Dynamic identification of functional brain networks by Bayesian tracking of electrophysiological data

Although functional brain imaging can uncover activity patterns encompassing multiple brain regions, the interplay of these regions is usually not directly addressed. Yet, neural processing supporting cognition may dynamically recruit brain functions implemented at distinct cortical regions. These short-lived networks are formed by dynamic functional connections between the participating regions. Electro- and magnetoencephalography (EEG/MEG) measure electric brain activity with high temporal resolution. However, neither method readily provides us with a network structure; they merely show the aggregated activity of all contributing regions. The challenge is to decompose the recorded MEG/EEG data into a sparse and dynamic set of brain signal sources. The goal of the proposed project is to tackle this challenge in a new way, surpassing current functional connectivity estimation methods, and to enable real-time tracking of these networks to allow their use in neurofeedback experiments.