Joshua W. Brown - US grants

[unreadable] DESCRIPTION (provided by applicant): Substance abuse is a widespread problem, yet some individuals are strongly drug avoidant. What brain mechanisms drive aversion to risky behavior such as illicit drug taking? The long-term goal of this research is to clarify the brain mechanisms that detect the risk associated with certain behaviors, such as illicit drug use, and how these brain mechanisms are activated to avoid risky behavior. The anterior cingulate cortex (ACC) is a critical area that predicts and avoids risk. It becomes active in proportion to both the likelihood that an individual will make a mistake and the severity of the potential consequences. Of note, individuals who are more likely to abuse drugs show reduced or absent ACC effects. The specific aims of this project are twofold. First, we will explore whether semantic framing in terms of gains or losses can increase the activity and risk prediction effects of ACC. If so, then it may be possible to trace how messages discouraging drug abuse modulate brain activity and predict the effectiveness of specific messages. To investigate this question, we will use a modified change signal task to manipulate the frequency of errors and the semantic framing of errors across conditions, and we will examine whole brain activity with functional magnetic resonance imaging. We will also examine the relationship between ACC activity and individual differences in stable dispositional traits using standard personality inventories. Second, using fMRI and a similar task and individual difference measures, we will investigate whether increasing the reward value of a response can improve error likelihood prediction by ACC when no explicit semantic framing is given. If so, then the results may suggest a neural basis for how environmental manipulations may support drug abstinence, and how the effectiveness of such manipulations can be predicted. "Just say no to drugs" sounds catchy, but how well do messages such as this actually increase brain activity associated with drug avoidance? This project will study how the brain avoids risky behavior such as drug abuse. Once we know how certain parts of the brain avoid risky behavior, then we will figure out how to help these parts of the brain to keep people from taking drugs. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Substance abuse is a widespread problem that is associated with a predisposition to increased risk-taking in general. Longitudinal and cross-sectional studies indicate that those who are more risk averse, or high harm avoidant, are less likely to use and abuse drugs (Masse &Tremblay 1997;Finn 2002). Such individuals appear to be strongly drug avoidant. What brain mechanisms drive aversion to risky behavior such as illicit drug taking? The long-term goal of this research is to clarify the brain and cognitive mechanisms that detect the risk associated with certain behaviors, including illicit drug use, and how these brain mechanisms are activated to avoid risky behavior. The proposed research goes beyond descriptive theories to leverage our separately developed new and integrative computational neural models. The medial prefrontal cortex (mPFC) and connected regions such as anterior insula are critically involved in the prediction and avoidance of risky outcomes and behaviors. Our computational model studies predict that a region of mPFC, the anterior cingulate cortex (ACC), becomes active in proportion to both the likelihood that an individual will make a mistake and the severity of the potential consequences, a prediction borne out by our subsequent fMRI studies. Of note, individuals who abuse drugs show reduced or absent ACC effects. The specific aims of this project are twofold and will use methods of stop-signal, gambling and task-switching behavioral tasks, individual trait difference measures, and fMRI in healthy individuals. First, we will use predictions of a new computational neural model to test the hypothesis that mPFC forms temporally-structured expectations of the outcome of a subject's planned actions, including both desirable and undesirable outcomes. Second, we will build on our existing computational neural model predictions to determine how implicit environmental manipulations of available reward as well as explicit positively and negatively-framed persuasive messages may interact with ACC sensitivity to risk. This will clarify how a network including ACC and anterior insula contributes to subjects'ability to avoid risky behavior. In turn, the results will lay a theoretical foundation that will help unify and reinterpret diverse effects found in mPFC, including effects of error and response conflict. PUBLIC HEALTH RELEVANCE: This project will use fMRI to study the cognitive and brain mechanisms of risk avoidance. Impairments in these mechanisms are associated with substance use and abuse. Knowledge of such mechanisms can inform the prevention and treatment of substance abuse problems.

DESCRIPTION (provided by applicant): Our long-range goal is to contribute to a better understanding of the basic mechanisms by which emotional/motivational and cognitive brain systems interact in the generation of complex behavior. The objective of the present application is to understand the mechanisms of interaction between these systems during tasks that recruit "top-down control", including inhibitory control and conflict processing. The project's central hypothesis is that affective significance confers a competitive advantage toward the processing of emotion-laden information (relative to neutral items) and that emotional stimuli are prioritized as a function of the stimuli's affective history and affective context. We will test our hypotheses by pursuing three specific aims, which will employ a combination of behavioral and functional magnetic resonance imaging (fMRI) studies: Aim #1: Determine how inhibition interacts with emotion;Aim #2: Determine how conflict processing interacts with emotion;Aim #3: Determine how executive function interacts with motivation. Affective significance will be manipulated in a number of ways, including aversive conditioning and reward/punishment (via monetary incentives). Throughout these aims, we will attempt to identify the brain networks underlying interactions between emotion and cognition. We anticipate that an item's affective history and context will bias processing in favor of emotion-laden information, and that such bias will be manifested in multiple ways across behavior, and, correspondingly, across multiple levels of the brain. Importantly, by providing a better understanding of cognitive-emotional interactions during normal behavior, our research can help understand the mechanisms that potentially go awry in many debilitating mental illnesses. PUBLIC HEALTH RELEVANCE: The proposed research will advance the knowledge database concerning the basic mechanisms of how cognition and emotion/motivation interact in the domains of inhibitory control and conflict processing in humans. Such knowledge is of importance because many neurological disorders and mental illnesses are characterized by profound deficits in cognitive and emotional interactions, including epilepsy, Alzheimer's disease, autism, major depression, anxiety disorders, and schizophrenia.

Project Summary/Abstract 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.