Neural Network Dynamics Supporting Adaptive Attentional Control in the Context of Nicotine Withdrawal: a Review with Empirical Example

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 an empirical example with preliminary data 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 suggest that SN’s modulatory capacity is also 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.