Evidence map›Paper›PMID 41479984›Full record

ArticleFrontiers in psychology2025

Longitudinal brain connectivity changes associated with successful smoking cessation.

Jooyeon Jamie Im, Hyeonjin Kim, Jeung-Hyun Lee, Hyung Jun Park, Hee-Kyung Joh, Woo-Young Ahn

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Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jooyeon Jamie ImDepartment of Psychology, Seoul National University, Seoul, Republic of Korea.
Hyeonjin KimDepartment of Psychology, Seoul National University, Seoul, Republic of Korea.
Jeung-Hyun LeeDepartment of Psychology, Seoul National University, Seoul, Republic of Korea.
Hyung Jun ParkCenter for Health Promotion, Samsung Medical Center, Seoul, Republic of Korea.
Hee-Kyung JohDepartment of Family Medicine, Seoul National University Health Service Center, Seoul, Republic of Korea.
Woo-Young AhnDepartment of Psychology, Seoul National University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tobacco smoking continues to be a leading cause of preventable morbidity and mortality globally, with the success rate of unaided cessation remaining consistently low. Understanding the neurobiological mechanisms of smoking cessation is crucial for improving quit rates. However, there has been a lack of studies examining brain network changes associated with smoking cessation over time. In this study, we aimed to investigate longitudinal changes in the functional connectivity (FC) of large-scale brain networks underlying smoking cessation outcomes using resting-state functional magnetic resonance imaging (fMRI). Methods: A total of 98 treatment-seeking smokers participated in a 5-week cessation program and underwent resting-state fMRI scans before and after the intervention. Independent component analysis identified the salience network (SN), executive control network (ECN), and default mode network (DMN) components, and region of interest (ROI)-to-ROI FC was compared between successful and unsuccessful quitters using a group × time mixed-effects model. Correlations with smoking-related measures were explored. Results: Significant group-by-time interaction effects were found in FC, particularly involving connections between SN and ECN, as well as between the SN and DMN. Specifically, successful quitters exhibited greater baseline FC in the SN-ECN and SN-DMN circuits, which tended to decrease and converge toward levels observed in unsuccessful quitters during the cessation process. Exploratory correlational analyses revealed trends suggesting that stronger pre-quit connectivity between the SN and ECN was associated with greater withdrawal severity and longer smoking history in successful quitters. Conclusion: Taken together, the reduction of initially elevated pre-quit FC in SN-ECN and SN-DMN circuits may reflect an adaptive neural process that supports successful withdrawal management and attentional reallocation during cessation. The identification of these neural substrates not only enhances our mechanistic understanding of smoking cessation over time but also underscores the need for targeted interventions that focus on these neural circuits to enhance quit outcomes.

Indexed as

default mode networkexecutive networkresting-state functional magnetic resonance imagingsalience networksmoking cessation

Identifiers

PMID41479984
PMCPMC12753871

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.