Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence

Network state dynamics underpin basal craving in a transdiagnostic population

Emerging fMRI methods quantifying brain dynamics present an opportunity to capture how fluctuations in brain responses give rise to individual variations in affective and motivation states. Although the experience and regulation of affective states affect psychopathology, their underlying time-varying brain responses remain unclear. Here, we present a novel framework to identify network states matched to an affective experience and examine how the dynamic engagement of these network states contributes to this experience. We apply this framework to investigate network state dynamics underlying basal craving, an affective experience with important clinical implications. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (total N = 252), we utilized connectome-based predictive modeling (CPM) to identify brain networks predictive of basal craving. An edge-centric timeseries approach was leveraged to quantify the moment-to-moment engagement of the craving-positive and craving-negative subnetworks during independent scan runs. We found that dynamic markers of network engagement, namely more persistence in a craving-positive network state and less dwelling in a craving-negative network state, characterized individuals with higher craving. We replicated the latter results in a separate dataset, incorporating distinct participants (N = 173) and experimental stimuli. The associations between basal craving and network state dynamics were consistently observed even when craving-predictive networks were defined in the replication dataset. These robust findings suggest that network state dynamics underpin individual differences in basal craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our affective experiences.

Altered executive control network and default model network topology are linked to acute electronic cigarette use: A resting-state fNIRS study

In recent years, electronic cigarettes (e-cigs) have gained popularity as stylish, safe, and effective smoking cessation aids, leading to widespread consumer acceptance. Although previous research has explored the acute effects of combustible cigarettes or nicotine replacement therapy on brain functional activities, studies on e-cigs have been limited. Using fNIRS, we conducted graph theory analysis on the resting-state functional connectivity of 61 male abstinent smokers both before and after vaping e-cigs. And we performed Pearson correlation analysis to investigate the relationship between alterations in network metrics and changes in craving. E-cig use resulted in increased degree centrality, nodal efficiency, and local efficiency within the executive control network (ECN), while causing a decrease in these properties within the default model network (DMN). These alterations were found to be correlated with reductions in craving, indicating a relationship between differing network topologies in the ECN and DMN and decreased craving. These findings suggest that the impact of e-cig usage on network topologies observed in male smokers resembles the effects observed with traditional cigarettes and other forms of nicotine delivery, providing valuable insights into their addictive potential and effectiveness as aids for smoking cessation.

Altered dynamic functional connectivity of insular subdivisions among male cigarette smokers

Background

Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers.

Methods

Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers.

Results

We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%.

Conclusions

Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.

Time-varying effect modeling with intensive longitudinal data: Examining dynamic links among craving, affect, self-efficacy and substance use during addiction recovery

Time-varying effect modeling (TVEM), a statistical technique for modeling dynamic patterns of change, presents new opportunities to study biobehavioral health processes. TVEM is particularly useful when applied to intensive longitudinal data (ILD) because it permits highly flexible modeling of outcomes over continuous time, as well as of associations between variables and moderation effects. TVEM coupled with ILD is ideal for the study of addiction. This article provides a general overview of using TVEM, particularly when applied to ILD, to better enable addiction scientists to conduct novel analyses that are important to realizing the dynamics of addiction-related processes. It presents an empirical example using ecological momentary assessment data from participants throughout their first 90 days of addiction recovery to estimate the (1) associations between morning craving and same-day recovery outcomes, (2) association between morning positive and negative affect and same-day recovery outcomes and (3) time-varying moderation effects of affect on the association between morning craving and recovery outcomes. We provide a didactic overview in implementing and interpreting the aims and results, including equations, computer syntax and reference resources. Our results highlight how affect operates as both a time-varying risk and protective factor on recovery outcomes, particularly when considered in combination with experiences of craving (i.e. dynamic moderation). We conclude by discussing our results, recent innovations and future directions of TVEM for advancing addiction science, including how 'time' can be operationalized to probe new research questions.

Not all smokers are alike: the hidden cost of sustained attention during nicotine abstinence

Nicotine Withdrawal Syndrome (NWS)-associated cognitive deficits are notably heterogeneous, suggesting underlying endophenotypic variance. However, parsing this variance in smokers has remained challenging. In this study, we identified smoker subgroups based on response accuracy during a Parametric Flanker Task (PFT) and then characterized distinct neuroimaging endophenotypes using a nicotine state manipulation. Smokers completed the PFT in two fMRI sessions (nicotine sated, abstinent). Based on response accuracy in the stressful, high cognitive demand PFT condition, smokers split into high (HTP, n = 21) and low task performer (LTP, n = 24) subgroups. Behaviorally, HTPs showed greater response accuracy (88.68% ± 5.19 SD) vs. LTPs (51.04% ± 4.72 SD), independent of nicotine state, and greater vulnerability to abstinence-induced errors of omission (EOm, p = 0.01). Neurobiologically, HTPs showed greater BOLD responses in attentional control brain regions, including bilateral insula, dorsal ACC, and frontoparietal Cx for the [correct responses (-) errors of commission] PFT contrast in both states. A whole-brain functional connectivity (FC) analysis with these subgroup-derived regions as seeds identified two circuits: Precentral Cx↔Insula and Insula↔Occipital Cx, with abstinence-induced FC strength increases seen only in HTPs. Finally, abstinence-induced FC and behavior (EOm) differences were positively correlated for HTPs in a Precentral Cx↔Orbitofrontal cortical circuit. In sum, only the HTP subgroup demonstrated sustained attention deficits following 48-hr nicotine abstinence, a stressor in dependent smokers. Unpacking underlying smoker heterogeneity with this 'dual (task and abstinence) stressor' approach revealed discrete smoker subgroups with differential attentional deficits to withdrawal that could be novel pharmacological/behavioral targets for therapeutic interventions to improve cessation outcomes.