Neurobehavioral Mechanisms Underlying an Olfactory Method for Controlling Cigarette Craving among Individuals who Smoke

Craving, a complex motivational state indexed across behavioral and biological responses, can increase risk of substance use and relapse. Research has struggled to develop treatments for craving due in part to limited mechanistic understanding of the construct. Promising evidence supports the strategic use of olfactory cues (OCs) to reduce cigarette craving. Black individuals who smoke (BIS) in the US face greater challenges in achieving smoking cessation compared to White individuals who smoke (WIS). BIS also tend to report higher levels of craving, but traditional craving scales often fail to capture their unique experiences and are prone to ceiling effects in this population. How OCs fare across varied populations remains unexplored. The proposed translational research integrates several methods, including functional magnetic resonance imaging (fMRI) and a set of novel behavioral measures, to examine varied mechanisms underlying craving during smoking abstinence across BIS and WIS. The proposal leverages a comprehensive dataset (n = 250; 50% female; 25% BIS) to address three aims: (1) identify neural signatures of craving states and examine how pleasant OCs shift these distributed brain patterns; (2) examine how racial identity relates to neural representations of craving and responses to pleasant OCs; and (3) integrate neural with behavioral data to offer a holistic understanding of craving across BIS and WIS. The behavioral urge battery includes traditional urge ratings, a pressure- sensitive dynamometer, and facial coding analyses, providing multiple indices of craving intensity, including some less susceptible to ceiling effects observed on standard scales, especially among BIS. Multivariate pattern analysis will characterize neural fingerprints of cognitive processes underlying craving and OC modulation and are proposed to predict behavioral measures of craving. Connectome-based predictive modeling will identify network configurations to predict ecological momentary assessmentmeasured smoking behavior. To achieve these aims, the candidate will complete a comprehensive three-prong training plan in (1) advanced machine learning techniques; (2) addiction science and racial identity; and (3) multimodal data integration. This F31 fellowship would accelerate the applicant’s trajectory toward becoming an independent researcher, focused on understanding and modulating craving mechanisms to develop effective interventions for SUDs. The proposal promises significant conceptual and methodological contributions by: (1) advancing understanding of craving through sophisticated analyses of neural mechanisms; (2) illuminating variation in craving experiences and intervention responses among BIS and WIS; and (3) developing multimodal approaches for measuring and predicting smoking outcomes across populations. Findings will inform the development of more effective craving assessment methods and clinical intervention delivery through OCs, addressing NIDA’s Priority Scientific Areas 1 and 4. The framework established here offers potential to be extended to craving mechanisms and modulation in other SUDs.