Brain Network Dynamics Study for Smoking Relapse Prevention
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Penn State University
Disqualifiers: Pacemakers, Metallic objects, others
No Placebo Group
Trial Summary
What is the purpose of this trial?
This trial uses brain scans to understand why people trying to quit smoking end up smoking again. It focuses on adults who smoke and examines how their brain activity changes right before they start smoking again. By studying these changes, researchers hope to find better ways to help people quit smoking for good.
Do I have to stop taking my current medications for the trial?
The trial protocol does not specify whether you need to stop taking your current medications. However, you must refrain from using nicotine for 12 hours before the lab visit.
What data supports the idea that Brain Network Dynamics Study for Smoking Relapse Prevention is an effective treatment?
The available research shows that the Brain Network Dynamics Study for Smoking Relapse Prevention can help identify smokers who are more likely to relapse. For example, one study found that certain brain areas, like the dorsolateral prefrontal cortex, showed different activity patterns in people who relapsed compared to those who successfully quit. This means that the treatment could be used to tailor personalized plans to help people quit smoking more effectively. Another study demonstrated that understanding brain connectivity during the first day of quitting can predict who might resist smoking. These findings suggest that this treatment could be useful in preventing smoking relapse by focusing on brain activity patterns.12345
What safety data exists for the Brain Network Dynamics Study for Smoking Relapse Prevention?
The provided research does not directly address safety data for the Brain Network Dynamics Study for Smoking Relapse Prevention or the Laboratory task modeling smoking lapse behavior. The studies focus on functional connectivity and brain activity related to smoking relapse and cessation, using fMRI to identify neural patterns associated with relapse risk and cessation success. However, they do not provide specific safety data or evaluations of the treatment's safety profile.12567
Is the treatment 'Laboratory task modeling smoking lapse behavior' promising for preventing smoking relapse?
Eligibility Criteria
This trial is for smokers aged 21-65 who have smoked at least six cigarettes daily over the past year and can speak English fluently. They must pass an MRI safety screening and show a carbon monoxide level above 10 ppm to confirm smoking status. Those unwilling to abstain from nicotine for 12 hours before lab visits or with risks related to MRIs, like pacemakers or metallic objects in their body, cannot join.Inclusion Criteria
I am between 21 and 65 years old.
Participants must be fluent English speakers
Participants must pass an MRI safety screening
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Exclusion Criteria
Individuals will be excluded if they report that they are not willing to refrain from using nicotine for 12 hours before the experimental lab visit
Individuals will be excluded if they have any known risk from exposure to high-field strength magnetic fields (e.g., pacemakers), any irremovable metallic foreign objects in their body (e.g., braces), or a questionable history of metallic fragments that are likely to create artifact on the MRI scans
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Pre-Scan Abstinence
Participants abstain from cigarettes for 12 hours before completing the fMRI lapse paradigm
12 hours
fMRI Lapse Paradigm
Participants undergo an fMRI scan to measure brain activity during a lapse task, including an in-scanner delay period and a post-scan ad-lib period
1 day
1 visit (in-person)
Follow-up
Participants are monitored for safety and effectiveness after the fMRI task
4 weeks
Treatment Details
Interventions
- Laboratory task modeling smoking lapse behavior (Behavioral Intervention)
Trial OverviewThe study uses fMRI scans while participants perform tasks that simulate situations leading to a smoking lapse. It aims to identify brain activity patterns linked to the urge of smoking and understand mental processes preceding a lapse, which could help prevent relapses.
Participant Groups
1Treatment groups
Experimental Treatment
Group I: fMRI smoking lapse taskExperimental Treatment1 Intervention
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
The Pennsylvania State UniversityUniversity Park, PA
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Who Is Running the Clinical Trial?
Penn State UniversityLead Sponsor
References
Increased network centrality as markers of relapse risk in nicotine-dependent individuals treated with varenicline. [2018]Identifying smokers at high risk of relapse could improve the effectiveness of cessation therapies. Although altered regional brain function in smokers has been reported, whether the whole-brain functional organization differs smokers with relapse vulnerability from others remains unclear. Thus, the goal of this study is to investigate the baseline functional connectivity differences between relapsers and quitters. Using resting-state fMRI, we acquired images from 57 smokers prior to quitting attempts. After 12-week treatment with varenicline, smokers were divided into relapsers (n=36) and quitters (n=21) (quitter: continuously abstinent for weeks 9-12). The smoking cessation outcomes were cross-validated by self-reports and expired carbon monoxide. We then used eigenvector centrality (EC) mapping to identify the functional connectivity differences between relapsers and quitters. When compared to quitters, increased EC in the right dorsolateral prefrontal cortex (DLPFC), left middle temporal gyrus (MTG) and cerebellum anterior lobe was observed in relapsers. In addition, a logistic regression analysis of EC data (with DLPFC, MTG and cerebellum included) predicted relapse with 80.7% accuracy. These findings suggest that the DLPFC, MTG and cerebellum may be important substrates of smoking relapse vulnerability. The data also suggest that relapse-vulnerable smokers can be identified before quit attempts, which could enable personalized treatment and improve smoking cessation outcomes.
The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt. [2018]Quitting smoking is the single best change in behavior that smokers can make to improve their health and extend their lives. Although most smokers express a strong desire to stop using cigarettes, the vast majority of quit attempts end in relapse. Relapse is particularly likely when smokers encounter cigarette cues. A striking number of relapses occur very quickly, with many occurring within as little as 24h. Characterizing what distinguishes successful quit attempts from unsuccessful ones, particularly just after cessation is initiated, is a research priority. We addressed this significant issue by examining the association between functional connectivity during cigarette cue exposure and smoking behavior during the first 24h of a quit attempt. Functional MRI was used to measure brain activity during cue exposure in nicotine-deprived daily smokers during the first day of a quit attempt. Participants were then given the opportunity to smoke. Using data collected in two parent studies, we identified a subset of participants who chose to smoke and a matched subset who declined (n=38). Smokers who were able to resist smoking displayed significant functional connectivity between the left anterior insula and the dorsolateral prefrontal cortex, whereas there was no such connectivity for those who chose to smoke. Notably, there were no differences in mean levels of activation in brain regions of interest, underscoring the importance of assessing interregional connectivity when investigating the links between cue-related neural responses and overt behavior. To our knowledge, this is the first study to link patterns of functional connectivity and actual cigarette use during the pivotal first hours of attempt to change smoking behavior.
Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse. [2021]The thalamus, with the highest density of nicotinic acetylcholine receptor (nAChR) in the brain, plays a central role in thalamo-cortical circuits that are implicated in nicotine addiction. However, little is known about whether the thalamo-cortical circuits are potentially predictive of smoking relapse. In the current study, a total of 125 participants (84 treatment-seeking male smokers and 41 age-matched male nonsmokers) were recruited. Structural and functional magnetic resonance images (MRI) were acquired from all participants. After a 12-week smoking cessation treatment with varenicline, the smokers were then divided into relapsers (n = 54) and nonrelapsers (n = 30). Then, we compared thalamic volume and seed-based thalamo-cortical resting state functional connectivity (rsFC) prior to the cessation treatment among relapsers, nonrelapsers and nonsmokers to investigate the associations between thalamic structure/function and smoking relapse. Increased thalamic volume was detected in smokers relative to nonsmokers, and in relapsers relative to nonrelapsers, especially on the left side. Moreover, decreased left thalamo-precuneus rsFC was detected in relapsers relative to nonrelapsers. Additionally, a logistic regression analysis showed that the thalamic volume and thalamo-precuneus rsFC predicted smoking relapse with an accuracy of 75.7%. These novel findings indicate that increased thalamic volume and decreased thalamo-precuneus rsFC are associated with smoking relapse, and these thalamic measures may be used to predict treatment efficacy of nicotine addiction and serve as a potential biomarker for personalized medicine.
Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder. [2019]Altered resting state functional connectivity (rsFC) and functional network connectivity (FNC), which is a measure of coherence between brain networks, may be associated with nicotine use disorder (NUD). We hypothesized that higher connectivity between insula and 1) dorsal anterior cingulate cortex (dACC) and 2) dorsolateral prefrontal cortex (dlPFC) would predict better treatment outcomes. We also performed an exploratory analysis of the associations between FNC values between additional key frontal and striatal regions and treatment outcomes. One hundred and forty four individuals with NUD underwent a resting state session during functional MRI prior to randomization to treatment with varenicline (n=82) or placebo. Group independent component analysis (ICA) was utilized to extract individual subject components and time series from intrinsic connectivity networks in aforementioned regions, and FNC between all possible pairs were calculated. Higher FNC between insula and dACC (rho=0.21) was significantly correlated with lower levels of baseline smoking quantity but did not predict treatment outcome upon controlling for baseline smoking. Higher FNC between putamen and dACC, caudate and dACC, and caudate and dlPFC significantly predicted worse treatment outcome in participants reporting high subjective withdrawal before the scan. FNC between key regions hold promise as biomarkers to predict outcome in NUD.
The feasibility of an in-scanner smoking lapse paradigm to examine the neural correlates of lapses. [2023]Quitting smoking is notoriously difficult. Models of nicotine dependence posit that strength of cognitive control contributes to maintaining smoking abstinence during smoking cessation attempts. We examine the role for large-scale functional brain systems associated with cognitive control in smoking lapse using a novel adaption of a well-validated behavioral paradigm. We use data from 17 daily smokers (five females) after 12 h of smoking abstinence. Participants completed up to 10 sequential 5-min functional magnetic resonance imaging (fMRI) runs, within a single scanning session. After each run, participants decided whether to stay in the scanner in order to earn additional money or to terminate the session in order to smoke a cigarette (i.e., lapse) and forego additional monetary reward. Cox regression results indicate that decreased segregation of the default mode system from the frontoparietal system undermines the ability to resist smoking. This study demonstrates the feasibility of modifying an established behavioral model of smoking lapse behavior for use in the neuro imaging environment, and it provides initial evidence that this approach yields valuable information regarding fine-grained, time-varying changes in patterns of neural activity in the moments leading up to a decision to smoke. Specifically, results lend support to the hypothesis that the time-varying interplay between large-scale functional brain systems associated with cognitive control is implicated in smoking lapse behavior.
Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence. [2022]Developing the means to identify smokers at high risk for relapse could advance relapse prevention therapy. We hypothesized that functional magnetic resonance imaging (fMRI) reactivity to smoking-related cues, measured before a quit attempt, could identify smokers with heightened relapse vulnerability.
Altered spontaneous activity of posterior cingulate cortex and superior temporal gyrus are associated with a smoking cessation treatment outcome using varenicline revealed by regional homogeneity. [2018]Compared to nonsmokers, smokers exhibit a number of potentially important differences in regional brain function. However, little is known about the associations between the local spontaneous brain activity and smoking cessation treatment outcomes. In the present analysis, we aimed to evaluate whether the local features of spontaneous brain activity prior to the target quit date was associated with the smoking cessation outcomes. All the participants underwent magnetic resonance imaging scans and smoking-related behavioral assessments. After a 12-week treatment with varenicline, 23 smokers succeeded in quitting smoking and 32 failed. Smokers underwent functional magnetic resonance imaging (fMRI) scanning prior to an open label smoking cessation treatment trial. Regional homogeneity (ReHo) was used to measure spontaneous brain activity, and whole-brain voxel-wise comparisons of ReHo were performed to detect brain regions with altered spontaneous brain activity between relapser and quitter groups. After controlling for potentially confounding factors including years of education, years smoked, cigarettes smoked per day and FTND score as covariates, compared to quitters, relapsers displayed significantly decreased ReHo in bilateral posterior cingulate cortex (PCC), as well as increased ReHo in left superior temporal gyrus (STG). These preliminary results suggest that regional brain function variables may be promising predictors of smoking relapse. This study provided novel insights into the neurobiological mechanisms underlying smoking relapse. A deeper understanding of the neurobiological mechanisms associated with relapse may result in novel pharmacological and behavioral interventions.
Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity. [2020]Although 60% of the US population have tried smoking cigarettes, only 16% smoke regularly. Identifying this susceptible subset of the population before the onset of nicotine dependence may encourage targeted early interventions to prevent regular smoking and/or minimize severity. While prospective neuroimaging in human populations can be challenging, preclinical neuroimaging models before chronic nicotine administration can help to develop translational biomarkers of disease risk. Chronic, intermittent nicotine (0, 1.2, or 4.8 mg/kg/d; N = 10-11/group) was administered to male Sprague Dawley rats for 14 d; dependence severity was quantified using precipitated withdrawal behaviors collected before, during, and following forced nicotine abstinence. Resting-state fMRI functional connectivity (FC) before drug administration was subjected to a graph theory analytical framework to form a predictive model of subsequent individual differences in nicotine dependence. Whole-brain modularity analysis identified five modules in the rat brain. A metric of intermodule connectivity, participation coefficient, of an identified insular-frontal cortical module predicted subsequent dependence severity, independent of nicotine dose. To better spatially isolate this effect, this module was subjected to a secondary exploratory modularity analysis, which segregated it into three submodules (frontal-motor, insular, and sensory). Higher FC among these three submodules and three of the five originally identified modules (striatal, frontal-executive, and sensory association) also predicted dependence severity. These data suggest that predispositional, intrinsic differences in circuit strength between insular-frontal-based brain networks before drug exposure may identify those at highest risk for the development of nicotine dependence.SIGNIFICANCE STATEMENT Developing biomarkers of individuals at high risk for addiction before the onset of this brain-based disease is essential for prevention, early intervention, and/or subsequent treatment decisions. Using a rodent model of nicotine dependence and a novel data-driven, network-based analysis of resting-state fMRI data collected before drug exposure, functional connections centered on an intrinsic insular-frontal module predicted the severity of nicotine dependence after drug exposure. The predictive capacity of baseline network measures was specific to inter-regional but not within-region connectivity. While insular and frontal regions have consistently been implicated in nicotine dependence, this is the first study to reveal that innate, individual differences in their circuit strength have the predictive capacity to identify those at greatest risk for and resilience to drug dependence.