In the present study, we developed a methamphetamine (METH)-related virtual social environment to elicit subjective craving and physiological reactivity. Sixty-one male patients who were abstinent from METH use and 45 age-matched healthy males (i.e., normal controls) were recruited. The physiological electrocardiogram (ECG) signals were recorded before (resting-state condition) and during viewing of a METH-cue video in the virtual environment (cue-induced condition). The cue-induced subjective craving was measured with a visual analogue scale (VAS) for patients with METH dependence. The results indicated that the cue-induced condition elicited significant differences in heart rate variability (HRV) between patients with METH dependence and normal controls. The changes of HRV indexes on time domain and non-linear domain from the resting-state condition to the cue-induced condition were positively correlated with the score on VAS of METH craving. Using a supervised machine learning algorithm with the features extracted from HRV changes, our results showed that the discriminant model provided a high predictive power for distinguishing patients with METH dependence from normal controls. Our findings support that immersing subjects with METH dependence in a METH-related virtual social environment can successfully induce physiological reactivity, and cue-induced physiological signal changes may have a potential implication in clinical practice.
Keywords: Cue-induced craving; Heart rate variability; Methamphetamine; Supervised machine learning; Virtual reality.
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