Delayed recovery from a crisis can cause damage to a system. However, the mechanism by which systems efficiently recover from crises is unknown. Here, we show that the strength of conditions for explosive synchronization, i.e., the proximity of a complex dynamical network to a first-order phase transition, determines whether a perturbed network will recover quickly or slowly. We used computational modeling, empirical data from the human brain undergoing anesthetic state transitions, and empirical data from the stock market during an economic crisis to demonstrate that, because of their high susceptibility at a tipping point, networks with stronger explosive synchronization conditions are disrupted more easily and recover more slowly after external perturbations. Furthermore, we show that it is possible to systematically predict early and prolonged recoveries in anesthesia and economic crises. This study has implications for the design of resilient networks that can withstand perturbation and recover quickly.
Contact: UnCheol Lee, uclee@umich.edu
Department of Anesthesiology, Center for Consciousness Science, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA,
Additional Authors:
HyoungKyu Kim, Department of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA; kim.hyoungkyu@gmail.com Minkyung Kim, Department of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA; kmg6257@gmail.com Gabjin Oh, Division of Business Administration, College of Business, Chosun University, Gwangju, Republic of Korea; phecogjoh@gmail.com Ayoung Park, Division of Business Administration, College of Business, Chosun University, Gwangju, Republic of Korea; ayppark@gmail.com Pangyu Joo, Department of Anesthesiology, Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan, USA; pangyuj@med.umich.edu Dinesh Pal, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, USA; dineshp@umich.edu Irene Tracey, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom; irene.tracey@ndcn.ox.ac.uk Catherine E. Warnaby, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Nuffield Division of Anaesthetics, University of Oxford, Oxford, United Kingdom; katie.warnaby@ndcn.ox.ac.uk Jamie Sleigh, Department of Anesthesiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand; Jamie.Sleigh@waikatodhb.health.nz George A. Mashour, Department of Anesthesiology, Center for Consciousness Science, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA; gmashour@med.umich.edu