Optical measurement of causal functional connectivity in posterior parietal cortex

后顶叶皮质因果功能连接的光学测量

• 批准号: 10064584
• 负责人: Daniel E Wilson
• 金额: $ 7.03万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10064584
• 关键词: Adopted; Animals; Architecture; Area; Behavior; Behavioral; Behavioral trial; Calcium; Cells; Complex; Data; Decision Making; Electron Microscopy; Electrophysiology (science); Encapsulated; Exhibits; Fluorescence; Foundations; Functional disorder; Generations; Goals; Image; In Vitro; Individual; Label; Light; Measurable; Measurement; Measures; Mediating; Memory; Methods; Modeling; Mus; Neurons; Opsin; Optics; Parietal Lobe; Pattern; Population; Recurrence; Resolution; Signal Transduction; Specificity; Speed; Spottings; Structure; Techniques; Technology; Testing; Time; Training; Visual Cortex; Work; awake; base; calcium indicator; excitatory neuron; experimental study; high dimensionality; holographic stimulation; in vivo; inhibitory neuron; insight; nervous system disorder; network architecture; neural circuit; neural model; neural network; new technology; novel; operation; optogenetics; patch clamp; preference; response; tool; two-photon

Project Summary The mouse posterior parietal cortex (PPC) has emerged as an essential region for decision-making during memory-guided decision-making tasks. Neurons in the PPC typically respond selectively at a single point during a unique behavioral trial type; activity at the population level can be thought of as a choice-specific trajectory through state space. Many neurons respond selectively for behavioral choice; these responses resemble those observed in models incorporating winner-take-all dynamics, but the extent to which PPC functions as a winner- take-all network remains unknown. Key features of winner-take-all neural networks include choice-specific excitatory connectivity and mutual inhibition between pools of neurons with different choice preferences. To test models of neural circuit architecture in decision-making areas, we propose to develop a high-speed, high- throughput two-photon photostimulation approach to optically measure causal functional connectivity between neurons. We will first develop and characterize this photostimulation approach with in vivo cell-attached recordings. Next, we will apply this photostimulation approach to measure causal functional connectivity between excitatory neurons in PPC to test the degree of functional specificity in excitatory networks. These experiments will relate separately measured activity-behavior relationships to causal functional connectivity measurements between excitatory neurons and test whether neurons exhibiting similar choice preferences preferentially excite one another. Finally, we will measure the effect of stimulating GABAergic neurons in PPC while the mouse performs a memory-guided two-alternative forced choice task to measure causal functional connectivity between GABAergic neurons and excitatory neurons. If PPC exhibits winner-take-all dynamics, we would expect to find that GABAergic neurons preferentially inhibit excitatory neurons with opposite choice preferences. As a whole, we predict that this technique will generate powerful data that provide new insight into the microcircuitry of decision-making in PPC.

项目摘要 小鼠后顶叶皮层（PPC）已成为一个重要的决策区域， 记忆引导的决策任务。PPC中的神经元通常选择性地在单个点响应， 一种独特的行为试验类型;群体水平的活动可以被认为是一种选择特定的轨迹 通过状态空间。许多神经元对行为选择做出选择性反应，这些反应类似于 观察模型纳入赢家通吃的动态，但在何种程度上PPC功能作为赢家- 全取网络仍然未知。赢家通吃神经网络的关键特征包括选择特定 不同选择偏好的神经元池之间的兴奋性连接和相互抑制。 为了测试决策领域的神经电路结构模型，我们建议开发一种高速、高性能的 通过双光子光刺激方法光学测量因果功能连接 神经元我们将首先开发和表征这种光刺激方法， 录音.接下来，我们将应用这种光刺激方法来测量因果功能之间的连接， PPC中的兴奋性神经元，以测试兴奋性网络的功能特异性程度。这些实验 将把单独测量的活动-行为关系与因果功能连接测量相关联 并测试表现出相似选择偏好的神经元是否优先兴奋 彼此最后，我们将测量刺激PPC中的GABA能神经元的作用， 执行记忆引导的两个选择的强迫选择任务，以测量因果功能连接之间 GABA能神经元和兴奋性神经元。如果PPC表现出赢家通吃的动态，我们预计会发现， GABA能神经元优先抑制具有相反选择偏好的兴奋性神经元。总的来说， 我们预测，这项技术将产生强大的数据，提供新的洞察微电路的 PPC中的决策。