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(申请人提供)：人类在大约十分之一秒内识别视觉输入并对其进行分类。然而，大脑是如何获得这种非凡能力的，仍然是一个谜。大脑皮层视觉识别系统由一个处理流组成，该处理流从V1开始，上升到与腹侧颞叶皮质(VTC)识别相关的高级视觉区域。这项研究的目的是通过研究人类VTC中神经执行、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。我们实验室先前的研究使用高分辨率功能磁共振成像(HR-fMRI)通过生成详细描述VTC的神经解剖学和拓扑特征的组织框架来促进对VTC功能组织的理解。利用这些发现，这项建议使用了一种创新的方法，结合了HR-fMRI、宏观解剖学、细胞结构和骨髓结构测量、高空间和角度分辨率扩散成像(HARDI)、先进的跟踪算法和计算建模等尖端技术，以解决以下三个关键问题：(1)神经微结构是VTC中功能表示拓扑组织的实现限制吗？(2)结构和功能连通性是否调节VTC的功能表示？(3)VTC中的神经实现与计算有何关系？AIM 1将告知VTC中功能表示的拓扑是否/如何由潜在的细胞结构和骨髓结构决定，这些结构可能已经进化到优化特定的计算。目标2将研究高级视觉皮质的精细、功能和结构连接，确定信息如何在相邻的专门皮质网络内和跨相邻的专门皮质网络进行分离和整合。AIM 3将开发第一个生成和量化的职训局计算模型，能够预测对形状、位置和大小不同的刺激的反应，同时还确定整个职训局是否存在与感知相关的信息分层处理。这项研究对识别个体大脑中VTC功能神经解剖的异常具有重要的临床应用价值，因此，对于具有解剖学或功能性VTC缺陷的患者群体，以及具有非典型感知或识别的个人而言，该研究具有重要的临床应用价值。总体而言，这项研究将通过阐明人类VTC中神经执行、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天地。