New Statistical Methods for fMRI Applied to Remapping

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，解决功能性神经成像数据分析中经常出现的问题，（B）人类皮层视觉重映射的神经科学研究，这将确定我们新方法的需求并指导我们新方法的开发。 该提案中的新统计方法直接适用于功能神经成像之外的各种应用，从星系聚类到DNA微阵列。本提案中的三个神经科学问题解决了有关人类重新映射神经机制的基本问题。视觉重映射是协调视觉系统和眼动系统以维持眼睛运动时对世界的稳定感知的过程。该项目将实现以下具体目标，每个目标都与一个具体的科学问题有关。 目标1.开发新的多种测试方法以控制错误发现。 问题1.重新映射是否发生在顶叶皮层之外？ 该项目扩展了最近关于控制错误发现率的工作，将开发程序，以指定的置信度约束未观察到的错误发现比例（或数量）。该方法将用于调查问题1。 目标二。开发用于寻找非线性最佳fMRI设计的工具。 问题2.重新映射的时间过程是什么？ 这个项目将实现设计工具，优化有针对性的推理-线性和非线性。这些工具将用于设计解决问题2的实验。 目标3。建立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，在形状约束和相关噪声下为未知函数构建置信集。该方法将应用于问题3。

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.