2011 |
Yu, Angela Jie |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
A Neurocognitive and Computational Study of Inhibitory Control in Substance Use @ University of California San Diego
DESCRIPTION (provided by applicant): Stimulant abuse and dependence are significant problems facing young adults in the United States. Around 20% of young adults report the use of illicit drugs between the ages of 18-20 years, and approximately one in seven subjects initiating stimulant use progress eventually to dependence. There is strong evidence that drug use and dependence are associated with deficits in inhibitory control. However, the basic cognitive and neural processes underlying such disinhibitory psychopathology are poorly understood. The long-term goal of this project is to delineate the neural provenance and behavioral consequence of inhibitory dysregulation in casual stimulant users, which would be valuable for developing novel tools for the identification of at-risk individuals, for preventative intervention, and for behavioral and pharmacological remediation. The specific aims are (1) to investigate the cognitive differences between stimulant users and normal controls, specifically evaluating the hypotheses that altered error processing and/or temporal perception in stimulant users contribute to impaired inhibitory control, (2) to identify the differential contributions of various brain regions in stopping behavior, as well as neurophysiological differences between stimulant users and matched controls, and (3) to uncover early behavioral and neurophysiological markers that predict whether a casual stimulant user will eventually develop dependent use or not. To achieve Aim 1, a novel, quantitative model for inhibitory control, differentiating the contributions of various cognitive processes such as sensory discrimination, temporal perception, reward/error processing, learning and adaptation, will be used to analyze stimulant users and matched controls performing the stop signal paradigm. To achieve Aim 2, subject- and trial-specific parametric regressors, corresponding to distinct cognitive processes of the quantitative model of Aim 1, will be used to identify the neural substrate (using fMRI data) of the various cognitive processes, and to characterize any neurophysiological differences between stimulant users and matches controls. To achieve Aim 3, three-year follow-up surveys, indicating which casual users eventually develop dependence, will be correlated with behavioral and neurophysiological measures collected in the original fMRI/behavioral experiments, to identify early behavioral and neurophysiological markers that predict transition from casual use to dependence. The behavioral and fMRI data have already been collected under a different grant;the follow-up survey, funded by another grant, will soon be completed. PUBLIC HEALTH RELEVANCE: Stimulant abuse and dependence are significant problems among young adults, making it imperative to identify the causative factors for stimulant use, as well as the nature of cognitive deficits in problem use, in order to develop more powerful diagnostic, preventative, and remedial procedures. In this project, a sophisticated, novel, quantitative model of inhibitory control will be developed, yielding a rich set of cognitive and behavioral measures that can characterizes subtle differences between stimulant users and control populations at different stages of drug use and addiction. The model will be applied to a large set of behavioral and functional MRI data of casual stimulant users and matched control subjects, with the goal of identifying behavioral and neural features that best predict whether an individual eventually develops addiction or not.
|
1 |
2013 — 2017 |
Yu, Angela |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Crcns: Collaborative Research: a Computational and Neuroimaging Investigation of Prediction and Learning in Cognitive Control @ University of California-San Diego
An important component of healthy human brain function is inhibitory control, the ability to stop the currently adopted behavioral strategies in response to changing task demands. Traditionally, it has been thought that the capacity for inhibitory control is fairly stable in an individual; however, recent experimental data have revealed that human subjects readily alter their measured inhibitory control capacity in response to environmental changes. In this proposal, Dr. Angela Yu of the University of California, San Diego and Dr. Chiang-Shan Li of Yale University will use a new computational framework to account for such flexibility in terms of the underlying component processes in the brain. They will record brain activity from the volunteers when they must interrupt a habitual response and utilize a Bayesian ideal observer framework to model subjects, internal representation of the task demands, and dynamic adjustment of that representation and consequent changes in behavioral strategy based on experienced outcomes. This project is expected to facilitate an integrated theoretical and neurobiological understanding of human cognitive control as a more dynamic and adaptive phenomenon than traditionally envisioned.
Deficits in inhibitory control afflict a number of psychiatric conditions, such as attention deficit hyperactivity disorder, depression, drug abuse, and obsessive-compulsive disorder. Understanding the brain's computational and organizational principles underlying inhibitory control would help to unravel how malfunctions of the underlying components may lead to different types of pathology, a focus of Dr. Li's current research. This proposal will allow Dr. Yu to continue to train graduate and undergraduate students in cognitive neuroscience research and, in particular, to support women scientists, who are under-represented in this field. This effort will also allow Dr. Li to broaden his participation in the Perspectives in Science and Engineering, a research program at Yale University dedicated to training undergraduates with advanced knowledge in science and engineering. Finally, Dr. Yu and Dr. Li will participate in the big data sharing effort by making the data available to support other efforts that aim to make use of real data in the teaching of STEM-related courses and to enable participation in discovery science by those who would otherwise have no access to such data.
|
0.915 |
2020 — 2021 |
Yu, Angela |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Crcns: Neurocomputational Study of Reward-Related Decision-Making & Uncertainty @ University of California, San Diego
Humans and animals often make decisions under uncertainty, whereby each decision affects not only the immediate reward gain but also longer-term information gain. While important advances have been made in understanding human learning and decision-making, there is still a lack of understanding of the different motivational factors that come into play when the behavioral context confers systematically varying amounts of reward and information gain. This project tackles this problem using a combination of sophisticated cognitive modeling, innovative behavioral experiments, fMRI data, physiological (pupillometry, cardiac, and respiratory) data, and psychiatric measures (questionnaires addressing depressiveness, anxiety, anhedonia, locus of control, pessimism, and substance abuse). The objectives are (1) to develop a statistically grounded and neurobiologically informed theory for how different motivational factors (immediate reward, long-term reward, reduction of uncertainties, and random stochasticity) jointly influence human decision making; (2) use this theoretical framework to guide the understanding of how different brain regions, in particular neuromodulatory systems, work separately and conjointly to implement behavioral choices in response to the reward and informational structure of the environment; (3) characterize individual differences in terms of motivations, subjective monitoring of uncertainties, neural and physiological responses, and psychiatric profile. This work builds on multiple theoretic approaches: Bayesian ideal observer, reinforcement learning, Markov decision process, and control theory; and multiple neuroscientific research areas: learning, information seeking, confidence, decision making, change-point detection. It will advance an integrated understanding of computational theory, neuro-cognitive processes, behavioral manifestations, physiological signals, and psychiatric traits in choice behavior under uncertainty. It will help to clarify how different cortical and subcortical (especially neuromodulatory) brain regions differentially and cooperatively contribute to reward- and information-based learning, decision making, and exploration. These outcomes can be expected to contribute to advancements in basic scientific understanding of brain circuits, mechanisms, and functions related to the use and abuse of addictive substances, as well as their prevention and treatment. RELEVANCE (See instructions): Drug use and abuse often involve alterations in reward learning, decision-making, and uncertainty-related processing. This project contributes to basic computational and neurobiological understanding of these processes in the healthy brain, and may help to elucidate how these processes go awry in substance use and addiction disorders.
|
0.915 |