Electroencephalographic Correlates of Conscious States and Contents

JUI 18
GIGA Seminar
Srivas Chennu (University of Cambridge, UK) invited by GIGA-Consciousness
18/07/2018 11:00 --> 18/07/2018 12:00 Léon Fredericq Auditorium
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Electroencephalographic Correlates of Conscious States and Contents

Srivas Chennu1,2,3

1 School of Computing, University of Kent

2 Department of Clinical Neurosciences, University of Cambridge

3 The Alan Turing Institute, London, UK

The study of human brain connectivity rests on the notion that the brain spontaneously generates networks of correlated activations involving functionally specific regions. A vast body of research into so-called resting state brain activity suggests that these networks are metastable, in that they exist in a state of dynamic equilibrium perturbed by changes in the state of consciousness. In my talk, I will bring together this prior evidence with my own work on the characterisation of brain networks measured using high-density electroencephalography in altered states of consciousness, including sleep, anaesthesia, and clinical disorders of consciousness. I will show that, during the process of falling asleep, topographical dynamics of the electroencephalogram correlate with specific patterns of brain connectivity and behavioural responsiveness to stimuli. In anaesthesia, changes in the topology of brain connectivity correlate with drug-induced changes in the state of consciousness, and can predict individual variability in the behavioural response to the drug. In clinical disorders of consciousness, brain networks correlate with diagnosis, prognosis and neuroimaging in patients. Finally, I will highlight how we are talking this research out of the lab, moving from cross-sectional group studies to longitudinal assessment of individual patients in the field, to track their behavioural recovery right at their bedside. Our findings highlight the feasibility of brain-based assessments in the rehabilitation context, showing that they can complement clinical evaluation with portable, accurate and timely generation of patient profiles. Going further, these assessments could also be used to evaluate the efficacy of medical interventions tailored to individual patients.