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: 8721703
• 负责人: Kalanit Grill-Spector
• 金额: $ 38.18万
• 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 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8721703
• 关键词: Accounting; Address; Affect; Agnosia; Algorithms; Anatomy; Architecture; Autistic Disorder; Brain; Cereals; Characteristics; Computer Simulation; Computing Methodologies; Coupling; Data; Diffusion; Dimensions; Disease; Face; Functional Magnetic Resonance Imaging; Goals; Health; Human; Human Characteristics; Image; Individual; Knowledge; Link; Location; Magnetic Resonance Imaging; Measurement; Methods; Mission; Modeling; Neuroanatomy; Perception; Play; Population; Positioning Attribute; Process; Property; Psychophysics; 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; in vivo; information processing; innovation; neuromechanism; novel; patient population; 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）：人类在大约十分之一秒内识别并分类视觉输入。然而，大脑如何实现这种非凡的能力仍然是一个谜。皮层视觉识别系统由从 V1 开始并上升到与腹侧颞叶皮层 (VTC) 识别相关的高级视觉区域的处理流组成。本研究的目标是通过研究人类 VTC 中神经实现、表示和计算之间的相互作用，在理解识别的神经基础方面取得重要的理论和实证进展。我们实验室之前的研究使用高分辨率功能磁共振成像 (HR-fMRI)，通过生成详细说明其神经解剖学和拓扑特征的组织框架来加深对 VTC 功能组织的理解。利用这些发现，本提案采用创新方法和尖端技术，结合 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建模来解决以下问题三个关键问题：（1）神经微架构是否是 VTC 中功能表示的拓扑组织背后的实现约束？ (2)结构和功能连接是否调节VTC的功能表征？ (3) 神经实现与 VTC 中的计算有何关系？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，这些细胞结构和骨髓结构可能已经进化以优化特定的计算。目标 2 将研究高级视觉皮层的精细功能和结构连接，确定信息如何在相邻的专门皮层网络内部和之间进行分离和集成。目标 3 将开发第一个 VTC 计算的生成和定量模型，能够预测对形状、位置和大小变化的刺激的反应，同时还确定 VTC 中是否存在感知相关的信息分层处理。这项研究对于识别个体大脑内 VTC 功能性神经解剖学异常具有重要的临床应用，因此，对于具有解剖或功能性 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。总体而言，该研究将通过阐明人类 VTC 中神经实现、表示和计算之间的相互作用，为理解人类视觉识别的神经基础开辟新天地。