Michael J. Wesley - US grants

ABSTRACT Cocaine use disorder (CUD) is characterized by the decision to use cocaine at the expense of other activities. Lab-based efforts to address this problem have therefore included cocaine choice self-administration procedures that incorporate a non-drug alternative to model this defining feature. Studies using these procedures have typically scheduled competing reinforcers so that the probabilities are certain. However, such deterministic outcomes are not representative of real-world scenarios in which the consequences from drug-related decisions are often unpredictable. Importantly, decision-making in a dynamic, uncertain context significantly alters the value of choice options and requires continuous updating of option values, which engages learning processes and related corticostriatal networks that might be functioning abnormally in CUD. Decision-making in dynamic environments has been successfully modeled using probabilistic reinforcement-learning choice (PRLC) tasks. The integration of these tasks with reinforcement-learning (RL) modeling has been used to capture moment-to- moment changes in the mechanisms of dynamic choice, and the application of neuroscience techniques has begun to identify the underlying neurobiology. This approach has uncovered biologically-based decision-making abnormalities in multiple brain disorders, but has yet to be systematically applied to the experimental study of CUD, The translation of combined RL and neuroscience approaches to CUD is logical considering the maladaptive choice behavior that typifies the disorder, the varying reinforcement probabilities in cocaine users? natural environments, and the learning impairments and neuroadaptations that have been documented in individuals with CUD. Thus, there are critical gaps in our understanding of the mechanisms underlying dynamic cocaine use decisions, and a strong scientific premise for applying an RL framework to fill these gaps. This project proposes rigorous PRLC tasks, RL modeling, neuromodulation/fMRI neuroimaging techniques and complementary, translational study designs in rats and humans to study dynamic choice in CUD. The first set of cross-species experiments will demonstrate the impact of problematic cocaine use on dynamic decision-making and reveal the neurobehavioral and neurobiological processes underlying this abnormal task performance. The second set of experiments will use a PRLC task in which intravenous cocaine is available as an alternative to the non-drug reinforcer to determine the behavioral and neural ?profile? associated with the decision to use cocaine and reduced cocaine choice during treatment. Amphetamine maintenance and non-drug alternative reinforcer treatments reduce cocaine choice, which will be leveraged here to uncover behavioral and neural mechanisms that can be targeted for future treatment development. This project will have a significant impact on the field by establishing the experimental application of reinforcement-learning theory to the study of maladaptive dynamic decision-making in CUD.

PROJECT ABSTRACT This mentored career development award (K01) will enable Dr. Michael Wesley to achieve his long-term goal of becoming an independent investigator with a clinical neuroscience research program examining cannabis use disorder (CUD) in emerging adults, which is a current NIDA funding priority. Dr. Wesley is a new Assistant Professor in the University of Kentucky (UK) College of Medicine. The activities proposed in this award build on Dr. Wesley?s background expertise in neuroimaging and drug abuse research and will allow him to accomplish the following short-term objectives: Become an expert in (1) clinical pharmacology and (2) non-invasive brain stimulation research in an emerging adult population with CUD, and enhance/develop his (3) knowledge of the responsible conduct of research, (4) skills for scientific communication and grant writing, and (5) ability to manage an independent research program. UK has numerous faculty and projects focused on drug abuse research and is an ideal environment for Dr. Wesley to complete this award. Dr. Wesley has assembled a stellar mentoring team consisting of Dr. Josh Lile (Mentor), who has a successful NIH-funded clinical pharmacology research program at UK and Dr. Mark George (Co-Mentor), who pioneered the use of non-invasive brain stimulation for treating depression. In addition, Dr. Lumy Sawaki (Internal Preceptor) and Dr. Lon Hays (Study Physician) will provide local safety oversight and medical supervision, and Dr. Terry Lohrenz (External Preceptor) will provide guidance with advanced statistical analysis of neuroimaging data. Dr. Wesley will also engage in a series of formal classes, lab exchanges, and research seminars/meetings to assist him in accomplishing the objectives of this award. The proposed research project is based on the premise that cannabis impairs decision-making processes and these impairing effects contribute to an increased risk of developing CUD in emerging adults. This research will combine the acute administration of ?9-tetrahydrocannabinol (THC), the main psychoactive ingredient in cannabis, with high-definition transcranial direct current stimulation (HD-tDCS) and neuroimaging, using rigorous methods, to examine the role of prefrontal cortex regions of interest in cannabis-impaired decision-making in emerging adults. Aim 1 will test the hypotheses that raising intrinsic PFC activity (Exp. 1) will attenuate, whereas lowering activity (Exp.2) will enhance, the impairing effects of THC on measures of decision-making and associated neurocognitive processes in emerging adults. Aim 2 will test the hypothesis that brain activity and connectivity during baseline task performance is associated with the subsequent response to THC and HD-tDCS. This highly innovative project will improve our understanding of the mechanisms involved in cannabis-impaired decision-making, which will inform CUD management and address a growing public health concern.

ABSTRACT Opioid use disorder (OUD) is characterized by the decision to use opioids at the expense of other activities. Lab-based efforts to address this problem have therefore included opioid choice self-administration procedures that incorporate a non-drug alternative to model this defining feature. Studies using these procedures have typically scheduled competing reinforcers so that the probabilities are certain. However, such deterministic outcomes are not representative of real-world experiences in which the consequences from drug-related choices are often unpredictable. Importantly, decision-making in a dynamic, uncertain context significantly alters the value of choice options and requires continuous updating of option values, which engages learning processes and related corticostriatal networks that function abnormally in OUD. Decision-making in dynamic environments has been successfully modeled using probabilistic reinforcement-learning choice (PRLC) tasks. The integration of these tasks with reinforcement-learning (RL) computational modeling has been used to capture moment-to- moment changes in the mechanisms of dynamic choice, and the application of neuroscience techniques has begun to identify the underlying neurobiology. This approach has uncovered biologically-based decision-making abnormalities in multiple brain disorders, but has yet to be systematically applied to the experimental study of OUD, The translation of combined RL and neuroscience approaches to OUD is logical considering the maladaptive choice behavior that typifies the disorder, the varying reinforcement probabilities in opioid users? natural environments, and the learning impairments that have been documented in individuals with OUD. Thus, there are critical gaps in our understanding of the mechanisms underlying dynamic opioid use decisions, and a strong scientific premise for applying an RL framework to fill these gaps. This project proposes rigorous PRLC tasks, RL modeling, neurorecording/fMRI neuroimaging techniques and complementary, translational study designs in rats and humans. The first set of cross-species experiments will demonstrate the impact of opioid exposure and withdrawal on dynamic decision-making and reveal the neurobehavioral and neurobiological processes underlying abnormal task performance. The second set of experiments will use a PRLC task in which intravenous remifentanil, a prototypical opioid agonist with a favorable safety profile, is available as an alternative to a non-drug reinforcer to determine the behavioral and neural ?profiles? associated with drug choice, as well as the increases and decreases in drug choice that occur during withdrawal and in the presence of a large magnitude alternative reinforcer, respectively. This project will have a significant impact on the field by establishing the experimental application of reinforcement-learning theory to the study of maladaptive dynamic drug-use decision-making in OUD to reveal behavioral and neural mechanisms that can be targeted for future prevention and treatment development.