Abstract
Introduction: Despite substantial health benefits, smoking cessation attempts have high relapse rates. Neuroimaging measures can sometimes predict individual differences in substance use phenotypes – including relapse – better than behavioral metrics alone. No study to date has compared the relative prediction ability of changes in psychological processes across prolonged abstinence with corresponding changes in brain activity. Methods: Here, in a longitudinal design, measurements were made one day prior to smoking cessation, and at 1 and 4 weeks post-cessation (total n=120). Next, we tested the relative role of changes in psychosocial variables vs. task-based functional brain measures predicting time to nicotine relapse up to 12 months. Abstinence was bioverified 4-5 times during the first month. Data were analyzed with a novel machine learning approach to predict relapse. Results: Results showed that increased electrophysiological brain activity during inhibitory control predicted longer time-to-relapse (c-index=0.56). However, reward-related brain activity was not predictive (c-index=0.45). Psychological variables, notably an increase during abstinence in psychological flexibility when experiencing negative smoking-related sensations, predicted longer time-to-relapse (c-index=0.63). A model combining psychosocial and brain data was predictive (c-index=0.68). Using a best-practice approach, we demonstrated generalizability of the combined model on a previously unseen holdout validation dataset (c-index=0.59 vs. 0.42 for a null model). Conclusion: These results show that changes during abstinence – increased smoking-specific psychological flexibility and increased inhibitory control brain function – are important in predicting time to relapse from smoking cessation. In the future, monitoring and augmenting changes in these variables could help improve the chances of successful nicotine smoking abstinence.
| Original language | English |
|---|---|
| Pages (from-to) | 99-112 |
| Number of pages | 14 |
| Journal | European Addiction Research |
| Volume | 31 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 26 Apr 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Electroencephalography
- Inhibitory control
- Machine learning
- Smoking cessation
- Time to relapse
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