Although nicotine dependent cigarette smokers who quit are less likely to experience life threatening health problems and improve their quality of life, unfortunately, long-term abstinence rates are low. Models of nicotine dependence suggest the importance of multifaceted approach to understanding relapse involving biological, motivational, cognitive, and behavioral factors. While biological factors, such as genetic predisposition are directly observable, other known risk factors for relapse such as craving and impulsivity are often indirectly measured using self-report questionnaires and behavioral observations. We propose to examine predictors of smoking cessation outcome by directly measuring neural activity associated with managing cravings, decision- making about rewards, and cognitive persistence using functional magnetic resonance imaging (FMRI). Assessed behaviorally, these constructs have been shown to predict smoking cessation outcome;however, it is expected that more direct fMRI assessment of neural activity will enhance sensitivity and specificity of quantitative measurement and avoid confounds associated with subjective ratings and behavioral observation of these factors. We expect fMRI activity to yield more sensitive markers of relapse relative to behavioral and subjective measures. To accomplish this we will challenge motivational and cognitive systems while measuring activity in brain regions normally associated with these functions. When grouped by cessation outcome (i.e., lapse) we predict different brain activity in regions of interest related to these systems. Specifically, we will demonstrate that nicotine-dependent smokers who lapse early exhibit different levels of brain activity compared to smokers who exhibit prolonged abstinence. We expect that groups will also differ on behavioral ratings of craving and measures of accuracy and impulsivity during these challenges. The results of the proposed study will refine understanding the neurobehavioral correlates of known risk factors for smoking relapse, and advance development of neurobehavioral models of neural activity that predispose smokers to relapse. Neurobehavioral methods can facilitate identification of smokers at greater risk for relapse, isolate targets of neural activity for clinical interventions and facilitate delivery of specialized behavioral and pharmacologic cessation treatments. Characterization of expected brain recruitment, compensatory responses, and disorganization of active and deactivated networks provide novel information that is likely to complement our existing knowledge on the neural mechanisms related to relapse risk.
Smokers who quit are less likely to experience life-threatening health problems and improve their quality of life, yet cessation rates are low. If we find that fMRI provides more sensitive markers of relapse than traditional methods, it would allow us to better identify those in need of specialized or tailored treatments, and better understand how neurocognitive and neurobehavioral treatments may act at the level of the brain to forestall relapse. Both of these advances should lead to greater cessation rates.
|Owens, Max M; MacKillop, James; Gray, Joshua C et al. (2017) Neural correlates of graphic cigarette warning labels predict smoking cessation relapse. Psychiatry Res 262:63-70|
|Owens, Max M; MacKillop, James; Gray, Joshua C et al. (2017) Neural correlates of tobacco cue reactivity predict duration to lapse and continuous abstinence in smoking cessation treatment. Addict Biol :|
|Murphy, Cara M; Owens, Max M; Sweet, Lawrence H et al. (2016) The substitutability of cigarettes and food: A behavioral economic comparison in normal weight and overweight or obese smokers. Psychol Addict Behav 30:857-867|
|Gray, Joshua C; Amlung, Michael T; Acker, John et al. (2014) Clarifying the neural basis for incentive salience of tobacco cues in smokers. Psychiatry Res 223:218-25|
|MacKillop, James; Amlung, Michael T; Acker, John et al. (2014) The neuroeconomics of alcohol demand: an initial investigation of the neural correlates of alcohol cost-benefit decision making in heavy drinking men. Neuropsychopharmacology 39:1988-95|
|Gray, Joshua C; Amlung, Michael T; Acker, John D et al. (2014) Item-based analysis of delayed reward discounting decision making. Behav Processes 103:256-60|