Laura Päeske

The functioning of the brain is based on neuronal cooperation and communication between different brain regions. Electroencephalography (EEG) functional connectivity (FC) analysis contributes to neuroscience and holds great potential for developing novel biomarkers to objectively assess signs of brain disorders. The project proposes a novel approach to the study of functional network analysis, focusing on the interactions between EEG FC and small-world (SW) organization. The project tests the hypothesis that interactions between FC and SW are related to Default Mode Network (DMN) activity, thereby linking electrode-level EEG features to underlying neurophysiological processes. The project uses biophysical modeling for EEG source-level analysis and machine learning to uncover complex patterns and relationships between FC and SW measures. The FC-SW interaction could lead to the development of a prospective biomarker that may be more specific for differentiating between disorders.