Defining individual differences in tobacco smokers using multimodal neuroimaging



 DESCRIPTION (provided by applicant): Nicotine dependence is a prevalent and costly public health epidemic. While several treatment options exist, the majority of individuals who try to quit will eventually relapse. Finding a single efficacious treatment for all smokers is unlikely given that numerous underlying factors can contribute to nicotine dependence. Guiding treatment based on individual risk profiles is a more effective approach to enhance abstinence rates and is in line with NIDA's treatment objective of "developing knowledge that leads to personalized or customized treatments". A major strength of the current proposal is that it will meet this objectiv by identifying differences in smokers using a unique combination of multi-modal neuroimaging techniques. First, we will use functional magnetic resonance imaging (fMRI) to directly measure individual smoker's brain reactivity to smoking cues. This is relevant given our prior work showing that enhanced brain reactivity to smoking cues predicts relapse vulnerability even when smokers are aided by nicotine replacement therapy (NRT). We will then determine whether heightened brain reactivity to smoking cues can be blunted when smokers quit with the aid of treatment specifically aimed at reducing cue-reactivity (e.g., nicotine-free e-cigarettes), which could become a possible personalized treatment option. We also will identify whether differences in cue reactivity are due to disruptions in brain chemistry, as measured by magnetic resonance spectroscopy (MRS) and inherent brain organization, as quantified by functional connectivity. These measures will identify additional molecular and neuroanatomical targets for developing future treatments and will define the underlying neurobiological factors contributing to heightened cue-reactivity. Collectively, the results of this study will have a significant impac on the development of novel and much-needed treatment strategies, which target specific risk factors.