Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study
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• 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.
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Alterations in large-scale brain networks involved in cognitive control may predict the severity of problematic social media use in youth.
“Emerging neuroscientific evidence further supports this link, showing that alterations in large-scale brain networks involved in cognitive control may predict the severity of problematic social media use in youth (Park, Lacadie, Zhao, & Potenza, 2026).”
From Problematic social media use and cognitive performance in young adults: Associations with selective attention and inhibitory control (2026) · cites this paper as Park, Lacadie, Zhao, & Potenza, 2026
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Sisí, C., Pallesen, S., & Fernández-Martín, M. P. (2026) · Journal of Behavioral Addictions · doi:10.1556/2006.2025.00576
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Park, J. J., Chen, B. B., & Potenza, M. N. (2026) · Future Science OA · doi:10.1080/20565623.2026.2681577
Self-citationPark, J. J., & Potenza, M. N. (2026) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2026.06.001
Self-citationDergaa, I., Chalghaf, N., Razzak, M., Muntean, R. I., Ștefănică, V., & Ceylan, H. İ. (2026) · JMIR Mental Health · doi:10.2196/98200
Park, J. J., Lee, G., Huh, S., & Potenza, M. N. (2026) · Brain and Environment · doi:10.1016/j.braen.2026.100031
Self-citationBai, K., Wu, B.-J., Wang, Z.-Z., Cao, X.-J., Dou, J.-Q., Jiang, X.-Y., Zhao, S.-S., Chen, Z., Li, Y.-T., Shang, Y., et al. (2026) · JMIR Medical Informatics · doi:10.2196/86963
- Authors
- Park, J. J., Lacadie, C. M., Zhao, Y., & Potenza, M. N.
- Affiliations
- Yale University (US)
- Columbia University (US)
- Corresponding author
- Park, Jennifer J.
- Potenza, Marc N.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2026-04
- Volume
- 329
- Pages
- 121829
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC13449941
- OpenAlex
- W7131113777
- Cited by
- 5
- References
- 77
- Citations by year
- 2026: 5
- FWCI
- 20.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (U01DA051037, U01DA041028, U01DA041106, U01DA041093, U24DA041123, U01DA041134, U01DA041120, U24DA041147, U01DA041022, U01DA051016, U01DA041117, U01DA050987, U01DA041174, U01DA041025, U01DA051039, U01DA050988, U01DA041148, U01DA041156, U01DA050989, U01DA041089, U01DA051038, U01DA041048, U01DA051018, 2147)
- National Institute on Alcohol Abuse and Alcoholism (R01 AA029611)
- National Institute of Mental Health (RF1 MH128614)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- adolescents, resting-state functional connectivity, large-scale brain networks, cognitive control, self-regulation, default mode network, frontoparietal network, sensorimotor network, Adolescent Brain Cognitive Development Study, longitudinal prediction
- MeSH terms
- Machine Learning, Internet Addiction Disorder, Predictive Learning Models, Adolescent, Brain, Child, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Social Media, Connectome
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