In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function.

GD is supported by the ERC Advanced Grant DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P. MLK is supported by the ERC Consolidator Grant: CAREGIVING (n. 615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). VJ and GD are supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). VJ and PR are supported by the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082). VJ is supported by FHU EPINEXT [A*MIDEX project (ANR-11-IDEX-0001-02) funded by the ‘Investissements d’Avenir’ French Government]. PR is supported the German Ministry of Education and Research (US-German Collaboration in Computational Neuroscience 100258846 and Bernstein Focus State Dependencies of Learning 01GQ0971-5), the Max-Planck Society and funding from the European Union Horizon 2020 (ERC Consolidator grant BrainModes 683049).