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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

基本信息

批准号：
8243545
负责人：
CHRISTOPHER R GENOVESE
金额：
$ 11.49万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-01-01 至 2015-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8243545
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 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

项目摘要

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 wil 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.

功能性磁共振成像（fMRI）的最新技术进展使得研究大脑不仅仅是将其作为一个体积元素的集合（体素）而是作为相互作用的组件的系统。而不是考虑在单个区域，我们可以研究功能网络。而不是计算体素的个体的反应曲线，我们可以估计他们对刺激的集体反应。我们可以用精细的空间图像来代替大脑区域的平均反应，结构这种面向系统的方法需要成像和统计方法。该项目由两个相互交织的部分组成。第一正在进行功能磁共振成像实验，以解决有关大脑表征的三个问题人类大脑中的空间。第二个是开发和验证三个新的统计技术，允许系统级的推理需要回答神经科学问题.这些技术的动机和发展的建议实验研究，但有轻微的调整，他们将广泛适用于其他神经影像学研究。在目标1中，该项目将制定方法，表征分布式功能网络。这些方法将用于研究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。目标3：项目将为高分辨率功能磁共振成像数据开发自适应空间平滑技术。这些将使用工具来测试视觉皮层中的眼睛位置信号的精细尺度结构。本项目中开发的实验方案和理论原理将增进对人类视觉系统基本功能的了解。统计在这个项目中开发的技术将提供新的方法来理解功能系统与神经成像，并将推进广泛适用的方法，使关于时空数据中区域的推断。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.

DOI：
10.1214/12-aos1044
发表时间：
2013-04
期刊：
Annals of statistics
影响因子：
4.5
作者：
Shalizi CR;Rinaldo A
通讯作者：
Rinaldo A

Straight to the Source: Detecting Aggregate Objects in Astronomical Images with Proper Error Control.

DOI：
10.1080/01621459.2013.779829
发表时间：
2013-07-01
期刊：
Journal of the American Statistical Association
影响因子：
3.7
作者：
Friedenberg DA;Genovese CR
通讯作者：
Genovese CR

Motion direction biases and decoding in human visual cortex.

人类视觉皮层的运动方向偏差和解码。

DOI：
10.1523/jneurosci.1034-14.2014
发表时间：
2014
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
0
作者：
Wang,HelenaX;Merriam,ElishaP;Freeman,Jeremy;Heeger,DavidJ
通讯作者：
Heeger,DavidJ

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