LC-ACC interactions supporting adaptive, feedback-driven decisions

LC-ACC 交互支持自适应、反馈驱动的决策

• 批准号: 10529791
• 负责人: JOSHUA I GOLD
• 金额: $ 60.58万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-23 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10529791
• 关键词: Anterior; Basic Science; Behavior; Behavioral; Brain; Brain Stem; Caliber; Cell Nucleus; Cerebral hemisphere; Cognition; Cognitive; Complex; Data; Data Set; Decision Making; Diagnosis; Electroencephalography; Etiology; Event; Feedback; Foundations; Functional disorder; Goals; Individual; Link; Measurement; Measures; Medial; Mediating; Mental disorders; Modeling; Monitor; Monkeys; Motor; Motor Neurons; National Institute of Mental Health; Nervous system structure; Neuromodulator; Neurons; Norepinephrine; Output; Pathway interactions; Pattern; Performance; Pharmacology; Play; Population; Population Process; Process; Public Health; Pupil; Research Domain Criteria; Resolution; Rest; Rewards; Role; Sensory; Signal Transduction; Source; Specificity; Structure; Surface; System; Task Performances; Testing; Time; Training; Uncertainty; Work; awake; base; cingulate cortex; design; experience; flexibility; information processing; innovation; insight; interest; locus ceruleus structure; neuroregulation; novel; relating to nervous system; theories

PROJECT SUMMARY/ABSTRACT Our long-term goal is to understand how the brain processes information in a flexible, context-dependent manner to support effective decision-making. Many previous studies of decision-making focused on how the brain accumulates evidence used to select a particular action, which was shown to involve modulations of persistent activity of individual sensory-motor neurons that prepare the appropriate action. However, the brain must also often accumulate evidence in a flexible manner across actions, about which little is known. We propose that decisions requiring the flexible accumulation of feedback-related evidence across actions depends on interactions between the Anterior Cingulate Cortex (ACC), a cortical structure on the medial surface of each cerebral hemisphere that has widespread connectivity with other parts of the brain, and the brainstem nucleus locus coeruleus (LC), which is the primary source of the neuromodulator norepinephrine (NE) to the rest of the brain. The ACC and LC have strong, reciprocal connections and are thought to interact in ways that support key features of cognition, including adaptive information processing, but the details of these interactions are not well understood. Our primary hypothesis is that these interactions modulate activity patterns of populations of ACC neurons that implement a process of across-trial evidence accumulation that uses reward and error feedback to govern decisions to switch behavioral choices. We are particularly interested in understanding how these modulations relate to changes in coordinated variability in ACC that can have major effects on how neural populations process information. To test this hypothesis, we use simultaneous, complementary measurements of neuronal activity from single and populations of neurons from the two brain areasin the context of two tasks that require different forms of across-trial accumulation of feedback information to guide saccadic decisions. We have three Specific Aims. Aim 1 is to understand how activity patterns of individual neurons in the LC relate to performance on these tasks. Aim 2 is to understand how relationships between neuronal activity patterns in the LC and ACC relate to performance on these tasks. Aim 3 is to use a combination of manipulations to identify causal contributions of temporally precise, pathway specific activity patterns from LC to ACC on task performance. Together these Aims will provide new mechanistic and computational insights into how LC-related modulations of ACC population activity support ACC's role in flexibly linking performance monitoring and control across multiple trials. Our findings will have direct relevance to numerous constructs in the Research Domain Criteria (RDoC) framework and thus have broad significance to fields that aim to understand the neural substrates of complex behaviors and their dysfunction in certain mental disorders.

项目摘要/摘要 我们的长期目标是了解大脑是如何在灵活的、依赖于环境的 支持有效决策的方式。以前的许多决策研究都集中在决策是如何 大脑积累了用于选择特定动作的证据，这被证明涉及到对 个体感觉运动神经元的持续活动，准备适当的动作。然而，大脑 还必须经常以灵活的方式在各种行动中积累证据，而人们对此知之甚少。我们 建议需要跨行动灵活积累与反馈相关的证据的决策 取决于前扣带回(ACC)之间的相互作用，前扣带回是内侧的皮质结构 与大脑其他部分有广泛连接的每个大脑半球的表面，以及 脑干蓝斑(LC)是神经调节剂去甲肾上腺素的主要来源 (NE)到大脑的其余部分。ACC和LC具有强大的相互联系，并被认为是相互作用的 以支持认知关键特征的方式，包括自适应信息处理，但细节 这些相互作用还没有被很好地理解。我们的主要假设是，这些相互作用调节活动 实施交叉试验证据积累过程的ACC神经元群体模式 使用奖励和错误反馈来管理决策，以改变行为选择。我们特别是 有兴趣了解这些调节如何与ACC中协调变异性的变化相关， 对神经细胞群体处理信息的方式有重大影响。为了检验这一假设，我们使用 同时、互补地测量单个和群体神经元的活动 在两个任务的背景下，这两个大脑区域需要不同形式的交叉试验积累 反馈信息，以指导扫视决策。我们有三个具体目标。目标1是了解如何 LC中单个神经元的活动模式与这些任务的表现有关。目标2是理解 LC和ACC中神经元活动模式之间的关系如何与这些任务的表现相关。 目标3是使用操作的组合来识别时间上精确的路径的因果贡献 从LC到ACC的特定活动模式对任务绩效的影响。这些目标加在一起将提供新的 对LC相关的ACC群体活动调节如何支持的机制和计算洞察力 行政协调会在灵活地将多个试验的绩效监测和控制联系起来方面的作用。我们的发现将会有 与研究领域标准(RDoC)框架中的许多概念直接相关，因此具有 对于旨在理解复杂行为的神经底物及其 某些精神障碍中的功能障碍。