Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study.
Authors
Affiliations (4)
Affiliations (4)
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. Electronic address: [email protected].
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
- Columbia University School of Nursing, New York, NY, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Child Study Center, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA; Connecticut Council on Problem Gambling, Wethersfield, CT, USA; Connecticut Mental Health Center, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA. Electronic address: [email protected].
Abstract
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<sub>age</sub>āÆ=āÆ10.03, 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.