Basic neural processing mechanisms of live human face viewing

实时人脸观看的基本神经处理机制

• 批准号: 10610114
• 负责人: megan Kelley
• 金额: $ 3.27万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10610114
• 关键词: Academia; Address; Appearance; Arousal; Attention; Behavior; Binding; Brain; Characteristics; Clinical; Code; Complex; Computer Models; Coupled; Coupling; Data; Data Analyses; Data Set; Development; Disease; Dyslexia; Electroencephalography; Experimental Designs; Eye; Eye Movements; Face; Face Processing; Factor Analysis; Follow-Up Studies; Frequencies; Functional disorder; Funding; Goals; Health; Human; Impairment; Learning; Literature; Mental disorders; Mission; Motion; Neurocognition; Neurocognitive; Participant; Patients; Perception; Phase; Population; Positioning Attribute; Postdoctoral Fellow; Pupil; Research; Research Personnel; Research Project Grants; Robot; Role; Schizophrenia; Seeds; Social Functioning; Social Interaction; Special Population; Stimulus; System; Techniques; Testing; Time; Time Management; Training; Transcend; United States National Institutes of Health; Visual; advanced analytics; career; complex data; data acquisition; data management; design; experience; experimental study; functional near infrared spectroscopy; gaze; humanoid robot; multimodal data; multimodality; neuromechanism; patient population; post-doctoral training; relating to nervous system; showing emotion; simulation; skills; social; social cognition; source localization; statistics; visual tracking

Project Summary Significance. The human brain has a dedicated neural system for processing other humans. However relatively little is known about the basic mechanisms of this processing. Prior research has found that live human face viewing results in more activity in the right temporoparietal junction (TPJ) than does viewing a face simulation like a robot face. This suggests that live faces have characteristics that transcend appearance, motion, co- presence, and embodiment which give them access to sociocognitive systems that face simulations cannot access. Research question. What are the neural mechanisms of this access and how do they relate to stimulus acquisition? Further, how might these mechanisms contribute to dysfunction in psychiatric illnesses like schizophrenia? Addressing these gaps in the literature requires skills in: (1) multimodal data acquisition; (2) advanced analytics; and (3) experimental design for clinical populations. To pursue these questions long-term by becoming an independent researcher, I propose a two-phase training plan. In Aim 1, I will supplement existing skills for multimodal experimental design, acquisition, and preprocessing with intermediate multimodal analytics, advanced programming, dataset management, and professionalization. Approach. I will do this with a study on neurotypical neural processing during live human face viewing with robot face viewing as control and will acquire simultaneous functional Near Infra-Red Spectroscopy (fNIRS), electroencephalography (EEG), eye-tracking, and pupillometry. I will then apply eye-behavior-guided time frequency decomposition of EGG data, cross- frequency phase-amplitude coupling, and fNIRS-constrained EEG source localization. Hypotheses. Visual sensing is an active component of the neural processing of live faces. If true, then we hypothesize that eye movements and pupil size during live face viewing will be greater due to increased attention and arousal and greater acquisition of meaningful information from a live face. These differences are hypothesized to correspond to increased power in gamma (30-100hz)—reflecting configural processing—and theta (4-8hz)—reflecting perceptual binding. These frequencies are hypothesized to be coupled to each other—reflecting information transfer from local to global processing—and to localize to regions identified with fNIRS contrasts of live face and robot face viewing. This will identify the temporal and spatial features of the social processing network. Preliminary Results. Participants show greater pupil size and differences in dwell time during live face viewing. Right-TPJ-localized theta band power is greater during live face than robot face viewing. Aim 2, I will complete post-doctoral training on experimental design for those with schizophrenia and on advanced computational analytics like factor analysis and predictive coding. I will do this through research on live face viewing paradigms, such as emotional expression or direct gaze perception, during multimodal data acquisition with a population of patients and non-patients. This training will give me the skills necessary for long term research independence to explore the role of social neurocognitive function and dysfunction in health and psychiatric illness.

项目概要 意义：人类大脑有一个专门的神经系统来处理其他人类的信息，但相对而言。 人们对这种处理的基本机制知之甚少，之前的研究发现活人的脸部。 与观看面部模拟相比，观看会导致右侧颞顶交界处 (TPJ) 更加活跃 就像机器人的脸一样，这表明活人脸具有超越外观、动作、合作的特征。 存在和体现使他们能够访问面对模拟无法实现的社会认知系统 研究问题：这种访问的神经机制是什么以及它们与刺激有何关系。 此外，这些机制如何导致精神疾病（如精神疾病）的功能障碍？ 填补文献中的这些空白需要以下方面的技能：（1）多模式数据采集；（2） 先进的分析；（3）临床人群的实验设计长期解决这些问题。 通过成为一名独立研究员，我提出了一个两阶段的培训计划。在目标 1 中，我将补充现有的培训计划。 多模式实验设计、采集和预处理以及中间多模式分析的技能， 我将通过研究方法来实现这一点。 以机器人面部观察作为控制的实时人脸观察过程中的神经典型神经处理，并将获得 同步功能近红外光谱 (fNIRS)、脑电图 (EEG)、眼动追踪、 然后我将应用眼行为引导的 EGG 数据时频分解、交叉分析 频率相位幅度耦合和 fNIRS 约束脑电图源定位假设。 感知是活人面部神经处理的一个活跃组成部分如果这是真的，那么我们就用了那只眼睛。 由于注意力和唤醒力的增加，实时面部观察期间的运动和瞳孔大小将会更大 从真实面孔中获取更多有意义的信息是为了对应这些差异。 提高 gamma (30-100hz)（反映配置处理）和 theta (4-8hz)（反映）的功率 这些频率相互耦合——反映信息。 从局部处理转移到全局处理，并定位到通过活体面部的 fNIRS 对比识别的区域 和机器人面部观察，这将识别社会处理网络的时间和空间特征。 初步结果。参与者在实时面部观察中表现出更大的瞳孔尺寸和停留时间差异。 实时脸部时右 TPJ 定位的 θ 波段功率比机器人脸部观看时更大，我将完成目标 2。 精神分裂症患者实验设计和高级计算的博士后培训 我将通过对实时观看面部范例的研究来实现这一点，例如因子分析和预测编码。 在对人群进行多模式数据采集期间，例如情感表达或直接凝视感知 这项培训将为我提供长期独立研究所需的技能。 探索社会神经认知功能和功能障碍在健康和精神疾病中的作用。