An Implementation of Integrated Information Theory in Resting-State fMRI
Abstract
Integrated Information Theory (IIT) is a framework developed to explain consciousness, arguing that conscious systems consist of interacting elements that are integrated through their causal properties. In this study, we present the first application of IIT to functional magnetic resonance imaging (fMRI) data and investigate whether its principal metric, Phi, can meaningfully quantify resting-state cortical activity patterns. Data was acquired from 17 healthy subjects who underwent sedation with propofol, a short acting anesthetic. Using PyPhi, a software package developed for IIT, we thoroughly analyze how Phi varies across different networks and throughout sedation. Our findings indicate that variations in Phi closely reflect the conscious state of patients in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. Despite existing limitations in applying IIT to empirical data, we obtained promising results that merit further applications of this framework to fMRI.