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.

项目总结 了解大型神经系综中的因果相互作用是开发改变 通过有针对性地操纵大脑而产生的认知行为。这是一个具有挑战性的目标，因为通常 用来记录人脑神经反应的方法不能提供有关生理反应的信息 神经元连接，并且只允许对电路中的神经元进行极其稀疏的采样(通常为1%)。这里, 我们使用多学科方法开发了一条创新的前进道路，将最近的理论和 两个PI(Kiani和Mazzucato)的实验进展。在目标1中，我们介绍了一种新的理论 基于稀疏采样的神经元推断因果功能连通性(CFC)图的框架 巡回赛。我们的框架成功地恢复了功能相互作用的结构，识别了中枢神经元 电路，并具有多尺度属性，使其适用于各种数据，从尖峰 单个神经元到聚集的相邻神经元簇的尖峰电位到局部场电位。在目标2中， 我们测试从同时记录的一组前额叶神经元中推断出的CFC是否成功 预测微刺激如何扰乱回路中的神经活动。具体地说，我们证明了集线器的存在 通过cfc识别的神经簇，其微刺激对 群体反应动态。最后，在目标3中，我们探讨了静止时的CFC值和微扰效应是否可以预测 微刺激如何在知觉决策任务中改变行为。我们假设休息的时间 Cfc结合微刺激前的群体活动成功地预测了 微刺激对电路活动和行为都有影响。建议的方法、数据和分析 这些目标中的每一个都是新颖的，它们的结合将为一个长期存在的问题提供切实可行的解决方案。 在系统神经科学方面。