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

尽管在心理学和精神病学方面有大量的循证研究，但作为社会互动和交流基础并促进社会互动和交流的神经机制仍然难以捉摸。 这部分是由于缺乏可以直接测量自然人类互动的仪器和技术。 该项目旨在开发一种二元功能磁共振成像（dfMRI）系统，以直接观察成对个体的大脑，同时他们通过视觉和触觉相互作用。 该项目还希望开发一个计算机模型来量化这些相互作用。 开发这项技术将提高人们对大脑如何在复杂环境中工作的认识水平。 该项目的研究结果将通过协助设计社交机器人来服务于国家利益，这些机器人将更自然地与人类互动。 该项目将利用一种开创性的dfMRI技术和方法，可以在一台商用MRI扫描仪上同时扫描两个人，并在观察和分析面对面交流中的脑-脑耦合方面进行了成功的可行性测试。 第一个目标将是开发一个国家的最先进的dfMRI技术，将显着提高时间分辨率和成像质量，通过实施局部静磁场匀场和并行成像。 第二个目标将是使用新系统来解决脑与脑互动中的两个基本假设：（1）面对面交流（通过dfMRI测量）比基于互联网的交流（通过MR超扫描测量）招募更多的大脑功能;因此，它传递了更多的社会和情感信息。(2)一个多通道脑网络模型-来自dfMRI数据-可以被训练来推断一些基本的社会归因过程中的二元互动，因此，社会行为和脑网络之间的因果关系可以被识别。 总的来说，这是一个变革性的、高风险的、高回报的跨学科提案，将工程学与社会和行为科学相结合，使新技术和方法能够研究人类社会互动、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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.