Control Systems Approach to Predicting Individualized Dynamics of Nicotine Cravings

Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

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.

Study Overview

• Status: Unknown
• Conditions: Cigarette Smoking; Nicotine Addiction
• Intervention / Treatment: Drug: Nicotine; Device: MR Compatible Nicotine Delivery Device

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Actual): 23
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • Stony Brook, New York, United States, 11794
      • Bioengineering Building , Stony Brook University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 21 years to 65 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Nicotine Cravings	Drug: Nicotine; Other Names: Nicotrol NS; Device: MR Compatible Nicotine Delivery Device

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: Stony Brook University
• Investigators: Principal Investigator: Lilianne Mujica-Parodi, PhD, Stony Brook University

Publications and helpful links

General Publications

• Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991 Sep;86(9):1119-27. doi: 10.1111/j.1360-0443.1991.tb01879.x.
• Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010 Jan;35(1):217-38. doi: 10.1038/npp.2009.110. Erratum In: Neuropsychopharmacology. 2010 Mar;35(4):1051.
• Fagerstrom KO. Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addict Behav. 1978;3(3-4):235-41. doi: 10.1016/0306-4603(78)90024-2. No abstract available.
• Koob GF. Addiction is a Reward Deficit and Stress Surfeit Disorder. Front Psychiatry. 2013 Aug 1;4:72. doi: 10.3389/fpsyt.2013.00072. eCollection 2013.
• Volkow ND, Fowler JS, Wang GJ. The addicted human brain viewed in the light of imaging studies: brain circuits and treatment strategies. Neuropharmacology. 2004;47 Suppl 1:3-13. doi: 10.1016/j.neuropharm.2004.07.019.
• Mujica-Parodi LR, Strey HH, Frederick B, Savoy R, Cox D, Botanov Y, Tolkunov D, Rubin D, Weber J. Chemosensory cues to conspecific emotional stress activate amygdala in humans. PLoS One. 2009 Jul 29;4(7):e6415. doi: 10.1371/journal.pone.0006415.

Study record dates

Study Major Dates

• Study Start: September 1, 2015
• Primary Completion (Actual): June 1, 2017
• Study Completion (Anticipated): December 1, 2017

Study Registration Dates

• First Submitted: November 24, 2015
• First Submitted That Met QC Criteria: December 28, 2015
• First Posted (Estimate): December 31, 2015

Study Record Updates

• Last Update Posted (Actual): July 2, 2017
• Last Update Submitted That Met QC Criteria: June 29, 2017
• Last Verified: June 1, 2017

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Mental Disorders; Chemically-Induced Disorders; Substance-Related Disorders; Tobacco Use Disorder; Physiological Effects of Drugs; Neurotransmitter Agents; Molecular Mechanisms of Pharmacological Action; Autonomic Agents; Peripheral Nervous System Agents; Cholinergic Agents; Ganglionic Stimulants; Nicotinic Agonists; Cholinergic Agonists; Nicotine
• Other Study ID Numbers: 1R21DA038467-01 (U.S. NIH Grant/Contract)