• CPM successfully identified neural networks predictive of PUSM severity in adolescents. • Resting-state fMRI data could be utilized to predict PUSM severity four years later. • Connectivity within and between several large-scale networks was identified. • Identified networks have been implicated in self-control and sensorimotor functions. Problematic use of social media (PUSM) is a major public health concern estimated to affect 35% of adolescents. However, data-driven research to identify neural networks predictive of PUSM in adolescents remains limited. The aim of this study was to utilize connectome-based predictive modelling (CPM), a machine-learning approach that employs whole-brain functional connectivity data, to predict PUSM severity and identify underlying neural networks in adolescents. We included 2294 participants from the Adolescent Brain Cognitive Development study (M age = 10.03, 50.6% female) who had resting-state functional magnetic resonance imaging (fMRI) data at baseline and PUSM scores at the four-year follow-up. CPM with 10-fold cross-validation was applied to resting-state fMRI data and PUSM scores. CPM successfully predicted PUSM scores and identified connectivity within and between multiple large-scale neural networks predictive of PUSM severity, which could be categorized into two key systems: (i) a cognitive control and self-regulation system consisting of the default mode, frontoparietal, and medial frontal networks, and (ii) a perceptual-motor integration system consisting of the visual area 1 and sensorimotor networks. The large-scale networks identified in the present study provide mechanistic insight into PUSM vulnerability and represent potential targets for personalized interventions. Future research should aim to replicate and extend the current results to refine prevention and treatment approaches.
Park et al. (Sun,) studied this question.