Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness
| Autor: | Stefanie Blain-Moraes, Tarek Lajnef, Karim Jerbi, George A. Mashour, Pierre Rainville, Etienne Combrisson, Thomas Thiery, Arthur Dehgan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
Consciousness Cognitive Neuroscience media_common.quotation_subject Alpha (ethology) Unconsciousness Electroencephalography Article 03 medical and health sciences Sevoflurane Young Adult 0302 clinical medicine Rhythm 030202 anesthesiology medicine Humans Wakefulness media_common medicine.diagnostic_test Oscillation Brain Amplitude Neurology Anesthetics Inhalation Female medicine.symptom Psychology Neuroscience 030217 neurology & neurosurgery |
| Popis: | Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15-30Hz) amplitude over frontocentral channels and with a suppression of alpha (8-13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness. |
| Databáze: | OpenAIRE |
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