Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder

Effect of Intensified Transcranial Direct-current Stimulation Targeting Bilateral Dorsolateral Prefrontal Cortex on Craving Reduction in Patients with Opioid (Heroin) Use Disorder

Objective

: This study aimed to analyze the effect of the intensified transcranial direct-current stimulation (tDCS) targeting bilateral dorsolateral prefrontal cortex (DLPFC) on craving reduction in patients with opioid use disorder.

Methods

: This quasi-experimental study was conducted on 30 individuals who participated voluntarily at Baharan Camp of Shahid Mahalati. The participants had already completed the detoxification phase and stayed at the camp to resolve their craving and gain occupational skills to reintegrate into the community. The participants were selected using convenience and purposive sampling methods and were then assigned to an experimental group (n = 15) and a control group (n = 15). The experimental group received ten 20-minute tDCS sessions twice a day for 5 consecutive days. There was a 20-minute break between the two stimulations. The DLPFC was stimulated with a current intensity of 2 mA (anode: F3 and cathode: F4). The control group received a sham stimulation. Both groups completed Franken's Desires for Drug Questionnaire at baseline and after the stimulation sessions. Additionally, they completed the questionnaires once again three months after the end of the treatment to assess treatment retention.

Results

: At the posttest stage, the intensified tDCS had significant effects on momentary opioid craving reduction in all measured factors, e.g., desire and intention, negative reinforcement, and control (p ＜ 0.001). However, the results concerning treatment retention at the 3-month follow-up stage were insignificant for all factors (p ＜ 0.001).

Conclusion

: Apparently, tDCS can be used as a tool to reduce craving. However, its application as an independent and sustainable treatment remains debatable.

Non-Opioid Anesthetics Addiction: A Review of Current Situation and Mechanism

Drug addiction is one of the major worldwide health problems, which will have serious adverse consequences on human health and significantly burden the social economy and public health. Drug abuse is more common in anesthesiologists than in the general population because of their easier access to controlled substances. Although opioids have been generally considered the most commonly abused drugs among anesthesiologists and nurse anesthetists, the abuse of non-opioid anesthetics has been increasingly severe in recent years. The purpose of this review is to provide an overview of the clinical situation and potential molecular mechanisms of non-opioid anesthetics addiction. This review incorporates the clinical and biomolecular evidence supporting the abuse potential of non-opioid anesthetics and the foreseeable mechanism causing the non-opioid anesthetics addiction phenotypes, promoting a better understanding of its pathogenesis and helping to find effective preventive and curative strategies.

Misconfigured striatal connectivity profiles in smokers

Dysregulation of frontal cortical inputs to the striatum is foundational in the neural basis of substance use disorder (SUD). Neuroanatomical and electrophysiological data increasingly show that striatal nodes receive appreciable input from numerous cortical areas, and that the combinational properties of these multivariate "connectivity profiles" play a predominant role in shaping striatal activity and function. Yet, how abnormal configuration of striatal connectivity profiles might contribute to SUD is unknown. Here, we implemented a novel "connectivity profile analysis" (CPA) approach using resting-state functional connectivity data to facilitate detection of different types of connectivity profile "misconfiguration" that may reflect distinct forms of aberrant circuit plasticity in SUD. We examined 46 nicotine-dependent smokers and 33 non-smokers and showed that both dorsal striatum (DS) and ventral striatum (VS) connectivity profiles with frontal cortex were misconfigured in smokers-but in doubly distinct fashions. DS misconfigurations were stable across sated and acute abstinent states (indicative of a "trait" circuit adaptation) whereas VS misconfigurations emerged only during acute abstinence (indicative of a "state" circuit adaptation). Moreover, DS misconfigurations involved abnormal connection strength rank order arrangement, whereas VS misconfigurations involved abnormal aggregate strength. We found that caudal ventral putamen in smokers uniquely displayed multiple types of connectivity profile misconfiguration, whose interactive magnitude was linked to dependence severity, and that VS misconfiguration magnitude correlated positively with withdrawal severity during acute abstinence. Findings underscore the potential for approaches that more aptly model the neurobiological composition of corticostriatal circuits to yield deeper insights into the neural basis of SUD.

Hypothalamic connectivities predict individual differences in ADT-elicited changes in working memory and quality of life in prostate cancer patients

Androgen deprivation therapy (ADT) has been associated with adverse effects on cognition. However, we currently lack understanding of the neurobiology and prognostic markers of these effects. Given that ADT acts via the hypothalamus-pituitary-gonadal axis, we assessed whether baseline hypothalamic resting state functional connectivity (rsFC) could predict changes in working memory and quality of life in prostate cancer patients following androgen deprivation. In a prospective observational study, 28 men with non-metastatic prostate cancer receiving ADT and 38 patients not receiving ADT (controls), matched in age, years of education and Montreal Cognitive Assessment score, participated in brain imaging at baseline, and N-back task and quality-of-life (QoL) assessments at baseline and at 6 months follow-up. Imaging data were processed with published routines and evaluated at a corrected threshold. ADT and control groups did not differ in N-back performance or QoL across time points. In ADT, the changes in 0-back correct response rate (follow-up-baseline) were correlated with baseline hypothalamus-precentral gyrus rsFC; the changes in 1-back correct response rate and reaction time were each correlated with hypothalamus-middle frontal gyrus and superior parietal lobule rsFC. The changes in physical well-being subscore of QoL were correlated with baseline hypothalamus-anterior cingulate and cuneus rsFC. The hypothalamus rsFCs predicted N-back and QoL change with an area under the receiver operating characteristic curve of 0.93 and 0.73, respectively. Baseline hypothalamus-frontoparietal and salience network rsFC's predict inter-subject variations in the changes in working-memory and QoL following 6 months of ADT. Whether and how hypothalamic rsFCs may predict the cognitive and QoL effects with longer-term ADT remain to be investigated.

Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Simple Summary

Addiction to tobacco is a serious health and economical problem because it is one of the most addictive and the most consumed substance in the world. Although well documented, and despite the desire of numerous smokers to quit, maintenance of abstinence is a daily challenge for most of them. The heterogeneity in achieving this maintenance raises the question of potential differences in brain reactivity. An emerging field of research has been interested in brain markers helping to identify individuals who are the most likely to relapse. Using brain imaging techniques such as Magnetic Resonance Imaging (MRI), one can hope it will be possible to offer tailored care for each patient.

Abstract

Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.

Functional connectivity of the anterior insula during withdrawal from cigarette smoking

Currently available therapies for smoking cessation have limited efficacy, and potential treatments that target specific brain regions are under evaluation, with a focus on the insula. The ventral and dorsal anterior subregions of the insula serve distinct functional networks, yet our understanding of how these subregions contribute to smoking behavior is unclear. Resting-state functional connectivity (RSFC) provides a window into network-level function associated with smoking-related internal states. The goal of this study was to determine potentially distinct relationships of ventral and dorsal anterior insula RSFC with cigarette withdrawal after brief abstinence from smoking. Forty-seven participants (24 women; 18-45 years old), who smoked cigarettes daily and were abstinent from smoking overnight (~12 h), provided self-reports of withdrawal and underwent resting-state fMRI before and after smoking the first cigarette of the day. Correlations between withdrawal and RSFC were computed separately for ventral and dorsal anterior insula seed regions in whole-brain voxel-wise analyses. Withdrawal was positively correlated with RSFC of the right ventral anterior insula and dorsal anterior cingulate cortex (dACC) before but not after smoking. The correlation was mainly due to a composite effect of craving and physical symptoms of withdrawal. These results suggest a role of right ventral anterior insula-dACC connectivity in the internal states that maintain smoking behavior (e.g., withdrawal) and present a specific neural target for brain-based therapies seeking to attenuate withdrawal symptoms in the critical early stages of smoking cessation.

Exploration of the influence of body mass index on intra-network resting-state connectivity in chronic cigarette smokers

BACKGROUND

Obesity and cigarette smoking are two leading preventable causes of death. Previous research suggests that comorbid smoking and obesity likely share neurobehavioral underpinnings; however, the influence of body mass index (BMI) on resting-state functional connectivity (rsFC) in smokers remains unknown. In this study, we explore how BMI affects rsFC and associations between rsFC and smoking-related behavior.

METHODS

Treatment-seeking cigarette smokers (N = 87; 54 % men) completed a BOLD resting-state fMRI scan session. We grouped smokers into BMI groups (N = 23 with obesity, N = 33 with overweight, N = 31 lean) and used independent components analysis (ICA) to identify the resting state networks commonly associated with cigarette smoking: salience network (SN), right and left executive control networks (ECN) and default mode network (DMN). Average rsFC values were extracted (p < 0.001, k = 100) to determine group differences in rsFC and relationship to self-reported smoking and dependence.

RESULTS

Analyses revealed a significant relationship between BMI and connectivity in the SN and a significant quadratic effect of BMI on DMN connectivity. Heavier smoking was related to greater rsFC in the SN among lean and obese groups but reduced rsFC in the overweight group.

CONCLUSIONS

Findings build on research suggesting an influence of BMI on the neurobiology of smokers. In particular, dysfunction of SN-DMN-ECN circuitry in smokers with overweight may lead to a failure to modulate attention and behavior and subsequent difficulty quitting smoking. Future research is needed to elucidate the mechanism underlying the interaction of BMI and smoking and its impact on treatment.

Neural cue reactivity during acute abstinence predicts short-term smoking relapse

In smokers, neural responses to smoking cues can be sensitive to acute abstinence, but the degree to which abstinence-related cue reactivity contributes to relapse is not fully understood. This study addressed this question in a sample of 75 smokers who were motivated to quit smoking. Participants underwent blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) during presentation of visual smoking cues and neutral stimuli on two occasions: once during smoking satiety and once following 24-hour abstinence (order counterbalanced). Following the imaging sessions, participants received brief smoking cessation counseling prior to a short-term (7-day) quit attempt. The primary smoking cessation outcome was biochemically confirmed 7-day relapse. The secondary smoking cessation outcome measure was total number of self-reported days of abstinence. During abstinence (vs satiety), smoking cue reactivity was significantly increased only in the anterior cingulate cortex (ACC); other regions showing a cue (vs neutral) response did not exhibit an abstinence effect in the stringent whole-brain analysis. Participants who showed greater smoking cue reactivity in the ACC during acute abstinence (compared with smoking satiety) were more likely to relapse (OR = 2.10 per standard deviation increase in percent signal change [abstinence minus smoking satiety], 95% CI: 1.05 to 4.20, P = 0.036). Greater abstinence-induced change in ACC activation also predicted fewer total days abstinent (β = -0.63, 95% CI = 0.43 to 0.66, P < 0.0001). This study provides the first evidence that changes in smoking cue reactivity in the ACC during acute abstinence predict smoking relapse, thereby improving our understanding of the neurobiology of smoking cessation.

Resting-State Connectivity in Former, Current, and Never Smokers

INTRODUCTION

Understanding the neural mechanisms that support successful smoking cessation is vital to the development of novel treatments for nicotine dependence.

METHOD

To this end, we compared resting-state functional connectivity across three smoking groups: current, never, and former smokers. We used an independent component analysis (ICA) that allowed us to compare differences in intrinsic, large-scale networks across our groups. Using this technique, we were able to compare group differences across resting-state networks without the requirement of identifying coordinate-based regions of interest.

RESULTS

Overall, the ICA resulted in networks that were largely consistent with previous reports, including bilateral executive control networks, salience, and a default mode network. Group comparisons among the three groups revealed differences in three networks including sensorimotor, dorsal attention, and default mode networks, with differences localized to pre/postcentral gyrus, lateral occipital cortex, and superior parietal lobe. In all regions showing a difference, current smokers showed increased network amplitude compared to former and never smokers.

CONCLUSION

Although some theoretical models of recovery have suggested an important role of frontal cortex and cognitive control, the current results seem to suggest that reductions in posterior regions including superior parietal lobe and somatosensory cortex may play a key role in maintaining long-term abstinence from cigarettes.

IMPLICATIONS

The submitted research is a novel contribution to the study of successful nicotine abstinence, in part, because it includes individuals who have successfully overcome nicotine dependence. The use of ICA allowed for examination of large-scale resting-state networks throughout the brain without the need for specifying numerous regions of interest. This research supports the view that overcoming nicotine dependence may depend on reducing spontaneous activity in posterior regions of the brain rather than solely enhancing frontal control.

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI

Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n = 108; 63 male) and matched nonsmoking controls (n = 108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting-state networks that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1 percent and an average area under the curve of 0.93. Compared with controls, individuals with NUD had weaker functional connectivity measures within these networks (P < 0.05, false discovery rate corrected). Further, multi-dimensional scaling visualization demonstrated that controls were similar to each other whereas individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine learning-based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in resting-state networks of individuals with NUD.

The insula in nicotine use disorder: Functional neuroimaging and implications for neuromodulation

Insula dysfunction contributes to nicotine use disorders. Yet, much remains unknown about how insular functions promote nicotine use. We review current models of brain networks in smoking and propose an extension to those models that emphasizes the role of the insula in craving. During acute withdrawal, the insula provides the sensation of craving to the cerebrum and is thought to negotiate craving sensations with cognitive control to guide behavior - either to smoke or abstain. Recent studies have shown that insula processing is saturable, such that different insular functions compete for limited resources. We propose that this saturability explains how craving during withdrawal can overload insular processing to the exclusion of other functions, such as saliency and network homeostasis. A novel signal flow model illustrates how limited insular capacity leads to breakdown of normal function. Finally, we discuss suitability of insula as a neuromodulation target to promote cessation. Given the limited efficacy of standard-of-care treatments for nicotine use disorder, insular neuromodulation offers an innovative, potentially therapeutic target for improving smoking cessation.

Alterations in resting-state functional connectivity in substance use disorders and treatment implications

Substance use disorders (SUD) are diseases of the brain, characterized by aberrant functioning in the neural circuitry of the brain. Resting state functional connectivity (rsFC) can illuminate these functional changes by measuring the temporal coherence of low-frequency fluctuations of the blood oxygenation level-dependent magnetic resonance imaging signal in contiguous or non-contiguous regions of the brain. Because this data is easy to obtain and analyze, and therefore fairly inexpensive, it holds promise for defining biological treatment targets in SUD, which could help maximize the efficacy of existing clinical interventions and develop new ones. In an effort to identify the most likely "treatment targets" obtainable with rsFC we summarize existing research in SUD focused on 1) the relationships between rsFC and functionality within important psychological domains which are believed to underlie relapse vulnerability 2) changes in rsFC from satiety to deprived or abstinent states 3) baseline rsFC correlates of treatment outcome and 4) changes in rsFC induced by treatment interventions which improve clinical outcomes and reduce relapse risk. Converging evidence indicates that likely "treatment target" candidates, emerging consistently in all four sections, are reduced connectivity within executive control network (ECN) and between ECN and salience network (SN). Other potential treatment targets also show promise, but the literature is sparse and more research is needed. Future research directions include data-driven prediction analyses and rsFC analyses with longitudinal datasets that incorporate time since last use into analysis to account for drug withdrawal. Once the most reliable biological markers are identified, they can be used for treatment matching, during preliminary testing of new pharmacological compounds to establish clinical potential ("target engagement") prior to carrying out costly clinical trials, and for generating hypotheses for medication repurposing.

Lack of effect of transcranial direct current stimulation (tDCS) on short-term smoking cessation: Results of a randomized, sham-controlled clinical trial

BACKGROUND

Transcranial direct current stimulation (tDCS) has been shown to improve measures of executive cognitive function and reduce cigarette consumption. Studies conducted to date have been small, and the results are mixed.

METHODS

This randomized, double-blind, parallel arm clinical trial tested the effects of active anodal tDCS targeted to the left dorsolateral prefrontal cortex (versus sham) on 7-day smoking cessation in 106 treatment-seeking smokers. Participants received three sessions of sham (n = 35), 1 mA (n = 35), or 2 mA (n = 36) tDCS in the context of a validated smoking lapse paradigm then received brief smoking cessation counseling and completed a monitored quit attempt. The primary outcome was total number of days of abstinence confirmed via exhaled carbon monoxide.

RESULTS

During the quit period, there were no effects of dose group on days of abstinence (sham, M (SD): 2.5 days (±2.5); 1 mA: 2.5 days (±2.5); 2 mA: 2.4 days (±2.3); β = -0.08; p = 0.76) or on change in daily smoking rate (sham, M (SD): 12.6 CPD (±4.8); 1 mA: -11.8 CPD (±4.4); 2 mA: -11.7 CPD (±5.3); β = 0.42, p = 0.49), nor were there effects of dose group on latency to smoke or number of cigarettes smoked during the smoking lapse paradigm. Side effects of tDCS were generally mild (<5 out of 10), and participants were not able to distinguish between active and sham treatment.

CONCLUSIONS

These results do not support the efficacy of tDCS targeted to the left dorsolateral prefrontal cortex (DLPFC) for smoking cessation.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.

Menthol enhances nicotine-induced locomotor sensitization and in vivo functional connectivity in adolescence

Mentholated cigarettes capture a quarter of the US market, and are disproportionately smoked by adolescents. Menthol allosterically modulates nicotinic acetylcholine receptor function, but its effects on the brain and nicotine addiction are unclear. To determine if menthol is psychoactive, we assessed locomotor sensitization and brain functional connectivity. Adolescent male Sprague Dawley rats were administered nicotine (0.4 mg/kg) daily with or without menthol (0.05 mg/kg or 5.38 mg/kg) for nine days. Following each injection, distance traveled in an open field was recorded. One day after the sensitization experiment, functional connectivity was assessed in awake animals before and after drug administration using magnetic resonance imaging. Menthol (5.38 mg/kg) augmented nicotine-induced locomotor sensitization. Functional connectivity was compared in animals that had received nicotine with or without the 5.38 mg/kg dosage of menthol. Twenty-four hours into withdrawal after the last drug administration, increased functional connectivity was observed for ventral tegmental area and retrosplenial cortex with nicotine+menthol compared to nicotine-only exposure. Upon drug re-administration, the nicotine-only, but not the menthol groups, exhibited altered functional connectivity of the dorsal striatum with the amygdala. Menthol, when administered with nicotine, showed evidence of psychoactive properties by affecting brain activity and behavior compared to nicotine administration alone.

Salience network coupling is linked to both tobacco smoking and symptoms of attention deficit hyperactivity disorder (ADHD)

INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) symptoms, even those below diagnostic threshold, enhance the likelihood of nicotine dependence, suggesting a neurobiological link between disorders. Of particular interest is the salience network (SN), which mediates attention to salient internal/external stimuli to guide behavior and is anchored by the dorsal anterior cingulate cortex (dACC) and bilateral anterior insula (AI). Disrupted interactions between the SN and the default mode (DMN) and central executive networks (CEN) have been noted in both ADHD and nicotine dependence. Further, enhanced intra-SN coupling between the dACC-AI influences aspects of nicotine dependence such as reactivity to smoking cues.

METHODS

To identify links between SN functional connectivity and ADHD symptoms in nicotine dependence, we compared 21 nicotine dependent individuals with 17 non-smokers on ADHD symptoms as measured by the ADHD self-report scale (ASRS) and resting state intra and inter-SN functional connectivity.

RESULTS

Relative to healthy controls, nicotine dependent individuals had significantly higher ASRS scores and greater dACC-AI coupling. No group differences were noted on inter-SN network coupling. A significant association was found between ASRS and dACC-AI coupling both in the entire cohort and specifically when evaluating nicotine dependent individuals alone.

CONCLUSIONS

The greater ASRS scores in nicotine dependent individuals is in line with existent literature and the stronger dACC-AI coupling in smokers further supports the role of this network in nicotine dependence. The significant association between dACC-AI coupling and ASRS suggests that intra-SN coupling strength may impact neurocognitive functioning associated with both ADHD symptoms and nicotine dependence.