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Neural Network Dynamics Supporting Adaptive Attentional Control in the Context of Nicotine Withdrawal: a Review with
Xu Yan1, Alexander S Weigard2, Stephen J Wilson1
1Department of Psychology, The Pennsylvania State University, University Park, PA, United States.
Introduction:
Tobacco use is a leading preventable cause of disability and death, with nicotine withdrawal-related affective and cognitive sequelae posing a key barrier to cessation. Understanding how the dynamics between salience (SN), default mode (DMN), and frontoparietal networks (FPN) support or lead to failures of adaptive attentional control - and how these network interactions may be altered by the cognitive and affective challenges of withdrawal - may help illuminate mechanisms that contribute to smoking relapse.
Current Review:
The goal of the current review is to synthesize existing literature on resting-state and task-based network connectivity studies of SN, DMN and FPN under nicotine withdrawal, while situating the interpretations of this literature in the broader cognitive neuroscientific understanding of the three-network dynamics. In particular, there is a need to clarify context-dependent network connectivity under nicotine withdrawal, particularly when cognitive and affective demands co-occur.
Empirical Example:
In addition to the review, we provide a proof-of-concept empirical example examining SN, DMN, and FPN dynamics in daily smokers following nicotine satiation and 12-hour abstinence when cognitive and affective demands were being manipulated. Although prior studies link nicotine withdrawal with increased salience signaling, our data provides initial evidence that SN's modulatory capacity may be reduced - impairing the SN's ability to toggle between the DMN and FPN under high demand.
Conclusion:
Together, results from the literature and our empirical example underscore the importance of context-dependent connectivity analyses for understanding how affective and environmental demands shape attentional control and for informing precise risk prediction and intervention strategies.
Implications:
This review and empirical example highlight the importance of examining context-dependent brain network dynamics in nicotine withdrawal. Preliminary findings suggest that under cognitive and affective load, the SN not only signals detection of salience or increased demand, but also modulates engagement of the DMN and FPN. Nicotine withdrawal may impair this modulatory capacity, thereby disrupting attentional control. These insights emphasize the utility of task-based connectivity paradigms for identifying mechanisms of relapse vulnerability.
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