As riveting as it can be, going to the movies only slightly changes this complex dynamic landscape as only few connections are actually driven by the task. Thus, the overall panorama does not change much. Yet the movie has a much stronger effect on the overall hue of this landscape. Major hills (networks) change their color from shades of blue/green to orange/red. Reducing cool colors (low frequency cortical noise) may be necessary LBH589 concentration to allow hot colors (high frequency task-related activity) to manifest.

Future studies will need to determine whether the shape of this landscape or its colors could be affected by more extreme behavioral manipulations. Twelve subjects (mean age 24.7, range 21–31 years; six females; all right-handed) performed separate MEG and fMRI recording sessions during fixation and movie watching. Neuromagnetic signals were recorded using

a 153-magnetometer MEG system built at the University Pomalidomide of Chieti (Della Penna et al., 2000) while fMRI was acquired on 3T MR Philips Achieva scanner. All participants signed prior to the experiment an informed consent form approved by the Ethics Committee of the University of Chieti. An overview of the MEG data preprocessing is depicted in Figure S1. After ICA identification and classification algorithm (Supplemental Information), the source-space band-limited power (BLP) were computed as in de Pasquale et al., 2010 and de Pasquale et al.,

2012), equation(Equation 1) pj(t)=(1Tp)∫tt+Tp|qj(τ)|2dτin Sitaxentan which Tp = 150 ms and qj (t) = [qjx (t) qjy(t) qjz(t)] is the source-space current density vector at voxel j at time t. Correlation time series between voxels j and s (the seed) were computed using the Pearson product moment formula: equation(Equation 2) rsj(t)=∫t−Tr/2t+Tr/2[ps(t+τ)−ps¯][pj(t+τ)−pj¯]dτ∫t−Tr/2t+Tr/2[ps(τ)−ps¯]2dτ∫t−Tr/2t+Tr/2[pj(τ)−pj¯]2dτwhere Tr is the epoch duration and overbars denote the mean over the appropriate interval. In the analysis assuming the stationarity rsj was computed over nonoverlapping windows spanning the whole recording (Tr ≈ 37 s). This approach was applied to obtain Z score differences maps over the whole brain and difference covariance matrices. In the analysis considering the nonstationarity, time courses of correlation were obtained evaluating rsj over 10 s window with 200 ms time step. Functional MRI (fMRI) data were preprocessed as in Mantini et al. (2012) ( Supplemental Information). This research was funded by the European Community’s Seventh Framework Programme Grant Agreement HEALTH-F2-2008-200728 (BrainSynch) and by the Human Connectome Project (1U54MH091657-01).V.B. was additionally supported by a fellowship from the University of Chieti. M.C. was supported by R01 MH096482-01 (NIMH) and 5R01HD061117-08 (NICHD).