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 前额皮质 (PFC) 对于一系列高级认知功能至关重要，例如注意力和决策 研究这些过程是很困难的，因为它们是隐蔽的、动态的并且受到控制。 他们的测量传统上依赖于通过推断他们的存在。 最近的技术进步，特别是神经元数量的增加。 同时记录，提高了直接从神经活动解码认知过程的可能性。 在拟议的项目中，我们将利用高密度、高通道数、硅 探针（神经像素）可以将神经元记录产量提高 20 倍以上 我们将使用这些新的探针和解码算法来识别蜂窝和电路。 我们将在灵长类 PFC 中进行注意力控制和决策的水平机制。 实时并使用输出来控制刺激扰动或电微刺激的应用， 这将“闭合循环”并确定解码信号对于认知的必要性。 通过将解码器限制为特定神经元亚群的贡献。 第一个目标侧重于了解外侧前额叶皮层（LPFC）如何实现注意力控制。 我们将同时记录额叶视野和区域 46 内的大量神经元 执行选择性注意任务的猴子的特征是其层流位置。 感觉、运动和记忆相关的特性，以及假定的锥体或中间神经元的贡献。 注意力控制的特定亚群将通过构建解码器来报告动物的 反过来，将使用来自感兴趣子群的神经活动的当前注意力轨迹。 控制刺激扰动，评估解码信号对注意力的行为影响。 第二个目标，我们将研究眶额皮质（OFC）神经亚群对价值的贡献 基于决策，包括不同皮质层、细胞类型和 OFC 分区的贡献。 此外，解码器输出将用于控制电微刺激的应用来检查 选择行为是否会偏向于特定的选择方案 在最后一个目标中，我们将以此为基础。 前两项工作旨在确定价值和注意力如何相互作用。 尽管人们优先考虑高价值的刺激，但其他证据表明事实恰恰相反。 目标 1 和 2 将用于优化 OFC 中的解码器，该解码器将输出当前下的对象的值 考虑，以及 LPFC 中的解码器将输出隐蔽空间注意力的当前位置。 然后将两个解码器输出信号互相关，以确定一个解码器输出信号是否可靠地落后或领先另一个。 拟议的工作利用了三位 PI 的专业知识：Moore 是 LPFC 在注意力方面的作用方面的专家， Wallis在OFC中扮演决策的角色，Shenoy在实时解码和闭环控制中扮演角色。 项目总结/摘要第 6 页