Control Systems Approach to Predicting 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 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.

Inclusion Criteria:

21-65years of age

Moderate to severe addiction to smoking/nicotine (smoke at least 5 cigarettes a day,
confirmed by a score 6 on the Fagerstrom Test for Nicotine Dependence)

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