Abstract
General anesthesia (GA) is a reversible manipulation of consciousness whose mechanism is mysterious at the level of neural networks leaving space for several competing hypotheses. We recorded electrocorticography (ECoG) signals in patients who underwent intracranial monitoring during awake surgery for the treatment of cerebral tumors in functional areas of the brain. Therefore, we recorded the transition from unconsciousness to consciousness directly on the brain surface. Using frequency resolved interferometry; we studied the intermediate ECoG frequencies (4–40 Hz). In the theoretical study, we used a computational Jansen and Rit neuron model to simulate recovery of consciousness (ROC). During ROC, we found that f increased by a factor equal to 1.62 ± 0.09, and δf varied by the same factor (1.61 ± 0.09) suggesting the existence of a scaling factor. We accelerated the time course of an unconscious EEG trace by an approximate factor 1.6 and we showed that the resulting EEG trace match the conscious state. Using the theoretical model, we successfully reproduced this behavior. We show that the recovery of consciousness corresponds to a transition in the frequency (f, δf) space, which is exactly reproduced by a simple time rescaling. These findings may perhaps be applied to other altered consciousness states.
Highlights
General anesthesia (GA) is an example of a reversible manipulation of consciousness, which is performed every day in hospitals around the world
The ECoG grid was placed on the brain cortical surface after opening the skull under GA using total intravenous anesthesia with propofol and remifentanil
recovery of consciousness (ROC) occurred over a broad time range, 6 minutes to 21 minutes, after stopping the anesthesia supply
Summary
General anesthesia (GA) is an example of a reversible manipulation of consciousness, which is performed every day in hospitals around the world. The changes in the EEG during sleep or anesthesia are empirically well described but their function and generation are still unknown[4]. We analysed the data using time-frequency analysis and we found a scaling factor between the conscious and the unconscious state. This scaling factor was demonstrated by formally time compressing an unconscious state and compared the obtained artificial state with a true conscious state.
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