New Statistical Methods for fMRI Applied to Remapping

应用于重映射的功能磁共振成像新统计方法

• 批准号: 6837683
• 负责人: CHRISTOPHER R GENOVESE
• 金额: $ 18.82万
• 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 2007-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6837683
• 关键词: bioimaging /biomedical imaging; clinical research; data collection methodology /evaluation; eye movements; functional magnetic resonance imaging; handedness; human subject; method development; microarray technology; parietal lobe /cortex; statistics /biometry; visual perception; visual stimulus

DESCRIPTION (provided by applicant): This project consists of two intertwined components: (a) development of new statistical methods that address recurrent problems in the analysis of functional neuroimaging data and (b) neuroscientific studies of visual remapping in human cortex, which will frame the need for and guide the development of our new methods. The new statistical methods in this proposal apply directly to diverse applications beyond functional neuroimaging, from galaxy clustering to DNA microarrays. The three neurosciences questions in this proposal address fundamental issues regarding neural mechanisms of remapping in humans. Visual remapping is the process that coordinates the visual and eye-movement systems in order to maintain stable perception of the world when the eyes move. This project will achieve the following specific aims, each tied to a specific scientific question. Aim 1. To develop new multiple testing methods for false discovery control. Question 1. Does remapping occur outside parietal cortex? Extending recent work on controlling the False Discovery Rate this project will develop procedures that bound the unobserved proportion (or number) of false discoveries with specified confidence. The method will be applied to investigate Question 1. Aim 2. To develop tools for finding nonlinearly optimal fMRI designs. Question 2. What is the time course of remapping? This project will implement design tools that optimize targeted inferences -- linear and nonlinear. The tools will be applied to design experiments that address Question 2. Aim 3. To develop nonparametric confidence sets for structured function estimation. Question 3. How do the shapes of the visual and remapped responses differ? This project will build on recent work in nonparametric regression to construct confidence sets for an unknown function under shape constraints and with dependent noise. The method will be applied to Question 3.

描述(由申请人提供)：该项目由两个相互交织的部分组成：(A)开发新的统计方法，以解决功能神经成像数据分析中反复出现的问题；(B)人类皮质视觉重绘的神经科学研究，这将为我们的新方法的开发提供需求和指导。 这项建议中的新统计方法直接适用于功能神经成像以外的各种应用，从星系团到DNA微阵列。这项提案中的三个神经科学问题解决了与人类重新映射的神经机制有关的基本问题。视觉重新映射是协调视觉和眼动系统的过程，以便在眼睛移动时保持对世界的稳定感知。这个项目将实现以下具体目标，每个目标都与一个特定的科学问题有关。 目的1.建立新的防伪发现多重检测方法。 问题1.重新定位是否发生在顶叶皮质之外？ 这个项目扩展了最近关于控制错误发现率的工作，将制定程序，以特定的置信度限制未观察到的错误发现的比例(或数量)。该方法将用于调查问题1。 目的2.开发寻找非线性最优fMRI设计的工具。 问题2.重新映射的时间进程是什么？ 这个项目将实施优化目标推理的设计工具--线性和非线性。这些工具将被应用于设计解决问题2的实验。 目的3.建立用于结构函数估计的非参数置信集。 问题3：视觉反应和重新映射反应的形状有何不同？ 这个项目将建立在非参数回归的最新工作的基础上，为形状约束和相关噪声下的未知函数构建置信集。该方法将应用于问题3。