Control Systems Approach to Predicting Individualized Dynamics of Nicotine Cravings

Study Overview
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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.

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.

Official Title
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Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

Conditions
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Nicotine Addiction, Cigarette Smoking

Intervention / Treatment
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* Drug: Nicotine
* Device: MR Compatible Nicotine Delivery Device


Participation Criteria
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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

Ages Eligible for Study
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Minimum Age: 21 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Nicotine Cravings<br> | Drug: Nicotine<br> <br> * Other names: Nicotrol NS;Device: MR Compatible Nicotine Delivery Device<br> <br> |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Autonomic nervous system activity will be measured by analysis of heart rate variability and electric dermal activity alongside a 0-10 craving scale. |  | through study completion, an average of 1 year |