The evaluation of the topological properties of brain networks is an emergent 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 brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.

Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks / DE VICO FALLANI, Fabrizio; Astolfi, Laura; Cincotti, Febo; D., Mattia; Maglione, ANTON GIULIO; Vecchiato, Giovanni; Toppi, Jlenia; F., Della Penna; Salinari, Serenella; Babiloni, Fabio; G., Zouridakis. - 2010:(2010), pp. 1738-1741. ((Intervento presentato al convegno 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10) tenutosi a Buenos Aires nel AUG 30-SEP 04, 2010 [10.1109/iembs.2010.5626710].

Large-scale cortical networks estimated from scalp EEG signals during performance of goal-directed motor tasks.

DE VICO FALLANI, FABRIZIO;ASTOLFI, LAURA;CINCOTTI, FEBO;MAGLIONE, ANTON GIULIO;VECCHIATO, GIOVANNI;TOPPI, JLENIA;SALINARI, Serenella;BABILONI, Fabio;
2010

Abstract

The evaluation of the topological properties of brain networks is an emergent 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 brain connectivity network estimated using neuroimaging techniques. In the present study, we analyze the changes in brain synchronization networks using high-resolution EEG signals obtained during performance of a complex goal-directed visuomotor task. Our results show that the cortical network is more stable when subjects reach the goal than when they fail by hitting an obstacle. These findings suggest the presence of a possible cerebral "marker" for motor actions that result in successful reaching of a target.
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