Linking brain network dynamics to imminent smoking lapse risk and behavior

Project: Research project

Project Details


Project Summary Most attempts to quit smoking end in relapse, or a return to regular smoking. One of the biggest threats to cessation is a lapse (i.e., any cigarette use during a quit attempt). Thus, characterizing why lapses occur is essential to understanding and preventing smoking relapse. Functional magnetic resonance imaging (fMRI) is a promising method for characterizing the psychological processes that lead to smoking lapses because it provides a way to measures patterns of brain activity thought to reflect relevant mental processes as they change over time. However, methodological issues have hindered the ability to capitalize on this potential and prevented an understanding of how brain activity and corresponding psychological processes unfold in the critical moments that immediately precede a smoking lapse. The proposed project will address this knowledge gap using a novel fMRI paradigm adapted from a well-validated behavioral lapse task. This novel fMRI paradigm includes an in-scanner delay period that models the ability to resist smoking during acute nicotine abstinence and a post-scan ad-lib period that captures key aspects of the smoking behavior that follows. Adults who smoke will abstain from cigarettes for 12 hours before completing the fMRI lapse paradigm. The goals of the project are to characterize changes in brain activity that lead up to a lapse and to investigate how these changes are related to concurrent affect and subsequent cigarette use. The study will focus specifically on linking lapse-related outcomes to time-dependent interactions between two large-scale brain networks: the executive control network, which includes parts of the lateral prefrontal and parietal cortices, and the default mode network, which includes parts of the medial prefrontal and posterior cingulate cortices. The central hypothesis guiding the proposed research is that lapse-related behavior and affect will be predicted by the extent to which the default mode network and the executive control network are functionally segregated (i.e., the strength of the connectivity within the default mode and executive control networks, relative to connectivity between the networks). The aims of the project are: 1) To examine the association between time-dependent changes in brain network dynamics and subsequent risk of smoking lapse; 2) To examine the association between time-dependent changes in brain network dynamics and self-reported affect leading up to a smoking lapse; and 3) To examine the association between brain network dynamics directly before a lapse and reinforcement from the smoking that follows. An additional exploratory aim of the study is to evaluate potential moderators of the association between brain network dynamics and lapse-related outcomes. The proposed study capitalizes on an innovative experimental fMRI approach to study in real time the neural underpinnings of discrete smoking lapse episodes. Successful completion of the proposed research will advance theoretical knowledge regarding the neural and psychological antecedents of smoking lapses. Results will also have implications for improving the effectiveness of strategies designed to prevent smoking lapses.
Effective start/end date9/30/227/31/24




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