Criticality as a Determinant of Integrated Information Φ in Human Brain Networks

Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large [Formula: see text] , a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and [Formula: see text] decreases. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness under general anesthesia. In the modeling study, maximal criticality coincided with maximal [Formula: see text]. The EEG study demonstrated an explicit relationship between [Formula: see text] , criticality, and level of consciousness. The conscious resting state showed the largest [Formula: see text] and criticality, whereas the balance between variation and constraint in the brain network broke down as the response rate dwindled. The results suggest network criticality as a necessary condition of a large [Formula: see text] in the human brain.