Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness. Author summary: How can different states of reduced consciousness be characterized and classified? This question carries great significance both for basic and clinical neuroscience, since loss of consciousness is transient when induced by anesthesia or deep sleep, but can be permanent in certain brain-injured patients. We demonstrated that perturbational analyses applied to semi-empirical whole-brain models can disentangle conscious states of different stability, providing information that complements data-driven metrics, and opening the way for new computational tools for the diagnosis and prognosis of disorders of consciousness. [ABSTRACT FROM AUTHOR]