Working memory-related neural activity predicts future smoking relapse

Abstract

Brief abstinence from smoking impairs cognition, particularly executive function, and this has a role in relapse to smoking. This study examined whether working memory-related brain activity predicts subsequent smoking relapse above and beyond standard clinical and behavioral measures. Eighty treatment-seeking smokers completed two functional magnetic resonance imaging sessions (smoking satiety vs 24 h abstinence challenge) during performance of a visual N-back task. Brief counseling and a short-term quit attempt followed. Relapse during the first 7 days was biochemically confirmed by the presence of the nicotine metabolite cotinine. Mean percent blood oxygen level-dependent (BOLD) signal change was extracted from a priori regions of interest: bilateral dorsolateral prefrontal cortex (DLPFC), medial frontal/cingulate gyrus, posterior cingulate cortex (PCC), and ventromedial prefrontal cortex. Signal from these brain regions and additional clinical measures were used to model outcome status, which was then validated with resampling techniques. Relapse to smoking was predicted by increased withdrawal symptoms, decreased left DLPFC and increased PCC BOLD percent signal change (abstinence vs smoking satiety). Receiver operating characteristic analysis demonstrated 81% area under the curve using these predictors, a significant improvement over the model with clinical variables only. The combination of abstinence-induced decreases in left DLPFC activation and reduced suppression of PCC may be a prognostic marker for poor outcome, specifically early smoking relapse.

Figure 1

(a) Visualization of functionally defined N-back ROIs in the left dorsolateral prefrontal cortex (DLPFC) and posterior cingulate cortex (PCC). (b) Forward stepwise logistic regression retained the left DLPFC, left PCC, and withdrawal scores. Greater abstinence-induced change in withdrawal (increase), left DLPFC percent signal change (reduced activation), and PCC percent signal change (less deactivation) were predictive of relapse.

Figure 2

ROC curves for three predictive models of 7-day quit status. The full model (black) includes clinical, withdrawal and brain variables, yielding an AUC of 81%. Clinical predictors alone (red) achieved an AUC of 67% and the intermediate model, using only clinical and withdrawal scores, an AUC of 73% (blue).

Figure 3

(a) Whole-brain condition (abstinence challenge, smoking satiety) by group (relapse, quit) voxelwise ANOVA interaction effect. Clusters (orange/yellow) are corrected for multiple comparison (Z>1.96 and probability of spatial extent p<0.05) and superimposed outlines (blue) delineate a priori ROIs. (b) MF/CG and right DLPFC clusters (see above) show comparable activation under the smoking satiety condition in both groups. There is greater activation in the left DLPFC in the smoking satiety condition for the relapse group (vs quit). Under abstinence, challenge signal in all clusters decreases for the relapse group and increases for the quit group.