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

应用于人类视觉参考系的功能磁共振成像新统计方法

• 批准号: 8060476
• 负责人: CHRISTOPHER R GENOVESE
• 金额: $ 27.67万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8060476
• 关键词: Address; Algorithms; Area; Attention deficit hyperactivity disorder; Biometry; Brain; Brain region; Cerebral cortex; Collection; Data; Data Set; Dependence; Development; Diagnostic tests; Elements; Exhibits; Eye; Eye Movements; Functional Magnetic Resonance Imaging; Health; Human; Image; Individual; Knowledge; Lead; Location; Macular degeneration; Methodology; Methods; Minor; Monkeys; Neighborhoods; Neurons; Noise; Ocular orbit; Performance; Peripheral; Positioning Attribute; Process; Protocols documentation; Public Health; Resolution; Retina; Saccades; Schizophrenia; Series; Shapes; Signal Transduction; Sorting - Cell Movement; Stimulus; Structure; System; Techniques; Testing; Time; Vision; Visual; Visual Cortex; Visual Fields; Visual attention; Visual system structure; human subject; improved; neglect; neuroimaging; neuropsychological; novel; novel strategies; patient population; receptive field; reconstruction; research study; response; tool

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.

描述(申请人提供)：功能磁共振成像(FMRI)的最新技术进步使研究大脑成为可能，而不仅仅是体积元素(体素)的集合，而是一个相互作用的组成部分的系统。我们可以研究功能网络，而不是考虑个别地区。我们可以估计体素对刺激的集体反应，而不是计算体素的个人反应曲线。我们可以想象精细的空间结构，而不是满足于大脑区域的平均反应。这种以系统为导向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，以解决关于人脑中空间表示的三个问题。第二个是开发和验证三种新的统计技术，这些技术允许进行回答神经科学问题所需的系统级推断。这些技术是由拟议的实验研究激发和开发的，但只要稍加适应，它们将广泛适用于其他神经成像研究。在目标1中，该项目将开发识别和表征分布式功能网络的方法。这些方法将被用来研究视觉重新映射背后的大脑皮层回路。在目标2中，该项目将开发同时估计功能磁共振反应场的方法。这些方法将被用来测试视觉和眼动信号的相互作用。在目标3中，该项目将为高分辨率功能磁共振数据开发自适应空间平滑技术。这些工具将被用来测试视觉皮质中眼睛位置信号的精细结构。在这个项目中开发的实验方案和理论原理将增加对人类视觉系统基本功能的理解。在这个项目中开发的统计技术将提供新的方法来理解神经成像的功能系统，并将促进关于时空数据中的区域的广泛适用的推断方法。公共卫生相关性：我们为研究健康人类受试者的视力和重新映射而开发的实验方案和理论原则将很容易适用于患者群体。更好地了解视觉重映射将有助于更好地了解限制周边视力的因素，当中心视力因黄斑变性和相关的视觉缺陷而受损时，这些因素是至关重要的。视觉注意的基本知识对我们理解几种神经心理疾病，包括单侧忽视、精神分裂症和多动症，以及为诊断测试的发展提供信息都有意义。该项目开发的统计技术将广泛适用于对公共卫生有直接影响的神经成像和生物统计学中的其他问题。