Predictive neural signature of internet gaming disorder severity revealed by cross-network connectivity

While internet gaming disorder (IGD) correlates with regional brain responses and functional connectivity, the brain network architecture underlying addiction severity remains poorly characterized.

Using resting-state functional magentic resonance imaging data and addiction severity metrics from 586 participants (443 IGD, 143 recreational game users), we employed connectome-based predictive modeling (CPM) with leave-one-out cross-validation to identify neural networks predictive of IGD severity. The resulting network was evaluated for replicability in independent datasets, with key predictive networks and nodes further analyzed.

CPM identified a replicable addiction severity network. CPM significantly predicted individual gaming addiction scores (r = 0.19, P < 0.001), with features selected using a threshold of P < 0.01. Predictive power primarily derived from internetwork connectivity linking the subcortical, subvisual, and frontoparietal networks. Validation in independent data showed a directional trend (r = 0.17, P = 0.011).

Individual variability in subcortical-subvisual-frontoparietal network connectivity predicts IGD addiction severity, highlighting these circuits as potential targets for neuromodulation interventions.