Regulation of craving and underlying resting-state neural circuitry predict hazard of smoking lapse
Abstract Among individuals with substance use disorders, clinical outcomes may be improved by identifying brain-behavior models that predict drug re/lapse vulnerabilities such as the ability to regulate drug cravings and inhibit drug use. In a sample of nicotine-dependent adult cigarette smokers (N ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-03-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03319-1 |
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| Summary: | Abstract Among individuals with substance use disorders, clinical outcomes may be improved by identifying brain-behavior models that predict drug re/lapse vulnerabilities such as the ability to regulate drug cravings and inhibit drug use. In a sample of nicotine-dependent adult cigarette smokers (N = 213), this laboratory study examined associations between regulation of craving (ROC) efficacy and smoking lapse, utilized functional connectivity multivariate pattern analysis (FC-MVPA) and seed-based connectivity (SBC) analyses to identify resting-state neural circuitry underlying ROC efficacy, and then examined if the identified ROC-mediated circuitry predicted hazard of smoking lapse. Regarding behavior, worse ROC efficacy predicted a greater hazard of smoking lapse. Regarding brain and behavior, FC-MVPA identified 29 brain-wide functional clusters associated with ROC efficacy. Follow-up SBC analyses using 9 of the FC-MVPA-derived clusters identified a total of 64 resting-state edges (i.e., cluster-to-cluster connections) underlying ROC efficacy, 10 of which were also associated with the hazard of smoking lapse. ROC efficacy edges also associated with smoking lapse were largely composed of connections between frontal-striatal-limbic clusters and sensory-motor clusters and better behavioral outcomes were associated with stronger resting-state FC. Findings suggest that both ROC efficacy and underlying resting-state neural circuitry may inform prediction models of re/lapse vulnerabilities and serve as treatment targets for cessation interventions. |
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| ISSN: | 2158-3188 |