The assessment of consciousness using neuroimaging has increasingly focused on brain complexity and entropy, intrinsic features of critical systems that provide insight into transitions between dynamic regimes. Complexity measures have varied relationships to entropy, such as positively linear (type 1) or parabolic (type 2) interactions. By combining a type 2 complexity measure with entropy, we can deepen our understanding of the neural dynamics that underlie different levels of consciousness. The complexity-entropy causal plane, involving statistical complexity and permutation entropy, has been proposed to disentangle the chaoticity and stochasticity of time series by quantifying the degree of organization within a system in respect to its randomness. In this study, we analyzed EEG data from participants (n=9) undergoing an anesthetic protocol, comparing resting-state baseline (conscious) and propofol-induced (unconscious) states. By comparing these states, we explored how measures of brain complexity and entropy, mapped onto the complexity-entropy causal plane, reflect transitions to anesthetically induced unconsciousness. We further correlated these features with criticality measures—such as the Hurst exponent, the spectral slope and Higuchi fractal dimension— to assess the brain’s critical dynamics during these transitions. Preliminary findings show that the complexity-entropy causal plane and criticality metrics effectively differentiates consciousness from anesthetically induced unconsciousness.
Contact: Derek Newman, derek.newman@mail.mcgill.ca
McGill University, Montreal, Quebec, Canada
Additional Authors:
Dr. Stefanie Blain-Moraes, School of Physical and Occupational Therapy, McGill University, Montreal Québec, Canada