Electrophysiological signatures of resting state networks in the human brain. Export

@article{citeulike:1610931, abstract = {Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.}, address = {Institute of Advanced Biomedical Technologies and Department of Clinical Sciences and Bio-imaging, G. D'Annunzio University Foundation, G. D'Annunzio University, Chieti 66013, Italy.}, author = {Mantini, D. and Perrucci, M. G. and Del Gratta, C. and Romani, G. L. and Corbetta, M.}, citeulike-article-id = {1610931}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0700668104}, citeulike-linkout-1 = {http://www.pnas.org/cgi/content/abstract/104/32/13170}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17670949}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17670949}, citeulike-linkout-4 = {http://www.scopus.com/record/display.url?view=extended&origin=resultslist&eid=2-s2.0-34548797958}, day = {7}, doi = {10.1073/pnas.0700668104}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {default-network}, month = {August}, number = {32}, pages = {13170--13175}, posted-at = {2009-10-07 16:44:11}, priority = {2}, title = {Electrophysiological signatures of resting state networks in the human brain.}, url = {http://dx.doi.org/10.1073/pnas.0700668104}, volume = {104}, year = {2007} }

TY - JOUR ID - citeulike:1610931 L3 - citeulike-article-id:1610931 N2 - Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies. IS - 32 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 AD - Institute of Advanced Biomedical Technologies and Department of Clinical Sciences and Bio-imaging, G. D'Annunzio University Foundation, G. D'Annunzio University, Chieti 66013, Italy. EP - 13175 TI - Electrophysiological signatures of resting state networks in the human brain. VL - 104 SP - 13170 KW - default-network AU - Mantini, D AU - Perrucci, MG AU - Del Gratta, C AU - Romani, GL AU - Corbetta, M PY - 2007/08/07/ UR - http://dx.doi.org/10.1073/pnas.0700668104 ER -