al., 2020), which included 300 seeds affiliated with 13 networks. We hypothesized that the relationship between binge drinking and functional connectivity will mirror connectivity abnormalities found in addiction, as outlined by Zilverstand and colleagues (2018). Therefore, we focused on the following eights networks often implicated in addiction: Cingulo-Opercular (CO), DMN, Dorsal Attention Network (DAN), Fronto-Parietal (FP), Medial Temporal Lobe (MTL), Reward, Salience, and VAN. This network-level analysis does have some limitations. For instance, focusing solely on canonical addiction networks can lead to overlooking non-canonical connectivity that might also be important in binge drinking. Indeed, when whole-brain connectivity is considered, aberrant connectivity in non-canonical networks such as visual or sensorimotor networks have emerged (Fede et al., 2019; Ruan et al., 2019). Moreover, the network connectivity approach simply averages across all edges affiliated with the same network. Although this calculation is more objective than picking a small number of seeds to represent a whole network (seed-based method), important edge-level information may be lost, as the calculation implies that all edges affiliated with the same network contribute to network connectivity equally. Therefore, we also used the connectome-based predictive modeling (Shen et al., 2017), which is an edge-level analysis that can address the issues
