Prediction of smoking cessation with treatment: the emerging contribution of brain imaging research.
Prediction of smoking cessation with treatment: the emerging contribution of brain imaging research.
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通过治疗预测戒烟：脑成像研究的新兴贡献。
DOI：
10.1038/npp.2015.31
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
McClernon,FrancisJoseph
中科院分区：
文献类型：
--
作者：
Brody,ArthurL;McClernon,FrancisJoseph
Modern medications for smoking cessation started to become available roughly 35 years ago, with the development and release of nicotine gum. Since that time and with the introduction of other effective smoking cessation medications, approximately 50 studies have been published examining clinical predictors of smoking cessation treatment response. While these clinical predictor studies are helpful in guiding basic aspects of smoking cessation treatment, only recently have studies begun to emerge that demonstrate associations between brain function and smoking cessation treatment response. In this issue of Neuropsychopharmacology, Loughead et al,(2015) report an examination of whether functional magnetic resonance imaging (fMRI) measurements of working memory (WM)-related brain activity could be used to predict smoking relapse above and beyond clinical measures following a brief smoking cessation intervention. Study results link abstinence-induced decreases in left dorsolateral prefrontal cortical (DLPFC) activation and reduced suppression of posterior cingulate cortex (PCC) activity to treatment outcome, thereby implicating the executive control and default mode brain networks, respectively, to the ability to maintain abstinence.Knowledge of predictors of smoking cessation treatment response is useful in clinical practice. The most commonly replicated clinical predictors of successful smoking cessation include lower levels of nicotine dependence, fewer cigarettes smoked per day, less craving in early abstinence, and high self-efficacy, with other predictors of cessation including high desire to quit, low negative effect, no history of depression, low levels of anger, slow nicotine metabolism, no lapses during early treatment, and lower difficulty reducing smoking over time (for a review, see the introduction to Brody et al, 2014). These predictors are currently most useful for guiding treatment intensity and duration. For example, while a typical smoking cessation treatment course may include serial medication trials and weekly group
