Upton Spencer, Froeliger Brett
Department of Psychological Sciences, University of Missouri, Columbia, MO, USA.
Department of Psychiatry, University of Missouri, Columbia, MO, USA.
Transl Psychiatry. 2025 Mar 28;15(1):101. doi: 10.1038/s41398-025-03319-1.
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.
在患有物质使用障碍的个体中，通过识别能够预测药物复吸易感性的脑-行为模型，如调节药物渴望和抑制药物使用的能力，临床结局可能会得到改善。在一个尼古丁依赖的成年吸烟者样本（N = 213）中，这项实验室研究考察了渴望调节（ROC）效能与吸烟复吸之间的关联，利用功能连接多元模式分析（FC-MVPA）和基于种子点的连接性（SBC）分析来识别ROC效能背后的静息态神经回路，然后检验所识别的由ROC介导的神经回路是否能预测吸烟复吸的风险。在行为方面，较差的ROC效能预示着更高的吸烟复吸风险。在脑与行为方面，FC-MVPA识别出29个与ROC效能相关的全脑功能簇。使用FC-MVPA得出的9个簇进行后续的SBC分析，共识别出64条构成ROC效能基础的静息态边（即簇与簇之间的连接），其中10条也与吸烟复吸风险相关。与吸烟复吸相关的ROC效能边主要由额叶-纹状体-边缘簇与感觉运动簇之间的连接组成，更好的行为结局与更强的静息态功能连接相关。研究结果表明，ROC效能和潜在的静息态神经回路都可能为复吸易感性预测模型提供信息，并可作为戒烟干预的治疗靶点。
