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.

项目摘要 意义人类大脑有一个专门的神经系统来处理其他人。然而，相对 对这种加工的基本机制知之甚少。先前的研究发现， 观看导致右颞顶联合区（TPJ）比观看面部模拟更活跃 就像机器人的脸这表明，活面孔具有超越外表、运动、合作、 存在，和具体化，使他们能够访问社会认知系统，面对模拟不能 access.研究问题。这种接触的神经机制是什么？它们与刺激有什么关系 收购？此外，这些机制如何导致精神疾病的功能障碍， 精神分裂症？填补文献中的这些空白需要以下技能：（1）多模式数据采集;（2） 先进的分析;（3）临床人群的实验设计。为了长期研究这些问题 通过成为一名独立的研究者，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 多模态实验设计，采集和预处理与中间多模态分析的技能， 高级编程、数据集管理和专业化。Approach.我将通过一项关于 在以机器人面部观看作为控制的真人面部观看期间的典型神经处理， 同步功能近红外光谱（fNIRS），脑电图（EEG），眼动追踪， 和瞳孔测量然后，我将应用眼行为引导的EGG数据的时频分解，交叉- 频率相位振幅耦合和fNIRS约束的EEG源定位。假设。视觉 感知是活体面部的神经处理的活跃组成部分。如果是真的，那么我们假设 由于注意力和唤醒的增加，在实时面部观看期间的运动和瞳孔大小将更大， 从一个活生生的面孔中获取更多有意义的信息。这些差异被假设为对应于 增加伽玛（30- 100赫兹）反射的电磁波处理和θ（4- 8赫兹）反射的功率 知觉绑定这些频率被假设为相互耦合-反映信息 从局部转移到全局处理-并定位到用活体面部的fNIRS对比度识别的区域 和机器人面部观察这将确定社会处理网络的时间和空间特征。 初步结果。参与者显示更大的瞳孔大小和差异停留时间在现场观看。 右-TPJ-本地化的θ波段功率是更大的活的脸比机器人的脸观看。目标2，我将完成 精神分裂症患者实验设计和高级计算的博士后培训 分析，如因子分析和预测编码。我将通过对实时人脸观察范例的研究来做到这一点， 例如情绪表达或直接注视感知， 病人和非病人。这项培训将使我获得长期独立研究所需的技能 探讨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。