Control Systems Approach to Predicting Individualized Dynamics of Nicotine Cravings

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

23 participants

Study Classification

INTERVENTIONAL

Study Start Date

2015-09-30

Study Completion Date

2017-12-31

Brief Summary

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Nicotine is the most common drug of abuse in the United States, and has addiction strength comparable to cocaine, heroin, and alcohol. It is the primary addictive component of tobacco, and its use markedly increases risk for cancer, heart disease, asthma, miscarriage, and infant mortality. Addiction is thought to be caused primarily by the intersection of two components: 1) the impact of drug pharmacokinetics on the dynamics of dopamine response, and 2) dysregulation of the brain's reward circuit. While the term 'dysregulated' tends to be used qualitatively within the neuroscience literature, regulation has a precise and testable meaning in control systems engineering, which has yet to be addressed in a quantitative manner by current neuroimaging methods or models of addiction. Current approaches to neuroimaging have primarily focused on identifying nodes and causal connections within the meso-circuit of interest, but have yet to take the next step in treating these nodes and connection as a self-interacting dynamical system evolving over time. Such an approach is critical for improving our understanding, and therefore prediction, of trajectories for addiction as well as recovery.

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Detailed Description

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Nicotine is the most common drug of abuse in the United States, and has addiction strength comparable to cocaine, heroin, and alcohol. It is the primary addictive component of tobacco, and its use markedly increases risk for cancer, heart disease, asthma, miscarriage, and infant mortality. Addiction is thought to be caused primarily by the intersection of two components: 1) the impact of drug pharmacokinetics on the dynamics of dopamine response, and 2) dysregulation of the brain's reward circuit. While the term 'dysregulated' tends to be used qualitatively within the neuroscience literature, regulation has a precise and testable meaning in control systems engineering, which has yet to be addressed in a quantitative manner by current neuroimaging methods or models of addiction. Current approaches to neuroimaging have primarily focused on identifying nodes and causal connections within the meso-circuit of interest, but have yet to take the next step in treating these nodes and connection as a self-interacting dynamical system evolving over time. Such an approach is critical for improving the understanding, and therefore prediction, of trajectories for addiction as well as recovery. These trajectories are likely to be nonlinear (e.g., involving thresholds, saturation, and self-reinforcement), as well as highly specific to each individual. This study is designed to provide the first step towards addressing this gap: integrating ultra-high-field (7T) and ultra-fast (\<1s) fMRI with computational modeling, to provide a bridge between the dynamics of meso-circuit regulation and the dynamics of human addictive behavior. The investigators propose to test the hypothesis that control systems regulation, measured by dynamic analyses of fMRI data, can predict-on an individual basis-exactly when an addicted smoker will want to take his next puff. This will be achieved by first validating a MR-compatible nicotine delivery system, by comparing its neurobiological and autonomic effects against those of a cigarette and e-cigarette. Once this is achieved, the investigators will then acquire fMRI data from addicted smokers while they 'smoke.' Using individual subjects' neuroimaging data, the investigators will derive coupled differential equations for a control system that predicts craving and behavioral response for that individual. Using independent data sets to estimate the parameters and to test them, the investigators will assess the model's accuracy in predicting each individual subject's cravings, as measured behaviorally by the frequency at which each smoker self-administers nicotine. If successful, this approach could then be exploited to develop individualized prevention and treatment of addiction by identifying individual-specific amplitude, duration, and frequency of dosing in nicotine replacement therapy that is least likely to trigger cravings. More generally, the methods proposed have the potential to rigorously examine system-wide dysregulation in addiction for the first time, opening the door to exploration of other dysregulatory brain-based diseases in humans.

Conditions

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Nicotine Addiction Cigarette Smoking

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Nicotine Cravings

Group Type EXPERIMENTAL

Nicotine

Intervention Type DRUG

MR Compatible Nicotine Delivery Device

Intervention Type DEVICE

Interventions

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Nicotine

Intervention Type DRUG

MR Compatible Nicotine Delivery Device

Intervention Type DEVICE

Other Intervention Names

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Nicotrol NS

Eligibility Criteria

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Inclusion Criteria

21-65years of age

Moderate to severe addiction to smoking/nicotine

Willingness to withdraw from nicotine for 12 hours prior to testing

Eyesight correctable to 20/20 with contact lenses.

Exclusion Criteria

Electrical implants such as cardiac pacemakers or perfusion pumps

Ferromagnetic implants such as aneurysm clips, surgical clips, prostheses, artificial hearts, valves with steel parts, metal fragments, shrapnel, facial tattoos, or steel implants

Claustrophobia

Pregnancy or breastfeeding (for females, pregnancy status will be confirmed with urine test)

Chronic nasal congestion, sinusitis, or common cold Use of nicotine cessation therapy (patch, gum, inhaler, nasal spray)

History of asthma, cardiovascular or peripheral vascular disease (anginas, arrhythmias, myocardial infarction, Raynaud's disease, insulin dependent diabetes)

History of neurological disease (brain tumor, stroke, traumatic brain injury, epilepsy)

Current use of psychotropic medication

Minimum Eligible Age

21 Years

Maximum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes