Resting-state electroencephalography (EEG) is a cost-effective, non-invasive tool for capturing brain network dynamics, offering insights into neural dysfunctions across clinical populations. This PhD project will investigate resting-state EEG microstates, using a data-driven approach, as biomarkers of altered brain function in anxiety, psychotic, mood, neurodevelopmental, and neurodegenerative disorders. By analyzing temporal microstate parameters the study aims to identify both disorder-specific alterations and shared patterns of dysfunction. Human Neocortical Neurosolver (HNN) simulations with implement biophysical model will be used to link these abnormalities to cellular and circuit-level mechanisms, providing insights into neurochemical contributions such as altered glutamate/GABA ratios or dopaminergic dysfunction. Study will integrate newly collected and open-source EEG data and will be conducted in collaboration with Swiss, Czech, Iranian and Turkish partners. By merging neuroscience and computational modeling, this work seeks to establish microstate-based biomarkers for early diagnosis and disease monitoring, advancing the understanding of brain network disruptions across diverse clinical populations.
Mokslinis vadovas / Supervisor: Dr. Evaldas Pipinis
Kontaktai / Contacts:
Programme: Biophysics N 011