroencephalography (EEG) and blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) scans of 40 healthy volunteers. Firstly, thalamo cortical loop of the visual system is the subject of interest. Our ndings prove that high correlation between the frequency response characteristics of the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) supports the oscillatory tuning property of the thalamo cortical interactions. Secondly, contribution of oscillations in the modeling of hemodynamic response is discussed based on the sensitivity of BOLD components (phasic and tonic) to temporal frequency. Our results show that, tonic BOLD component is decreasing more sharply than phasic component with increasing frequencies pointing higher dependency of tonic BOLD response to the stimulation frequency. Finally, EEG informed fMRI analysis is conducted for the sake of testing resonance phenomena. The correlation maps between the BOLD responses and the steady state visually evoked potential (SSVEP) amplitudes show signi cant correlation for the beta and gamma bands but not for alpha band. This result supports the view that the global amplitude maximum of the SSVEP in the alpha band is due to the synchronization without synaptic activity (BOLD) increase.|Keywords : Electroencephalography, Functional magnetic resonance imaging, Oscillator networks, Steady state visually evoked potentials, BOLD transients, Resonance phenomena, EEG informed fMRI analysis.