Integrated information to predict consciousness state - An exploratory analysis

Integrated Information Theory (IIT) is considered the most advanced formal theory of consciousness within neuroscience literature. However, only limited and indirect empirical evidence supports IIT. Computational, empirical, and theoretical limitations make it hard to test the predictions of IIT. To verify the hypothesis that higher values of integrated information (PHI) are associated with a higher level of consciousness, we leveraged data collected by two previous studies (Taghia et al., 2018; Huang et al., 2020). Such data is amenable to an IIT analysis employing the PyPhi toolbox (Mayner et al., 2018). In both studies there are conditions associated with different levels of consciousness (e.g., sedated participants vs controls as in Huang et al., 2020) and a transition probability matrix between brain states, obtained by means of machine learning techniques. We investigated if integrated information is able to predict consciousness level based on the state-by-state matrix generated according to transition probabilities. We observed that the PHI values are not related with conditions where brain states are characterized, according to neuroscience literature, by a greater consciousness level. Finally, we discussed limitations and future opportunities of our approach.