Functional Connectivity based on EEG is a technique used to study the interrelationships between different regions of the brain by analyzing the statistical dependencies of their electrical activity. This method involves recording brain waves through electrodes placed on the scalp and then applying various mathematical and computational approaches to assess how different brain regions interact with each other. Functional connectivity provides insights into the dynamic communication between neuronal populations and is crucial for understanding the brain’s functional organization. By examining these connections, researchers can explore how different parts of the brain coordinate and integrate information.
This technique is closely related to Network Graph Theory, a field of mathematics that studies the properties of networks through nodes (representing brain regions) and edges (representing connections between these regions). By modeling the brain’s functional connectivity as a graph, researchers can use metrics from graph theory, such as degree, centrality, and clustering coefficient, to quantify the brain’s connectivity patterns. In epileptic patients, analyzing functional connectivity can reveal abnormalities in brain networks, such as hyper-synchronization or disrupted connectivity, which are often associated with seizure activity. This approach helps map the connectome, the comprehensive map of neural connections, and understanding how epilepsy alters brain network dynamics.
Furthermore, functional connectivity analysis can provide valuable insights into how vagus nerve stimulation (VNS) modulates brain networks.