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Influence of Temporal Difference Reward Prediction Errors on Brain Network Connectivity during Learning and Decision-Making

时间差异奖励预测误差对学习和决策过程中大脑网络连接的影响

基本信息

批准号：
10374789
负责人：
Lester Paul Sands
金额：
$ 2.41万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2022-05-21
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10374789
关键词：
Address Adult Anatomy Animals Area Back Basal Ganglia Behavior Behavior Control Behavior Disorders Behavioral Behavioral Symptoms Brain Brain region Characteristics Choice Behavior Computer Models Computing Methodologies Corpus striatum structure Coupling Data Decision Making Disease Electrophysiology (science)Experimental Designs Feedback Fellowship Foundations Functional Magnetic Resonance Imaging Human Impulse Control Disorders Individual Learning Link Magnetoencephalography Maps Measures Mental Health Methods Modeling National Research Service Awards Participant Pathologic Pattern Pharmaceutical Preparations Plant Roots Process Psychological reinforcement Psychophysics Punishment Research Resolution Rewards Signal Transduction Source Stimulus Substance abuse problem Testing Thalamencephalon Time Training Update adaptive learning addiction base behavior measurement blood oxygen level dependent career cingulate cortex computational neuroscience design expectation experience experimental study functional MRI scan human model innovation insight interest learned behavior learning outcome multimodality neural patterning neurobehavioral neuroimaging reinforcer relating to nervous system response reward circuitry reward processing skills somatosensory source localization spatiotemporal theories

项目摘要

PROJECT SUMMARY Disorders of choice behavior, such as substance abuse and impulse control disorders, involve over-valuing and repeatedly choosing certain reinforcers (e.g., drugs, risky actions), even in the face of recurring consequences. Understanding the link between adaptive choice behaviors and underlying neural activity is a strategic focus for substance abuse and mental health research. Much empirical evidence demonstrates that features of distinct pathological behaviors map onto distinct patterns of interactions between distributed brain regions. Nevertheless, how adaptive learning signals concerning rewards and punishments alter region-to-region functional interactions in real-time lies at the limit of our current understanding. As such, we seek to identify neurobehavioral markers that reflect how reinforcement learning (RL) signals alter functional brain network interactions and associated choice behaviors in healthy adults. Along this line of inquiry, this proposal’s central objective is to understand how real-time changes in inter-regional functional interactions – between, for instance, regions of the basal ganglia and limbic, prefrontal, and sensorimotor cortices – in response to RL signals influence adaptive choice behaviors. Our approach uses computational methods to investigate the quantitative relationship between measures of human choice behavior and brain network interactions at high-resolution spatiotemporal scales. Specifically, we pair computational RL models of human behavior on a probabilistic reward and punishment learning task with multi-modal functional neuroimaging to investigate changes in functional brain networks responsive to learning signals called ‘temporal difference reward prediction errors’ (TD RPEs). We will identify functional network interactions related to TD RPE signals to address, through two Specific Aims, our overarching hypothesis that TD RPE signals alter – in real time – the coupling (synchrony) of functional interactions between brain regions involved in processing rewards and punishments to direct changes in choice behavior. In Aim 1, we will measure functional networks interactions using magnetoencephalography (MEG) to test the hypotheses that (1) positive TD RPE signals increase, in real time, the coupling of inter-regional functional interactions and (2) negative or zero TD RPE signals decrease this coupling. In Aim 2, we use functional magnetic resonance imaging (fMRI) – within the same subjects from Aim 1 – to investigate individual-specific functional networks associated with TD RPE signals. Here, we will localize TD RPE-responsive functional regions-of-interest (ROIs) and incorporate these ROIs as individual-specific spatial priors for within-subject MEG analysis of functional network interactions. In all, this fellowship will provide training in contemporary modeling methods and experimental design that address fundamental questions in computational neuroscience regarding the functional organization of the human brain during adaptive learning. The central findings of this research will advance our understanding of the principles of brain activity supporting adaptive learning processes that are characteristically the root cause of many behavioral symptoms of substance abuse and related behavioral disorders.

项目总结