Criticality as a Determinant of Integrated Information - In Human Brain Networks Hyoungkyu Kim , Uncheol Lee (Anesthesiology, University of Michigan, Ann Arbor, MI ) C15
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 barPHI, a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and barPHI 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 barPHI. The EEG study demonstrated an explicit relationship between barPHI, criticality, and level of consciousness. The conscious resting state showed the largest barPHI 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 barPHI in the human brain. 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 PHI. We also hypothesized that if the brain deviates from criticality, the cause-effect structure is obscured and PHI 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 PHI. The constraint of the structural network on the functional network is maximized in the maximal criticality. The EEG study demonstrated an explicit relationship between PHI, 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 PHI.