Integrated information theory (IIT) offers an axiomatic framework based on phenomenological properties, allowing the quantification and characterization of consciousness through a measure known as Φ. According to IIT, Φ reflects the level of consciousness and is expected to decrease with loss of consciousness, although empirical data supporting this claim remain limited. In this study, we analyzed two functional magnetic resonance imaging (fMRI) datasets acquired during anesthesia (propofol-induced) and natural sleep to determine whether Φ changes with the loss and recovery of consciousness. Our analysis was conducted using the fourth version of IIT. We constructed systems composed of five functional brain networks, computed transition probability matrices from fMRI time series data, and derived Φ values based on these matrices. As predicted by IIT, Φ decreased during anesthesia-induced loss of consciousness at both global and local levels. Similarly, Φ was locally reduced within a system centered on posterior brain regions during sleep-induced loss of consciousness. Considering functional networks as system units, we found that the integrated information (Φ) of the brain is linked to fluctuations in consciousness levels. These findings indicate a strong association between consciousness and integrated information within the large-scale functional networks.