## 1 Introduction
Since feature selection methods can not only remove the redundant information or invalid features but also keep the key information of the original data set, they have been widely used in functional connectivity network studies [21]. Based on the combination of feature selection search and the construction of the classification model, filter and wrapper are two typical types of feature selection methods. Additionally, ensemble methods are another popular kind of feature selection method [22]. Filter methods mainly depend on the attribute of features, and the evaluation criteria are independent from classifiers [23]. Wrapper methods embed the feature selection search with the classification performance of the classifier, but the drawbacks of this method are the higher risk of overfitting, and computational intensity when the classifier has a high computational cost [24]. Ensemble methods are based on different sampling strategies to extract multiple sample sets, and then, a specific feature selection algorithm is used to obtain multiple sets of feature subsets [25]. For ICN studies in recent years, special attention has been paid to investigating the differences of ICN with regard to brain development. For example, dynamic functional sparse connectivity patterns on rs-fMRI data were extracted to analyze children from ages 8 to 21 years [26]. The analysis suggested that, compared with young adults, children's brains showed reduced connectivity among the sensorimotor network and auditory network and increased connectivity between the default mode network and other networks. Some co-activated brain regions were revealed [27]. They found that in the default mode network, auditory system, and executive control system, there is an increased connectivity in children compared to young adults. For subjects of 10-20 years old, nodes at multiple scales, e.g., voxel-wise or regions of interest (ROIs) within the brain, were examined in [28]. The results showed that connectivity between hub and non-hub regions changed with the development from childhood to adolescence. Based on 264 ROIs identified by parcellation analysis from rs-fMRI data, functional modules in the brain were detected for subjects 8-22 years old [29; 30; 31]. The results indicated that the network organization is stable throughout adolescence, and cross-network integration increases with age. In this paper, we propose a two-step feature selection method to identify the differences of ICNs during brain development. The two-step feature selection methods can couple two effective feature selection methods to assemble an optimal feature set for building the prediction model [32]. This method takes full advantages of the support vector machine recursive feature elimination (SVM-RFE), which can efficiently reduce noise and irrelevant features in the classification task. In addition, by combining the F-score with the correlation score, only the discriminative features can be kept. Our results suggest that there exist many remarkable differences of ICNs with age. Among them, the significant differences in connectivity are strongly associated with task control, cognition, information processing, attention, alertness, and other functions. Compared with children, we find that young adults experience increased connectivity between the sensory/somatomotor network and the cingulo-opercular task control network, visual network, and ventral attention network. At the same time, young adults experience reduced connectivity among the default mode network and the fronto-parietal task control network. Our discoveries coincide with many prior studies, e.g., [26; 27; 33; 34; 35; 36; 37; 38]. Moreover, we reveal that with aging, most of the connections among different resting state networks become remarkably enhanced and those within rest state networks generally decline. This is coincident with the fact that the connectivity of brains alters from segregation to integration as one grows.