NCS-FO: Developing dyadic fMRI methodology to quantify and model human brain-to-brain interactions

NCS-FO：开发二元功能磁共振成像方法来量化和模拟人脑之间的相互作用

• 批准号: 1926789
• 负责人: Ray Lee
• 金额: $ 99.98万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926789&HistoricalAwards=false
• 关键词: NCS; FO; Developing; dyadic; fMRI

The neural mechanisms that underlie and promote social interaction and communication remain elusive, despite a large number of evidence-based studies in psychology and psychiatry. This is partially due to the lack of instruments and technology that can directly measure natural human interactions. The proposed project intends to develop a dyadic functional magnetic resonance imaging (dfMRI) system to directly observe the brains of pairs of individuals while they are interacting with each other visually and by touch. The project also hopes to develop a computer model to quantify these interactions. Developing this technology will improve the level of knowledge about how brains work in complex environments. Findings from this project will serve the national interest by assisting in the design of social robots, which will interact more naturally with humans. Project findings may also help in identifying the neural underpinnings of mental disorders, particularly those that affect communication.This project will leverage a pioneering dfMRI technology and methodology that can simultaneously scan two people in one commercial MRI scanner, and has generated successful feasibility tests in observing and analyzing brain-to-brain coupling in live face-to-face communication. The first aim will be to develop a state-of-the-art dfMRI technology that will significantly improve temporal resolution and imaging quality by implementing local static magnetic field shimming and parallel imaging. The second aim will be to use the new system to address two fundamental hypotheses in brain-to-brain interactions: (1) Face-to-face communication (measured by dfMRI) recruits more brain faculty that the Internet-based communications (measured by MR hyperscanning); therefore, it conveys more social and affective information. (2) A multi-channel cerebral network model - derived from dfMRI data - may be trained to deduce some basic social attribution processes in dyadic interaction; thus, a causal relationship between social behaviors and brain network can be identified. Overall, this is a transformative, high-risk, high-payoff interdisciplinary proposal integrating engineering and social and behavioral science to enable new technology and methods to study human social interaction, mental disorders, and social robots.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.

尽管心理学和精神病学中有大量循证研究，但支撑和促进社会互动和交流的神经机制仍然难以捉摸。这在一定程度上是因为缺乏能够直接测量自然人类互动的仪器和技术。这项拟议的项目旨在开发一种二进制功能磁共振成像(DfMRI)系统，当两个人通过视觉和触摸进行互动时，可以直接观察他们的大脑。该项目还希望开发一种计算机模型来量化这些相互作用。开发这项技术将提高关于大脑如何在复杂环境中工作的知识水平。该项目的发现将通过帮助设计社交机器人来服务于国家利益，社交机器人将更自然地与人类互动。项目发现还可能有助于确定精神障碍的神经基础，特别是那些影响沟通的神经基础。该项目将利用开创性的dfMRI技术和方法，可以在一台商业MRI扫描仪中同时扫描两个人，并已在观察和分析实时面对面交流中的脑对脑耦合方面产生了成功的可行性测试。第一个目标是开发一种最先进的dfMRI技术，通过实施局部静磁场匀场和并行成像，显著提高时间分辨率和成像质量。第二个目标是使用新的系统来解决脑与脑互动中的两个基本假设：(1)面对面的交流(通过dfMRI测量)比基于互联网的交流(由mr hyperscanning测量)招募了更多的大脑人员；因此，它传递了更多的社交和情感信息。(2)根据dfMRI数据建立的多通道脑网络模型可以用来推断二元互动中的一些基本的社会归因过程，从而识别社会行为和脑网络之间的因果关系。总体而言，这是一个变革性的、高风险、高回报的跨学科提案，将工程学与社会和行为科学相结合，使新的技术和方法能够研究人类社交互动、精神障碍和社交机器人。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。