Experimental Model of Study of Consciousness at the Awakening: FMRI, EEG and Behavioral Methods
For the study of neuronal correlates of consciousness, a simple and effective model is the comparison of sleeping and waking states. Consciousness turns off during sleep and turns on at waking. The moment of awakening from sleep is a promising model for the study of neurophysiological correlates of consciousness. We developed a psychomotor test, the monotonous performance of which, causes within 60 min alternating episodes with the disappearance of consciousness when falling asleep (the “microsleep”) and its restoration upon awakening (wakefulness). When performing this test, the subject with closed eyes counts from 1 to 10 and simultaneously presses sensitive buttons, alternately with the right and left hands. Spontaneous restoration of the test after the episode of “microsleep” requires the activation of consciousness, which is accompanied by consciously performing the test with counting and simultaneously pressing the buttons. EEG methods allow you to accurately assess the moments of the transition of sleep/wakefulness, the levels of wakefulness and the depth of sleep, and behavioral methods, by indicators of the correctness of the performance of the psychomotor test - to determine the levels of consciousness. We showed reproducibility of this test obtained both under normal conditions and in conditions of functional magnetic resonance imaging (fMRI) procedure. In 10 out of 14 subjects during a 60-min experiment performed in the MRI scanner, 3–48 episodes of “microsleep” were recorded with subsequent awakening. Preliminary results showed an increase in the activity of the visual regions (the region of the calcarine sulcus) of the cerebral cortex, left pre-cuneus/cuneus, etc. during sleep and regions of the right thalamus, left cuneus, cerebellar zones, stem structures, etc. at the moment of awakening and resumption of conscious activity.
KeywordsMicrosleep Awakening fMRI Levels of consciousness
This study was partially supported by the Russian Science Foundation, grant 18-11-00336 (data preprocessing algorithms) and by the Russian Foundation for Basic Research grant ofi-m 17-29-02518 (study of thinking levels). The authors are grateful to the MEPhI Academic Excellence Project for providing computing resources and facilities to perform experimental data processing.
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