## 4 Discussion
On the other hand, emerging evidence indicate that the strength of the resting-state functional connectivity is associated with individual differences in task performance [7; 8; 9]. Therefore, increased functional connectivity may facilitate communications between the pre-SMA and prefrontal regions and benefit successful performance in driving. Here, we showed that the pre-SMA, rather than SMA proper, showed significant resting-state functional connectivity differences between drivers and nondrivers (Table 2), which may help to shed light on the functional roles of SMA sub-regions. Additionally, we compared driving-related functional connectivity changes with the entire SMA and the pre-SMA, and found that the latter was more sensitive in detecting group differences (Table 2). A possible reason for that is that the use of functionally inaccurate ROIs will dilute meaningful signals. This emphasizes the great importance of specifying ROIs that are functionally homogeneous for resting-state fMRI studies. Functional connectivity analysis has provided candidate brain networks that showed significant driving-related functional integration (see Figure 2). Effective connectivity analysis using spectral DCM was then applied on this basis to further reveal the directionality of the connections. The results revealed that the pre-SMA had established a hierarchical architecture with the IOFG, the ISFG and the IMTG, which could be judged according to the sign of the effective connectivity strength--the bottom-up connection is a feedforward connection, which is excitatory (i.e., the posterior mean of the effective connectivity strength is positive), while the opposite is a top-down feedback connection, which is inhibitory (i.e., negative effective connectivity strength). This agrees with the predictive coding theory, showing that top-down feedback connection is used to suppress prediction errors from the lower levels [41; 42; 43]. According to the group means of the effective connectivity provided using PEB and BMR, the posterior means of the directed connections from the pre-SMA to the IOFG, the ISFG and the IMTG were all negative, while the effective connectivity of these brain areas to the pre-SMA were all positive (see Figures 3 and 4). Considering that various driving-related tasks (such as performing prepared actions, action planning and considering the traffic rules) were associated with the pre-SMA [4], the pre-SMA is more highlighted in task switching [44], response inhibition [45], learning of movement sequences [46] and motor selection [47]. Therefore, we infer that the pre-SMA is at a higher level of the brain functional network that we studied, which is responsible for integrating complex cognitive and motor functions, and suppressing the prediction errors introduced by lower levels of a hierarchy. The results of PEB and BMR also pointed out that there significant group differences existed between drivers and controls, in which the pre-SMA\(\rightarrow\)ISFG connection was significantly enhanced in the drivers with a posterior probability of 100% (Figure 4). We then conducted predictive validity assessment, and found that this connection could effectively identify group membership (see Figure 5 and Table 3), indicating that this effective connectivity could be used to judge whether the functional integration structure of the brain was reshaped by long-term driving behavior. In addition, considering that the effective connectivity of pre-SMA\(\rightarrow\)ISFG was an inhibitory feedback connection (see the \"Group Mean\" panel of Figure 4), the enhanced value in the driver group may indicate that the driving behavior weakens the inhibitory effects of this feedback connection; namely, the pre-SMA does not need to use a strong inhibitory strength to suppress the prediction errors from the ISFG.