Fig 3. Co-activation graphs for each emotion category.
A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see S5 Fig).