HNDS-R: Brain network mechanisms of task-general cognition

HNDS-R：任务一般认知的脑网络机制

• 批准号: 2219323
• 负责人: Michael Cole
• 金额: $ 62.5万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219323&HistoricalAwards=false
• 关键词: HNDS; Brain; network; mechanisms; task

Abstract for "Brain network mechanisms of task-general cognition" What are the special properties of the human brain that make us intelligent? What is the brain circuitry underlying the intelligence of geniuses such as Leonardo da Vinci, Marie Curie, Albert Einstein and the neural basis for normal human intelligence, which surpasses machine and artificial intelligence in a broad spectrum of cognitive and social tasks? Understanding the neural basis of different varieties of human intelligence is one of the great challenges in neuroscience. In addition to the scientific quest to understand the nature of human cognition and intelligence, knowing what makes the human brain intelligent would have many useful applications. For example, this knowledge could potentially be used to guide brain stimulation to help those with learning disabilities. This knowledge could also help develop brain-computer interfaces and enhance artificial intelligence, with many applications in science, engineering, and business. This project will use brain imaging and brain stimulation to learn about some of the key brain network processes that make human intelligence possible.Converging evidence indicates that general human intelligence is primarily implemented by activity and connectivity in subnetworks of the brain termed cognitive control networks (CCNs). However, there is a critical need to determine how CCN activity and connectivity together generate intelligent goal-directed behavior. The overall objective of this project is to determine how CCNs implement intelligent behavior across a wide variety of different tasks. Researchers will use brain activity flow models – a novel method for determining how activity and connectivity together generate brain function –to investigate how CCNs implement task-general cognition underlying intelligent behavior. The project will utilize brain imaging and brain stimulation to build and test activity flow models of intelligent human behavior. This combined experimental and computational approach will allow testing of the hypothesis that CCNs dynamically re-route activity flows between sensory inputs and motor outputs, implementing intelligent behavior via conjunctive activations as well as dynamic connectivity changes. In addition to scientific research, this project will enhance Outreach and increase participation of underrepresented groups in STEM via supporting continuation and expansion of participation in the NSF-funded nationwide LSAMP (Louis Stokes Alliance for Minority Participation) program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

摘要“任务一般认知的脑网络机制”人类大脑的特殊属性是什么使我们聪明？列奥纳多达芬奇、玛丽居里、阿尔伯特爱因斯坦等天才的智力背后的大脑回路是什么？正常人类智力的神经基础是什么？正常人类智力在广泛的认知和社会任务中超过了机器和人工智能？了解不同种类人类智能的神经基础是神经科学的巨大挑战之一。除了对人类认知和智力本质的科学探索之外，了解是什么使人类大脑变得聪明将有许多有用的应用。例如，这些知识可能被用来指导大脑刺激，以帮助那些有学习障碍的人。这些知识还可以帮助开发脑机接口并增强人工智能，在科学，工程和商业中有许多应用。该项目将使用脑成像和脑刺激来了解一些使人类智能成为可能的关键脑网络过程。汇聚的证据表明，一般人类智能主要是通过被称为认知控制网络（CCN）的大脑子网络中的活动和连接来实现的。然而，迫切需要确定CCN活动和连接如何共同产生智能目标导向行为。该项目的总体目标是确定CCN如何在各种不同的任务中实现智能行为。研究人员将使用大脑活动流模型-一种确定活动和连接如何共同产生大脑功能的新方法-来研究CCN如何实现智能行为背后的任务一般认知。该项目将利用大脑成像和大脑刺激来建立和测试智能人类行为的活动流模型。这种结合实验和计算的方法将允许测试CCN动态地重新路由感觉输入和运动输出之间的活动流的假设，通过联合激活以及动态连接变化实现智能行为。除了科学研究之外，该项目还将通过支持持续和扩大参与NSF资助的全国性LSAMP（Louis Stokes Alliance for Minority Participation）计划，加强外展并增加代表性不足的群体在STEM中的参与。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.

• DOI: 10.1016/j.neuroimage.2023.120300
• 发表时间: 2023-09
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Sanchez-Romero, Ruben;Ito, Takuya;Mill, Ravi D.;Hanson, Stephen Jose;Cole, Michael W.
• 通讯作者: Cole, Michael W.