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.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College
