Preprint open access publication

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

bioRxiv, Cold Spring Harbor Laboratory,

2019

DOI:10.1101/846667, Dimensions: pub.1122682300,

Authors

Perl, Y. S. (3) (4)
Kringelbach, M.L. (5) (6) (7)
Gayte, I. (1)
Laufs, H. (8)
Deco, G. (2)

* Corresponding author

Affiliations

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  1. (1) University of Seville, grid.9224.d
  2. (2) Pompeu Fabra University, grid.5612.0
  3. (3) Laboratory of Experimental Psychology and Neuroscience, Institute of Cognitive and Translational Neuroscience, INECO Foundation, Favaloro University, Argentina
  4. (4) University of Buenos Aires, grid.7345.5
  5. (5) Aarhus University, grid.7048.b, AU
  6. (6) University of Minho, grid.10328.38
  7. (7) University of Oxford, grid.4991.5
  8. (8) Kiel University, grid.9764.c

Description

ABSTRACT The dynamical activity of the human brain describes an extremely complex energy landscape changing over time and its characterisation is central unsolved problem in neuroscience. We propose a novel mathematical formalism for characterizing how the landscape of attractors sustained by a dynamical system evolves in time. This mathematical formalism is used to distinguish quantitatively and rigorously between the different human brain states of wakefulness and deep sleep. In particular, by using a whole-brain dynamical ansatz integrating the underlying anatomical structure with the local node dynamics based on a Lotka-Volterra description, we compute analytically the global attractors of this cooperative system and their associated directed graphs, here called the informational structures . The informational structure of the global attractor of a dynamical system describes precisely the past and future behaviour in terms of a directed graph composed of invariant sets (nodes) and their corresponding connections (links). We characterize a brain state by the time variability of these informational structures. This theoretical framework is potentially highly relevant for developing reliable biomarkers of patients with e.g. neuropsychiatric disorders or different levels of coma.

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Green, Published