Clinical trial NCT02643914: Control Systems Approach to Predicting Individualized Dynamics of Nicotine Cravings
Official title: Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Nicotine Addiction
Cigarette Smoking
Lead sponsor: Stony Brook University
Sponsor class: OTHER
First posted: 2015-12-31
Enrollment: 23
Sex: ALL
Minimum age (years): 21
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Autonomic nervous system activity will be measured by analysis of heart rate variability and electric dermal activity alongside a 0-10 craving scale.
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Stony Brook
country: United States
facility: Bioengineering Building , Stony Brook University
state: New York
Study officials:
affiliation: Stony Brook University
name: Lilianne Mujica-Parodi, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010 Jan;35(1):217-38. doi: 10.1038/npp.2009.110.
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.
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. doi: 10.1016/j.neuropharm.2004.07.019.
citation: 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.
citation: 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.
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.