Functional Neuroimaging of Electrophysiological Rhythms in Pathological and Normal Brains

Abstract

Imaging of electrophysiological activity in the brain plays a critical role in neuroscience research. Shown by emerging neuroimaging studies, rhythmic oscillations in electrophysiology reflect important functional changes in the brain. More importantly, the mapping of electrophysiological neural signals can serve as a diagnostic tool for neurological diseases. One typical example is the electroencephalography (EEG) technique, which has been established as a core component of pre-surgical evaluation in epilepsy treatment. However, despite the recent advances of functional neuroimaging techniques, a non-invasive, high resolution, electrophysiological imaging approach still remains challenging. In the clinical application of epilepsy, there is not an established protocol that can image, non-invasively, the electrophysiological signals during the most important epileptic event - epileptic seizures. The present dissertation research aims at developing electrophysiological imaging approaches with focus on the rhythmic activity in pathological and normal brains. Towards this goal, we have developed a spatiotemporal EEG imaging method, which is suited to image dynamic changes of ictal discharges during epileptic seizures. As evaluated in a group of epilepsy patients in clinical environment, such a non-invasive seizure imaging approach could potentially translate into a more precise and less risky pre-surgical imaging tool for epilepsy diagnosis. In addition to the direct impact of seizures, we have studied the electrophysiological changes in the widespread brain networks. The spatial and spectral features of EEG rhythms can reflect important correlation with the impact of seizures and the change of cognitive functions. The electrophysiological imaging in epilepsy, therefore, can serve as a useful tool in a pathological model to study cognition and consciousness in human brains. In order to achieve higher spatial resolution, we also improved the EEG source imaging by adding a multimodal component of functional MRI. From all the results we have obtained so far in these studies, it is suggested that the spatiotemporal EEG source imaging has the potential to improve clinical diagnosis and treatment of neurological disorders. It can also advance our understanding of basic neuroscience questions.