Smoking Cessation After Initial Treatment Failure With Varenicline or Nicotine Replacement: A Randomized Clinical Trial.

Importance: Most people who smoke do not quit on their initial attempt. Objective: To determine the best subsequent strategy for nonabstinence following initial treatment with varenicline or combined nicotine replacement therapy (CNRT). Design, Setting, and Participants: Using a double-blind, placebo-controlled, sequential multiple assignment randomized trial, 490 volunteers were randomized to receive 6 weeks of varenicline or CNRT. After 6 weeks, nonabstainers were rerandomized to continue, switch, or increase medication dosage for 6 additional weeks. The study was conducted from June 2015 through October 2019 in a Texas tobacco treatment clinic. Interventions: The initial treatment was 2 mg/d of varenicline or the combined replacement therapy of a 21-mg patch plus 2-mg lozenge. The rerandomized participants either continued with their initial therapies, switched between varenicline and CNRT, or increased dosages either to 3-mg or more of varenicline or to a 42-mg patch and lozenges. All received weekly brief counseling. Main Outcomes and Measures: Biochemically verified 7-day point prevalence abstinence at the end of treatment at 12 weeks. Results: The 490 randomized participants (210 female [43%], 287 non-Hispanic White [58%], mean age, 48.1 years) smoked an average of 20 cigarettes per day. After the first phase, 54 participants in the CNRT group were abstinent and continued their therapy; of the 191 who were not abstinent, 151 were rerandomized, and the 40 who did not return for rerandomization were assigned to continue their initial CNRT condition in phase 2. The end-of-treatment abstinence rate for the 191 phase 1 nonabstainers was 8% (95% credible interval [CrI], 6% to 10%) for the 90 (47%) who continued at the dosage condition, 14% (CrI, 10% to 18%) for the 50 (33%) who increased their dosage, and 14% (95% CrI, 10% to 18%) for the 51 (34%) who switched to varenicline (absolute risk difference [RD], 6%; 95% CrI, 6% to 11%) with more than 99% posterior probability that either strategy conferred benefit over continuing the initial dosage. After the first phase, 88 participants in the varenicline group were abstinent and continued their therapy; of the 157 who were not abstinent, 122 were rerandomized and 35 who did not return for rerandomization were assigned to continue with the varenicline condition. The end-of-treatment abstinence rate for the 157 phase 1 nonabstainers was 20% (95% CrI, 16% to 26%) for the 39 (32%) who increased their varenicline dosage, 0 (95% CrI, 0 to 0) for the 41 (34%) who switched CNRT, and 3% (95% CrI, 1% to 4%) for the 77 (49%) who were assigned to the continued varenicline condition (absolute RD, -3%; 95% CrI, -4% to -1%) with more than 99% posterior probability that continuing varenicline at the initial dosage was worse than switching to a higher dosage. Furthermore, increasing the varenicline dosage had an absolute RD of 18% (95% CrI, 13% to 24%) and a more than 99% posterior probability of conferring benefit. The secondary outcome of continuous abstinence at 6 months indicated that only increased dosages of the CNRT and varenicline provided benefit over continuation of the initial treatment dosages. Conclusions and Relevance: For individuals who smoked but did not achieve abstinence after treatment with varenicline, increasing the dosage enhanced abstinence vs continuing, whereas for nonabstainers initially treated with CNRT, a dosage increase or switch to varenicline enhanced abstinence and may be viable rescue strategies. Trial Registration: ClinicalTrials.gov Identifier: NCT02271919.

Electrophysiological normative responses to emotional, neutral, and cigarette-related images.

To create reproducible emotional probes, affective scientists rely on sets of standardized pictures that are normed using subjective ratings of valence and emotional arousal. However, when emotional responses are investigated using neurophysiological measures, it might be more appropriate to select pictures integrating information from normative subjective reports and normative neurophysiological responses. Here, we provide electrophysiological normative responses for 323 emotional pictures (215 from the IAPS) covering a wide range of categories (erotica, romantic, appetizing foods, landscapes, people engaged in mundane activities, household objects, disgusting objects, accidents, sad people, violence, mutilations, and cigarette-related contents). Event-related potentials (ERPs) and subjective ratings of pleasure and emotional arousal were collected from 763 individuals (52% females, 41% white) aged between 18 and 65 (mean = 43). For each image, the mean amplitude of the late positive potential (LPP, an electrophysiological index of motivational relevance) and the mean subjective ratings of valence and arousal were calculated. We validated our procedure by showing that the subjective ratings of valence and arousal from this sample were highly correlated to the IAPS' published norms (Pearson r = .97 for pleasure and r = .82 for emotional arousal). LPP responses and subjective ratings of emotional arousal also were correlated (Pearson r = .61), but some categories reported being significantly more arousing than neutral (i.e., food, landscapes, and unpleasant objects) did not evoke LPPs significantly different from those evoked by neutral pictures. Researchers interested in probing the brain's affective systems can use these electrophysiological normative responses to create emotional probes that evoke reliable neuroaffective responses.

Topiramate decreases the salience of motivationally relevant visual cues among smokers with alcohol use disorder.

BACKGROUND: There is preliminary evidence that the anticonvulsant topiramate increases the likelihood of both smoking and alcohol abstinence among smokers with alcohol use disorder (AUD), but its therapeutic mechanism has not been determined. We used event-related potentials (ERPs) to evaluate topiramate's effect on the salience of drug-related, emotional, and neutral pictorial cues to identify whether one of its potential therapeutic mechanisms involves reduction of the salience of motivationally relevant cues. METHODS: Participants enrolled in a multisite clinical trial treating smokers with AUD were randomly assigned to receive placebo, low-dose topiramate (up to 125 mg/day), or high-dose topiramate (up to 250 mg/day), along with brief behavioral compliance enhancement treatment. A subsample (n = 101) completed ERP assessments at baseline (1 week pre-medication) and week 5 (5 weeks on medication; 1 week pre-quit). We assessed the salience of pleasant, unpleasant, cigarette-related, alcohol-related, and neutral pictorial cues using the late positive potential (LPP) ERP component and measured self-reported substance use, reinforcement, craving, and withdrawal. RESULTS: Five weeks of high-dose topiramate treatment decreased LPP amplitudes in response to both emotional (pleasant and unpleasant) and drug-related cues (alcohol and cigarette), but not to neutral cues. However, results showed that the LPPs were not significant mediators of the relationship between topiramate dose and post-quit measures of substance use, reinforcement, craving, or withdrawal. CONCLUSIONS: These findings suggest that high-dose topiramate (up to 250 mg/day) decreases the motivational salience of both drug-related and emotional cues among smokers with AUD. However, the nonsignificant mediation analyses preclude any firm conclusions about whether this effect represents one of topiramate's therapeutic mechanisms of action.

Food addiction symptoms are related to neuroaffective responses to preferred binge food and erotic cues.

It has been proposed that some individuals succumb to maladaptive eating behaviors because, like those with addiction, they attribute high incentive salience to food-associated cues. Here, we tested whether women that attribute high incentive salience to food-associated cues report high food addiction symptomatology. In 76 college women, we assessed self-reported food addiction symptoms using the Yale Food Addiction Scale and we recorded event-related potentials (ERPs, a direct measure of brain activity) to preferred food, erotic, unpleasant, and neutral images. We used the amplitude of the late positive potential (LPP, a component of the ERPs) as an index of the incentive salience attributed to the images. Using a multivariate classification algorithm (k-means cluster analysis), we identified two neuroaffective reactivity profiles that have been previously associated with individual differences in the tendency to attribute incentive salience to cues and with differences in vulnerability to addictive behaviors. Results showed that women with elevated LPP responses to preferred food cues relative to erotic images report higher food addiction symptoms than women with low LPP responses to preferred food cues relative to other motivationally relevant stimuli. These results support the hypothesis that individual differences in the tendency to attribute incentive salience to food cues play an important role in modulating food addiction symptomatology.

Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.

INTRODUCTION: By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C"). AIM AND METHOD: The goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers. RESULTS: The predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials. CONCLUSION: These results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm. IMPLICATIONS: We assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

Brain Responses to Cigarette-Related and Emotional Images in Smokers During Smoking Cessation: No Effect of Varenicline or Bupropion on the Late Positive Potential.

Introduction: Varenicline and bupropion are two effective smoking cessation pharmacotherapies. Researchers have hypothesized that they might be effective, in part, because they reduce cue reactivity and cue-induced cravings. Here, we used event-related potentials (ERPs) to directly measure brain responses to cigarette-related and other motivationally relevant images during a pharmacologically aided quit attempt. Methods: Smokers involved in a 12-week placebo-controlled double-blind clinical trial of smoking cessation medications (varenicline, bupropion, placebo) took part in the study. We assessed participants at two time points: 24 h (n = 140) and 4 weeks (n = 176) after the quit date. At both sessions, we measured the amplitude of the late positive potential (LPP), an ERP component reliably associated with motivational relevance, and self-reported tonic craving using the brief version of the Questionnaire of Smoking Urges (QSU-Brief). Results: At both sessions, emotional and cigarette-related images evoked significantly larger LPPs than neutral images. Neither drug type nor smoking abstinence altered this effect at either session. At both sessions, varenicline and bupropion significantly reduced self-reported tonic craving relative to the placebo condition. Conclusions: While both varenicline and bupropion reduced self-reported tonic craving, neither medication altered the amplitude of the LPP to cigarette-related or emotional pictures in smokers attempting to quit. These medications may influence abstinence by means other than by reducing neuroaffective responses to cigarette-related cues. Smokers should be prepared for the likelihood that even after several weeks of successful abstinence, once treatment ends, cigarette-related cues may remain motivationally relevant and trigger cravings that might lead to relapse. Implications: Bupropion and varenicline do not alter electrophysiological responses, as measured by the LPP, to cigarette-related and emotional images. These findings help explain why cigarette-related cues can trigger relapse when smoking cessation medication treatments end.

Robust and Gaussian spatial functional regression models for analysis of event-related potentials.

Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes. Motivated by ERP data in a smoking cessation study, we introduce a Bayesian spatial functional regression framework that models the entire ERPs as spatially correlated functional responses and the stimulus types as covariates. This novel framework relies on mixed models to characterize the effects of stimuli while simultaneously accounting for the multilevel correlation structure. The spatial correlation among the ERP profiles is captured through basis-space Matérn assumptions that allow either separable or nonseparable spatial correlations over time. We induce both adaptive regularization over time and spatial smoothness across electrodes via a correlated normal-exponential-gamma (CNEG) prior on the fixed effect coefficient functions. Our proposed framework includes both Gaussian models as well as robust models using heavier-tailed distributions to make the regression automatically robust to outliers. We introduce predictive methods to select among Gaussian vs. robust models and models with separable vs. non-separable spatiotemporal correlation structures. Our proposed analysis produces global tests for stimuli effects across entire time (or time-frequency) and electrode domains, plus multiplicity-adjusted pointwise inference based on experiment-wise error rate or false discovery rate to flag spatiotemporal (or spatio-temporal-frequency) regions that characterize stimuli differences, and can also produce inference for any prespecified waveform components. Our analysis of the smoking cessation ERP data set reveals numerous effects across different types of visual stimuli.

Beyond Cue Reactivity: Non-Drug-Related Motivationally Relevant Stimuli Are Necessary to Understand Reactivity to Drug-Related Cues.

Neurobiological models of addiction posit that drug use can alter reward processes in two ways: (1) by increasing the motivational relevance of drugs and drug-related cues and (2) by reducing the motivational relevance of non-drug-related rewards. Here, we discuss the results from a series of neuroimaging studies in which we assessed the extent to which these hypotheses apply to nicotine dependence. In these studies, we recorded smokers' and nonsmokers' brain responses to a wide array of motivationally relevant visual stimuli that included pleasant, unpleasant, cigarette-related, and neutral images. Based on these findings, we highlight the flaws of the traditional cue reactivity paradigm and we conclude that responses to non-drug-related motivationally relevant stimuli should be used to appropriately gauge the motivational relevance of cigarette-related cues and to identify smokers attributing higher motivational relevance to drug-related cues than to non-drug-related rewards. Identifying these individuals is clinically relevant as they achieve lower rates of long-term smoking abstinence when attempting to quit. Finally, we show how this approach may be extended beyond nicotine dependence to inform theoretical and clinical research in the study of obesity. Implications: The cue reactivity paradigm (ie, comparing responses evoked by drug-related cues to those evoked by neutral cues) cannot provide conclusive information about the motivational relevance of drug-related cues. Responses to non-drug-related motivationally relevant stimuli should be used to appropriately gauge the level of motivational relevance that substance-dependent individuals attribute to drug-related cues.

Bayesian function-on-function regression for multilevel functional data.

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images.

A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.

In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.

Prequit fMRI responses to pleasant cues and cigarette-related cues predict smoking cessation outcome.

INTRODUCTION: The reasons that some smokers find it harder to quit than others are unclear. Understanding how individual differences predict smoking cessation outcomes may allow the development of more successful personalized treatments for nicotine dependence. Theoretical models suggest that drug users might be characterized by increased sensitivity to drug cues and by reduced sensitivity to nondrug-related natural rewards. We hypothesized that baseline differences in brain sensitivity to natural rewards and cigarette-related cues would predict the outcome of a smoking cessation attempt. METHODS: Using functional magnetic resonance imaging, we recorded prequit brain responses to neutral, emotional (pleasant and unpleasant), and cigarette-related cues from 55 smokers interested in quitting. We then assessed smoking abstinence, mood, and nicotine withdrawal symptoms during the course of a smoking cessation attempt. RESULTS: Using cluster analysis, we identified 2 groups of smokers who differed in their baseline responses to pleasant cues and cigarette-related cues in the posterior visual association areas, the dorsal striatum, and the medial and dorsolateral prefrontal cortex. Smokers who showed lower prequit levels of brain reactivity to pleasant stimuli than to cigarette-related cues were less likely to be abstinent 6 months after their quit attempt, and they had higher levels of negative affect during the course of the quit attempt. CONCLUSIONS: Smokers with blunted brain responses to pleasant stimuli, relative to cigarette-related stimuli, had more difficulty quitting smoking. For these individuals, the lack of alternative forms of reinforcement when nicotine deprived might be an important factor underlying relapse. Normalizing these pathological neuroadaptations may help them achieve abstinence.

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes.

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.

Alpha oscillations in response to affective and cigarette-related stimuli in smokers.

INTRODUCTION: The presence of cigarette-related cues has been associated with smoking relapse. These cues are believed to activate brain mechanisms underlying emotion, attention, and memory. Electroencephalography (EEG) alpha desynchronization (i.e., reduction in alpha power) has been suggested to index the engagement of these mechanisms. Analyzing EEG alpha desynchronization in response to affective and smoking cues might improve our understanding of how smokers process these cues, and the potential impact of this processing on relapse. METHODS: Before the start of a medication-assisted cessation attempt, we recorded EEG from 179 smokers during the presentation of neutral, pleasant, unpleasant, and cigarette-related pictures. Wavelet analysis was used to extract EEG alpha oscillations (8-12 Hz) in response to these pictures. Alpha oscillations were analyzed as a function of picture valence and arousal dimensions. RESULTS: Emotional and cigarette-related stimuli induced a higher level of alpha desynchronization (i.e., less power in the alpha frequency band) than neutral stimuli. In addition, the level of alpha desynchronization induced by cigarette-related stimuli was similar to that induced by highly arousing stimuli (i.e., erotica and mutilations). CONCLUSIONS: These results suggest that, for smokers, cigarette-related cues are motivationally significant stimuli that may engage emotional, attentional, and memory-related neural mechanisms at a level comparable to that seen in response to highly arousing stimuli. This finding suggests that activation of emotional, attentional, and memory-related brain mechanisms may be an important contributor to cue-induced smoking relapse.

Neuroaffective reactivity profiles are associated with vulnerability to e-cigarette use.

BACKGROUND: We tested whether neuroaffective responses to motivationally salient stimuli are associated with vulnerability to cue-induced e-cigarette use in e-cigarette naïve adults who smoke daily. We hypothesized that individuals with stronger neuroaffective responses to nicotine-related cues than to pleasant stimuli (the C>P reactivity profile) would be more vulnerable to cue-induced nicotine self-administration than individuals with stronger neuroaffective responses to pleasant stimuli than to nicotine-related cues (the P>C reactivity profile). METHODS: We used event-related potentials (ERPs, a direct measure of cortical activity) to measure neuroaffective reactivity to pleasant, unpleasant, neutral, and nicotine-related cues indicating the opportunity to use an e-cigarette in 36 participants. For each picture category, we computed the amplitude of the late positive potential (LPP), a robust index of motivational salience. To identify each individual's neuroaffective reactivity profile we applied k-means cluster analysis on the LPP responses. We compared the e-cigarette use frequency across profiles using quantile regression for counts. RESULTS: K-means cluster analysis assigned 18 participants to the C>P profile and 18 participants to the P>C profile. Individuals with the C>P neuroaffective profile used the e-cigarette significantly more often than those with the P>C profile. Significant differences in the number of puffs persisted across different quantiles. CONCLUSIONS: These results support the hypothesis that individual differences in the tendency to attribute motivational salience to drug-related cues underlie vulnerability to cue-induced drug self-administration. Targeting the neuroaffective profiles that we identified with tailored treatments could improve clinical outcomes.

Towards neuromarkers for tailored smoking cessation treatments.

Vulnerability to compulsive drug use stems from dysregulated activity within the neural networks that underlie reward and executive functions. Empirical evidence suggests that a) attributing high motivational salience to drug-related stimuli leads to compulsive drug seeking and b) cognitive control deficits lead to compulsive drug taking. Noninvasive neuroimaging techniques enable brain activity monitoring during affective and cognitive processing and are paving the way to precision medicine for substance use disorders. Identifying robust neuromarkers of affective and cognitive dysregulation would allow clinicians to personalize treatments by targeting individual psychophysiological vulnerabilities. However, methodological choices have biased the field toward experimental paradigms that cannot optimally assess individual differences in the motivational salience of drug-related cues and in the ability to control drug-related decisions, choices which have hindered the identification of clinically relevant neuromarkers. Here, we show that once these shortcomings are amended, replicable neuromarkers of the tendency to attribute motivational salience to drug-related cues and the ability to control drug-related decisions emerge. While we use tobacco use disorder as a model, we also show that the methodological issues highlighted here are relevant to other disorders characterized by maladaptive appetitive behaviors.

Individual differences in late positive potential amplitude and theta power predict cue-induced eating.

Cue-induced reward-seeking behaviors are regulated by both the affective and cognitive control systems of the brain. This study aimed at investigating how individual differences in affective and cognitive responses to cues predicting food rewards contribute to the regulation of cue-induced eating. We recorded electroencephalogram (EEG) from 59 adults while they viewed emotional and food-related images that preceded the delivery of food rewards (candies) or non-food objects (beads). We measured the amplitude of the late positive potential (LPP) in response to a variety of motivationally relevant images and power in the theta (4-8 Hz) frequency band after candies or beads were dispensed to the participants. We found that individuals with larger LPP responses to food images than to pleasant images (C>P group) ate significantly more during the experiment than those with the opposite response pattern (P>C group, p < 0.001). Furthermore, we found that individuals with higher theta power after dispensation of the candy than of the bead (θCA>θBE) ate significantly more than those with the opposite response pattern (θBE>θCA, p < 0.001). Finally, we found that the crossed P>C and θBE>θCA group ate less (p < 0.001) than did the other three groups formed by crossing the LPP and theta group assignments, who exhibited similar eating behavior on average (p = 0.662). These findings demonstrate that individual differences in both affective and cognitive responses to reward-related cues underlie vulnerability to cue-induced behaviors, underscoring the need for individualized treatments to mitigate maladaptive behaviors.

Assessing cocaine motivational value: Comparison of brain reactivity bias toward cocaine cues and cocaine demand.

The behavioral economic measure drug demand and the neural measure late positive potential (LPP) are two measures of motivational value that have been associated with drug relapse risk and treatment outcomes. Despite having overlapping themes, no studies have directly compared drug demand and LPP. Participants (N = 59) included treatment-seeking individuals with cocaine use disorder that had completed both a baseline cocaine demand task and an electroencephalogram (EEG) picture-viewing task of drug-related and pleasant picture cues. Associations between the LPP difference score amplitude (drug-pleasant) and five demand indices (Q0, essential value [EV], Omax, Pmax, and breakpoint [BP]) were evaluated via Bayesian generalized linear modeling. Positive associations (posterior probabilities ≥ 75%) were found between LPP amplitude and four demand indices (Q0, EV, Omax, BP). These results suggest that individuals who attach greater relevance to cocaine cues also exhibit greater valuation of cocaine reward. Implications for incorporating methodology from behavioral science and brain imaging are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Exenatide as an adjunct to nicotine patch for smoking cessation and prevention of postcessation weight gain among treatment-seeking smokers with pre-diabetes and/or overweight: study protocol for a randomised, placebo-controlled clinical trial.

INTRODUCTION: Obesity and smoking are the two leading causes of preventable death in the USA. Unfortunately, most smokers gain weight after quitting. Postcessation weight gain (PCWG) is frequently cited as one of the primary barriers to a quit attempt and a common cause of relapse. Further, excessive PCWG may contribute to the onset or progression of metabolic conditions, such as hyperglycaemia and obesity. The efficacy of the current treatments for smoking cessation is modest, and these treatments have no clinically meaningful impact on mitigating PCWG. Here, we outline a novel approach using glucagon-like peptide 1 receptor agonists (GLP-1RA), which have demonstrated efficacy in reducing both food and nicotine intake. This report describes the design of a double-blind, placebo-controlled, randomised clinical trial that evaluates the effects of the GLP-1RA exenatide as an adjunct to nicotine patches on smoking abstinence and PCWG. METHODS AND ANALYSIS: The study will be conducted at two university-affiliated research sites in Houston, Texas, the UTHealth Center for Neurobehavioral Research on Addiction and Baylor College of Medicine Michael E. DeBakey VA Medical Centre. The sample will consist of 216 treatment-seeking smokers with pre-diabetes (haemoglobin A1c of 5.7%-6.4%) and/or overweight (body mass index of 25 kg/m2 or above). Participants will be randomised (1:1) to receive subcutaneous injections of placebo or 2 mg exenatide, once weekly for 14 weeks. All participants will receive transdermal nicotine replacement therapy and brief smoking cessation counselling for 14 weeks. The primary outcomes are 4-week continuous abstinence and changes in body weight at the end of treatment. The secondary outcomes are (1) abstinence and changes in body weight at 12 weeks post end of treatment and (2) changes in neuroaffective responses to cigarette-related and food-related cues as measured by electroencephalogram. ETHICS AND DISSEMINATION: The study has been approved by the UTHealth Committee for the Protection of Human Subjects (HSC-MS-21-0639) and Baylor College of Medicine Institutional Review Board (H-50543). All participants will sign informed consent. The study results will be disseminated via peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT05610800.

Neural substrates of smoking cue reactivity: a meta-analysis of fMRI studies.

Reactivity to smoking-related cues may be an important factor that precipitates relapse in smokers who are trying to quit. The neurobiology of smoking cue reactivity has been investigated in several fMRI studies. We combined the results of these studies using activation likelihood estimation, a meta-analytic technique for fMRI data. Results of the meta-analysis indicated that smoking cues reliably evoke larger fMRI responses than neutral cues in the extended visual system, precuneus, posterior cingulate gyrus, anterior cingulate gyrus, dorsal and medial prefrontal cortex, insula, and dorsal striatum. Subtraction meta-analyses revealed that parts of the extended visual system and dorsal prefrontal cortex are more reliably responsive to smoking cues in deprived smokers than in non-deprived smokers, and that short-duration cues presented in event-related designs produce larger responses in the extended visual system than long-duration cues presented in blocked designs. The areas that were found to be responsive to smoking cues agree with theories of the neurobiology of cue reactivity, with two exceptions. First, there was a reliable cue reactivity effect in the precuneus, which is not typically considered a brain region important to addiction. Second, we found no significant effect in the nucleus accumbens, an area that plays a critical role in addiction, but this effect may have been due to technical difficulties associated with measuring fMRI data in that region. The results of this meta-analysis suggest that the extended visual system should receive more attention in future studies of smoking cue reactivity.

Beyond cue reactivity: blunted brain responses to pleasant stimuli predict long-term smoking abstinence.

Identifying addicts with higher risk of relapse would provide the opportunity to implement individualized interventions and increase cessation success rates. Unfortunately, the ability to predict the long-term success of drug cessation treatments continues to elude researchers. We tested whether brain responses to emotional and cigarette-related pictures were predictive of the ability to abstain from smoking. Smokers interested in quitting (n=180) participated in a smoking cessation clinical trial. Before the initiation of any treatment, we recorded event-related potentials (ERPs) evoked by emotional (both pleasant and unpleasant), neutral, and cigarette-related images. Cluster analysis was used to assign smokers to two groups based on the amplitude of the late positive potential (LPP) to the experimental stimuli. While both groups showed enhanced responses to cigarette-related cues, one group (n=81) also showed blunted brain responses to intrinsically pleasant stimuli. Smokers in the latter group were significantly less likely to be abstinent at 10, 12 and 24 weeks after their quit date. In conclusion, using ERPs, a direct measure of brain activity, we found that smokers with blunted brain responses to intrinsically pleasant stimuli had lower rates of long-term smoking abstinence. This response offers a new biomarker for identifying smokers at higher risk of relapse and for testing the efficacy of new interventions aimed at normalizing brain reward systems' responses to intrinsically pleasant stimuli.