Towards a computationally precise characterization of the human ventral visual pathway

人类腹侧视觉通路的计算精确表征

• 批准号: 10460457
• 负责人: N Apurva Ratan Murty
• 金额: $ 11.09万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460457
• 关键词: Address; Algorithms; Animals; Area; Behavior; Brain; Brain Injuries; Brain region; Categories; Cognition; Computer Models; Computing Methodologies; Data; Data Analyses; Databases; Development; Disease; Encapsulated; Evolution; Eye; Face; Functional Magnetic Resonance Imaging; Goals; Human; Image; Impairment; Individual; Lead; Location; Mentors; Methods; Modeling; Monkeys; Network-based; Neurosciences; Pattern; Perception; Performance; Phase; Population; Positioning Attribute; Reading; Recovery; Research; Resolution; Rest; Series; Social Functioning; Stimulus; Supervision; System; Testing; Text; Vision; Visual; Visual Cortex; Visual Pathways; Visual Perception; Work; analytical method; base; cognitive ability; cohort; data quality; deep neural network; design; experimental study; extrastriate; fusiform face area; insight; interest; neuroimaging; neuroprosthesis; neurosurgery; object recognition; public health relevance; relating to nervous system; response; screening; theories; ultra high resolution; vision development; visual information; visual processing; visual stimulus

Project Summary/Abstract: Humans are extraordinarily visual animals, allocating a third of their cortex just to seeing what is in front of them. Visual recognition is supported by a series of hierarchically organized brain regions known collectively as the ventral visual cortex (VVC). Despite extensive research, we still lack a computationally precise understanding of how visual information is represented and transformed over stages of the human VVC. A key barrier has been the limitations of methods like functional MRI (fMRI) which make it difficult to test a large number of experimental stimuli. The research in this proposal will overcome this barrier by collecting fMRI responses to hundreds of stimuli, and analyzing these data using deep neural network based computational models and human interpretable algorithms such as image-synthesis and saliency mapping. In Aim 1 (K99 phase), I will focus on the category-selective regions of the VVC, that respond preferentially to images of faces (fusiform face area), scenes (parahippocampal place area), and bodies (extrastriate body area). I will develop and use new computational methods together with closed-loop experiments to address open questions such as: Is the hypothesized selectivity for these regions even correct? What is represented in the intermediate stages of processing? Are there functionally distinct regions within the category-selective regions? In Aim 2 (R00 phase), I will venture into the ~65% of VVC that lies outside the category-selective regions. I will develop and apply new data-driven clustering to divide these regions into their native components, and characterize them individually. Together, this endeavor will reveal the computational and neural basis of visual recognition in humans with an unprecedented precision. My background in experimental and analytical methods in monkey and human vision puts me in a unique position to accomplish this proposal which requires a seamless integration between neuroimaging experiments and state-of-the-art computational modeling. The proposed work will be initiated in the lab of Prof. Nancy Kanwisher (mentor). During the K99 phase, I will continue to be mentored by Prof. Kanwisher, and will also advance my expertise with computational modeling under the supervision of Dr. Jim DiCarlo (co-mentor), and ultra-high-resolution 7T neuroimaging with Dr. Jon Polimeni (collaborator). This proposed plan will significantly augment my theoretical understanding and experimental abilities, and put me on a path to independence.

项目摘要/摘要：人类是异常视觉动物，将其三分之一的皮质分配给 看到他们面前的东西。视觉识别得到一系列分层组织的大脑的支持 区域统称为腹视觉皮层（VVC）。尽管进行了广泛的研究，但我们仍然缺乏 计算精确地了解视觉信息的表示和转换 人类VVC。关键障碍是功能性MRI（fMRI）等方法的局限性 难以测试大量的实验刺激。该提案中的研究将通过 收集fMRI对数百种刺激的反应，并使用基于深神网络的数据分析这些数据 计算模型和人类可解释的算法，例如图像合成和显着映射。在 AIM 1（K99阶段），我将重点关注VVC的类别选择区域，优先响应于 面孔（梭形面部区域），场景（帕拉希帕克宫区域）和身体（体外身体区域）的图像。 我将开发和使用新的计算方法以及闭环实验来解决开放 诸如：这些地区的假设选择性甚至正确吗？在 处理的中级阶段？类别选择区域内是否存在功能不同的区域？ 在AIM 2（R00阶段）中，我将冒险进入位于类别选择区域之外的约65％的VVC。我会 开发和应用新的数据驱动聚类以将这些区域分为其本地组件，并 单独表征它们。这项努力一起揭示了视觉的计算和神经基础 具有前所未有的精度的人类认可。我的实验和分析方法背景 在猴子和人类的视野中，我处于独特的立场来完成这一建议，这需要一个无缝的 神经成像实验与最新计算建模之间的整合。拟议的工作 将在Nancy Kanwisher教授（导师）的实验室中启动。在K99阶段，我将继续受到指导 由Kanwisher教授，还将在我的专业知识下通过 吉姆·迪卡洛（Jim DiCarlo）博士（Co-entor）和乔恩·波利梅尼（Jon Polimeni）博士（合作者）和超高分辨率7T神经影像学。这 拟议的计划将大大增强我的理论理解和实验能力，并使我掌握 独立之路。