NCS-FO: Neural Correlates of Social States in Macaques

NCS-FO：猕猴社会状态的神经相关性

• 批准号: 2024581
• 负责人: Hyun Soo Park
• 金额: $ 99.86万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024581&HistoricalAwards=false
• 关键词: NCS; FO; Neural; Correlates; Social

Many biological organisms interact with one another by transmitting and perceiving social signals. These include gaze, facial expression, and body pose that convey information about the individual’s internal state, including their focus of attention, intended actions, and emotional level. Despite the ubiquity and potential value of these social interactions, understanding how neural activity gives rise to social interactions is still a largely uncharted area. Past experiments were conducted either by subjective behavioral observations in natural social settings or by quantifiable methods such as neuroimaging in restricted social settings. This project will address these limitations by leveraging the project team's recently developed high resolution motion capture system, which can measure, detect, and quantify natural social behaviors and their corresponding neural activity. This research will open new opportunities to study early behavioral markers, such as those for at-risk children with autism spectrum disorder, schizophrenia, and obsessive-compulsive disorder.The project team's main innovation is a new statistical model called social states, designed to encode the social context of joint behaviors. These social states will be associated with neurophysiological activities in two brain regions, the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), both located in the prefrontal cortex. Using the neural correlates of social states, the project will develop a novel method to model the dynamics of social state transitions, facilitating an understanding of how these two brain regions are responsible for processing social signals. While the project will focus on specific brain regions, the planned research will provide a general computational foundation for understanding the complex social behaviors of macaques. The planned research will advance the computational understanding of cognitive and neural processes by learning from millions of neurobehavioral data points in real, unrestricted environments. Developing such a computational model is a complex, real world problem, requiring a new holistic, transformative, and integrative approach. The planned solution is built upon domain knowledge from multiple disciplines, including machine learning, primate physiology, neuroscience, and computer vision.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.

许多生物有机体通过传递和感知社会信号相互作用。 这些包括凝视，面部表情和身体姿势，传达有关个人内部状态的信息，包括他们的注意力焦点，预期动作和情绪水平。 尽管这些社会互动的普遍性和潜在价值，了解神经活动如何引起社会互动仍然是一个未知的领域。 过去的实验要么是通过自然社会环境中的主观行为观察，要么是通过限制社会环境中的神经成像等量化方法进行的。 该项目将通过利用项目团队最近开发的高分辨率运动捕捉系统来解决这些限制，该系统可以测量，检测和量化自然社会行为及其相应的神经活动。 这项研究将为研究早期行为标志物提供新的机会，例如自闭症谱系障碍，精神分裂症和强迫症的高危儿童。项目团队的主要创新是一种名为社会状态的新统计模型，旨在编码联合行为的社会背景。这些社交状态将与两个大脑区域的神经生理活动相关，即背外侧前额叶皮层（dLPFC）和背侧前扣带皮层（dACC），两者都位于前额叶皮层。 该项目将利用社会状态的神经相关性开发一种新方法来模拟社会状态转变的动态，以促进了解这两个大脑区域如何负责处理社会信号。 虽然该项目将专注于特定的大脑区域，但计划中的研究将为理解猕猴复杂的社会行为提供通用的计算基础。 计划中的研究将通过从真实的、不受限制的环境中的数百万个神经行为数据点中学习，推进对认知和神经过程的计算理解。 开发这样一个计算模型是一个复杂的、真实的世界问题，需要一种新的整体的、变革的和综合的方法。 该计划的解决方案基于多个学科的领域知识，包括机器学习、灵长类动物生理学、神经科学和计算机视觉。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。

项目成果

OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Estimation of Non-human Primates.

• DOI: 10.1007/s11263-022-01698-2
• 发表时间: 2023-01
• 期刊: INTERNATIONAL JOURNAL OF COMPUTER VISION
• 影响因子: 19.5
• 作者: Yao, Yuan;Bala, Praneet;Mohan, Abhiraj;Bliss-Moreau, Eliza;Coleman, Kristine;Freeman, Sienna M.;Machado, Christopher J.;Raper, Jessica;Zimmermann, Jan;Hayden, Benjamin Y.;Park, Hyun Soo
• 通讯作者: Park, Hyun Soo

Self-supervised Secondary Landmark Detection via 3D Representation Learning

• DOI: 10.1007/s11263-023-01804-y
• 发表时间: 2023-06-01
• 期刊: INTERNATIONAL JOURNAL OF COMPUTER VISION
• 影响因子: 19.5
• 作者: Bala, Praneet;Zimmermann, Jan;Hayden, Benjamin Y.
• 通讯作者: Hayden, Benjamin Y.