Working Memory-Related Neural Activity Predicts Future Smoking Relapse

Loughead, James; Wileyto, E. Paul; Ruparel, Kosha; Falcone, Mary; Hopson, Ryan; Gur, Ruben C.; Lerman, Caryn

doi:10.1038/npp.2014.318

Cited by 93 publications

(76 citation statements)

References 73 publications

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“…Specifically, genes for nAChRs (and related proteins), determinants of nicotine metabolism, and brain imaging findings related to cigarette cue responses and nAChR levels have been found to predict response. Furthermore, several of these studies found that specific biomarkers may provide additional information for predicting response beyond subjective symptom or rating scale measures [137, 139, 144, 147]. Current research is focusing on clinical applicability of NMR [134], which is likely the most robust biomarker of treatment response found to date.…”

Section: Discussionmentioning

confidence: 99%

“…In both of the preceding studies, increased activity in the insula during a cognitive control task predicted an improved likelihood of quitting. For the third study [144], smokers underwent fMRI (in abstinence and satiety) during which they performed the n-back working memory task. These sessions were followed by brief counseling and an abstinence attempt for 1 week.…”

Section: Brain Imaging Biomarkersmentioning

confidence: 99%

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Biomarkers of Response to Smoking Cessation Pharmacotherapies: Progress to Date

et al. 2015

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For the past thirty years, research examining predictors of successful smoking cessation treatment response has focused primarily on clinical variables, such as levels of tobacco dependence, craving, and self-efficacy. Recent research, however, has begun to determine biomarkers (such as genotype, nicotine and metabolite levels, and brain imaging findings) that may have utility in predicting smoking cessation. For genotype, genes associated with nicotinic acetylcholine receptors (nAChRs) and related proteins have been found to predict response to first line medications (e.g., nicotine replacement therapy [NRT], bupropion, or varenicline) or quitting over time without a controlled treatment trial. For nicotine and metabolite levels, function of the CYP 2A6 liver enzyme, which can be assessed with the nicotine metabolite ratio or via genotype, has been found to predict response, with slow nicotine metabolizers having less severe nicotine dependence and a greater likelihood of quitting with NRT than normal metabolizers. And for brain imaging, decreased activation of brain regions associated with emotion regulation and increased connectivity in emotion regulation networks, increased responsiveness to pleasant cues, and altered activation with the Stroop effect have been found in smokers who quit with the first-line medications listed above or counseling. In addition, our group recently demonstrated that lower pre-treatment brain nAChR density is associated with a greater chance of quitting smoking with NRT or placebo. Several of these studies found that specific biomarkers may provide additional information for predicting response beyond subjective symptom or rating scale measures, thereby giving an initial indication that biomarkers may, in the future, be useful for guiding smoking cessation treatment intensity, duration, and type.

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Section: Discussionmentioning

confidence: 99%

Section: Brain Imaging Biomarkersmentioning

confidence: 99%

Biomarkers of Response to Smoking Cessation Pharmacotherapies: Progress to Date

et al. 2015

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“…Moreover, systemic galantamine administration improves cognitive performance in rodents and humans during nicotine withdrawal [113, 114]. Given that cognitive deficits during nicotine withdrawal predict smoking relapse [115, 116], these results suggest that AChEIs may improve cognitive performance in abstinent smokers and prevent smoking relapse. Taken together, these studies suggest that AChEIs could be re-purposed as pharmacotherapies for smoking cessation [117].…”

Section: Neurotransmitter Mechanisms Regulating Nicotine Seekingmentioning

confidence: 99%

Neurobiological and Neurophysiological Mechanisms Underlying Nicotine Seeking and Smoking Relapse

2018

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Tobacco-related morbidity and mortality continue to be a significant public health concern. Unfortunately, current FDA-approved smoking cessation pharmacotherapies have limited efficacy and are associated with high rates of relapse. Therefore, a better understanding of the neurobiological and neurophysiological mechanisms that promote smoking relapse is needed to develop novel smoking cessation medications. Here, we review preclinical studies focused on identifying the neurotransmitter and neuromodulator systems that mediate nicotine relapse, often modeled in laboratory animals using the reinstatement paradigm, as well as the plasticity-dependent neurophysiological mechanisms that facilitate nicotine reinstatement. Particular emphasis is placed on how these neuroadaptations relate to smoking relapse in humans. We also highlight a number of important gaps in our understanding of the neural mechanisms underlying nicotine reinstatement and critical future directions, which may lead toward the development of novel, target pharmacotherapies for smoking cessation.

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“…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.…”

mentioning

confidence: 99%