SYSTEM FOR MONITORING SOCIAL COGNITIVE BRAIN FUNCTION

描述（由申请人提供）：社会神经科学的新兴领域正在弥合认知和情感的大脑机制与影响人际态度和行为的社会和动机因素之间的知识差距。然而，到目前为止，可用于测量社会大脑功能的工具主要限制了实验，以记录个人的神经反应，被限制在扫描仪中，对社会重要性的刺激。为了解决这个问题，我们的目标是开发一个突破性的硬件/软件系统，同步采集，分析，访问和可视化神经生理数据从两个或两个以上的参与者从事一个共同的活动。这种低成本、高度自动化的系统将能够记录和分析来自参与社交互动的多个人的连续EEG和亚秒ERP信号。它将把积累的关于EEG信号分析的专业知识融入自动化软件中，这将大大减少从EEG信号中提取有用信息所需的劳动力和知识。在第一阶段，我们将实现一个原型系统，用于同时记录两个人的大脑功能。为了证明可行性，该系统将被用来实现一个小型实验，该实验将利用高度控制的两人计算机任务来研究在类似游戏的环境中的竞争性互动，以及更多的自然主义任务，这些任务将提供更具有结构化程度较低的社交互动特征的数据。由此产生的新的数据库将被用来证明通过测试假设的人际因素对神经生理参数的影响的可行性，并作为一种资源，用于设计新的分析功能，在这样的多人实验。在第二阶段将实施一个完整、完善的数据采集和分析系统，并在与其他实验室的合作研究中进行评价。因此，第二阶段的成果将是一项技术，该技术将使研究社会现象的科学家能够定期直接、连续地测量大脑功能。

DESCRIPTION (provided by applicant): The emerging field of social neuroscience is bridging the knowledge gap between brain mechanisms of cognition and affect and the social and motivational factors that impact interpersonal attitudes and behaviors. However, to date, the tools available for measuring social brain function have mainly restricted experimentation to recording the neural responses of an individual, confined in a scanner, to stimuli of social importance. To address this limitation we aim to develop a breakthrough hardware/software system for synchronously acquiring, analyzing, accessing and visualizing neurophysiological data from two or more participants engaged in a common activity. This low-cost, highly-automated system will enable the recording and analysis of continuous EEG and sub-second ERP signals from multiple people engaged in a social interaction. It will embody accumulated expertise about the analysis of EEG signals into automated software that will vastly reduce the labor and knowledge required to extract useful information from them. In Phase I we will implement a prototype system for simultaneously recording brain function from two people. To demonstrate feasibility, this system will be used to enable a small experiment that will utilize both highly controlled two-person computer-based tasks to study competitive interactions in a game-like context, and more naturalistic tasks that will provide data more characteristic of less structured social interactions. The resulting novel database will be used to demonstrate feasibility by testing hypotheses concerning the impact of interpersonal factors on neurophysiologic parameters, and as a resource for designing new analysis functionality for use in such multiple-person experiments. A complete, refined, data acquisition and analysis system would be implemented during Phase II and evaluated in collaborative studies with other laboratories. The outcome of Phase II will thus be a technology that will make direct, continuous measurements of brain function routinely available to scientists studying social phenomena.