## 3 Results
### Predictive Validity Assessment of Effective Connectivity  
In the previous section, we have found seven effective connections exhibiting significant group differences. Among these results, predictive validity assessment was conducted to further evaluate the classification ability of each connection. To be more specific, a PEB model was estimated while leaving out a subject from the total 40 subjects (i.e., leave-one  
Figure 4: Significance of the effective connectivity revealed using Bayesian model reduction (BMR) and Bayesian model averaging (BMA). The “Group Mean” panel illustrates the significance of the effective connectivity for the group commonality. Some connections were removed for a posterior probability of 0; the others establish a directed connectivity network of either excitatory (marked in red) or inhibitory (blue) connections among the four brain regions (the connection with a posterior probability of 100% is shown in bold). The “Group Difference” panel illustrates the effective connectivity of the significant group difference. The drivers establish a total of seven enhanced effective connections relative to the controls (marked in red). The posterior means and probabilities of all connections are shown as bar charts. The numbers next to the arrowhead label the corresponding connections, in accordance with the figures on the abscissa. out cross validation), and this PEB model was then used to calculate the posterior predictive density (i.e., posterior predictive expectation, posterior predictive covariance and posterior probability) of the between-subjects effect (i.e., driver or control) for the reserved subject, based on the significant effective connectivity chosen. Taking the predictive validity of the pre-SMA\(\rightarrow\)ISFG as an example, Figure 5 shows the posterior probability of each subject belonging to the control group (left panel) and the driver group (right panel). Considering the true label of each subject (i.e., subjects 1 to 20 are controls, and subjects 21 to 40 are drivers), the true negative results (i.e., control subjects were correctly classified to control group) are marked as blue bars in the left panel in Figure 5, and the true positive results (i.e., driver subjects were correctly classified to driver group) are highlighted as red bars in the right panel. The black dotted bars show the wrongly predicted results, and we can see that 7 controls were misidentified as drivers (false positive), and 4 drivers were misidentified as controls (false negative). Therefore, in terms of the classification performance of the pre-SMA\(\rightarrow\)ISFG connection, the classification accuracy, sensitivity and specificity are 72.5%, 80% and 65%, respectively. The Pearson's correlation coefficient between the posterior predictive expectation and the true value of the group membership (correlation = 0.4057) and its significance (\(p\) = 0.0047) were also calculated (see Table 3). These results clearly show that the pre-SMA\(\rightarrow\)SFG connection can be effectively used to identify driving behavior. Figure 5: The predictive validity of the pre-SMA\(\rightarrow\)ISFG connection. The bar chart shows the posterior probability of each subject (subjects 1 to 20 are controls, and subjects 21 to 40 are drivers) belonging to control group (**left**) and driver group (**right**), respectively. The bars marked with blue highlight the true negative results (i.e., control subjects were correctly classified to control group), whereas the bars marked with red highlight the true positive results (i.e., driver subjects were correctly classified to driver group). The black dotted bars show the misidentified results.