Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany; Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany; Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany; Correspondence authors at: Brain Simulation Section, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany.
Xiaolu Kong
Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Singapore; N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore
B.T. Thomas Yeo
Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Singapore; N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
Gustavo Deco
Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Clayton, Australia
Petra Ritter
Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany; Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany; Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany; Correspondence authors at: Brain Simulation Section, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany.
What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.
