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Updated: Jan 22, 2026
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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
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Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.
Jennifer J Park1, Cheryl M Lacadie2, Dustin Scheinost2
11Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Journal of Behavioral Addictions
|January 20, 2026
Summary
Researchers used machine learning to find brain networks linked to problematic gaming in adolescents. These findings could help develop new interventions for gaming disorder by targeting specific neural connections.
Area of Science:
- Neuroscience
- Adolescent Psychology
- Computational Psychiatry
Background:
- Problematic gaming is increasing among adolescents, yet brain network research is limited.
- Understanding the neural basis of gaming disorder is crucial for developing effective interventions.
- This study addresses the gap by identifying brain networks associated with problematic gaming severity in youth.
Purpose of the Study:
- To identify neural networks predictive of problematic gaming severity in adolescents.
- To utilize connectome-based predictive modeling (CPM) with functional magnetic resonance imaging (fMRI) data.
- To assess the generalizability of predictive networks across different brain states.
Main Methods:
- 1,036 adolescents (mean age 12.0) from the Adolescent Brain Cognitive Development study were analyzed.
- Connectome-based predictive modeling (CPM) was applied to problematic gaming scores and fMRI data during a reward-processing task.
- Generalizability was tested using other task-based and resting-state fMRI data.
Main Results:
- CPM successfully predicted problematic gaming scores (r = 0.12, p = 0.002).
- Predictive networks involved visual, cognitive control, salience, and sensorimotor networks.
- Findings were consistent across various brain states, indicating generalizability.
Conclusions:
- Identified large-scale networks offer potential targets for personalized interventions for problematic gaming.
- Replicability in external samples is needed to validate these findings for clinical application.
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