Predicting IGD treatment outcomes across interventions using a whole-brain functional connectome biomarker.
Although treatment approaches have been explored in internet gaming disorder (IGD), however, little is known about the specific neural activity that is associated with treatment outcomes. This study aims to establish a treatment-sensitive network that reliably predicts treatment outcomes across different interventions for IGD.
Resting-state fMRI data were collected from 90 individuals with IGD who received one of three different intervention protocols (mindfulness meditation, approach bias modification, or retrieval-aversive conditioning training). Connectome-based predictive modelling (CPM) was used to build a treatment-sensitive network to predict individual symptom reduction, then validated them on the external dataset.
The negative (r = 0.401, p <.001) and combined network (r = 0.480, p <.001) can significantly predict a reduction of addiction severity (baseline minus post-test). Specifically, the negative network was primarily centred on the motor, subcortical, and salience networks. The positive network primarily involved cerebellar-related connections. Incorporating these into a combined model significantly improved predictive accuracy, suggesting that cerebellar-related connectivity may contribute to the prediction of intervention-related clinical improvement. Finally, the treatment-sensitive network was validated in a separate data.
This study established a treatment-sensitive network based on the whole-brain connectome. These findings provide valuable empirical evidence for identifying biomarkers for IGD treatment and advance personalised therapeutic strategies.
The trial protocol was pre-registered with the Chinese Clinical Trial Registry (www.chictr.org.cn; ChiCTR2300077831(MM), ChiCTR2300075869(ApBm), ChiCTR2500095444(R-AC), ChiCTR2400090834(SAE)).
Resting-state fMRI data were collected from 90 individuals with IGD who received one of three different intervention protocols (mindfulness meditation, approach bias modification, or retrieval-aversive conditioning training). Connectome-based predictive modelling (CPM) was used to build a treatment-sensitive network to predict individual symptom reduction, then validated them on the external dataset.
The negative (r = 0.401, p <.001) and combined network (r = 0.480, p <.001) can significantly predict a reduction of addiction severity (baseline minus post-test). Specifically, the negative network was primarily centred on the motor, subcortical, and salience networks. The positive network primarily involved cerebellar-related connections. Incorporating these into a combined model significantly improved predictive accuracy, suggesting that cerebellar-related connectivity may contribute to the prediction of intervention-related clinical improvement. Finally, the treatment-sensitive network was validated in a separate data.
This study established a treatment-sensitive network based on the whole-brain connectome. These findings provide valuable empirical evidence for identifying biomarkers for IGD treatment and advance personalised therapeutic strategies.
The trial protocol was pre-registered with the Chinese Clinical Trial Registry (www.chictr.org.cn; ChiCTR2300077831(MM), ChiCTR2300075869(ApBm), ChiCTR2500095444(R-AC), ChiCTR2400090834(SAE)).