Functional-neuroanatomy of High-level Visual Cortex: A Quantitative Multimodal Ap

高级视觉皮层的功能神经解剖学：定量多模式应用

• 批准号: 9511829
• 负责人: Kalanit Grill-Spector
• 金额: $ 38.78万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9511829
• 关键词: Accounting; Address; Affect; Agnosia; Algorithms; Anatomy; Architecture; Autistic Disorder; Brain; Characteristics; Computer Simulation; Computing Methodologies; Coupling; Data; Diffusion; Dimensions; Disease; Face; Functional Magnetic Resonance Imaging; Goals; Grain; Health; Human; Human Characteristics; Image; Individual; Knowledge; Link; Location; Magnetic Resonance Imaging; Measurement; Methods; Mission; Modeling; Neuroanatomy; Perception; Play; Population; Positioning Attribute; Property; Psychophysics; Reproducibility; Research; Resolution; Rest; Role; Shapes; Specificity; Stimulus; Stream; Structure; System; Techniques; Temporal Lobe; Testing; Vision; Visual; Visual Cortex; Visual Perception; Visual system structure; Williams Syndrome; base; clinical application; developmental prosopagnosia; extrastriate visual cortex; human model; in vivo; information processing; innovation; multimodality; neuromechanism; novel; patient population; predicting response; public health relevance; relating to nervous system; response; white matter

DESCRIPTION (provided by applicant): Humans recognize and categorize the visual input in about a tenth of a second. However, it is still a mystery how the brain achieves this remarkable ability. The cortical visual recognition system consists of a processing stream starting in V1 and ascending into high-level visual areas associated with recognition in ventral temporal cortex (VTC). The goal of the proposed research is to make important theoretical and empirical progress in our understanding of the neural basis of recognition by examining the interplay between neural implementation, representations, and computations in human VTC. Prior research from our lab used high-resolution functional magnetic resonance imaging (HR-fMRI) to advance understanding of the functional organization of VTC by generating an organizational framework detailing its neuroanatomical and topological characteristics. Leveraging these findings, this proposal uses an innovative approach with cutting edge techniques combining HR-fMRI, macro-anatomical, cytoarchitechtonic and myeloarchitectonic measurements, high spatial and angular resolution diffusion imaging (HARDI), advanced tracking algorithms, and computational modeling to address the following three key questions: (1) Is neural microarchitecture an implementational constraint underlying the topological organization of functional representations in VTC? (2) Does structural and functional connectivity regulate functional representations of VTC? (3) How does the neural implementation relate to computations in VTC? Aim 1 will inform if/how the topology of functional representations in VTC is determined by the underlying cytoarchictecture and myeloarchitecture, which may have evolved to optimize particular computations. Aim 2 will investigate the fine-scale functional and structural connectivity of high-level visual cortex determining how information is segregated and integrated within and across adjacent specialized cortical networks. Aim 3 will develop the first generative and quantitative model of VTC computations with the ability to predict responses to stimuli varying in shape, position, and size, while also determining if there is a perceptually-relevant hierarchical processing of information across VTC. This research has important clinical applications for identifying abnormalities in the functional neuroanatomy of VTC within individual brains, and thus, is relevant for patient populations with anatomical or functional VTC deficits, and for individuals with atypical perception or recognition. Overall, the research will break new ground in understanding the neural bases of visual recognition in humans by elucidating the interplay between neural implementation, representations, and computations in human VTC.

描述（由申请人提供）：人类在十分之一秒内识别和分类视觉输入。然而，大脑是如何获得这种非凡能力的仍然是个谜。皮层视觉识别系统由一个始于V1并上升到与腹侧颞叶皮层（VTC）识别相关的高级视觉区域的加工流组成。本研究的目标是通过研究人类VTC中神经实现、表征和计算之间的相互作用，在我们对识别的神经基础的理解方面取得重要的理论和经验进展。我们实验室之前的研究使用了高分辨率的功能磁共振成像（HR-fMRI），通过生成一个详细描述其神经解剖和拓扑特征的组织框架，来提高对VTC功能组织的理解。基于这些发现，本研究采用了一种创新的方法，结合了HR-fMRI、宏观解剖、细胞结构和骨髓结构测量、高空间和角分辨率扩散成像（HARDI）、先进的跟踪算法和计算模型，以解决以下三个关键问题：(1)神经微架构是VTC中功能表征拓扑组织的实现约束吗？(2)结构和功能的连通性是否规管职训局的功能表征？(3)神经实现与VTC的计算有何关系？目标1将告知VTC中功能表示的拓扑结构是否/如何由潜在的细胞结构和骨髓结构决定，它们可能已经进化到优化特定的计算。目的2将研究高级视觉皮层的精细功能和结构连通性，这决定了信息如何在相邻的专门皮层网络内部和之间分离和整合。目标3将开发第一个职业训练场计算的生成和定量模型，以预测对不同形状、位置和大小的刺激的反应，同时确定整个职业训练场是否存在感知相关的信息分层处理。该研究对于识别个体大脑中VTC的功能神经解剖学异常具有重要的临床应用价值，因此，对于具有解剖或功能VTC缺陷的患者群体以及具有非典型感知或识别的个体具有重要意义。总的来说，该研究将通过阐明人类视觉识别中神经实现、表征和计算之间的相互作用，在理解人类视觉识别的神经基础方面开辟新的领域。