Substance abuse is a major public health issue, and millions continue to abuse substances despite their own attempts to stop. Current addiction treatments are inadequate. The ability to disrupt addiction directly at the neural level would open a whole new avenue for treatment. The problem is that we don?t know where exactly to intervene in the brain. Cognitive neuroscience studies of addiction typically compare addicted subjects and healthy controls, and the tasks usually involve pure cognitive functions or decision-making about money rather than drugs. This is problematic because addiction may change brain circuits to respond specifically to drug cues rather than other rewards, such as money. To the extent that is the case, monetary reward tasks will fail to tap the neural circuits of drug addiction. Understanding how the involved brain regions represent the specific risks and rewards of consuming drugs in addicted persons is also critical, but not well understood. One way to address these gaps is to observe addicted subjects in the MRI making real-time decisions that determine whether they will or will not receive the drug to which they are addicted. To this end, we have built and validated a modified electronic cigarette that allows us to deliver controlled and measurable doses of nicotine vapor to human subjects in the MR scanner. We have also developed a gambling task that dissociates the factors of expected drug reward amount, variance (risk) of the expected drug reward, and the probability of failing to get any drug reward on a given trial. Using the ?Gambling for Drugs? task among 40 heavy smokers, we propose to identify specific brain regions and networks involved in decisions about drug use (Aim 1), specifically: drug reward, variance and probability of failure. Using the same task with the same subjects but substituting monetary for drug reward, we will then compare these brain regions and networks (Aim 2), providing important information on the degree to which studies examining monetary reward accurately target brain regions involved in decision-making about drugs. Results will more accurately pinpoint the brain regions involved in drug addiction and decision-making, leading to the potential for more effective neural interventions, such as targeted transcranial magnetic or direct current stimulation, or invasive deep brain stimulation to treat addiction. The methods used also have the potential to be applied to studies of other inhaled drugs, such as THC.
Because no studies have measured brain activity while drug-addicted subjects make real-time, risky decisions to take drugs, it is difficult to know what is happening in the brain when an addict approaches and anticipates using a drug. Using an e-cigarette that has been modified for use in an MRI scanner and a ?gambling for drugs? task among heavy smokers, this project will 1) identify which areas of the brain are involved in making decisions that lead to real-time drug reward (nicotine), and 2) examine whether those brain areas are different when addicted subjects instead make decisions about monetary rewards.