NEW STATISTICAL METHODS FOR fMRI APPLIED TO VISUAL REFERENCE FRAMES IN HUMANS

描述（由申请人提供）：功能磁共振成像（fMRI）的最新技术进步使得研究大脑成为可能，而不是将其作为体积元素（体素）的集合，而是作为相互作用组件的系统。我们可以研究功能网络，而不是考虑单个区域。而不是计算个体的反应曲线，我们可以估计他们对刺激的集体反应。我们可以成像精细的空间结构，而不是满足于大脑区域的平均反应。这种面向系统的方法需要在成像和统计方法方面取得进展。这个项目由两个相互交织的部分组成。第一个是进行功能磁共振实验，以解决关于人类大脑空间表征的三个问题。第二个是开发和验证三种新的统计技术，这些技术允许回答神经科学问题所需的系统级推断。这些技术是由提出的实验研究所激发和发展的，但稍加调整，它们将广泛适用于其他神经影像学研究。在目标1中，该项目将开发用于识别和表征分布式功能网络的方法。这些方法将用于研究构成视觉重映射的皮层回路。在目标2中，该项目将开发同时估计fMRI响应场的方法。这些方法将用于测试视觉和眼动信号的相互作用。在目标3中，该项目将开发高分辨率fMRI数据的自适应空间平滑技术。这些工具将用于测试视觉皮层中眼睛位置信号的精细结构。在这个项目中发展的实验协议和理论原则将增加对人类视觉系统基本功能的理解。本项目中开发的统计技术将为理解神经成像的功能系统提供新的途径，并将推进对时空数据中区域进行推断的广泛适用方法。公共卫生相关性：我们为研究健康人类受试者的视觉和重新定位而制定的实验方案和理论原则将很容易适用于患者群体。更好地理解视觉重映射将导致更好地理解限制周边视觉的因素，当中央视觉因黄斑变性和相关的视觉缺陷而受损时，这是至关重要的。视觉注意的基本知识对我们理解包括单侧忽视、精神分裂症和多动症在内的几种神经心理疾病具有重要意义，并为诊断测试的发展提供了信息。本项目开发的统计技术将广泛适用于神经影像学和生物统计学中对公共卫生有直接影响的其他问题。

DESCRIPTION (provided by applicant): Recent technological improvements in functional Magnetic Resonance Imaging (fMRI) are making it possible to study the brain as more than a collection of volume elements (voxels) but rather as a system of interacting components. Instead of considering individual regions, we can study functional networks. Instead of computing voxels' individual response curves, we can estimate their collective response to a stimulus. Instead of settling for responses averaged over brain regions, we can image fine spatial structure. Such a system-oriented approach requires advances in both imaging and statistical methodology. This project consists of two intertwined components. The first is performing fMRI experiments to address three questions about the representation of space in the human brain. The second is developing and validating three new statistical techniques that allow the system-level inferences needed to answer the neuroscientific questions. These techniques are motivated by and developed for the proposed experimental studies, but with minor adaptation, they will be broadly applicable to other neuroimaging studies. In Aim 1, the project will develop methods for identifying and characterizing distributed functional networks. These methods will be used to study the cortical circuit that underlies visual remapping. In Aim 2, the project will develop methods for simultaneously estimating fMRI response fields. These methods will be used to test the interaction of visual and eye movement signals. In Aim 3, the project will develop adaptive spatial smoothing techniques for high-resolution fMRI data. These tools will be used to test the fine-scale structure of eye position signals in visual cortex. The experimental protocols and theoretical principles developed in this project will increase understanding of the basic function of the human visual system. The statistical techniques developed in this project will give new ways to understand of functional systems with neuroimaging and will advance broadly applicable methods for making inferences about regions in spatio-temporal data. PUBLIC HEALTH RELEVANCE: The experimental protocols and theoretical principles that we develop for studying vision and remapping in healthy human subjects will be readily applicable to patient populations. A better understanding of visual remapping will lead to a better understanding of factors limiting peripheral vision, which are critical when central vision is compromised due to macular degeneration and related visual deficits. Basic knowledge of visual attention has implications for our understanding of several neuropsychological conditions, including unilateral neglect, schizophrenia and ADHD, and for informing the development of diagnostic tests. The statistical techniques developed in this project will be broadly applicable to other problems in neuroimaging and biostatistics that have direct implications for public health.