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：人类是非凡的视觉动物，将三分之一的大脑皮质分配给 看着他们面前的一切。视觉识别是由一系列按层次组织的大脑支持的 这些区域统称为腹侧视觉皮质(VVC)。尽管进行了广泛的研究，但我们仍然缺乏 通过计算精确理解视觉信息在不同阶段的表示和转换方式 人类的VVC。一个关键的障碍是像功能磁共振(FMRI)这样的方法的局限性，这些方法使 难以测试大量的实验刺激物。这项提案中的研究将通过以下方式克服这一障碍 采集fMRI对数百个刺激的响应，并使用基于深度神经网络的方法分析这些数据 计算模型和人类可解释的算法，如图像合成和显著映射。在……里面 目标1(K99阶段)，我将重点介绍VVC的类别选择性区域，这些区域优先对 面部(梭形脸部区域)、场景(海马旁区域)和身体(纹状体外区域)的图像。 我将开发和使用新的计算方法，并结合闭环实验来解决开放问题 像这样的问题：假设的对这些地区的选择性是否正确？中所表示的内容 加工的中间阶段？在类别选择区域内是否有不同的功能区域？ 在目标2(R00阶段)，我将冒险进入~65%的VVC，这些VVC位于类别选择区域之外。这就做 开发和应用新的数据驱动的集群，将这些区域划分为它们的本机组件，以及 分别描述它们的特征。总之，这一努力将揭示视觉的计算和神经基础 在人类身上以前所未有的精确度识别。我的实验和分析方法背景 在猴子和人类的视觉中，我处于一个独特的位置来完成这个建议，这需要一个无缝的 神经成像实验与最先进的计算模型的结合。拟议中的工作 将在Nancy Kanwisher教授(导师)的实验室启动。在K99阶段，我将继续接受指导 由Kanwisher教授撰写，并将在 Jim DiCarlo博士(共同导师)和Jon Polimeni博士(合作者)的超高分辨率7T神经成像。这 提出的计划将显著增强我的理论理解和实验能力，并使我 一条通往独立的道路。