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Examining Oscillatory Dynamics with Magnetoencephalography and Intracranial Electroencephalography (Oscillatory Dynamics)
Date du début: 1 juil. 2008, Date de fin: 30 juin 2010 PROJET  TERMINÉ 

The scientific goal of this project is to use magnetoencephalography (MEG) and intracerebral electroencephalography (iEEG) to gain a better understanding of subcortical and abnormal cortical oscillatory dynamics. The first specific aim of this project is to apply the most sophisticated techniques currently available to resolve the activity of deep brain structures, such as the amygdala, hippocampus, brainstem, and cerebellum, using noninvasive MEG recordings. Time-frequency analysis may be better suited to the nature of the signals produced by these structures than traditional event-related averages. Therefore, advanced spatial filtering techniques will be combined with time-frequency analysis to produce five-dimensional space-time-frequency maps of brain activity. A realistic head model will be used in the construction of the spatial filters, enhancing the spatial resolution of deeper sources. The results will be validated and augmented with iEEG recordings from intractable epilepsy patients who receive clinically indicated depth electrode implants in these areas. Additionally, time-frequency analysis will be applied to EEG-based auditory brainstem response (ABR) recordings to investigate its utility compared to the thousands-of-trials average currently in clinical practice. The second specific aim is to examine the abnormal spontaneous oscillatory activity associated with tinnitus. Recent studies suggest that the phantom perception of many tinnitus patients arise from abnormal synchronization of auditory cortex. However, to date, most efforts in EEG/MEG source localization have concentrated on the localization of high-amplitude transient epileptic spikes or stimulus-evoked experimental designs. These methods require further technical development to allow the analysis of spontaneous changes in oscillatory activity. I propose to use adaptive spatial filtering techniques combined with time-frequency analysis to accomplish this aim.

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