Neurophysiological correlates of response inhibition predict relapse in detoxified alcoholic patients: some preliminary evidence from event-related potentials.

BACKGROUND

Alcohol dependence is a chronic relapsing disease. The impairment of response inhibition and alcohol-cue reactivity are the main cognitive mechanisms that trigger relapse. Despite the interaction suggested between the two processes, they have long been investigated as two different lines of research. The present study aimed to investigate the interaction between response inhibition and alcohol-cue reactivity and their potential link with relapse.

MATERIALS AND METHODS

Event-related potentials were recorded during a variant of a "go/no-go" task. Frequent and rare stimuli (to be inhibited) were superimposed on neutral, nonalcohol-related, and alcohol-related contexts. The task was administered following a 3-week detoxification course. Relapse outcome was measured after 3 months, using self-reported abstinence. There were 27 controls (seven females) and 27 patients (seven females), among whom 13 relapsed during the 3-month follow-up period. The no-go N2, no-go P3, and the "difference" wave (P3d) were examined with the aim of linking neural correlates of response inhibition on alcohol-related contexts to the observed relapse rate.

RESULTS

Results showed that 1) at the behavioral level, alcohol-dependent patients made significantly more commission errors than controls (P<0.001), independently of context; 2) through the subtraction no-go P3 minus go P3, this inhibition deficit was neurophysiologically indexed in patients with greater P3d amplitudes (P=0.034); and 3) within the patient group, increased P3d amplitude enabled us to differentiate between future relapsers and nonrelapsers (P=0.026).

CONCLUSION

Our findings suggest that recently detoxified alcoholics are characterized by poorer response-inhibition skills that demand greater neural resources. We propose that event-related potentials can be used in conjunction with behavioral data to predict relapse; this would identify patients that need a higher level of neural resources when suppressing a response is requested.