## 4 Discussion  
The two-step feature selection method proposed here is utilized to identify the essential differences of ICNs as an individual grows. With the first step, i.e., SVMRFE-CA, the noise and irrelevant features can be efficiently reduced. At the same time, since the data set is small but with high dimensions, if a feature in the training process is eliminated, although it may have some discriminative ability, it will not be considered again in the subsequent process by the original SVM-RFE method. In order to retain these latent significant features, we introduced a reconsidered way to keep these features, and the multiple subsampling strategies are also utilized to avoid mistakenly removing such features. In the second step, the F-score is combined with the correlation score to remove the highly correlated and relevant features selected in the SVMRFE-CA process. By the two-step feature selection method, only the features with the most discriminative ability will be retained. The analysis based on graph theory further confirms the significance of the revealed difference of ICNs between children and young adults. Compared with other feature selection methods, the two-step feature selection method also shows its competitive power. For this method, less than a third of features are needed to identify the developmental differences. Meanwhile, the redundant degree, and the classification performance both displayed the efficiency of the proposed method. The obtained results reveal that, compared with children, for young adults there exist many remarkable differences in the functional connections among SSN, COTCN, VN, VAN, DMN, FPTCN, and SCN, where the connectivity among SSN, COTCN, VN, and VAN, are significantly enhanced. The results can reflect the specific functional differences during brain development since these RSNs are strongly associated with cognition, information processing, attention, alertness, and other functions. In detail, it is reported that SSN is mainly related to cognitive activities [57]. Some brain regions in COTCN play a particularly important role in the coordination of information transfer between networks and are very active in performing many complex cognitive tasks [58]. VN is mainly involved in visual information processing. VAN is responsible for non-spatial attention, which enables a bottom-up attention selection by a stimulus-driven process, which includes awareness of significant events, reorientation of attention, and alertness [59]. Additionally, our findings are quite consistent with prior reports. For example, it has been observed that the connectivity among the SSN and certain other RSNs are relatively low in childhood [26]. The study by [PERSON] et al. showed that the hyper-connectivity of young adults' VN with other RSNs is enhanced, that is, young adults' visual information processing ability is higher than that of children [33]. In [35], it observed that some ICNs related to SSN and VN are quite complex in young adults' brains. Meanwhile, we found that compared with children, there exist some weakened functional connections in young adults' brains, i.e., DMN, SCN, and COTCN. These discoveries have also been found in prior studies. [PERSON] et al. revealed that children have stronger connectivity between DMN and the cognitive control system than young adults [34]. In [36], it was discovered that there is more consistent DMN connectivity in children' brains. In [37], they uncovered that with increasing age during adolescence and early adulthood, a clear pattern emerges, that is, the connectivity decreases between DMN and COTCN. In [38], it showed that for children, the cortical gray matter reaches a peak, which is usually reduced in adolescence, and this can be a reason leading to the weakening of the connection between DMN and SCN. We discovered that several decreases in connectivity exist between FPTCN and SSN for young adults, and it was also found in [26], that children have stronger connectivity between these two RSNs.