Onoda Keiichi, Miyauchi Satoru, Kan Shigeyuki, Akama Hiroyuki
Department of Psychology, Otemon Gakuin University, 2-1-15, Nishiai, Ibaraki, Osaka 567-8502, Japan.
Department of Physiology, Kansai Medical University, 2-5-1, Shinmachi, Hirakata, Osaka 573-1010, Japan.
Neurosci Conscious. 2025 Sep 1;2025(1):niaf024. doi: 10.1093/nc/niaf024. eCollection 2025.
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.
整合信息理论（IIT）提供了一个基于现象学特性的公理框架，通过一种称为Φ的度量方法，能够对意识进行量化和表征。根据IIT，Φ反映了意识水平，并且随着意识丧失预计会降低，尽管支持这一说法的实证数据仍然有限。在本研究中，我们分析了在麻醉（丙泊酚诱导）和自然睡眠期间获取的两个功能磁共振成像（fMRI）数据集，以确定Φ是否随着意识的丧失和恢复而变化。我们的分析使用了IIT的第四版。我们构建了由五个功能性脑网络组成的系统，从fMRI时间序列数据计算转移概率矩阵，并基于这些矩阵得出Φ值。正如IIT所预测的，在麻醉诱导的意识丧失期间，全局和局部水平的Φ均降低。同样，在睡眠诱导的意识丧失期间，以脑后部区域为中心的系统内的Φ在局部也降低。将功能网络视为系统单元，我们发现大脑的整合信息（Φ）与意识水平的波动有关。这些发现表明，在大规模功能网络中，意识与整合信息之间存在紧密关联。
