Multi-scale Orbitofrontal Networks Underlying Reward Processing

奖励处理背后的多尺度眶额网络

基本信息

批准号：
8868828
负责人：
Erin L Rich
金额：
$ 13.05万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-15 至 2020-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8868828
关键词：
Abstinence Accounting Acute Affective Anterior Area Brain Brain Mapping Characteristics Choice Behavior Code Communication Cues Decision Making Dimensions Disease Drug Addiction Electrocorticogram Epilepsy Goals Human Individual Language Lead Learning Lesion Link Measures Memory Mentors Monitor Monkeys Neurons Neurosurgeon Operative Surgical Procedures Output Patients Pharmaceutical Preparations Phonetics Play Population Positioning Attribute Prefrontal Cortex Preparation Process Property Psychological reinforcement Relapse Research Research Personnel Rewards Role Sensory Sensory Process Series Signal Transduction Site Stimulus Structure Substance abuse problem Superior temporal gyrus Temporal Lobe Testing Therapeutic Time Training Work abstracting addiction awake base cognitive ability cognitive process craving drug addict drug craving experience insight neuromechanism neurophysiology neuroregulation neurotransmission nonhuman primate novel novel therapeutic intervention preference programs public health relevance relating to nervous system research study reward processing sensory cortex sensory stimulus spatiotemporal stimulus processing theories translational approach

项目摘要

DESCRIPTION (provided by applicant): In substance abuse disorders, cravings and relapses can occur even after long periods of abstinence, posing a key challenge for successful long-term treatment. These phenomena arise when brain circuits that support reward-based learning and decision-making associate arbitrary cues with behaviorally reinforcing effects of drugs. The orbitofrontal cortex is a region crucial to reward-based decision-making, and has been strongly implicated in addiction. To date, it is known that the orbitofrontal cortex encodes the subjective value of predicted rewards, but the precise mechanism by which orbitofrontal networks use value information to inform learning and choice remains unclear. One theory suggests that orbitofrontal neurons combine multiple inputs, including sensory information to compute an abstract value, so that stimulus information contributes to orbitofrontal value coding in a bottom-up fashion. Another view holds that orbitofrontal cortex makes value-related predictions that influences downstream stimulus representations in a top-down fashion. Crucially, both accounts predict that orbitofrontal neurons encode value information; the distinction lies in how that information is used at a network level. Here, we will investigate how stimulus values are processed in both local orbitofrontal networks and broader functional circuits. To assess local network properties, we will use acute neurophysiology approaches in awake, behaving monkeys as they perform a reward preference task. We will record both single neurons and local field potentials, signals believed to reflect underlying network-level function, and focus on spatiotemporal dynamics in local field potentials, and their relationship to value-encoding neurons. To assess large-scale functional networks, we will record electrocorticography (ECoG) signals from the brains of human patients undergoing intracranial monitoring in preparation for epilepsy surgery. Patients will be tested in a reinforcement-learning task, while we simultaneously record from orbitofrontal cortex and anatomically linked sensory areas that process the stimuli used in the learning task. Over the course of learning, we will determine how orbitofrontal value representations interact with sensory stimulus representations. Overall, this work will use translational approaches to determine how orbitofrontal value coding relates to network-level activity at multiple scales, which could lead to novel therapeutic approaches for conditions of aberrant reward processing, such as addiction disorders. In addition, this proposal offers outstanding training potential. I will benefit from the expertise of two established mentors Dr. Wallis who specializes in non-human primate neurophysiology, and Dr. Chang, a neurosurgeon and researcher specializing in functional brain mapping and ECoG in human patients. Their training will help me build an independent research program that combines multiple approaches to understand how reward processing impacts higher cognitive abilities such as learning, memory and decision-making.

描述（由适用提供）：在滥用药物障碍中，即使在禁欲的长期后，也可能会发生渴望和继电器，这对成功的长期治疗构成了关键的挑战。当支持基于奖励的学习和决策的大脑电路将任意提示与药物的行为增强作用相关时，就会出现这些现象。眶额皮质是对基于奖励的决策至关重要的地区，并且在成瘾中得到了强有力的实施。迄今为止，众所周知，眶额皮质编码预测奖励的主题价值，但是轨道额外网络使用价值信息来告知学习和选择的精确机制尚不清楚。一种理论表明，眶额神经元结合了多个输入，包括感官信息来计算抽象值，因此刺激信息以自下而上的方式有助于轨道额值编码。另一种观点认为，眶额皮层可以做出与价值相关的预测，以自上而下的方式影响下游刺激表示。至关重要的是，两个帐户都预测轨道额神经元编码值信息。区别在于如何在网络级别使用该信息。在这里，我们将研究如何在局部轨道额网络和更广泛的功能电路中处理刺激值。为了评估本地网络属性，我们将在清醒中使用急性神经生理学方法，在执行奖励偏好任务时表现出猴子。我们将记录单个神经元和局部场电位，信号被认为反映了潜在的网络级函数，并专注于局部场电位中的空间时间动力学以及它们与价值编码神经元的关系。为了评估大规模的功能网络，我们将记录来自接受颅内监测的人的大脑的电视图（ECOG）信号，以准备癫痫手术。将在一项加强学习任务中对患者进行测试，而我们只是从轨道额皮层和解剖上连接的感觉区域记录了处理学习任务中使用的刺激的感觉区域。在学习过程中，我们将确定轨道额值表示如何与感觉刺激表示相互作用。总体而言，这项工作将采用翻译方法来确定在多个尺度上如何与网络级活动的轨道额值编码关系，这可能会导致新颖的治疗方法，以实现异常奖励处理的条件，例如成瘾障碍。此外，该建议还提供了出色的培训潜力。我将从专门从事非人类灵长类神经生理学的两位知名导师沃利斯博士的专业知识中受益。他们的培训将帮助我建立一个独立的研究计划，该计划结合了多种方法，以了解奖励处理如何影响更高的认知能力，例如学习，记忆和决策。