4.1 Article

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

期刊

ADDICTION BIOLOGY
卷 28, 期 6, 页码 -

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WILEY
DOI: 10.1111/adb.13284

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

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Proactive inhibition is crucial for smokers trying to reduce or quit smoking. This study aims to explore the impact of salient cues on proactive inhibition in smokers experiencing nicotine withdrawal. Findings reveal the involvement of specific brain modules and disrupted interactions due to smoking cues, providing insights for developing interventions.
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.

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