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Sci Rep. 2019 Mar 20;9(1):4927. doi: 10.1038/s41598-019-41345-4.

Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity.

Author information

1
Laboratory of integrative neuroscience, The Rockefeller University, New York, NY, 10065, USA. lalonso@brandeis.edu.
2
Volen Center for Complex Systems, Department of Biology, Brandeis University, Waltham, MA, 02454, USA. lalonso@brandeis.edu.
3
Instituto del Cálculo, FCEyN, Universidad de Buenos Aires, (C1428EGA), Buenos Aires, Argentina. gsolovey@gmail.com.
4
Laboratory for Adaptive Intelligence, Brain Science Institute, RIKEN, Saitama, 351-0198, Japan.
5
Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, 19104, USA.
6
IBM, Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
7
Laboratory of integrative neuroscience, The Rockefeller University, New York, NY, 10065, USA.
8
Volen Center for Complex Systems, Department of Biology, Brandeis University, Waltham, MA, 02454, USA.

Abstract

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

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