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

Decomposing the late positive potential to cannabis cues in regular cannabis users: A temporal-spatial principal component analysis

Cannabis use disorder (CUD) is increasing in the United States, yet, specific neural mechanisms of CUD are not well understood. Disordered substance use is characterized by heightened drug cue incentive salience, which can be measured using the late positive potential (LPP), an event-related potential (ERP) evoked by motivationally significant stimuli. The drug cue LPP is typically quantified by averaging the slow wave's scalp-recorded amplitude across its entire time course, which may obscure distinct underlying factors with differential predictive validity; however, no study to date has examined this possibility. In a sample of 105 cannabis users, temporo-spatial Principal Component Analysis was used to decompose cannabis cue modulation of the LPP into its underlying factors. Acute stress was also inducted to allow for identification of specific cannabis LPP factors sensitive to stress. Factor associations with CUD severity were also explored. Eight factors showed significantly increased amplitudes to cannabis images relative to neutral images. These factors spanned early (~372 ms), middle (~824 ms), and late (>1000 ms) windows across frontal, central, and parietal-occipital sites. CUD phenotype individual differences were primarily associated with frontal, middle/late latency factor amplitudes. Acute stress effects were limited to one early central and one late frontal factor. Taken together, results suggest that the cannabis LPP can be decomposed into distinct, temporal-spatial factors with differential responsivity to acute stress and CUD phenotype variability. Future individual difference studies examining drug cue modulation of the LPP should consider (1) frontalcentral poolings in addition to conventional central-parietal sites, and (2) later LPP time windows.

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.

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.

Toward 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.

The Use of Biomarkers to Guide Precision Treatment for Tobacco Use

This review summarizes the evidence to date on the development of biomarkers for personalizing the pharmacological treatment of combustible tobacco use. First, the latest evidence on FDA-approved medications is considered, demonstrating that, while these medications offer real benefits, they do not contribute to smoking cessation in approximately two-thirds of cases. Second, the case for using biomarkers to guide tobacco treatment is made based on the potential to increase medication effectiveness and uptake and reduce side effects. Next, the FDA framework of biomarker development is presented along with the state of science on biomarkers for tobacco treatment, including a review of the nicotine metabolite ratio, electroencephalographic event-related potentials, and other biomarkers utilized for risk feedback. We conclude with a discussion of the challenges and opportunities for the translation of biomarkers to guide tobacco treatment and propose priorities for future research.

Electrophysiological responses to emotional and cocaine cues reveal individual neuroaffective profiles in cocaine users

Smokers with stronger neuroaffective responses to drug-related cues compared to nondrug-related pleasant images (C > P) are more vulnerable to compulsive smoking than individuals with the opposite brain reactivity profile (P > C). However, it is unknown if these neurobehavioral profiles exist in individuals abusing other drugs. We tested whether individuals with cocaine use disorder (CUD) show similar neuroaffective profiles to smokers. We also monitored eye movements to assess attentional bias toward cues and we further performed exploratory analyses on demographics, personality, and drug use between profiles. Participants with CUD (n = 43) viewed pleasant, unpleasant, cocaine, and neutral images while we recorded electroencephalogram. For each picture category, we computed the amplitude of the late positive potential (LPP), an event-related potential component that reflects motivational relevance. k-means clustering classified participants based on their LPP responses. In line with what has been observed in smokers, clustering participants using LPP responses revealed the presence of two groups: one with larger LPPs to pleasant images compared to cocaine images (P > C) and one group with larger LPPs to cocaine images compared to pleasant images (C > P). Individuals with the C > P reactivity profile also had higher attentional bias toward drug cues. The two groups did not differ on demographic and drug use characteristics, however individuals with the C > P profile reported lower distress tolerance, higher anhedonia, and higher posttraumatic stress symptoms compared to the P > C group. This is the first study to report the presence of these neuroaffective profiles in individuals with CUD, indicating that this pattern may cut across addiction populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neural substrates of substance use disorders

PURPOSE OF REVIEW

Substance use disorders account for a tremendous burden to society, yet despite substantial progress in basic studies, our understanding of the brain-basis of these disorders is still emerging. This review summarizes the recent findings of neuroimaging studies with substance use disorder individuals.

RECENT FINDINGS

Resting-state functional connectivity studies support for some but not all substances of abuse and disruption in executive control. Structural neuroimaging findings point towards reduced subcortical volumes, which may emerge as an interaction between preexisting factors and recent substance use. Longitudinal studies implicate some of the same core brain structures and their functional role that have also been identified via case-control studies. Finally, meta-analyses support the idea of dysregulation of cortical control over subcortical salience processing.

SUMMARY

Although progress has been made and there is both structural and functional imaging evidence of an imbalance between brain structures involved in executive control and salience processing, there is emerging evidence that brain-behaviour relationships, which are core to discovering the neural processes that lead to and maintain substance use, are small and require larger consortia that prospectively examine individuals with substance use disorder.

Heavy Smoking Patients Receiving a Lung Cancer Screen Want to Quit: A Call for Tailored Cessation Interventions

Background

Lung cancer screening for current or former heavy smokers is now recommended among all asymptomatic adults 50–80 years old with a 20 pack-year history of smoking. However, little is known about the smoking-related attitudes of this population.

Method

An assessment was conducted among 1,472 current smokers who presented for an annual lung cancer screen at one of 12 diagnostic imaging sites in Rhode Island between April 2019 and May 2020. Patients were asked about their use of smoking products, interest in quitting, and smoking-related attitudes.

Results

Patients smoked a median of 16 cigarettes per day; 86.6% were daily cigarette smokers and 30.1% were daily cigar smokers. In total, 91.4% of patients were, to some degree, interested in quitting smoking and 71.4% were seriously thinking about quitting in the next 6 months or sooner. Patients planned on smoking less regardless of whether their lung screen was positive or negative for cancer, though they were more likely to plan on smoking less if negative (on 0–3 pt Likert scale: 0.31, 95% CI [0.27, 0.34] vs. 0.77, 95% CI [0.72, 0.81]). Confidence in quitting and belief in one’s inherent ability to quit smoking varied substantially within the sample.

Conclusion

Nearly all current smokers receiving a lung cancer screen have some interest in smoking cessation. Due to the heterogeneity in some smoking-related attitudes, tailored interventions for this population should be tested.

Classifying risky cannabis involvement in young adults using the Marijuana Consequences Questionnaire (MACQ)

BACKGROUND

Despite the growing prevalence of cannabis use and associated consequences among U.S. young adults, relatively little is known about precisely what level of marijuana involvement may be problematic.

METHOD

With this study we sought to identify empirically-derived cut-scores for the Marijuana Consequences Questionnaire (MACQ) that would distinguish among levels of cannabis risk in a sample of young adult college students (N = 496). We also examined how these levels of cannabis risk corresponded to a variety of indicators of cannabis involvement, including frequency of use, intoxication, other measures of cannabis consequences, and indicators of more severe cannabis involvement (e.g., physiological dependence, loss of control over use, cannabis use disorder).

RESULTS

Receiver operating characteristic analyses yielded cutoffs that distinguished among three distinct levels of risk, "Low", "Moderate", and "High". These empirically derived cut scores showed strong overall differentiation among classifications, with good sensitivity and specificity. MACQ-based risk levels were validated across several indices of cannabis involvement. Cutoffs differed across genders.

CONCLUSIONS

Findings offer a new application for the MACQ, allowing for the identification of those at greatest risk. As such, this measure may be used to facilitate appropriately targeted intervention.

The lateral hypothalamus and orexinergic transmission in the paraventricular thalamus promote the attribution of incentive salience to reward-associated cues

RATIONALE

Prior research suggests that the neural pathway from the lateral hypothalamic area (LHA) to the paraventricular nucleus of the thalamus (PVT) mediates the attribution of incentive salience to Pavlovian reward cues. However, a causal role for the LHA and the neurotransmitters involved have not been demonstrated in this regard.

OBJECTIVES

To examine (1) the role of LHA in the acquisition of Pavlovian conditioned approach (PavCA) behaviors, and (2) the role of PVT orexin 1 receptors (OX1r) and orexin 2 receptors (OX2r) in the expression of PavCA behaviors and conditioned reinforcement.

METHODS

Rats received excitotoxic lesions of the LHA prior to Pavlovian training. A separate cohort of rats characterized as sign-trackers (STs) or goal-trackers (GTs) received the OX1r antagonist SB-334867, or the OX2r antagonist TCS-OX2-29, into the PVT, to assess their effects on the expression of PavCA behavior and on the conditioned reinforcing properties of a Pavlovian reward cue.

RESULTS

LHA lesions attenuated the development of sign-tracking behavior. Administration of either the OX1r or OX2r antagonist into the PVT reduced sign-tracking behavior in STs. Further, OX2r antagonism reduced the conditioned reinforcing properties of a Pavlovian reward cue in STs.

CONCLUSIONS

The LHA is necessary for the development of sign-tracking behavior; and blockade of orexin signaling in the PVT attenuates the expression of sign-tracking behavior and the conditioned reinforcing properties of a Pavlovian reward cue. Together, these data suggest that LHA orexin inputs to the PVT are a key component of the circuitry that encodes the incentive motivational value of reward cues.

Modeling neuroaffective biomarkers of drug addiction: A Bayesian nonparametric approach using dirichlet process mixtures

BACKGROUND

The properties of neurophysiological processes related to addiction have received much attention in the literature. However, empirical evidence of meaningful and useful characterization of these processes is limited. Recent studies have found that electrophysiological responses to emotional and drug-related cues can be used to create profiles that reliably predict smoking relapse.

NEW METHOD

This paper evaluates the validity of classifying electrophysiological responses into distinct profiles using a Bayesian dirichlet process mixture (DPM) model. The DPM is a Bayesian nonparametric (BNP) method to modeling unknown number of profiles characterized by uncertainty in cluster membership and in cluster number.

RESULTS

The DPM model confirmed previously identified neuroaffective reactivity profiles, but also revealed a finer level of granularity in the clustering. Specifically, in addition to the two clusters previously identified in the literature, the BNP methods identified a cluster of individuals showing similar responses to smoking, pleasant, neutral and unpleasant cues.

COMPARISON WITH EXISTING METHODS

BNP models provide an alternative to the k-mean clustering approach to modeling EEG-based neuroaffective profiles. Unlike k-means clustering, BNP models compute the probability that a subject belongs to a cluster while taking into consideration uncertainty in the number of clusters.

CONCLUSIONS

Our results confirm the reliability of the two clusters previously identified in these data, but also provide new insights by revealing a cluster that presented similar responses to stimuli with different contents. This finding may be related to the uncertainty in classification or overlapping brain-reactivity profiles.