We propose to determine the neurobiological mechanisms that predict self-regulation of smoking urges while a person is under stress. Even after quitting or deciding to quit, the cravings for tobacco continue, particularly when exposed to acute stress. During stressful situations, self-control can fail, often resulting in a relapse. Previous behavioral and neuroimaging studies have not provided specific information about the neurobiological basis of self-control that could be used to prevent a self-control failure (i.e., relapse) at a particular moment (e.g., a single puff after abstinence). If smoking lapses are predictable before they actually occur, clinical interventions might be provided ahead of time as often imagined in science-fiction films (e.g., """"""""Minority Report""""""""). We will study how and why self-regulation fails by using a brain-as-predictor functional magnetic resonance imaging (fMRI) approach and our custom-made MRI-compatible electronic cigarette delivery system that allows us to investigate """"""""real"""""""" smoking decisions during fMRI scans. The main goal of this research is to elucidate the precise psychological and neurobiological mechanisms of self-control of smoking urges under cognitive overload and emotional distress on a moment-to-moment basis. Forty tobacco-dependent smokers (e10 cigarettes/day;18-50 years old) will be recruited from the local community. While in the fMRI scanner, subjects will make real choices regarding whether or not to take a puff of an electronic cigarette in three different types of dual-task conditions;working memory (WM), emotional distress (ED), and fixation control (FC). Stressful cognitive overload will be induced by a concurrent WM task and emotional distress will be induced by threat of electric shock stimulation. We hypothesize that (1) the moment-to-moment brain signals in affective (increased craving-related activity) and cognitive (decreased self- control-related activity) brain regions will predict subsequent self-regulation failures (lapses), and (2) cognitive overload and affective distress will modulate the pattern of functional connectivity of brain activation that predicts trial-by-trial self-regulation outcomes. The knowledge gained from our study that predicts real smoking-regulation choices will have strong ecological validity and provide valuable transformative information for developing novel clinical interventions that may prevent smoking lapses before they actually occur. Beyond smoking cessation treatments, our project outcomes will inform understanding of other self-control related maladaptive lifestyle behaviors (e.g., obesity, alcohol abuse, etc.) that increase one's risk for cancer.
Smokers often know the risks associated with tobacco use, yet struggle to quit smoking. In this study, we will study how the human brain works to inhibit smoking urges at a stressful moment and test whether brain signals can predict smoking lapses by using novel brain imaging techniques. If we can understand why smokers fail to inhibit smoking urges at a particular moment, and predict when they will fail, it might be possible to provide preventive clinical interventons before relapse occurs.
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