Integrated Information Theory aims to explain and measure consciousness by quantifying how integrated a system's causal properties are. This work implemented version 3.0 of the theory on functional MRI data from 17 healthy volunteers sedated with propofol. Using the PyPhi software, the measure Φmax was computed and compared with other proposed consciousness metrics: an earlier integrated information version, Granger causality, and correlation-based functional connectivity. Φmax showed varied responses to sedation across different brain networks. Changes in Φmax closely tracked changes in conscious level within the frontoparietal and dorsal attention networks, which support higher-order cognition. The findings offer guidance for future use of these measures in neuroimaging.
Integrated information, a measure proposed by Integrated Information Theory as a correlate of conscious experience, behaves as an order parameter that undergoes a phase transition at the critical point in generalized Ising models of small neural networks. In simulations of 159 random, positively weighted five-node excitatory networks, integrated information peaked at the critical temperature, where its generalized susceptibility was maximal. At this point, the system was maximally receptive and responsive to perturbations of its own states. The findings show that integrated information can capture critical behavior in an empirical dataset derived from the generalized Ising model.