NEUROSCIENCE Correction for “Key role of coupling, delay, and noise in resting brain fluctuations,” by Gustavo Deco, Viktor Jirsa, A. R. McIntosh, Olaf Sporns, and Rolf Kötter, which appeared in issue 25, June 23, 2009, of Proc Natl Acad Sci USA (106:10302–10307; first published June 3, 2009; 10.1073/pnas.0901831106).
The authors note that on page 10304, in Fig. 3D, the right-hand graph was incorrect as shown. Additionally, on page 10306, Fig. 5B was incorrect as shown. These errors do not affect the conclusions of the article. The corrected figures and their legends appear below.
Fig. 3.
Sychronization analysis of simulated neuroelectric activity. (Left) Level of synchronization for each of the 2 individual communities as measured by the Kuramoto order parameter (community 1, black; community 2, red; difference, blue). (Right) Power spectrum of the signal given by differences between the level of synchronization between both communities. (A) The results obtained by selecting the optimal working point P (see Fig. 2). (B) Simulations with a different level of noise (〈ν2〉 = 2). (C) Without delays. (D) For a different working point (α = 0.007 and ν = 3.5).
Fig. 5.
Stochastic resonance effects. (A) Maximum of the power spectrum peak of the signal given by differences between the level of synchronization between both communities versus the noise level (variance). (B) Maximum in the power spectrum of the signal given by differences between the level of synchronization between both communities versus the noise level. (C) Correlation between the level of synchronization between both communities versus the noise level. Note the stochastic resonance effect that for the same level of fluctuations reveals the optimal emergence of 0.1-Hz global slow oscillations and the emergence of anticorrelated spatiotemporal patterns for both communities. Points (diamonds) correspond to numerical simulations, whereas the line corresponds to a nonlinear least-squared fitting using an α-function.