A novel measure called Network Causal Activity, based on Compression-Complexity Causality, was used to analyze electrocorticographic signals from the lateral cortex of four monkeys. Network Causal Activity was consistently higher in the awake state compared with the anaesthetized state, suggesting it may serve as a quantitative indicator of consciousness.
A new measure called Φ^C bridges Integrated Information Theory (IIT) and the Perturbational Complexity Index (PCI) by using lossless data compression to quantify integrated information in brain networks. Unlike IIT's Φ, which is computationally expensive and dependent on current state, Φ^C is mathematically well bounded, has negligible state dependence, and scales linearly with network nodes, avoiding combinatorial explosion. Computer simulations show Φ^C produces similar hierarchies to Φ across multiple-node networks and reveals interactions between differentiation, integration, and entropy. It offers a faster heuristic for measuring integrated information—and thus a potential proxy for consciousness—in larger networks like the human brain, enabling tests of brain complexity predictions on real neural data.