Cortical-level activity was generated using a flexible neural mass model framework, named COALIA. This multi-population neural mass model enables the simulation of brain-scale electrophysiological activity while accounting for the macro- (between regions) and micro-circuitry (within a single region) of the brain, with one neural mass representing the local field potential of one Desikan-Killiany atlas region [for details, readers may refer to (Bensaid et al. 2019)]. The simulated cortical networks (DMN and DAN) each included six regions based on the Desikan-Killiany atlas (Desikan et al. 2006) in terms of region parcellation. The DMN consisted of the right and left posterior cingulate cortex (PCC), medial orbitofrontal (MOF) gyrus, and inferior parietal lobe (IPL). Regarding the DAN, this network consisted of the right and left inferior parietal lobe (IPL), caudal middle frontal gyrus (cMFG), and superior parietal lobe (SPL). Activity in the alpha band ([8-12] Hz) was attributed to the regions belonging to reference RSNs, while background activity was assigned to remaining cortical regions. A variability between simulated data segments was introduced at the subject level, as well as at the level of epochs per subject. Each “virtual subject” had different connectivity matrices provided to the model, while each epoch for the same subject had a different input noise (mean =90, standard deviation = 30) set within the model. More specifically, for each subject, a different fractional anisotropy matrix of the HCP dataset was used (Van Essen et al. 2013), and the weights corresponding to a RSN-connection were modified and set to a value of (1 ± 20%). A corresponding scaling of the matrices followed in accordance with COALIA’s requisites and the type of each input matrix (inhibitory/excitatory). A total of 50 “virtual subjects”, 4 epochs per subject (i.e., 200 data segments) were simulated; with a duration of 40 seconds each and a sampling rate of 2048 Hz. The time delay between NMMs was determined by the euclidean distance between the centroids of Desikan-Killainy’s regions divided by the velocity of action potentials propagation, which was set as 100 cm/s. Scalp EEG signals can be estimated from simulated cortical activity by solving the forward problem. Bensaid, Siouar, Julien Modolo, Isabelle Merlet, Fabrice Wendling, and Pascal Benquet. 2019. “COALIA: A Computational Model of Human EEG for Consciousness Research.” Frontiers in Systems Neuroscience 13: 1–18. Desikan, Rahul S., Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, et al. 2006. “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans into Gyral Based Regions of Interest.” NeuroImage 31: 968–80. Van Essen, David C., Stephen M. Smith, Deanna M. Barch, Timothy E. J. Behrens, Essa Yacoub, Kamil Ugurbil, and WU-Minn HCP Consortium. 2013. “The WU-Minn Human Connectome Project: An Overview.” NeuroImage 80 (October): 62–79.