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.

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