Individualized prediction of heroin cue-induced craving using task-based EEG functional connectivity.

Cue-induced craving is a core driver of addiction and relapse, and its significant heterogeneity represents a major barrier to precision intervention. Currently, there remains a lack of objective, quantifiable, and individualized neurobiological biomarkers. Here, we employed task-based electroencephalography (EEG) to capture the dynamic neural signatures underlying cue-induced craving in patients with heroin use disorder (HUD) and developed an individualized functional connectivity (FC)-based prediction model. We identified β-band power envelope connectivity (PEC) as a reliable biomarker capable of estimating subjective craving severity at the individual level. Notably, even after FC reconfiguration induced by intermittent theta burst stimulation (iTBS) over the left dorsolateral prefrontal cortex (L-DLPFC) or precuneus (PCu), the PEC-based framework's prediction of immediate craving levels following these perturbed states remained effective. Crucially, baseline β-band PEC demonstrated strong prognostic value for improvements in craving scores (L-DLPFC-iTBS: r = 0.856, P < 0.001; PCu-iTBS: r = 0.675, P = 0.008). This individualized predictive model was further validated in an independent dot-probe task dataset, demonstrating its generalizability across distinct cue-induced craving paradigms. Together, our study demonstrates that EEG FC features predict individual cue-induced craving levels and intervention outcomes, facilitating the advancement of digital biomarker-driven precision medicine.