Causal power of cortical neural ensembles: mechanisms and utility for brain perturbations

皮质神经元的因果力：大脑扰动的机制和效用

• 批准号: 10454002
• 负责人: Roozbeh Kiani
• 金额: $ 62.1万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10454002
• 关键词: Behavior; Behavior Control; Behavioral; Brain; Chronic; Cognition; Cognitive; Cortical Column; Data; Data Analyses; Decision Making; Development; Dimensions; Electrodes; Ethics; Foundations; Future; Genetic; Goals; Human; Impaired cognition; Individual; Investigation; Maps; Memory; Methods; Modeling; Monitor; Monkeys; Movement; Neurons; Neurosciences; Output; Pattern; Population; Prefrontal Cortex; Primates; Property; Protocols documentation; Pupil; Rest; Saccades; Sampling; Space Models; Statistical Methods; Statistical Models; Structure; System; Techniques; Technology; Testing; Therapeutic Intervention; Time; awake; base; brain machine interface; cognitive control; cognitive function; cognitive performance; gaze; improved; innovation; interdisciplinary approach; microstimulation; neural circuit; novel; relating to nervous system; response; simulation; tool

PROJECT SUMMARY Understanding the causal interactions in large neural ensembles is key for developing techniques to alter cognitive behavior through targeted manipulation of the brain. This is a challenging goal because commonly used methods for recording neural responses in the human brain do not provide information about physical connections of neurons and allow only extremely sparse sampling of neurons in a circuit (typically <1%). Here, we develop an innovative path forward using a multi-disciplinary approach that combines recent theoretical and experimental advances by the two PIs (Kiani and Mazzucato). In Aim 1, we introduce a novel theoretical framework to infer a map of causal functional connectivity (CFC) based on sparse sampling from neurons in a circuit. Our framework successfully recovers the structure of functional interactions, identifies hub neurons in the circuit, and has multi-scale properties that make it applicable on a variety of data, ranging from spiking of individual neurons to aggregated spiking of clusters of neighboring neurons to local field potentials. In Aim 2, we test if the CFC inferred from a population of simultaneously recorded prefrontal neurons successfully predicts how microstimulation perturbs neural activity in the circuit. Specifically, we show the existence of hub neural clusters, identified through CFC, whose microstimulation has large and predictable impacts on the population response dynamics. Finally, in Aim 3, we explore if the CFC and perturbation effects at rest predict how microstimulation alters behavior during a perceptual decision-making task. We hypothesize that resting CFC combined with the population activity prior to microstimulation successfully predicts the effect of microstimulation both on the circuit activity and the behavior. The approach, data and analyses proposed in each of these aims are novel and the combination will provide a practical solution for a long-standing problem in systems neuroscience.

项目摘要 了解大型神经合奏中的因果相互作用是开发改变技术的关键 通过针对大脑的有针对性操纵的认知行为。这是一个具有挑战性的目标，因为通常 记录人脑中神经反应的使用方法没有提供有关物理的信​​息 神经元的连接，仅允许在电路中对神经元的极稀疏采样（通常<1％）。这里， 我们使用多学科的方法开发了一种创新的途径，该方法结合了最新的理论和 两个PI（Kiani和Mazzucato）的实验进步。在AIM 1中，我们介绍了一个小说的理论 基于来自神经元的稀疏采样的因果功能连通性（CFC）的框架 电路。我们的框架成功恢复了功能相互作用的结构，确定了中心神经元 该电路具有多尺度属性，使其适用于各种数据，从峰值到 单个神经元将邻近神经元簇的聚集峰值到局部田间电位。在AIM 2中， 我们测试CFC是否从同时记录的前额叶神经元中推断出 预测微刺激如何使电路中的神经活动渗透。具体来说，我们显示了集线器的存在 通过CFC确定的神经簇，其微刺激对 人口反应动态。最后，在AIM 3中，我们探讨了CFC和扰动效果是否预测 在感知决策任务中，微刺激如何改变行为。我们假设休息 CFC在微刺激之前结合了种群活动，成功地预测了 在电路活动和行为上进行了微刺激。提出的方法，数据和分析 这些目标都是新颖的，结合将为长期存在的问题提供实用的解决方案 在系统神经科学中。