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2. Methods and materials 2.1. fMRI and DTI data In this study we consider anatomical and functional brain connectivities-extracted from diffusion-weighted DW-MRI and fMRI data, respectively- defined on the same brain regions. Brain images were partitioned into the 90 anatomical regions (N = 90 nodes of the networks) of the Tzourio-Mazoyer brain atlas (Tzourio-Mazoyer et al., 2002) using the automated anatomical labeling method. The anatomical connectivity network is based on the connectivity matrix obtained by Diffusion Magnetic Resonance Imaging (DW-MRI) data from 20 healthy participants, as described in Iturria-Medina et al. (2008). The elements of this matrix represent the probabilities of connection between the 90 brain regions of interest. These probabilities are proportional to the density of axonal fibers between different areas, so each element of the matrix represents  an approximation of the connection strength between the corresponding pair of brain regions. The functional brain connectivity was extracted from BOLD fMRI resting state recordings obtained as described in Valencia et al.  (2009). All acquired brain volumes were corrected for motion and differences in slice acquisition times using the SPM51 software package. All fMRI data sets (segments of 5 min recorded from healthy subjects) were co-registered to the anatomical data set and normalized to the standard MNI (Montreal Neurological Institute) template image, to allow comparisons between subjects. As for DW-MRI data, normalized and corrected functional scans were sub-sampled to the anatomical labeled template of the human brain (Tzourio-Mazoyer et al., 2002). Regional time series were estimated for each individual by averaging the fMRI time series over all voxels in each of the 90 regions. To eliminate low frequency noise (e.g., slow scanner drifts) and higher frequency artifacts from cardiac and respiratory oscillations, time-series were digitally filtered with a finite impulse response (FIR) filter with zero-phase distortion (bandwidth 0.01��0.1 Hz) as in Valencia et al. (2009).  A functional link between two time series xi(t) and xj(t) (normalized to zero mean and unit variance) was defined by means of the linear cross-correlation coefficient computed as rij = ?xi(t)xj(t)?, where ?��? denotes the temporal average. For the sake of simplicity, we only considered here correlations at zero lag. To determine the probability that correlation values are significantly higher than what is expected from independent time series, rij(0) values (denoted rij) were firstly variance-stabilized by applying the Fisher's Z transform. Zij=0.5ln(1+rij1?rij) (1) Under the hypothesis of independence, Zij has a normal distribution with expected value 0 and variance 1/(dfij?3), where df is the effective number of degrees of [http://en.wikipedia.org/wiki/Ramoplanin Ramoplanin] freedom (Bartlett, 1946; Bayley and Hammersley, 1946; Jenkins and Watts, 1968).