Awake to the Awakening

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Gustavo Deco. Abstract A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. Results In order to find accurate ways to promote a stimulus-driven transition from one brain state to another, we first provide a quantitative characterization of the dynamics underlying brain states, here called the PMS space, and then fit a whole-brain model to this.

Awakening: Forcing a Brain State Transition. Discussion We successfully used a whole-brain model to fit the PMS space of the 2 radically different brain states of sleep and wakefulness. Materials and Methods Experimental Data. Whole-Brain Computational Model. Empirical Fitting. Comparing empirical and simulated grand-averaged static functional connectivity. Comparing empirical and simulated FCD. Comparing empirical and simulated probability metastable space state measurements.

Comparing empirical and simulated transition probabilities between metastable substates. Methods for updating EC. Acknowledgments G. Email: gustavo. Reviewers: A. The authors declare no conflict of interest. Gu et al. Neuroimage , — Gervasoni et al. Muldoon et al.

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PLoS Comput. OpenUrl CrossRef. Deco , V. Jirsa , Ongoing cortical activity at rest: Criticality, multistability, and ghost attractors. Friston , L. Harrison , W. Penny , Dynamic causal modelling. Neuroimage 19 , — Jirsa , A.

McIntosh , O. Sporns , R. Deco , G. Tononi , M. Boly , M. Kringelbach , Rethinking segregation and integration: Contributions of whole-brain modelling. Laumann et al.

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Neuroimage , 34 — 44 Kringelbach , A. Green , T. Aziz , Balancing the brain: Resting state networks and deep brain stimulation. Schiff et al. Nature , — Erratum in: Nature , Pascual-Leone , B. Rubio , F. Lancet , — Rossini et al. An updated report from an I. Kringelbach , N.

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Jenkinson , S. Owen , T. Aziz , Translational principles of deep brain stimulation. Honey , R. Breakspear , O. Sporns , Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Ghosh , Y.

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Rho , A. McIntosh , R. Cabral , M. Kringelbach , G. Deco , Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. Neuroimage , 84 — 96 Breakspear , Dynamic models of large-scale brain activity. Tagliazucchi , H.

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Laufs , Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82 , — Stevner et al. Cabral et al. Neuroimage , 46 — 56 Deco , M. Kringelbach , V. Jirsa , P. Ritter , The dynamics of resting fluctuations in the brain: Metastability and its dynamical cortical core. Deco , T. Van Hartevelt , H.

Fernandes , A. Stevner , M. Kringelbach , The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling. Feynman , R. Leighton , M. Raichle , A. Snyder , A default mode of brain function: A brief history of an evolving idea.

Neuroimage 37 , — ; discussion — Kringelbach , Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders.

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Kringelbach , Brain songs framework used for discovering the relevant timescale of the human brain. Preti , T. Bolton , D. Van De Ville , The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage , 41 — 54 Keilholz , C.

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