Large-scale recordings in Primate Prefrontal Cortex: Mechanisms of Value and Attention

灵长类动物前额皮质的大规模记录：价值和注意力机制

• 批准号: 10455269
• 负责人: TIRIN MOORE
• 金额: $ 19.24万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10455269
• 关键词: Algorithms; Animals; Area; Attention; Attention deficit hyperactivity disorder; Behavior; Behavioral; Choice Behavior; Cognition; Cognitive; Decision Making; Development; Functional disorder; Interneurons; Lateral; Location; Measurement; Memory; Mental Health; Methods; Monkeys; Mood Disorders; Motor; Neurons; Obsessive-Compulsive Disorder; Output; Population; Prefrontal Cortex; Primates; Process; Property; Reporting; Research; Role; Sensory; Signal Transduction; Silicon; Stimulus; Time; Work; addiction; attentional control; base; cell type; cognitive function; cognitive process; density; electrical microstimulation; frontal eye fields; interest; neuropsychiatric disorder; public health relevance; relating to nervous system; selective attention; time use

Contact PD/PI: MOORE, TIRIN Prefrontal cortex (PFC) is critical for a range of high-level cognitive functions, such as attention and decision- making. Studying these processes is difficult, since they are covert, dynamic and under the control of the subject, rather than the experimenter. Their measurement traditionally relies on inferring their presence via behavior. Recent technical advances, particularly the increase in the number of neurons that can be simultaneously recorded, has raised the possibility of decoding cognitive processes directly from neural activity. In the proposed project, we will capitalize on the recent development of high-density, high-channel count, silicon probes (Neuropixels) that can produce a more than 20-fold increase in neuronal-recording yield over conventional methods. We will use these new probes and decoding algorithms to identify the cellular and circuit- level mechanisms of attentional control and decision-making in primate PFC. We will perform this decoding in real-time and use the output to control the application of stimulus perturbations or electrical microstimulation, which will ‘close the loop’ and determine the necessity of the decoded signals for cognition. Finally, we will study the contribution of specific subpopulations of neurons by restricting the decoder to those populations. The first aim focuses on understanding how attentional control is achieved by lateral prefrontal cortex (LPFC). We will record simultaneously from large populations of neurons within the frontal eye field and area 46 of monkeys performing a selective attention task. Neurons will be characterized by the laminar location, by their sensory, motor and memory-related properties, and as putative pyramidal or interneurons. The contribution of specific subpopulations to attentional control will be determined by building a decoder to report the animal’s current attentional locus using neural activity from the subpopulation of interest. In turn, the decoder will be used to control stimulus perturbations to assess the behavioral effect of the decoded signals on attention. In the second aim, we will examine the contribution of orbitofrontal cortex (OFC) neuronal subpopulations to value- based decision-making, including the contribution of different cortical layers, cell types, and OFC subregions. In addition, the decoder output will be used to control the application of electrical microstimulation to examine whether choice behavior can be biased towards a specific choice alternative. In the last Aim, we will build on the work from the first two aims to determine how value and attention interact. While some evidence suggests that attention is prioritized to stimuli with high value, other evidence suggests that the opposite is true. Results from Aims 1 and 2 will be used to optimize a decoder in OFC that will output the value of objects currently under consideration, and a decoder in LPFC that will output the current location of covert spatial attention. We will then cross-correlate both decoder output signals to determine whether one reliably lags or leads the other. The proposed work leverages the expertise of the three PIs: Moore is an expert in the role of LPFC in attention, Wallis in the role of OFC in decision-making, and Shenoy in real-time decoding and closed-loop control. Project Summary/Abstract Page 6

联系PD/PI：Moore，Tirin 前额叶皮层（PFC）对于一系列高级认知功能（例如注意力和决策）至关重要 制作。研究这些过程很困难，因为它们是覆盖，动态的，并且在控制之下 主题，而不是专家。他们的测量传统上依赖于通过 行为。最近的技术进步，尤其是可以增加的神经元数量 同样记录下来，也提高了直接从神经活动中解码认知过程的可能性。 在拟议的项目中，我们将利用最近的高密度，高通道数，硅的发展 问题（神经偶像）可能会产生超过20倍以上的神经元录制产率 常规方法。我们将使用这些新问题并解码算法来识别细胞和电路 - 灵长类动物PFC中注意力控制和决策的水平机制。我们将执行此解码 实时并使用输出来控制刺激扰动或电微刺激的应用， 这将“关闭循环”并确定解码信号的必要性。最后，我们将学习 神经元特定亚群的贡献通过将解码器限制在这些人群中。 第一个目的是了解如何通过前额叶皮层（LPFC）实现注意力控制。 我们将简单地记录在前眼田中的大量神经元和46区域内的46个区域的记录 猴子执行选择性关注任务。神经元将以层流位置为特征 感觉，运动和记忆相关的特性，以及推定的金字塔或中间神经元。的贡献 注意力控制的具体亚群将通过构建解码器来确定该动物的 目前，通过利益亚群来利用神经活动的注意力基因座。反过来，解码器将被使用 控制刺激扰动以评估解码信号对注意力的行为效应。在 第二个目的，我们将研究眶额皮层（OFC）神经元亚群对值的贡献。 基于决策，包括不同皮层，细胞类型和OFC子区域的贡献。 此外，解码器输出将用于控制电微刺激的应用以检查 选择行为是否可以偏向特定的选择替代方案。在最后一个目标中，我们将基础 前两个的工作旨在确定价值和注意力如何相互作用。虽然一些证据表明 其他证据表明，这种注意力优先考虑具有高价值的刺激。结果 来自AIMS 1和2将使用OFC中的解码器优化，该解码器将输出当前下面的对象的值 考虑和LPFC中的解码器，它将输出覆盖空间注意的当前位置。我们将 然后互相关的两个解码器输出信号，以确定一个可靠的滞后还是导致另一个可靠的滞后。 拟议的工作利用了三个PI的专业知识：Moore是LPFC在关注的角色方面的专家， Wallis在OFC在决策中的作用，以及Shenoy在实时解码和闭环控制中的作用。 项目摘要/摘要页面6