Abstract
The brain is possibly the most complex system known to mankind, and its complexity has been called upon to explain the emergence of consciousness. However, complexity has been defined in many ways by multiple different fields: here, we investigate measures of algorithmic and process complexity in both the temporal and topological domains, testing them on functional MRI BOLD signal data obtained from individuals undergoing various levels of sedation with the anaesthetic agent propofol, replicating our results in two separate datasets. We demonstrate that the various measures are differently able to discriminate between levels of sedation, with temporal measures showing higher sensitivity. Further, we show that all measures are strongly related to a single underlying construct explaining most of the variance, as assessed by Principal Component Analysis, which we interpret as a measure of “overall complexity” of our data. This overall complexity was also able to discriminate between levels of sedation and serum concentrations of propofol, supporting the hypothesis that consciousness is related to complexity - independent of how the latter is measured.
Highlights
The science of complex systems has gained increasing prominence in the 21st century
Our aim was twofold: first, to investigate the relationship between the different measures of complexity as applied to human functional MRI (fMRI) data, with the hypothesis that results should be consistent across measures; secondly, we sought to provide a comprehensive investigation of the hypothesis that whole-brain complexity of the human brain measured from fMRI BOLD data is reduced when consciousness is lost as a result of anaesthetic-induced unconsciousness
Despite the ability of algebraic connectivity to discriminate between conditions, there was no significant correlation with serum propofol concentration in the Mild and Moderate conditions of Dataset A. These results suggest that, while graph theoretical measures may be predictive of level of consciousness in propofol anaesthesia, algebraic connectivity in particular seems to lack the discriminative power of direct analysis on BOLD signals
Summary
The science of complex systems has gained increasing prominence in the 21st century. It combines the reductionist ideal of science, with the notion of emergence, whereby high-level phenomena can result from the interactions of simple constituent parts, confirming Aristotle’s saying that “the whole is more than the sum of its parts”[1]. Is the brain the source of humans’ widely diverse range of behaviours and accomplishments, which is itself suggestive of a highly complex underlying organisation; its structure is that of a complex network of sub-networks, in turn made of multiple kinds of neurons obeying nontrivial plasticity rules for their interactions. For these reasons, it has been proposed that the brain’s complexity may explain another unique property it possesses: consciousness. We chose to evaluate whole-brain measures of algorithmic and process complexity applied to the temporal and topological (network) dimensions, derived from fMRI blood-oxygen-level-dependent (BOLD) signals of volunteers undergoing sedation with propofol. We replicated our results in an independent dataset of propofol anaesthesia, in order to demonstrate their robustness
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