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.
Highlights
In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness
The dynamical aspect of criticality has been brought into focus, as a desirable feature not fully captured by steady-state statistics such as avalanche size distributions[20,21,22]; a perturbation in an extended dynamical system that is close to a critical point will neither decay nor explode, allowing for long range communication across the entire system
As a way to track changes in the dynamical stability (DS) of the fitted vector autoregressive (VAR)(1) models as anesthesia is induced, we compare the initial distribution of damping time scales (Re(λ)) against subsequent distributions using a Kolmogorv-Smirnov test
Summary
In the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. Www.nature.com/scientificreports sound measure of anesthetic depth should take into account the interactions between signals emitted by different parts of the cortex Consistent with this line of reasoning Massimini and colleagues demonstrated that loss of consciousness associated with sleep[11,12], anesthesia[13], and brain injury[14] result in decrease in the complexity of responses elicited by transcranial magnetic stimulation. The dynamical aspect of criticality has been brought into focus, as a desirable feature not fully captured by steady-state statistics such as avalanche size distributions[20,21,22]; a perturbation in an extended dynamical system that is close to a critical point will neither decay nor explode, allowing for long range communication across the entire system This will manifest as increase in functional connectivity and the complexity of responses. This will result in the apparent loss of functional connectivity and loss of complexity of responses
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