Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach.
There is growing interest in studying the role of connectivity patterns in brain functions. In recent years, functional brain networks were found to exhibit small-world properties during different brain states. In previous studies, time-independent networks were recovered from long time periods of brain activity. In this paper, we propose an approach, the event-related networks, that allows one to characterize the dynamical evolution of functional brain networks in time-frequency space. We illustrate this approach by characterizing connectivity patterns in magnetoencephalographic signals recorded during a visual stimulus paradigm. When compared with equivalent random and regular networks, the results reveal that functional connectivity varies with time and frequency during the processing of the stimulus, while maintaining a small-world structure. This approach may provide insights into the connectivity of other complex and spatially extended nonstationary systems.
Valencia
M
M
Laboratoire de Neurosciences Cognitives et Imagerie Cérébrale, LENA-CNRS UPR-640, MEG Center, Hôpital de la Salpêtrière, 47 Bd. de l'Hôpital, 75651 Paris CEDEX 13, France.
Martinerie
J
J
Dupont
S
S
Chavez
M
M
D000200:Action Potentials / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*