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Characterization of neocortical networks from high-resolution EEG: application to disorders of consciousness

Abstract : The human brain is a complex network. Cognitive function is guaranteed when the brain dynamically reconfigures its network organization over time. Studies have showed that most brain disorders, including neurodegenerative and mental diseases, are characterized by changes in the structural and/or functional brain networks. Thus, there is a strong demand for new, non-invasive, network-based and easy-to-use methods to identify these pathological networks. Electroencephalography (EEG) source connectivity method enables the tracking of large scale brain networks dynamics with an excellent temporal resolution. It is in this context that my thesis was carried out. My work here extends the methodological and clinical developments of our research team on functional connectivity at cortical level.The aim of my thesis work is twofold: i) to progress on the methodological aspects of the EEG source connectivity method and ii) to use this method in a clinical application related to the disorders of consciousness. My thesis is divided into two main parts, with two studies realized in each part. In the first part (methodological aspects), I approached, in a first study, the capacity of the EEG source connectivity method to track the brain network dynamic alterations during a fast cognitive task. Then in a second study, I tested the effect of the spatial leakage problem on the reconstructed functional brain networks. In the second part (clinical applications), I analyzed brain networks alterations in patients with disorders of consciousness, using static analysis in the first study and dynamic analysis in the second one.
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Submitted on : Wednesday, November 20, 2019 - 12:27:07 PM
Last modification on : Wednesday, September 14, 2022 - 10:20:04 AM


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  • HAL Id : tel-02372280, version 1


Jennifer Rizkallah. Characterization of neocortical networks from high-resolution EEG: application to disorders of consciousness. Signal and Image processing. Université de Rennes 1 [UR1], 2019. English. ⟨tel-02372280⟩



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