Dynamic reorganization of brain functional networks during cognition

- Does cognitive processing change organization of brain functional networks?
- Source-space phase-synchronized event related networks (ERNs) estimated from EEG
- Dynamic frequency-resolved ERNs were characterized with graph measures.
- Cognition related to high clustering, low modularity, strong interactions among hubs
- Such reorganization facilitates global integration and creates a “global workspace”.

How does cognition emerge from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes the strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG while subjects performed a visual discrimination task. We conducted an event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. ERNA revealed rapid, transient, and frequency-specific reorganization of the network's topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the “network fingerprint” of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a “global workspace” necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.