Brain Network Dynamics Study for Smoking Relapse Prevention

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Penn State University

Disqualifiers: Pacemakers, Metallic objects, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

150 Participants Needed

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. ^{1 2 3 4 5}

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. ^{1 2 5 6 7}

Is the treatment 'Laboratory task modeling smoking lapse behavior' promising for preventing smoking relapse?

Yes, this treatment is promising because it helps understand how brain activity changes when someone is about to smoke again. This knowledge can be used to improve ways to help people quit smoking by focusing on brain systems that control behavior. ^{1 2 3 5 8}

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

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

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

Trial Overview The 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

Group I: fMRI smoking lapse task Experimental Treatment 1 Intervention

Who Is Running the Clinical Trial?

Penn State University

Lead Sponsor

Trials

380

Recruited

131,000+

Findings from Research

A study involving 57 smokers used resting-state fMRI to identify brain connectivity differences between those who relapsed after quitting and those who successfully abstained, revealing that relapsers had increased functional connectivity in key brain areas like the dorsolateral prefrontal cortex (DLPFC).

The findings suggest that by analyzing brain connectivity patterns, it is possible to predict relapse vulnerability with 80.7% accuracy, which could lead to more personalized and effective smoking cessation treatments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Increased network centrality as markers of relapse risk in nicotine-dependent individuals treated with varenicline. Shen, Z., Huang, P., Wang, C., et al.[2018]

A study using functional MRI on 38 nicotine-deprived smokers found that those who successfully resisted smoking after exposure to cigarette cues showed significant functional connectivity between the left anterior insula and the dorsolateral prefrontal cortex.

This research highlights the importance of brain connectivity patterns over mere activation levels in understanding why some smokers can resist the urge to smoke during the critical first 24 hours of quitting.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Zelle, SL., Gates, KM., Fiez, JA., et al.[2018]

In a study of 125 participants, smokers showed increased thalamic volume compared to nonsmokers, with relapsers having even greater volume than nonrelapsers, suggesting structural changes in the thalamus may be linked to smoking relapse.

Decreased resting state functional connectivity between the thalamus and precuneus was found in relapsers, and both thalamic volume and connectivity measures could predict smoking relapse with 75.7% accuracy, indicating their potential as biomarkers for personalized treatment in nicotine addiction.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse. Wang, C., Wang, S., Shen, Z., et al.[2021]

In a study of 144 individuals with nicotine use disorder (NUD), higher functional network connectivity (FNC) between the insula and dorsal anterior cingulate cortex (dACC) was associated with lower baseline smoking levels, suggesting a potential biomarker for nicotine dependence.

However, higher FNC between certain brain regions (putamen and dACC, caudate and dACC, caudate and dorsolateral prefrontal cortex) predicted worse treatment outcomes, particularly in participants experiencing high withdrawal symptoms, indicating that brain connectivity may influence treatment efficacy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder. Wilcox, CE., Calhoun, VD., Rachakonda, S., et al.[2019]

A study involving 17 daily smokers used fMRI to explore how brain systems related to cognitive control affect the ability to resist smoking after 12 hours of abstinence.

Findings suggest that when the default mode system and frontoparietal system are less segregated, it may weaken a person's ability to resist the urge to smoke, highlighting the importance of brain activity patterns in smoking lapse behavior.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The feasibility of an in-scanner smoking lapse paradigm to examine the neural correlates of lapses. Lydon-Staley, DM., MacLean, RR., Falk, EB., et al.[2023]

In a study of 21 nicotine-dependent women, those who were more likely to relapse after a quit attempt showed increased brain reactivity to smoking-related images, particularly in areas associated with emotion and motor planning, indicating a heightened vulnerability to relapse.

The research found that fMRI and attentional bias measurements could predict relapse outcomes with 79% accuracy, suggesting that these methods could help identify smokers at risk before they attempt to quit, allowing for more personalized and effective treatment strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence. Janes, AC., Pizzagalli, DA., Richardt, S., et al.[2022]

In a study of smokers undergoing treatment with varenicline, differences in spontaneous brain activity were observed between those who successfully quit smoking and those who relapsed, suggesting that brain function may influence cessation outcomes.

Specifically, relapsers showed decreased spontaneous brain activity in the posterior cingulate cortex and increased activity in the superior temporal gyrus, indicating that these brain regions could be potential predictors of smoking relapse.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Wang, C., Shen, Z., Huang, P., et al.[2018]

A study using male Sprague Dawley rats identified that differences in brain connectivity, particularly in an insular-frontal module, can predict the severity of nicotine dependence after exposure to nicotine, suggesting that these brain network characteristics may serve as biomarkers for addiction risk.

The research highlights the importance of understanding individual differences in brain circuitry before drug exposure, which could lead to targeted interventions to prevent nicotine dependence in susceptible individuals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity. Hsu, LM., Keeley, RJ., Liang, X., et al.[2020]

References

1. England pubmed.ncbi.nlm.nih.gov

Increased network centrality as markers of relapse risk in nicotine-dependent individuals treated with varenicline. [2018]

2. United States pubmed.ncbi.nlm.nih.gov

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]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse. [2021]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder. [2019]

5. United States pubmed.ncbi.nlm.nih.gov

The feasibility of an in-scanner smoking lapse paradigm to examine the neural correlates of lapses. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence. [2022]

7. United States pubmed.ncbi.nlm.nih.gov

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]

8. United States pubmed.ncbi.nlm.nih.gov

Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity. [2020]