Criticality as a determinant of integrated information Φ in human brain networks

Integrated information theory (IIT) postulates that consciousness arises from the cause-effect structure of a system but the optimal network conditions for this structure have not been elucidated. In the study, we test the hypothesis that network criticality, a dynamically balanced state between a large variation of functional network configurations and a large constraint of structural network configurations, is a necessary condition for the emergence of a cause-effect structure that results in a large Φ, a surrogate of integrated information. We also hypothesized that if the brain deviates from criticality, the cause-effect structure is obscured and Φ diminishes. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness during general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The constraint of the structural network on the functional network is maximized in the maximal criticality. The EEG study demonstrated an explicit relationship between Φ, criticality, and level of consciousness. Functional brain network significantly correlated with structural brain network only in conscious states. The results support the hypothesis that network criticality maximizes Φ.