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Imbalance of Neuronal Activity as the Underlying Principle of Unconsciousness in Sleep and General Anesthesia

Applicant Dr. Janna Lendner
Subject Area Anaesthesiology
Anatomy and Physiology
Cognitive, Systems and Behavioural Neurobiology
Term from 2018 to 2020
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 407711355
 
Final Report Year 2020

Final Report Abstract

During general anesthesia and every night during sleep, our brains disconnect from the world around us. In the progression from wakefulness to sleep, the electrical signals in our brains change substantially: Low-amplitude arrhythmic patterns during waking give way to prominent rhythmic patterns, so-called neuronal oscillations. We investigated how the interplay between the three cardinal sleep oscillations (slow waves, spindles and ripples) orchestrates interregional communication, that enable information transfer from short- to long-term memory storage. So far, ripples from the seahorse-shaped hippocampus in the depth of the human brain were thought to initiate a dialogue with the cortex to replay newly learned information. Over the course of this dialogue, the memories would become less hippocampus- and more cortexdependent. Here, we showed that it is indeed a bidirectional dialogue between these brain regions: The prefrontal cortex uses the slow and spindle oscillations as pacemakers to invite the hippocampus for a timed transfer of information packages. However, while general anesthesia and deep sleep stages are characterized by prominent oscillations, rapid eye movement (REM) sleep looks similar to the desynchronized brain signals during wakefulness. Therefore, additional monitoring of the typical rapid eye movements and muscle activity is needed to identify this sleep stage from waking. Traditionally, neuroscientific research has mostly focused on the more salient brain oscillations while the arrhythmic part of the electrical signal was often discarded as “noise” – although it constitutes the lion’s share of the electrical activity. Here, we showed that a measure of arrhythmic brain activity, the so-called spectral slope, was able to discern wakefulness from general anesthesia and sleep. Not only was this marker able to reliably track changes in arousal level on different time scales (millisecond to full night recordings), it could for the first enable a classification of REM sleep directly from the brains electrical signal, without the help of traditional markers. In addition, the spectral slope can be quickly extracted from scalp recordings and integrated into existing depth of anesthesia und sleep staging algorithms. In the future, we will extend and evaluate this marker in patients that suffer from generalized epileptic seizures and coma. The results from this study were featured in The Daily Californian, Berkeley News, EurekAlert!, reddit, ONRede Tech News, Newzealand Online News, Indian Flipboard, The Science Breaker and Quanta Magazine.

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