Tracking brain dynamics across transitions of consciousness

The loss and regain of consciousness involves gradual and rapid brain-activity changes measurable via electroencephalogram (EEG). Falling asleep: a specific EEG microstate whose increased duration predicts behavioral unresponsiveness to auditory stimuli during drowsiness, and which captures increased frontoparietal theta connectivity, a putative marker of losing consciousness before sleep onset. During mild-to-moderate sedation, responsiveness is best predicted by temporal signal complexity at single-channel and low-frequency integration, while drug level is best predicted by spatial-pattern complexity and high-frequency integration. In acute coma, increased variability in delta-network integration early after injury predicts eventual recovery score; a case study shows frontoparietal connectivity re-emergence predicted full recovery long before behavioral improvement. The thesis argues that diversity of dynamical states, especially in the temporal domain, and information integration across brain networks are fundamental to sustaining consciousness.

Study at a glance

Abstract

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.