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.

Mental health disorders are widespread, and their impact on individual functioning, work ability, and the healthcare system is substantial. At present, the effects of interventions delivered as part of mental health support or treatment are assessed mainly through clinical interviews and self-reports, usually during infrequent visits. The project adds a regular data-driven measurement layer to this process and develops an interpretable individualized model and a standardized workflow to help detect clinically meaningful changes earlier, more consistently, and more objectively. In this way, the project lays the foundation for a new data-driven service layer in occupational health and mental health services, as well as for the further development of well-being digital solutions based on neurophysiological input.