Control Systems Approach to Predicting Individualized Dynamics of Nicotine Cravings

Cigarette Smoking Clinical Trial

Official title:

Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT02643914
• Other study ID #: 1R21DA038467-01
• Status: Active, not recruiting
• Phase: N/A
• First received: November 24, 2015
• Last updated: June 29, 2017
• Start date: September 2015
• Est. completion date: December 2017

Study information

• Verified date: June 2017
• Source: Stony Brook University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 23
• Est. completion date: December 2017
• Est. primary completion date: June 2017
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 21 Years to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Cigarette Smoking
• Nicotine Addiction

Intervention

Drug:
• Nicotine

Device:
• MR Compatible Nicotine Delivery Device

Locations

Country	Name	City	State
United States	Bioengineering Building , Stony Brook University	Stony Brook	New York

Sponsors (1)

Lead Sponsor	Collaborator
Stony Brook University

Country where clinical trial is conducted

United States, 

References & Publications (6)

Fagerström KO. Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addict Behav. 1978;3(3-4):235-41. — View Citation

Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict. 1991 Sep;86(9):1119-27. — View Citation

Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010 Jan;35(1):217-38. doi: 10.1038/npp.2009.110. Review. Erratum in: Neuropsychopharmacology. 2010 Mar;35(4):1051. — View Citation

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. — View Citation

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. — View Citation

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. Review. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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