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

项目摘要 在行为过程中，眼球运动系统的任务是从不断变化的视野中选择对象，并 引导眼球运动到这些位置。在选择过程中对感觉目标的注意优先 受外部刺激属性(自下而上)或基于以前的内部目标的强烈影响 体验(“自上而下”)。尽管已知这些外源和内源的选择驱动因素 在部分重叠的时间尺度上，神经电路如何机械地支持自上而下和自下而上 处理过程一直难以理清。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质上，并在多个空间尺度上进行操作 横跨单个神经元和神经元群体的活动。在这个有针对性的大脑电路R01项目中 提议，一个实验小组(佩萨兰/纽约大学)和一个理论小组(沙内奇/南加州大学)将使用尖端技术 在NIH大脑倡议支持下开发的验证神经元预测模型的技术 关于额叶-顶叶皮质如何执行注意选择的动力学和测试假设。一种行为 分离自下而上和自上而下处理的任务将允许我们定义自下而上和自上而下的目标 各州。然后，我们将建立额叶和顶叶内部和之间的神经元动力学预测模型 并通过刺激神经活动以达到所需的神经状态来经验地验证模型。 目标1验证本地电路动力学的预测模型。我们将在PFC内部进行刺激，以实现目标 PFC中的州。目标2验证了远程电路动力学的预测模型。我们将在以下方面刺激网站 在功能上连接到PFC以实现PFC中的目标状态的PPC。AIM 3验证预测 分布式电路动力学模型。我们将同时刺激PFC和PPC，以实现 目标州。在每种情况下，成功地将活动定向到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中的因果作用，而不是与注意过程相关，我们 预测行为选择将是有偏见的。这项建议解决了 大脑2025年报告。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(主题5)。我们将通过开发和应用来产生大脑功能的动态图像 改进了大规模监测神经活动的方法(专题3)。我们将通过以下方式来证明因果关系 通过改变神经回路动力学的精确干预工具，将大脑活动与行为联系起来(主题4)。 近年来，我们在与灵长类动物大脑的实验接口能力方面取得了戏剧性的进步 不断增加的精度刻度。多尺度实验和预测建模之间卓有成效的相互作用，我们 Proposal将让我们测试有关大规模神经电路如何控制灵活行为的假设。

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.