Figure 6.
Linear regression of (A) <LZΦC> and (B) <ETCΦC> as a function of entropy of nodes for all 3, 4 and 5-node networks. A linear fit is obtained between the dependent variable <LZΦC> (or <ETCΦC>) and the explanatory variables – ‘entropy’ of nodes and ‘number of nodes’. In each of the graphs above, X-axis of each graph represents the different configurations of networks and Y-axis represents the mean value of brain network complexity. The leftmost, middle and rightmost graph in both (A) and (B) shows the mean value of integrated information for ten configurations 3-node networks, 15 configurations of 4-node networks, 21 configurations of 5-node networks respectively (refer to ‘Supplementary Tables’ for the network configurations). For each network configuration, the blue plot represents <LZΦC> or <ETCΦC> values respectively in (A) and (B) and the red plot represents their predicted values as a function of ‘entropy’. For further details, please refer to ‘Supplementary Text’.