August 7, 2007 | vol. 104 | no. 32 | D. Mantini†‡§, M. G. Perrucci†, C. Del Gratta†, G. L. Romani†‡, and M. Corbetta†‡||
The study by Mantini et al. investigates the relationship between hemodynamic and electrical oscillations in the human brain during resting wakefulness. Using a data-driven approach combining electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), the researchers identified six widely distributed resting state networks (RSNs) in the brain. These RSNs are characterized by slow (~0.1 Hz) fluctuations of the blood oxygen level-dependent (BOLD) signal and faster (1–80 Hz) electrical oscillations. The study found that each RSN is associated with a specific combination of EEG rhythms (delta, theta, alpha, beta, and gamma), forming a unique electrophysiological signature. This signature helps to fine-tune the physiological fractionation of brain networks in the resting state. The results support the hypothesis that multiple brain rhythms coexist within large-scale brain networks, as suggested by biophysical studies, and provide a deeper understanding of the functional role of spontaneous brain activity.The study by Mantini et al. investigates the relationship between hemodynamic and electrical oscillations in the human brain during resting wakefulness. Using a data-driven approach combining electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), the researchers identified six widely distributed resting state networks (RSNs) in the brain. These RSNs are characterized by slow (~0.1 Hz) fluctuations of the blood oxygen level-dependent (BOLD) signal and faster (1–80 Hz) electrical oscillations. The study found that each RSN is associated with a specific combination of EEG rhythms (delta, theta, alpha, beta, and gamma), forming a unique electrophysiological signature. This signature helps to fine-tune the physiological fractionation of brain networks in the resting state. The results support the hypothesis that multiple brain rhythms coexist within large-scale brain networks, as suggested by biophysical studies, and provide a deeper understanding of the functional role of spontaneous brain activity.