Fig. 5.

The complex network properties of the brain network based on the PAC algorithm during the WM tasks (10 rats, 100 trials). A Dynamic changes in the average degree of the brain network based on the PAC algorithm. The average degree of the brain network during the WM process increased significantly with an increase in learning days (one-way ANOVA: $F_{(9,149)}=4.017$, ***$P<0.001$). B Dynamic changes in the average clustering coefficient of the brain network based on the PAC algorithm. Learning days had no significant effect on the average clustering coefficients of the brain networks during WM. (one-way ANOVA: $F_{(9,149)}^{}=0.401$, $P=0.9332$) C Dynamic changes in average shortest path length of the brain network based on the PAC algorithm. The average shortest path length of the brain network during the WM process increased significantly with an increase in learning days. (one-way ANOVA: $F_{(9,149)}=3.264$, **$P<0.01$). D Dynamic changes in the global efficiency of the brain network based on the PAC algorithm. The global efficiency of the brain network during the WM process decreased significantly with an increase in learning days. (one-way ANOVA: $F_{(9,149)}=2.752$, **$P<0.01$). E Dynamic changes in the small-world attribute of the brain network based on the PAC algorithm. There was no significant difference of the small-world attribute of the brain networks during WM in learning days (one-way ANOVA: $F_{(9,149)}=0.068$, $P>0.9999$)