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. 2021 Jul 13;10:e65582. doi: 10.7554/eLife.65582

Figure 3. The nucleus accumbens (Nac) is selectively active for the helping behavior test (HBT) ingroup condition.

The Nac was activated selectively for trapped ingroup members. (A) Several brain regions (in orange) were significantly more active in the HBT ingroup compared to the HBT outgroup condition (p<0.05). c-Fos numbers are also shown for the brief, 2 free, chocolate (ch), trapped (trp), and baseline (base) conditions. (B, C) Network graph depicting the top 10% inter-region correlations for the HBT ingroup and outgroup conditions. Positive correlations are shown in solid lines, negative correlations in dashed lines. Central hubs were determined as the top 20% of regions with highest in both degree and betweenness (yellow). In bold, regions that were more active in the HBT ingroup condition than the outgroup. Circle color represents clusters identified via a Louvain algorithm, circle size represents the number of degrees for each region. (D) A series of multiple logistical regression tests on all test conditions identified clusters of brain regions that aligned with the distinct brain activity in the helping test conditions. The figure contrasts regions uniquely observed for the ingroup condition (x-axis) with regions observed for both ingroup and outgroup conditions (y-axis). The nucleus accumbens shell (NacSh) and nucleus accumbens core (NacC) were present uniquely in the ingroup condition in 85 and 67.5% of tests, respectively. Dashed line represents the boundary for the regions that are required to identify the ingroup condition based on brain activity. Diagram describes how the graph was derived. (E) Activity in the NacC and NacSh was positively correlated with door-opening behavior. No other regions were significantly correlated with helping.

Figure 3.

Figure 3—figure supplement 1. Network analyses.

Figure 3—figure supplement 1.

(A, B) Louvain clustered heatmap of pairwise correlation values for the helping behavior test (HBT) ingroup and outgroup conditions. Bar on left visualizes the identified clusters. (C–E) Network parameters used for selecting a threshold. The 10% top-ranking correlation values were used in the network map. This threshold was determined according to the network parameters. Increasing the threshold results in a fragmented network, that is, overly scale-free and reduced in connectivity. Decreasing the threshold undermines the small-worldness of the network. (C) Scale-free topology index shows the correlation threshold cutoff of the transition to scale-free network. (D) The percent connectivity graph represents the correlation threshold for 10% connectivity. (E) The small-worldness of the network for the HBT ingroup and outgroup condition is displayed for each correlation threshold. (F, G) Central hubs of the HBT ingroup and outgroup networks. To identify central hubs, brain regions were ranked by degree and betweenness. The top 20% of regions are shown for each parameter. Brain areas appearing in the top 20% of both parameters were classified as central hubs (in yellow).