ArticleSocial cognitive and affective neuroscience2024
Connectome-based predictive modeling of Internet addiction symptomatology.
Article in Social cognitive and affective neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
- Identifying internet addiction profiles and bridge connectors among Chinese college students and evaluating CBT vs. CBT+MBI interventions via a randomized controlled trial.Journal of behavioral addictions · 2025Trial
- Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.Journal of behavioral addictions · 2026Article
- Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study.NeuroImage · 2026Article
- Predictive neural signature of internet gaming disorder severity revealed by cross-network connectivity.Psychoradiology · 2026Article
- Connectome-based predictive modelling predicts frailty levels in older adults.bioRxiv : the preprint server for biology · 2025Article
- Limitations of Correlation Coefficients in Research on Functional Connectomes and Psychological Processes.Human brain mapping · 2025Review
- Connectome-based predictive modeling of grip strength: a marker of physical frailty.Frontiers in neuroscience · 2025Article
- Mindfulness as a Path to Freedom from Internet Addiction in Adolescents: A Narrative Review.Cureus · 2024Review
- Article
- A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.bioRxiv : the preprint server for biology · 2024Article
- Distinct value-modulated connectomic signatures during monetary incentive anticipation predict severity and craving in internet gaming disorder.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
Abstract
Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.