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Brain modular connectivity interactions can predict proactive inhibition in smokers when facing smoking cues

Brain modular connectivity interactions can predict proactive inhibition in smokers when facing... Proactive inhibition is a critical ability for smokers who seek to moderate or quit smoking. It allows them to pre‐emptively refrain from seeking and using nicotine products, especially when facing salient smoking cues in daily life. Nevertheless, there is limited knowledge on the impact of salient cues on behavioural and neural aspects of proactive inhibition, especially in smokers with nicotine withdrawal. Here, we seek to bridge this gap. To this end, we recruited 26 smokers to complete a stop‐signal anticipant task (SSAT) in two separate sessions: once in the neutral cue condition and once in the smoking cue condition. We used graph‐based modularity analysis to identify the modular structures of proactive inhibition‐related network during the SSAT and further investigated how the interactions within and between these modules could be modulated by different proactive inhibition demands and salient smoking cues. Findings pointed to three stable brain modules involved in the dynamical processes of proactive inhibition: the sensorimotor network (SMN), cognitive control network (CCN) and default‐mode network (DMN). With the increase in demands, functional connectivity increased within the SMN, CCN and between SMN‐CCN and decreased within the DMN and between SMN‐DMN and CCN‐DMN. Salient smoking cues disturbed the effective dynamic interactions of brain modules. The profiles for those functional interactions successfully predicted the behavioural performance of proactive inhibition in abstinent smokers. These findings advance our understanding of the neural mechanisms of proactive inhibition from a large‐scale network perspective. They can shed light on developing specific interventions for abstinent smokers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Addiction Biology Wiley

Brain modular connectivity interactions can predict proactive inhibition in smokers when facing smoking cues

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References (51)

Publisher
Wiley
Copyright
© 2023 Society for the Study of Addiction
ISSN
1355-6215
eISSN
1369-1600
DOI
10.1111/adb.13284
Publisher site
See Article on Publisher Site

Abstract

Proactive inhibition is a critical ability for smokers who seek to moderate or quit smoking. It allows them to pre‐emptively refrain from seeking and using nicotine products, especially when facing salient smoking cues in daily life. Nevertheless, there is limited knowledge on the impact of salient cues on behavioural and neural aspects of proactive inhibition, especially in smokers with nicotine withdrawal. Here, we seek to bridge this gap. To this end, we recruited 26 smokers to complete a stop‐signal anticipant task (SSAT) in two separate sessions: once in the neutral cue condition and once in the smoking cue condition. We used graph‐based modularity analysis to identify the modular structures of proactive inhibition‐related network during the SSAT and further investigated how the interactions within and between these modules could be modulated by different proactive inhibition demands and salient smoking cues. Findings pointed to three stable brain modules involved in the dynamical processes of proactive inhibition: the sensorimotor network (SMN), cognitive control network (CCN) and default‐mode network (DMN). With the increase in demands, functional connectivity increased within the SMN, CCN and between SMN‐CCN and decreased within the DMN and between SMN‐DMN and CCN‐DMN. Salient smoking cues disturbed the effective dynamic interactions of brain modules. The profiles for those functional interactions successfully predicted the behavioural performance of proactive inhibition in abstinent smokers. These findings advance our understanding of the neural mechanisms of proactive inhibition from a large‐scale network perspective. They can shed light on developing specific interventions for abstinent smokers.

Journal

Addiction BiologyWiley

Published: Jun 1, 2023

Keywords: abstinent smokers; brain networks; fMRI; machine learning; modularity; proactive inhibition

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