Predicting IGD treatment outcomes across interventions using a whole-brain functional connectome biomarker.

In 30 seconds

This clinical trial study examined whether resting-state whole-brain functional connectivity could predict symptom improvement in 90 individuals with internet gaming disorder (IGD) receiving mindfulness meditation, approach bias modification, or retrieval-aversive conditioning. Connectome-based predictive modelling identified networks associated with reduction in addiction severity from baseline to post-test, with validation in a separate dataset. The negative network predicted symptom reduction (r = 0.401, p <.001), while a combined network showed stronger prediction (r = 0.480, p <.001). The combined model incorporated cerebellar-related connections alongside networks centred on motor, subcortical, and salience systems.

Key findings

• The negative connectivity network predicted reduction in addiction severity (r = 0.401, p <.001).
• The combined network predicted reduction in addiction severity (r = 0.480, p <.001), improving on the negative network.
• The negative network centred mainly on motor, subcortical, and salience networks; positive-network connections were primarily cerebellar-related.
• The treatment-sensitive network was validated in a separate dataset.

Why it matters

The findings suggest that whole-brain connectivity patterns may help explain variation in IGD symptom improvement across different interventions. In particular, the stronger prediction from the combined model points to potential value in considering cerebellar-related connectivity alongside other network features when developing biomarkers for treatment response. This is an early step toward evaluating whether neural measures can support more individualised treatment research.

What to keep in mind

The study reports predictive associations and external validation, but the abstract does not provide details on the validation dataset or predictive performance there.