bioRxivNeurosciencePreprint
Neuroaffective profiles are associated with e-cigarette use
Abstract
IntroductionIdentifying the psychophysiological underpinnings of cue-induced compulsive nicotine use will provide new targets for relapse prevention treatments. We tested whether neuroaffective responses to motivationally relevant stimuli are associated with cue-induced nicotine self-administration. We hypothesized that smokers with stronger neuroaffective responses to nicotine-related cues than to pleasant stimuli (C>P) are more vulnerable to cue-induced nicotine self-administration than smokers with stronger neuroaffective responses to pleasant stimuli than to nicotine-related cues (P>C). MethodsSmokers (N=36) looked at pleasant, unpleasant, neutral, and nicotine-related images signaling that an electronic nicotine delivery system (ENDS) was immediately available for use. We measured event-related potentials (a direct measure of cortical activity) and computed the amplitude of the late positive potential, a robust index of motivational salience. We used k-means cluster analysis to identify individuals characterized by the C>P or the P>C neuroaffective profile. We compared the ENDS use frequency in the two groups using quantile regression for counts. ResultsCluster analysis assigned 18 smokers to the C>P profile and 18 smokers to the P>C profile. Smokers with the C>P neuroaffective profile used the ENDS significantly more often than smokers with the P>C profile. Significant differences in the number of puffs persisted across different quantiles. ConclusionsThese results support the hypothesis that individual differences in the tendency to attribute motivational salience to drug-related cues underlie vulnerability to cue-induced drug self-administration. ImplicationsBy linking brain reactivity profiles to nicotine self-administration, we identified a neuroaffective biomarker that could guide the development of personalized treatments to prevent smoking relapse.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 2
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