1. Large Scale Brain Network Coupling Predicts Acute Nicotine Abstinence Effects on Craving and Cognitive Function: Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. We tested the hypothesis that the strength of coupling between salience, executive control, and default mode networks (DMN) - will reflect the state of nicotine withdrawal vs. smoking satiety;predict abstinence-induced craving and cognitive deficits;and develop a composite metric (Resource Allocation Index, RAI) that reflects the combined strength of interactions between the three largescale networks. This within-subject study compared rs-FC coherence strength after 24 hours of abstinence vs. smoking satiety and examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural function. The RAI was significantly lower in the abstinent compared to smoking satiety condition, suggesting weaker inhibition between the DMN and the salience network. Reduced RAI predicted abstinence-induced cravings to smoke and less suppression of DMN activity during performance of a subsequent working memory task. These findings suggest that alterations in salience network-DMN coupling, and inability to disengage from the DMN, may be critical in cognitive/affective alterations that underlie nicotine dependence. 2. Individual differences in amygdala reactivity following nicotinic receptor stimulation in abstinent smokers. Hyperactive amygdala functioning may underlie emotional dysregulation during abstinence and represents one neurobiological target for pharmacological cessation aids. We assessed task performance and amygdala functioning during an emotional face matching paradigm following administration of nicotine and varenicline to abstinent smokers and nonsmokers. Nicotinic acetylcholine receptor (nAChR) stimulation by nicotine and varenicline decreased reaction time (RT) in abstinent smokers only. When considering all smokers as a single homogenous group, no drug-induced effects on amygdala reactivity were detected. However, after parsing subjects into subgroups according to individual differences in task performance, drugs modulated amygdala reactivity in only one smoker subgroup but not in either nonsmoker subgroup. The SI-smoker cohort abstinence-induced elevated amygdala reactivity was down-regulated by nAChR stimulation. In contrast, varenicline and nicotine did not modulate amygdala functioning in the VI-smoker cohort who displayed moderate levels of amygdala reactivity in the absence of drug. This suggests that pharmacotherapies most robustly dampened amygdala functioning in smokers appearing susceptible to abstinence-induced effects. Such findings provide a step toward fractionating the smoker phenotype by discrete neurobiological characteristics. 3. Down-Regulation of Amygdala and Insula Functional Circuits by Varenicline and Nicotine in Abstinent Cigarette Smokers. Although the amygdala and insula are regarded as critical neural substrates perpetuating cigarette smoking, little is known about their circuit-level interactions with interconnected regions during nicotine withdrawal or following pharmacotherapy administration. Using seed-based rsFC to characterize the influence of nicotine withdrawal and pharmacotherapy administration on neurocircuitry implicated in addiction, we found that insulas rsFC with multiple brain regions (e.g., posterior cingulate cortex, ventro/dorsomedial prefrontal cortex, amygdala) was elevated during abstinence and decreased by varenicline and nicotine. These results suggest that nicotine withdrawal is associated with elevated amygdala-insula and insula-default-mode network interactions. Decreased rsFC in these circuits may contribute to amelioration of subjective withdrawal symptoms. 4. Nicotine and prefrontal dopamine affect cortico-striatal areas in smokers during performance feedback. Nicotine and tonic dopamine (DA) levels interact to affect pfc processing involved in response to performance feedback. We asked whether there is a nicotine COMT genotype interaction in brain circuitry during performance feedback during a reward task. We found a significant nicotine COMT genotype interaction for BOLD signal during performance feedback in cortico-striatal areas. Activation in these areas during nicotine patch was greater in Val/Val homozygotes and reduced in Met allele carriers. During negative performance feedback, the change in activation in error detection areas was greater in Val/Val homozygotes compared to Met allele carriers with nicotine, while Val/Val homozygotes showed greater activation with performance feedback in areas associated with habitual responding. In response to negative feedback, Val/Val homozygotes had greater activation in error detection areas, suggesting increased sensitivity to loss with nicotine exposure. These results suggest a possible neurobiological mechanism underlying the observation that Val/Val homozygotes, presumably with elevated COMT activity and therefore reduced prefrontal DA levels, have poorer outcomes with nicotine replacement therapy. 5. Acute nicotine differentially impacts anticipatory valence- and magnitude-related striatal activity. Dopaminergic activity plays a role in mediating the rewarding aspects of abused drugs, including nicotine, while nicotine modulates the reinforcing properties of other motivational stimuli. While performing an MID task, both nicotine and placebo patch conditions were associated with reduced activity in regions supporting anticipatory valence, including ventral striatum. In contrast, relative to controls, acute nicotine increased activity in dorsal striatum for anticipated magnitude. Across conditions, anticipatory valence-related activity in the striatum was negatively associated with plasma nicotine concentration, whereas the number of daily cigarettes correlated negatively with loss anticipation activity in the mPFC during abstinence. These data suggest a partial dissociation in the state- and trait-specific effects of smoking and nicotine exposure on magnitude- and valence-dependent anticipatory activity within discrete reward processing brain regions and may partially explain nicotine's impact on the reinforcing properties of nondrug stimuli and the continued motivation to smoke and cessation difficulty. 6. Predicting nicotine addiction: Through the use of machine learning-based approaches, we examined fMRI data in a multivariate manner and extracted features predictive of group membership. We applied support vector machine (SVM)-based classification, augmented by the AdaBoost algorithm, to rs-FC data from 21 nicotine-dependent smokers and 21 controls to identify features predictive of nicotine dependence. We used a network-centered approach, focusing on 16 resting state networks (RSNs) previously identified as being critical to common cognitive paradigms (Smith et al., 2009). As a large number of input features were involved, we tested the classification process with and without feature elimination techniques. Our data suggests that within-network functional connectivity measures offer maximal information for predicting smoking status (accuracy = 78.6%;precision = 83.3.%), as opposed to between-network connectivity (52.4%;52.6%), or the representativeness of each individual node with respect to its parent network (76.2%;73.9%). Further, connectivity measures within higher-order resting state networks, including the executive control and frontoparietal networks, seem particularly informative in predicting smoking status. This suggests that machine learning-based approaches offer a valuable alternative technique to understanding large-scale differences in addiction-related neurobiology.
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