Uncovering the Functional Organization and Cell Type Composition of Cortical Face Areas

揭示面部皮质区域的功能组织和细胞类型组成

• 批准号: 10227904
• 负责人: Winrich Freiwald
• 金额: $ 24.66万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10227904
• 关键词: Address; Area; Biological Models; Brain; Calcium; Callithrix; Callithrix jacchus jacchus; Cells; Characteristics; Code; Computer Models; Development; Dimensions; Disease; Disease model; Electrophysiology (science); Face; Face Processing; Fill-It; Foundations; Fragile X Syndrome; Functional Imaging; Functional Magnetic Resonance Imaging; Future; Goals; Head; Human; Image; Imaging Techniques; Immunohistochemistry; Impairment; In Situ; Knowledge; Lead; Life; Location; Macaca; Maps; Mental disorders; Mission; Modeling; Molecular; Monkeys; Mus; Neurons; Outcome; Personal Satisfaction; Photic Stimulation; Population; Primates; Prosopagnosia; Public Health; Research; Resolution; Social Perception; Specificity; Sports; Syndrome; System; Technology; Testing; Tissues; Transgenic Organisms; United States National Institutes of Health; Vision; Visual; Visual Cortex; Visual system structure; Williams Syndrome; Work; autism spectrum disorder; base; cell type; developmental prosopagnosia; disability; experimental study; improved; information processing; innovation; insight; lissencephaly; neuromechanism; novel strategies; object recognition; operation; optical imaging; relating to nervous system; social; transcriptomics; two-photon; visual information

PROJECT SUMMARY There is a fundamental gap in our understanding of how cortical circuit operations aid in high-level visual information-processing like face recognition. The existence of this conceptual gap constitutes an important problem because, until it is filled, it will neither be possible to explain face recognition and the computations face- selective networks implement, nor understand the reasons for face-recognition impairments in disorders like developmental prosopagnosia (face blindness). The long-term goal is to understand the neural mechanisms of face recognition and build an artificial face-recognition system implementing neural computations and thus explain face recognition mechanistically. The overall objective of this proposal presents a major step towards this goal: the establishment of a new approach and a new model system that permits imaging of large neural populations with single-cell resolution and cell-type differentiation within face-selective areas and surrounding regions. These technological advances are expected to lead to the understanding of the functional organization of face areas and how it impacts population codes for faces. The central hypotheses that will be tested, are that face areas are composed of multiple columns with different functional specializations, and that facial codes are highly cell type specific. The rationale for this proposal is that, after completion of the proposed research, the central gap in the understanding of how cortical circuit operations enable high-level vision will have been narrowed through the establishment of a new model system with unprecedented power to uncover the functional organization and circuit mechanisms of population codes of object recognition. The hypothesis will be tested by pursuing two specific aims: 1) Uncover the Spatial Organization of Face-Specializations of the Marmoset Brain; and 2) Determine the Cell-Type Specificity of Face Representations in Face-Selective areas. Two-photon calcium imaging during visual stimulation, combined with tissue clearing and cell type identification through immunohistochemistry will identify the functional organization of face areas and their surroundings with single- cell resolution. The approach is innovative because it presents a new and substantive departure from the status quo and because it addresses an NEI-relevant problem, the neural mechanisms of social perception, in a new way. The proposed research is significant, because it will provide a critical step forward towards a mechanistic understanding of the neural computations performed inside face areas, allow for the development of highly improved artificial face-processing systems, and advance our understanding of the functional organization of face areas in a new dimension and from the level of single cells to the level of face areas. The outcomes will lay the foundation for the determination of the molecular organization of high-level visual circuits and the development of transgenic disease models. The project, therefore, is of direct relevance for the understanding of prosopagnosia, as well as altered social information processing in syndromes like autism spectrum disorders, fragile X, and Williams syndrome.

项目概要 我们对皮层回路操作如何帮助高级视觉的理解存在根本性差距 信息处理，如人脸识别。这种概念鸿沟的存在构成了一个重要的 问题是，在它被填满之前，不可能解释人脸识别和人脸计算 选择性网络实施，也不了解诸如此类的疾病中面部识别障碍的原因 发育性面容失认症（面盲）。长期目标是了解神经机制 人脸识别并构建实现神经计算的人工人脸识别系统，从而 从机制上解释人脸识别。该提案的总体目标是朝着 这个目标：建立一种新方法和新模型系统，允许对大神经进行成像 面部选择性区域和周围区域内具有单细胞分辨率和细胞类型分化的群体 地区。这些技术进步预计将导致人们对职能组织的理解 面部区域及其如何影响面部人口代码。将要检验的中心假设是 人脸区域由多个具有不同功能专业的列组成，并且人脸代码是 高度细胞类型特异性。该提案的基本原理是，在完成拟议的研究后， 对皮层回路操作如何实现高级视觉的理解的中心差距将是 通过建立一个新的模型系统来缩小范围，该系统具有前所未有的能力来揭示功能 物体识别总体代码的组织和电路机制。该假设将通过以下方式进行检验 追求两个具体目标：1）揭示狨猴大脑面部特化的空间组织； 2) 确定人脸选择性区域中人脸表示的细胞类型特异性。双光子 视觉刺激期间的钙成像，结合组织透明化和细胞类型识别 免疫组织化学将识别面部区域及其周围环境的功能组织 细胞分辨率。这种方法是创新的，因为它提出了一种新的、实质性的背离现状 现状，因为它以一种新的方式解决了与 NEI 相关的问题，即社会感知的神经机制 方式。拟议的研究意义重大，因为它将为迈向机械化方向迈出关键一步。 了解面部区域内执行的神经计算，可以开发高度 改进的人工面部处理系统，并增进我们对人脸功能组织的理解 新维度的人脸区域，从单细胞层面到人脸区域层面。结果将奠定 确定高级视觉电路的分子组织和 转基因疾病模型的开发。因此，该项目与理解直接相关 面部失认症，以及自闭症谱系障碍等综合症中社会信息处理的改变， 脆性 X 病和威廉姆斯综合征。