STRUCTURAL ORGANIZATION OF FUNCTIONAL NETWORKS FROM EEG SIGNALS DURING MOTOR LEARNING TASKS

The evaluation of the topological properties of brain networks is an emerging research topic, since the estimated cerebral connectivity patterns often have relatively large size and complex structure. Since a graph is a mathematical representation of a network, the use of a theoretical graph approach would describe concisely the topological features of the functional network estimated from neuroimaging techniques. In particular, by applying the process of coherence analysis to high-density EEG recordings, rich visualizations can be developed that provide a means for spatiotemporal analysis of changes in synchronous brain activity. In the present work, we studied the changes in brain synchronization networks during performance of a complex visuomotor task with strategic components in normal subjects. In particular, we evaluated the differences in the functional network topology associated with human learning by calculating global and local efficiency indexes. Our results suggest that during an initial period of learning, which is probably related to the most significant cognitive processes, the particular organization of functional links in the alpha frequency band (8-12Hz) tends to increase the efficiency of communication within the cerebral network. Such evidence could be interpreted as due to the need for a new strategy formulation. Overall, this approach enabled us to capture a shift in topology made during the process of learning and thus helped us to shed more light on the neural correlates of strategy formulation. Our findings provide strong support for the efficacy of theoretical graph analysis to study complex brain networks.