Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation

决策过程中大脑动力学的预测模型及其使用分布式光遗传学刺激的验证

• 批准号: 10240643
• 负责人: Roozbeh Kiani
• 金额: $ 66.72万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10240643
• 关键词: Address; Algorithms; Area; Attention; BRAIN initiative; Behavior; Behavioral; Biological; Biophysics; Brain; Choice Behavior; Collaborations; Computer Models; Data; Data Analyses; Decision Making; Development; Etiology; Eye Movements; Feedback; Goals; Individual; Intervention; Link; Location; Measures; Mediating; Mental Processes; Methodology; Methods; Modeling; Monitor; Movement; Neurons; Non-linear Models; Nonlinear Dynamics; Output; Parietal; Parietal Lobe; Pattern; Performance; Phase; Physiologic pulse; Play; Population; Primates; Process; Property; Reporting; Resolution; Role; Sensory; Site; Space Models; Stimulus; System; Techniques; Testing; Time; Training; United States National Institutes of Health; Validation; Visual Fields; Work; base; behavioral response; computer framework; experience; experimental group; experimental study; flexibility; frontal lobe; improved; loss of function; millisecond; multi-scale modeling; neural circuit; neural patterning; neuroregulation; nonhuman primate; oculomotor; optogenetics; outcome prediction; parietal-frontal circuits; predictive modeling; relating to nervous system; response; sensory input; stem; theories; tool

Project Summary During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to sensory targets during selection can be strongly influenced by external stimulus properties (“bottom-up”) or internal goals based on previous experience (“top-down”). Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping time scales, how neural circuits mechanistically support top-down and bottom-up processing has been difficult to disentangle. This is because the neural circuits for spatial attention and selection are distributed across the frontal and parietal cortices and operate across multiple spatial scales spanning the activity of individual neurons and neuronal populations. In this Targeted Brain Circuit R01 Project proposal, an experimental group (Pesaran/NYU) and a theory group (Shanechi/USC) will use cutting-edge techniques developed under the NIH BRAIN Initiative support to validate predictive models of neuronal dynamics and test hypotheses about how frontal-parietal cortices perform attentional selection. A behavioral task that dissociates bottom up and top-down processing will let us define bottom-up and top-down target states. We will then build predictive models of neuronal dynamics within and between frontal and parietal cortex and empirically validate the models by stimulating neural activity to achieve the desired neural state. Aim 1 validates predictive models of local circuit dynamics. We will stimulate within PFC to achieve target states in PFC. Aim 2 validates predictive models of long-range circuit dynamics. We will stimulate sites in PPC that functionally connect to PFC in order to achieve target states in PFC. Aim 3 validates predictive models of distributed circuit dynamics. We will simultaneously stimulate both PFC and PPC to achieve the target states. In each case, successfully directing activity toward the target state will indicate the model is valid. If the target state reflects a causal role in attention, as opposed to correlating with attentional processes, we predict that behavioral choices will be biased. This proposal tackles several of the major topic areas of the BRAIN 2025 report. We will identify fundamental principles about circuit dynamics and functional connectivity for understanding the biological basis of mental processes through development of new theoretical and data analysis tools (Topic 5). We will produce a dynamic picture of the functioning brain by developing and applying improved methods for large-scale monitoring of neural activity (Topic 3). We will demonstrate causality by linking brain activity to behavior with precise interventional tools that change neural circuit dynamics (Topic 4). Recent years have seen dramatic advances in our ability to experimentally interface with the primate brain with increasing precision scale. A fruitful interplay between multiscale experiments and predictive modeling that we propose will let us test hypotheses about how flexible behaviors are controlled by large-scale neural circuits.

项目摘要 在行为过程中，视觉系统的任务是从不断变化的视野中选择物体， 引导眼球运动到这些位置。在选择过程中给予感觉目标的注意优先级 可以强烈影响外部刺激属性（“自下而上”）或内部目标的基础上，以前 自上而下（top-down）。尽管已知这些外生和内生的选择驱动因素 在部分重叠的时间尺度上，神经回路如何机械地支持自上而下和自下而上 处理一直难以理清。这是因为空间注意力的神经回路和 选择分布在额叶和顶叶皮层，并在多个空间尺度上运作 跨越单个神经元和神经元群体的活动。在这个有针对性的脑回路R 01项目中 一个实验小组（Pesaran/NYU）和一个理论小组（Shanechi/USC）将使用尖端的 在NIH BRAIN Initiative支持下开发的技术，用于验证神经元预测模型 动态和测试关于额顶叶皮层如何执行注意力选择的假设。一个行为 将自下而上和自上而下处理分离的任务将允许我们定义自下而上和自上而下目标 states.然后，我们将建立额叶和顶叶内和之间的神经元动力学预测模型 皮层，并通过刺激神经活动以实现期望的神经状态来经验地验证模型。 目的1验证局部电路动态的预测模型。我们将在PFC内部进行激励，以实现目标 目标2验证了长程电路动态的预测模型。我们将刺激网站在 PPC在功能上连接到PFC，以便在PFC中实现目标状态。目标3验证预测性 分布式电路动态模型。我们将同时刺激PFC和PPC，以实现 目标国家。在每种情况下，成功地将活动导向目标状态将表明模型是有效的。 如果目标状态反映了注意力的因果作用，而不是与注意力过程相关，我们 预测行为选择会有偏差。本提案涉及《公约》的几个主要专题领域， 《大脑2025》报告。我们将确定有关电路动力学和功能连接的基本原则 通过发展新的理论和数据来理解心理过程的生物学基础 分析工具（专题5）。我们将通过开发和应用 大规模监测神经活动的改进方法（专题3）。我们将证明因果关系， 通过精确的干预工具将大脑活动与行为联系起来，改变神经回路动力学（主题4）。 近年来，我们在实验上与灵长类动物的大脑进行交互的能力取得了巨大的进步， 增加精度刻度。多尺度实验和预测建模之间富有成效的相互作用， 该计划将让我们测试关于大规模神经回路如何控制灵活行为的假设。

项目成果

Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.

• DOI: 10.1038/s41467-020-20197-x
• 发表时间: 2021-01-27
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Abbaspourazad H;Choudhury M;Wong YT;Pesaran B;Shanechi MM
• 通讯作者: Shanechi MM

Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation.

• DOI: 10.1038/s41593-018-0171-8
• 发表时间: 2018-07
• 期刊: Nature neuroscience
• 影响因子: 25
• 作者: Pesaran B;Vinck M;Einevoll GT;Sirota A;Fries P;Siegel M;Truccolo W;Schroeder CE;Srinivasan R
• 通讯作者: Srinivasan R

Multiregional communication and the channel modulation hypothesis.

• DOI: 10.1016/j.conb.2020.11.016
• 发表时间: 2021-03
• 期刊: Current opinion in neurobiology
• 影响因子: 5.7
• 作者: Pesaran B;Hagan M;Qiao S;Shewcraft R
• 通讯作者: Shewcraft R